Current measurement device

Abstract

A current measuring device includes a magnetic core including a first air gap; a first magnetic flux sensor disposed at the first air gap; one or more memories configured to store instructions; and one or more processors configured to execute the instructions to perform operations, wherein, when a conductor is positioned within an interior portion of the magnetic core, the operations include: outputting a current value associated with current flowing through the conductor, wherein the current value is determined based on a magnetic flux measured by the first magnetic flux sensor and a first correction factor determined based on capacitance information associated with the first air gap.

Description

CURRENT MEASUREMENT DEVICECROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S. Provisional Application No. 63 / 599,550 filed on November 15, 2023, the contents of which are incorporated by reference herein in its entirety for all purposes.FIELD

[0002] The disclosure relates generally to current sensing technologies, and in particular to direct current (DC) current sensing technologies, measurement devices, and methods.BACKGROUND

[0003] Current sensing is any one of several techniques used to measure electric cunent. A cunent sensor is a device that detects electric cunent in a wire and generates a signal indicative of that cunent. A cunent clamp, also known as current probe, is an electrical device with jaws which open to allow clamping around an electrical conductor. This allows measurement of the cunent in a conductor without the need to make physical contact with the conductor or without the need to disconnect the conductor so that it can be inserted through the probe.

[0004] Typical non-contact cunent sensors capable of measuring direct current (DC) utilize clamp-type or jaw-type sensors having a rigid clamp which can be positioned around an electrical component for measurement. Arms or jaws of a clamp meter need to be opened and closed to receive a conductor under test. Valid measurements require accurate and tight jaw alignment.SUMMARY

[0005] Aspects and advantages of embodiments of the disclosure will be set forth in part in the following description, or can be learned from the description, or can be learned through practice of the embodiments.

[0006] A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.

[0007] Example aspects of the disclosure include a computer-implemented method for a current measuring device including a magnetic flux sensor disposed at an air gap included in a magnetic core of the current measuring device. The computer-implemented method includes measuring, by the magnetic flux sensor when a conductor is positioned at an interior portion of the magnetic core, a magnetic flux; and outputting a current value associated with current flowing through the conductor, wherein the current value is determined based on the magnetic flux and a correction factor determined according to capacitance information associated with the air gap.

[0008] Example aspects of the disclosure include a computing device (e.g., a current sensing device or current measuring device) comprising a magnetic core including a first air gap; a first magnetic flux sensor disposed at the first air gap; one or more memories configured to store instructions; and one or more processors configured to execute the instructions to perform operations. The operations can include, when a conductor is positioned within an interior portion of the magnetic core, outputting a current value associated with current flowing through the conductor, wherein the current value is determined based on a magnetic flux measured by the first magnetic flux sensor and a first correction factor determined based on capacitance information associated with the first air gap.

[0009] The computing device may be configured to execute instructions to perform operations associated with any of the other aspects and operations of the computer- implemented methods described herein.

[0010] Example aspects of the disclosure include a non-transitory computer readable medium storing instructions which, when executed by one or more processors of a current measuring device including a magnetic flux sensor and a magnetic core with an air gap, cause the one or more processors to perform operations, the operations comprising: measuring, by the magnetic flux sensor when a conductor is positioned at an interior portion of the magnetic core, a magnetic flux; and outputting a current value associated with current flowing through the conductor, wherein the current value is determined based on the magnetic flux and a correction factor determined according to capacitance information associated with the air gap.

[0011] The non-transitory computer-readable medium may store additional instructions to execute any of the other aspects and operations of the computing devices, computing systems, and computer-implemented methods described herein.

[0012] Other example aspects of the disclosure are directed to other systems, methods, apparatuses, tangible non-transitory computer-readable media, and devices for performing functions described herein. These and other features, aspects, and advantages of various implementations will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate implementations of the disclosure and, together with the description, help explain the related principles.BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Detailed discussion of embodiments directed to one of ordinary skill in the art is set forth in the specification, which makes reference to the appended figures, in which:

[0014] FIG. 1 A depicts a block diagram of an example computing system according to example embodiments of the disclosure.

[0015] FIG. IB depicts an example block diagram of a computing system including a current sensing application for measuring current carried by a conductor, according to example embodiments of the disclosure.

[0016] FIGS. 2A through 2B are example methods for measuring current via a current measuring device, according to example embodiments of the disclosure.

[0017] FIGS. 3A through 7B are example current measuring devices, according to example embodiments of the disclosure.

[0018] FIG. 8 is a flow chart diagram illustrating an example method for training a machine-learned model, according to example embodiments of the disclosure.

[0019] FIG. 9 is a block diagram of an example sequence processing model, according to example embodiments of the disclosure.

[0020] FIG. 10 is a block diagram of an example implementation of a multi-modal sequence processing model, according to example embodiments of the disclosure.

[0021] FIG. 11 is an example training flow for training a machine-learned model, according to example embodiments of the disclosure.

[0022] FIG. 12 is a block diagram of an example networked computing system, according to example embodiments of the disclosure.

[0023] Reference numerals that are repeated across plural figures are intended to identify the same features in various implementations.DETAILED DESCRIPTIONOverview

[0024] Measuring current with a current clamp or clamp meter (which has jaws which open to allow clamping around an electrical conductor) requires the arms or jaws of the clamp meter to be opened and closed to receive the electrical conductor under test. The conductor conducts cunent that induces a magnetic field and that magnetic field travels as magnetic flux through a magnetic core included in the clamp meter. The lower the magnetic reluctance of the core, the more magnetic flux will travel through the core. Ideally, 100% of the magnetic flux will travel through the core when the magnetic air gap has a small size.

[0025] Valid measurements require accurate and tight jaw alignment. The clamp meter includes air gaps in which a magnetic flux sensor (e.g., a Hall effect sensor) can be provided that measures magnetic flux through the air gap. However, due to differences in air gap geometry, errors in calculating electrical current can occur based on variations in the amount of leakage flux through the air gap. Further, if there is a magnetic flux leak then an erroneous measurement is obtained as the measurement detects less current than the actual amount of current through the conductor.

[0026] Existing methods which attempt to minimize the error from the leakage of magnetic flux include apply ing a significant force to hold the gaps at a controlled distance when the clamp meter is in the closed position, aligning features to guide the ends of the magnetic core into repeatable locations relative to each other, or utilizing robust hinge mechanisms that reduce the freedom of movement of the cores.

[0027] Some clamp meters are configured with two cores whose ends are forced together to complete a magnetic path for measurement. This configuration can include a hinge that opens those cores away from each other so that the clamp can be placed around a conductor. This results in a cumbersome clamp configuration. For example, in crowded panel boxes or areas where several conductors are located in a tight space, there is insufficient room for the opened clamp jaws to be placed around each side of the conductor to be measured. Thus, existing clamp meters are restricted to certain configurations that limit where the clamp meter can be utilized or positioned, and which require a large force to maintain the ends of the clamp meter closed. If an insufficient force is applied, the ends of the clamp will not reliably close and a reading from the clamp meter will include more error.

[0028] According to example computing devices and methods of the disclosure, accurate current measurements can be obtained while also increasing the flexibility and usability of the computing device (e.g., the clamp meter). Example computing devices and methodsdescribed herein can accurately measure current while reducing geometric boundary conditions, for example, by computing magnetic reluctance based on capacitance information. First, a correlation between capacitance and magnetic reluctance can be utilized to determine (infer) information about flux leakage and / or magnetic flux. Then the output of the magnetic flux sensor can be adjusted (corrected) to reflect an amount of leakage flux inferred based on the correlation. For example, as a capacitance value decreases (indicating an air gap which is increasing), it can be inferred that magnetic flux leakage is increasing, and the electric current value can be corrected accordingly (e.g., increased).

[0029] In some implementations, the capacitance across an air gap can be measured by applying a voltage or an electric current across the air gap. The measured capacitance value can inform a degree of adjustment (correction) to the calculation of the electric current associated with the conductor. Calibration data can be obtained relating capacitance to magnetic flux for various conditions (e g., for various air gap conditions, for various frequencies, etc.) at various points of the air gap. Correction factors can be derived for both the measured capacitance values and the measured magnetic flux values, based on the calibration data. The correction factors can then be applied to the measured electric current value to obtain a corrected electric current value.

[0030] According to examples of the disclosure, the disclosed clamp meter utilizes capacitance measurements for correcting an electric current value, which can enable different configurations of the clamp meter. In some implementations, ends of the magnetic core can overlap with one another (e.g., in a variable manner) rather than be forced to be held in a closed position at a particular location. In some implementations, at least some portions of the magnetic core can be formed of a flexible material rather than be completely rigid.

[0031] Aspects of the disclosure provide technical effects, benefits, and / or improvements in current measuring devices including clamp meter technology and the technology of measuring electric current via a clamp meter. According to examples of the disclosure, the disclosed clamp meter can accurately measure electric current of a conductor by applying one or more correction factors to an electric current measured via one or more magnetic flux sensors. The one or more correction factors can be derived or determined based on a capacitance value associated with an air gap. According to examples of the disclosure, the disclosed clamp meter can be realized or embodied according to various configurations compared to existing clamp meters, thereby resulting in clamp meters which can be implemented in an increased number of environments. Accordingly, the disclosed clamp meter has increased utility compared to existing clamp meters.

[0032] With reference now to the drawings, example embodiments of the disclosure will be discussed in further detail.

[0033] FIG. 1A depicts a block diagram of an example computing system 1100 according to example embodiments of the disclosure. The computing system 1100 includes a user computing system 100 and a server computing system 300 that are communicatively coupled over a network 400.

[0034] The user computing system 100 can include any type of computing device capable of measuring a current, such as, for example, a current measuring device (current sensing device), a current measuring device which does not contact a conductor carrying current to be measured, a clamp meter, a non-contact current sensing device, a current probe (e.g., a flexible current probe), etc. For example, the user computing system 100 can be configured to implement some or all of the operations implemented by computing system 1200 described herein with respect to FIG. IB and some or all of the operations of the computer-implemented method of FIGS. 2A-2B.

[0035] The server computing system 300 can include or otherwise be implemented by one or more server computing devices. In instances in which the server computing system 300 includes plural server computing devices, such server computing devices can operate according to sequential computing architectures, parallel computing architectures, or some combination thereof. In some implementations, the server computing system 300 can perform some of the operations implemented by computing system 1200 described herein with respect to FIG. IB and some of the operations of the computer-implemented method of FIGS. 2A-2B.

[0036] The network 400 may include any type of communications network including a wired or wireless network, or a combination thereof. The network 400 may include a local area network (LAN), wireless local area network (WLAN), wide area network (WAN), personal area network (PAN), virtual private network (VPN), or the like. For example, wireless communication between elements of the example embodiments may be performed via a wireless LAN, Wi-Fi, Bluetooth, ZigBee, Wi-Fi direct (WFD), ultra wideband (UWB), infrared data association (IrDA), Bluetooth low energy (BLE), near field communication (NFC), a radio frequency (RF) signal, and the like. For example, wired communication between elements of the example embodiments may be performed via a pair cable, a coaxial cable, an optical fiber cable, an Ethernet cable, and the like. Communication over the network 400 can use a wide variety of communication protocols (e.g., TCP / IP, HTTP, SMTP,FTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0037] As will be explained in more detail below, in some implementations the user computing system 100 and / or server computing system 300 may form part of an application system which can measure current and output the current value, for example, for display to a user.

[0038] The user computing system 100 includes one or more processors 110, one or more memory devices 120, an application system 130, one or more sensors 140, an input device 150, a display device 160, and an output device 170. The server computing system 300 may include one or more processors 310, one or more memory devices 320, and an application system 330.

[0039] For example, the one or more processors 110, 310 can be any suitable processing device that can be included in a user computing system 100 or server computing system 300. For example, the one or more processors 110, 310 may include one or more of a processor, processor cores, a controller and an arithmetic logic unit, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an image processor, a microcomputer, a field programmable array, a programmable logic unit, an applicationspecific integrated circuit (ASIC), a microprocessor, a microcontroller, etc., and combinations thereof, including any other device capable of responding to and executing instructions in a defined manner. The one or more processors 110, 310 can be a single processor or a plurality of processors that are operatively connected, for example in parallel.

[0040] The one or more memory devices 120, 320 can include one or more non- transitory computer-readable storage mediums, including a Read Only Memory (ROM), Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), and flash memory, a USB drive, a volatile memory device including a Random Access Memory (RAM), a hard disk, floppy disks, a Blu-ray disk, or optical media such as CD ROM discs and DVDs, and combinations thereof. However, examples of the one or more memory devices 120, 320 are not limited to the above description, and the one or more memory devices 120, 320 may be realized by other various devices and structures as would be understood by those skilled in the art.

[0041] The one or more memory devices 120, 320 can store data 122, 322 and instructions 124, 324 which are executed by the one or more processors 110, 310 to cause theuser computing system 100 and server computing system 300 to perform operations (e.g., operations associated with the methods described herein).

[0042] In some example embodiments, the user computing system 100 includes an application system 130. For example, the application system 130 may include a current sensing application 132. The application system 130 can include various other applications including document applications, text messaging applications, email applications, media (image, video, etc.) applications, dictation applications, virtual keyboard applications, browser applications, map applications, social media applications, navigation applications, etc.

[0043] The user computing system 100 may include one or more sensors 140. The one or more sensors 140 may be housed in a housing component that houses the one or more processors 110, the one or more memory devices 120, and / or one or more hardware components, which may store, and / or cause to perform, one or more software packets. For example, the one or more sensors 190 may include one or more magnetic flux sensors 142 (e.g., one or more Hall effect sensors) and one or more capacitance sensors 144, which can be used to measure current in a conductor as will be described herein. The one or more sensors 140 can also include transformer type current sensors which may be formed of a conductor wrapped around the magnetic core, which are configured to convert magnetic flux into current in a secondary conductor. The one or more sensors 140 can also include other types of sensors including an inertial measurement unit which includes one or more accelerometers and / or one or more gy roscopes, one or more magnetometers, one or more proximity sensors, one or more infrared sensors, one or more LIDAR sensors, one or more biological sensors (e.g., a heart rate sensor, a pulse sensor, a retinal sensor, and / or a fingerprint sensor), one or more touch sensors (e.g., a conductive touch sensor and / or a mechanical touch sensor), etc.

[0044] The user computing system 100 may include an input device 150 configured to receive an input from a user and may include, for example, one or more of a keyboard (e.g., a physical keyboard, virtual keyboard, etc.), a mouse, a joystick, a button, a switch, an electronic pen or stylus, a gesture recognition sensor (e.g., to recognize gestures of a user including movements of a body part), an input sound device or speech recognition sensor (e.g., a microphone to receive a voice input such as a voice command or a voice query), a track ball, a remote controller, a portable (e.g., a cellular or smart) phone, a tablet PC, a pedal or footswitch, a virtual-reality' device, and so on. The input device 150 may also be embodied by a touch-sensitive display having a touchscreen capability, for example. Forexample, the input device 150 may be configured to receive an input from a user associated with the input device 150 for executing the current sensing application 132, for accepting or declining suggestions or recommendations provided by the user computing system 100 with respect to performing operations associated with the current sensing application 132, with respect to selecting various options for taking a current measurement, etc.

[0045] The user computing system 100 may include a display device 160 which displays information viewable by the user (e.g., via a user interface screen provided via user interface 162). For example, the display device 160 may be a non-touch sensitive display or a touch- sensitive display. The display device 160 may include a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, active matrix organic light emitting diode (AMOLED), flexible display, 3D display, a plasma display panel (PDP), a cathode ray tube (CRT) display, and the like, for example. However, the disclosure is not limited to these example displays and may include other types of displays. The display device 160 can be used by the application system 130 provided at the user computing system 100 to display information to a user relating to a cunent which is being measured, errors regarding a current measurement, instructions for measuring current, etc. The user interface 162 can be configured to receive inputs and / or provide data for display (e.g., image data, text data, audio data, one or more user interface elements, an augmented-reality experience, a virtual reality experience, and / or other data for display). The user interface 162 may be associated with one or more other computing systems (e.g., the server computing system 300). The display device 160 can be configured to provide, for presentation to a user, one or more user interface screens via the user interface 162 having user interface elements which are selectable by the user. The user interface elements which are selectable by the user can be configured to confirm the completion of a current measurement operation, confirm the selection of an option of the current sensing application 132, provide feedback regarding an error message, etc.

[0046] The user computing system 100 may include an output device 170 to provide an output to the user and may include, for example, one or more of an audio device (e.g., one or more speakers), a haptic device to provide haptic feedback to a user (e.g., a vibration device), a light source (e.g., one or more light sources such as LEDs which provide visual feedback to a user), a thermal feedback system, and the like. For example, the output device 170 may provide an output relating to the completion of a cunent measurement operation, confirmingthe selection of an option of the current sensing application 132, regarding an error message, etc.

[0047] Though not shown in FIG. 1 A, in some implementations the user computing system 100 can include a position determination device that can determine a current geographic location of the user computing system 100 which can be communicated to the server computing system 300 over network 400. The position determination device can be any device or circuitry for analyzing the position of the user computing system 100 (e.g. a GPS system, a Galileo positioning system, the GLObal Navigation satellite system (GLONASS), the BeiDou Satellite Navigation and Positioning system, an inertial navigation system, a dead reckoning system, based on an IP address, by using triangulation and / or proximity to cellular towers or WiFi hotspots, and / or other suitable techniques for determining a position of the user computing system 100).

[0048] Though not shown in FIG. 1 A, in some implementations the user computing system 100 can also include a capture device that is capable of capturing images (e.g., photos, videos, etc.). For example, the image capturer can include one or more cameras having an imaging sensor (e.g., a complementary metal-oxide-semiconductor (CMOS) or charge- coupled device (CCD)) to capture, detect, or recognize objects, materials, and the like, at an inspection site or structure. For example, the camera can be an infrared camera, a thermal camera, a visible light camera, etc. Other image types may include ultraviolet images, x-ray images, multi-spectral images, etc. For example, the capture device 180 can include a sound capturer (e.g., a microphone) which is configured to capture sound or audio (e.g., an audio recording).

[0049] Referring again to the server computing system 300, in some example embodiments, the server computing system 300 includes an application system 330, For example, the application system 330 may include a current sensing application 132. The application system 330 can include various other applications including document applications, text messaging applications, email applications, media (image, video, etc.) applications, dictation applications, virtual keyboard applications, browser applications, map applications, social media applications, navigation applications, etc. The server computing system 300 can communicate with the user computing system 100 according to a client- server relationship. For example, the current sensing application 132 can be implemented by the server computing system 300 as a portion of a web service (e.g., via an application programming interface). The server computing system 300 may store and / or provide one ormore user interfaces 340 for obtaining input data and / or providing output data to one or more users. The one or more user interfaces 340 can include one or more user interface elements, which may include input fields, navigation tools, content chips, selectable tiles, widgets, data display carousels, dynamic animation, informational pop-ups, image augmentations, text-to- speech, speech-to-text, augmented-reality, virtual -reality, feedback loops, and / or other interface elements.

[0050] FIG. IB illustrates an example block diagram of a computing system including a current sensing application for measuring current carried by a conductor. For example, the computing system can be implemented to perform electrical inspections on electrical systems and equipment (e.g., motors, pumps, gearboxes, HVAC systems and equipment, transformer circuits, renewable energy systems including solar inverters and battery banks, automotive and marine circuits, household appliances, etc.) in a variety of environments (e.g., residential, commercial, and industrial settings).

[0051] In FIG. IB, the example computing system 1200 includes a current sensing application 1210 having a capacitance determiner 1212, magnetic reluctance determiner 1213, first correction factor determiner 1214, second correction factor determiner 1216, current determiner 1218, and cunent corrector 1219. The current sensing application 1210 of FIG. IB is merely an example, and the cunent sensing application 1210 may have fewer components or more components than that shown in FIG. IB. Further, some components may be combined. The cunent sensing application 1210 of FIG. IB may conespond to the current sensing application 132 and / or current sensing application 332.

[0052] Referring to FIG. IB, the computing system 1200 receives information (data) from the magnetic flux sensor 142 and the capacitance sensor 144,

[0053] In some implementations, the magnetic flux sensor 142 may be disposed or embedded in the magnetic core, for example at the air gap, and can include a sensor (e.g., a Hall Effect sensor) that is configured to provide an output in response to the magnetic field produced by the current. The magnetic flux induces a voltage in the magnetic flux sensor 142 which is proportional to the magnetic field strength, which is directly related to the current. For example, the current determiner 1218 may be configured to determine a first current value based on the output signal (e.g., the voltage) of the magnetic flux sensor 142. In some implementations, the first correction factor determiner 1214 may be configured to determine or calculate a first correction factor based on correlation data between the magnetic flux and the first current value (e.g., measured current value prior to correction). In someimplementations, the first correction factor determiner 1214 may be configured to retrieve the first correction factor (“KI”) from the first correction factor correlation database 1220 according to the magnetic flux measured by the magnetic flux sensor 142, the first current value, and / or the voltage measured by the magnetic flux sensor 142. The first correction factor may account for losses, core characteristics, and sensor sensitivity of the magnetic flux sensor 142, etc. The first correction factor correlation database 1220 may store a plurality of correction factors that are associated with various conditions and parameters including varying current amounts, varying frequencies, vary ing magnetic flux values, varying air gap distances, varying air gap geometries, etc. For example, the plurality of correction factors may be determined at the factory or manufacturer and stored on device and / or stored remotely. In some implementations, the current determiner 1218 may be configured to determine the first current value based on the output signal (e.g., the voltage) of the magnetic flux sensor 142 and the first correction factor determined by the first correction factor determiner 1214.

[0054] In some implementations, the capacitance sensor 144 may include probes that are disposed across two (opposing) surfaces of the air gap to measure the capacitance. In some implementations, the capacitance sensor 144 may comprise a circuit (e.g., an AC circuit) in which an electrical voltage is applied across the air gap and the capacitance value is obtained (determined) by the capacitance determiner 1212 based on the measured current (displacement current across the air gap) according to the relationship of I=V-®-C where I is the displacement current, V is the applied AC voltage across the air gap, co is the angular frequency of the AC signal, and C is the capacitance across the air gap. In some implementations, the capacitance sensor 144 may comprise a circuit (e.g., an AC circuit) in which an electrical voltage is applied across the air gap and the capacitance value is obtained (determined) by the capacitance determiner 1212 indirectly based on the ratio of current to voltage which serves as an indicator of the effective capacitance across the gap. For example, the ratio of I / V is proportional to the capacitance when the frequency is constant. Thus, changes in the ratio of I / V directly indicates changes in capacitance. Observing the changes in the ratio can reflect changes in the air gap’s characteristics, for example. Thus, the capacitance sensor 144 may be configured to apply an AC voltage across the magnetic air gap and measure the resulting current, and the capacitance determiner 1212 can either calculate the capacitance directly or monitor the ratio of I / V to determine changes in the system’s capacitance. In some implementations, the capacitance sensor 144 can further include an electrically isolated plate that is localized to (positioned at) the magnetic air gap.The plate can be electrically isolated from the magnetic core to ensure that only the capacitance is measured across the magnetic air gap to avoid parasitic capacitances from the magnetic core or other components. When there are a plurality of magnetic air gaps, the capacitance sensor 144 can include respective electrically isolated plates that are localized to (positioned at) each of the magnetic air gaps to determine capacitance information at each magnetic air gap. For example, variations in air gap size can be determined for each magnetic air gap based on the respective capacitance information.

[0055] For example, the current determiner 1218 may be configured to determine a second current value based on the first cunent value and a corrective amount determined by the current corrector 1219 which is determined based on the capacitance information determined by the capacitance determiner 1212. In some implementations, the current determiner 1218 may be configured to determine a current value (without first determining the first current value) based on the output signal (e.g., the voltage) of the magnetic flux sensor 142 and the corrective amount determined by the current corrector 1219 which is determined based on the capacitance information determined by the capacitance determiner 1212. In some implementations, the current determiner 1218 may be configured to determine the current value (without first determining the first current value) based on the output signal (e.g., the voltage) of the magnetic flux sensor 142, the first correction factor (“KI”) and a second correction factor (“K2”) determined by the second correction factor determiner 1216.

[0056] In some implementations, the second correction factor determiner 1216 may be configured to determine or calculate the second correction factor based on correlation data (relationship information) between the capacitance information and the magnetic flux measured by the magnetic flux sensor 142 and a measured current value (e.g., prior to correction). In some implementations, the second correction factor determiner 1216 may be configured to retrieve the second correction factor (“K2”) from the second correction factor correlation database 1230 according to the magnetic flux measured by the magnetic flux sensor 142 and / or the voltage measured by the magnetic flux sensor 142 and the capacitance information obtained via the capacitance determiner 1212 and the capacitance sensor 144.The second correction factor may account for magnetic reluctance losses, core characteristics, and sensor sensitivity of the magnetic flux sensor 142, capacitance sensor 144, etc. The second correction factor correlation database 1230 may store a plurality of correction factors that are associated with various conditions and parameters including varying current amounts, varying frequencies, varying capacitance values, varying magnetic flux values, varying air gap distances, varying air gap geometries, etc. For example, the plurality of correctionfactors may be determined at the factory or manufacturer and stored on device and / or stored remotely. Factory' calibration can be done at multiple points around the air gap to establish the inverse relationship between the capacitance and the size of the air gap, considering various factors, e.g., the overlapping surface area around the air gap, the geometry' of the air gap and associated surfaces of the core, the size of the air gap, the distance of the air gap, range of the air gap, etc. In some implementations, the current determiner 1218 may be configured to determine the current value (or second current value) based on the output signal (e.g., the voltage) of the magnetic flux sensor 142, the first correction factor determined by the first correction factor determiner 1214, and the second correction factor determined by the second correction factor determiner 1216.

[0057] The current sensing application 1210 may be configured to provide as an output 1240 the current value (the corrected current value) as determined according to the methods described herein. For example, the output 1240 can be provided for presentation via a display, via a speaker, etc.

[0058] In some implementations, the magnetic reluctance determiner 1213 is configured to compute magnetic reluctance information based on the capacitance information determined by the capacitance determiner 1212. For example, the relationship between capacitance and magnetic reluctance may be represented as a curved, inverse relationship. The magnetic reluctance information can also be used to determine an accurate current measurement similar to the capacitance information. The magnetic reluctance information predicts or is indicative of what percentage of the magnetic flux is being captured in the magnetic core versus what percentage of the magnetic flux is being leaked. As the magnetic reluctance increases, the amount of magnetic flux leakage increases. Likewise, as the magnetic reluctance decreases, the amount of magnetic flux leakage decreases, and a higher percentage of the magnetic flux will pass through the core. Based on the measurement of the magnetic flux that passes through the core, the current determiner 1218 and / or current corrector 1219 may be configured to correlate the capacitance via magnetic reluctance to the percentage of magnetic flux that goes through the magnetic flux sensor 142 to determine the corrected current value.

[0059] FIG. 2A is an example method 2100 for measuring current via a current measuring device, according to example embodiments of the disclosure. Although shown in a particular sequence or order, unless otherwise specified, the order of the processes can be modified. Thus, the illustrated embodiments should be understood only as examples, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel. Additionally, one or more processes can be omitted in variousembodiments. Thus, not all processes are required in every embodiment. Other process flows are possible.

[0060] Referring to FIG. 2A, at operation 2110 the method 2100 includes a computing device encircling or enclosing a conductor with a magnetic core of the computing device. As described herein, the computing device may be embodied as the user computing system 100 and / or computing system 1200 which may include a cunent measuring device including a clamp meter. For example, a user can position the conductor at an interior portion of the computing device such that the conductor is surrounded by the magnetic core of the computing device. Example clamp meters are shown with respect to FIGS. 3A-7B and are described further herein. According to examples of the disclosure, air gaps are formed at ends of the magnetic cores of the clamp meter, and the ends may be secured or held together to keep the air gaps at a controlled distance. However, less rigid clamping can be applied compared to previous devices by using a capacitive correlation while maintaining a measurement accuracy.

[0061] At operation 2120 the method 2100 includes the computing device measuring magnetic flux. For example, as described with respect to FIG. IB, the magnetic flux sensor 142 may be disposed or embedded in the magnetic core, for example at the air gap, and can include a sensor (e.g., a Hall Effect sensor) that is configured to measure the magnetic flux and provide an output in response to the magnetic field produced by the current which passes through the conductor. The magnetic flux induces a voltage in the magnetic flux sensor 142 which is proportional to the magnetic field strength, which is directly related to the current.

[0062] At operation 2130 the method 2100 includes the computing device measuring a value related to a capacitance of the air gap. For example, as described with respect to FIG. IB, the capacitance sensor 144 may be configured to measure an actual capacitance or value indicative of capacitance through a relative measure of current and voltage. The capacitance sensor 144 can include probes that are disposed across two (opposing) surfaces of the air gap to measure the capacitance. In some implementations, the capacitance sensor 144 may comprise a circuit (e.g., an AC circuit) in which an electrical voltage is applied across the air gap and the capacitance value is obtained (determined) by the capacitance determiner 1212 based on the measured current (displacement current across the air gap) or based on the ratio of current to voltage which serves as an indicator of the effective capacitance across the gap.

[0063] At operation 2140, the method 2100 includes the computing device calculating or obtaining a first correction factor (“KI”) which can be used for calculating a current value. For example, as described with respect to FIG. IB, the first correction factor determiner 1214may be configured to determine or calculate the first correction factor based on correlation data relating to the magnetic flux and the measured current prior to correction.

[0064] At operation 2150, the method 2100 includes the computing device retneving or obtaining magnetic flux correlation to electrical current data that can be used to determine the first correction factor (“KI”) which can be used for calculating the current value. In some implementations, the first correction factor determiner 1214 may be configured to retrieve or receive the first correction factor (“KI”) from the first correction factor correlation database 1220 according to the magnetic flux measured by the magnetic flux sensor 142 and / or the voltage measured by the magnetic flux sensor 142. The first correction factor may account for losses, core characteristics or properties, and sensor sensitivity of the magnetic flux sensor 142, etc. That is, the first correction factor correlation database 1220 may include calibration data that is associated with one or more magnetic core parameters associated with the magnetic core, which can include properties of the magnetic core, sensor sensitivity of the first magnetic flux sensor, etc. The calibration data can be empirically gathered data that is obtained through testing of the current measuring device under various conditions and environments (e.g., utilizing different voltages, frequencies, currents, varying air gap geometries and spacing, etc.).

[0065] At operation 2160, the method 2100 includes the computing device calculating or obtaining a second correction factor (“K2”) which can be used for calculating a current value. For example, as described with respect to FIG. IB, the second correction factor determiner 1216 may be configured to determine or calculate the second correction factor based on correlation data relating to the magnetic flux and the capacitance value measured at operation 2130.

[0066] At operation 2170, the method 2100 includes the computing device retneving or obtaining electrical capacitance correlation to electrical current data that can be used to determine the second correction factor (“K2”) which can be used for calculating the current value. In some implementations, the second correction factor determiner 1216 may be configured to determine or calculate the second correction factor based on a correlation (relationship) between the capacitance information and the current which is measured (prior to correction), and in some implementations based on a correlation to the magnetic flux measured by the magnetic flux sensor 142. In some implementations, the second correction factor determiner 1216 may be configured to retrieve the second correction factor (“K2”) from the second correction factor correlation database 1230 according to the measured current value, the magnetic flux measured by the magnetic flux sensor 142 and / or the voltagemeasured by the magnetic flux sensor 142, and the capacitance information obtained via the capacitance determiner 1212 and the capacitance sensor 144. The second correction factor may account for magnetic reluctance losses associated with the air gap, core characteristics, and sensor sensitivity of the magnetic flux sensor 142, capacitance sensor 144, etc. That is, the second correction factor correlation database 1230 may include calibration data that is associated with one or more air gap parameters associated with the air gap, which can include properties of the air gap, including an area of the air gap, a distance of the air gap, overlapping surface areas around the air gap, etc. The calibration data can be empirically gathered data that is obtained through testing of the current measuring device under various conditions and environments (e.g., utilizing different voltages, frequencies, currents, varying air gap geometries and spacing, etc.).

[0067] At operation 2180, the method 2100 includes the computing device generating a corrected reading of the electrical current as a function of the magnetic flux, first correction factor, and the second correction factor. For example, as described with respect to FIG. IB, the current sensing application 1210 (e.g., current determiner 1218) may be configured to provide as an output 1240 the current value (the corrected cunent value) as determined according to the methods described herein. That is, the current value associated with the magnetic flux measured by the magnetic flux sensor 142 may be corrected according to the first correction factor and the second correction factor. For example, the current determiner 1218 may be configured to increase the current value associated with the magnetic flux measured by the magnetic flux sensor 142 based on the second correction factor when the capacitance information indicates flux leakage is present according to the magnetic reluctance which is inferred from the capacitance information. In some implementations, the current determiner 1218 may be embodied as one or more processors which are configured to apply the corrections (e.g., the first correction factor and / or the second correction factor) to correct the current value. In some implementations, the current determiner 1218 may be embodied as an analog amplification circuit that is configured to output corrected current values based on the value or output from the capacitance measurement. For example, the current value associated with the magnetic flux measured by the magnetic flux sensor 142 may be corrected according to the capacitance measurement via an analog amplification circuit.

[0068] At operation 2190, the method 2100 includes the computing device outputting the electrical current which is corrected for the air gap. For example, as described with respect toFIG. IB, the current sensing application 1210 may be configured to provide the output 1240 (e.g., for presentation via a display, via a speaker, etc.).

[0069] FIG. 2B is another example method 2200 for measuring current via a current measuring device, according to example embodiments of the disclosure. Although shown in a particular sequence or order, unless otherwise specified, the order of the processes can be modified. Thus, the illustrated embodiments should be understood only as examples, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel. Additionally, one or more processes can be omitted in various embodiments. Thus, not all processes are required in every embodiment. Other process flows are possible.

[0070] Referring to FIG. 2B, at operation 2210 the method 2200 includes a computing device encircling or enclosing a conductor with a magnetic core of the computing device. As described herein, the computing device may be embodied as the user computing system 100 and / or computing system 1200 which may include a cunent measuring device including a clamp meter. For example, a user can position the conductor at an interior portion of the computing device such that the conductor is surrounded by the magnetic core of the computing device. Example clamp meters are shown with respect to FIGS. 3A-7B and are described further herein. According to examples of the disclosure, air gaps are formed at ends of the magnetic cores of the clamp meter, and the ends may be secured or held together to keep the air gaps at a controlled distance. However, less rigid clamping can be applied compared to previous devices by using a capacitive correlation while maintaining a measurement accuracy.

[0071] At operation 2220 the method 2200 includes the computing device applying an electrical voltage across the magnetic air gap. At operation 2230 the method 2200 includes the computing device measuring an electrical current. For example, as described with respect to FIG. IB, the capacitance sensor 144 may be configured to measure an actual capacitance or value indicative of capacitance through a relative measure of current and voltage. In some implementations, the applied electrical voltage may be represented by the equation of V = Vmax*sin(co * t). The capacitance sensor 144 can include probes that are disposed across two (opposing) surfaces of the air gap to measure the capacitance. In some implementations, the capacitance sensor 144 may comprise a circuit (e.g., an AC circuit) in which the electrical voltage V is applied across the air gap and the capacitance value is obtained (determined) by the capacitance determiner 1212 based on the measured current (displacement current across the air gap) or based on the ratio of current to voltage which serves as an indicator of theeffective capacitance across the gap. In some implementations, the measured electrical current may be represented by the equation of I = Imax*cos(co * t).

[0072] At operation 2240 the method 2200 includes the computing device (e.g., current sensing application 1210 including one or more of the current determiner 1218, current corrector 1219, and second correction factor determiner 1216) comparing the value of Imax to values from the calibration data to calculate an adjustment or correction factor of "K". For example, as described with respect to FIG. IB, the correction factor (“K”) can be used for calculating a corrected current value. In some implementations, the second correction factor determiner 1216 may be configured to determine or calculate the second correction factor based on correlation data or a relationship between the maximum current Imax and the capacitance value measured at operation 2220 to a corresponding correction factor among a plurality of correction factors that may be stored. In some implementations, the second correction factor determiner 1216 may be configured to determine or calculate the second correction factor based on correlation data or a relationship between the maximum current Imax to a corresponding correction factor among a plurality of correction factors that may be stored.

[0073] At operation 2250, the method 2200 includes the computing device measuring or obtaining magnetic flux information from the magnetic flux sensor. For example, as described with respect to FIG. IB, the magnetic flux sensor 142 may be disposed or embedded in the magnetic core, for example at the air gap, and can include a sensor (e.g., a Hall Effect sensor) that is configured to measure the magnetic flux and provide an output in response to the magnetic field produced by the current which passes through the conductor. In some implementations, the magnetic flux can induce a voltage in the magnetic flux sensor 142 which is proportional to the magnetic field strength, which is directly related to the current. Other methods can be implemented to measure the electrical current (e.g., prior to correction) via the magnetic flux sensor 142. For example, sensors which might have varying resistance (e.g., magnetoresistive sensors) can indicate the current or a current might be induced rather than a voltage which is proportional to the magnetic field strength. In some implementations, the magnetic flux sensor 142 can include a fluxgate sensor that can be driven with electrical current such that the output (magnetic flux) of the magnetic sensor is nulled. The current needed to null the magnetic sensor can indicate the current flowing through the conductor.

[0074] At operation 2260, the method 2200 includes the computing device generating or obtaining a corrected reading of the electrical current as a function of the magnetic flux andthe correction factor K. For example, as described with respect to FIG. IB, the current sensing application 1210 (e.g., current determiner 1218) may be configured to provide as an output 1240 the current value (the corrected current value) as determined according to the methods described herein. That is, the cunent value associated with the magnetic flux measured by the magnetic flux sensor 142 may be corrected according to the correction factor. For example, the current determiner 1218 may be configured to increase the current value associated with the magnetic flux measured by the magnetic flux sensor 142 based on the correction factor when the capacitance information indicates flux leakage is present according to the magnetic reluctance which is inferred from the capacitance information. For example, as the magnetic air gap distance increases, capacitance will decrease, and magnetic reluctance (and correspondingly flux leakage) will increase. Therefore, applying the correction factor to a value of the current will increase the current to account for the loss due to the magnetic reluctance. For example, as described with respect to FIG. IB, the current sensing application 1210 may be configured to provide the corrected current value as an output 1240 (e.g., for presentation via a display, via a speaker, etc.).

[0075] While the example methods of FIGS. 2A and 2B have been described in the context of measuring capacitance associated with one air gap and measuring magnetic flux with one magnetic flux sensor, it will be appreciated that these are merely examples, and the disclosure is not limited to these examples. For example, in some implementations the example methods of FIGS. 2A and 2B can also be applied to current measuring devices having a plurality of magnetic flux sensors and / or a plurality of air gaps. In some implementations, capacitance information can be obtained for each of the air gaps and magnetic flux measurements can be obtained by each of the magnetic flux sensors. For example, capacitance information can be obtained at each of the air gaps by implementing respective electrical circuits and alternating times at which measurements (e.g., voltage measurements, capacitance measurements, etc.) are taken. In some implementations, the corrected current value can be determined as a function of each of the magnetic flux measurements, a first correction factor that is determined based on the properties of each of the magnetic flux sensors and the magnetic core, and a second correction factor that is determined based on the respective capacitance information associated with each of the air gaps. In some implementations, the corrected current value can be determined as a function of an overall magnetic flux measurement determined based on the measurements of the plurality of magnetic flux sensors, a first correction factor that is determined based on the overall magnetic flux measurement and the properties of each of the magnetic flux sensorsand the magnetic core, and a second correction factor that is determined based on an overall capacitance measurement which is determined based on the respective capacitance information associated with each of the air gaps.

[0076] FIGS. 3A-7B illustrate example current measuring devices, according to examples of the disclosure. Referring to FIG. 3 A, in a first example current measuring device configuration, the cunent measuring device 3100 (e.g., a clamp meter) includes a magnetic core 3110 through which magnetic flux 3120 flows, where the magnetic core 3110 include a plurality of air gaps 3130, 3140. For example, the magnetic core 3110 may have a high magnetic permeability. A material with high magnetic permeability is more easily magnetized in the presence of an applied magnetic field than a material with low magnetic permeability. A magnetic circuit having a matenal with a high magnetic permeability can have a higher magnetic flux in that circuit compared to a magnetic circuit having a material with low magnetic permeability. The plurality of air gaps 3130, 3140 have a low magnetic permeability. At the locations of the plurality of air gaps 3130, 3140 the magnetic flux will exhibit fringing (e.g., as shown in FIG. 3A). Each of the plurality of air gaps 3130, 3140 have a gap with a distance D, for example. In some implementations, the current measuring device 3100 may be configured to measure the capacitance across the air gap 3140 by implementing an electric circuit 3150 which is configured to apply a voltage (e.g., an AC voltage) across the air gap 3140. The current measuring device 3100 may be configured to output a current value determined based on a magnetic flux measured by a magnetic flux sensor (which can be disposed at the air gap 3140) and a correction factor determined according to the capacitance information associated with the air gap 3140 which is obtained based on the voltage applied across the air gap 3140. Similarly, the cunent measuring device 3100 may be configured to output another current value determined based on a magnetic flux measured by a magnetic flux sensor (which can be disposed at the air gap 3130) and a correction factor determined according to the capacitance information associated with the air gap 3130 which can also be obtained based on a voltage applied across the air gap 3130 by implementing an electric circuit (not shown) which is configured to apply a voltage (e.g., an AC voltage) across the air gap 3130. In some implementations, capacitance information for each of the air gap 3130 and the air gap 3140 can be determined via the capacitance sensor 144 which can include respective electrically isolated plates that are localized to (positioned at) each of the air gap 3130 and the air gap 3140. For example, small electrically isolated plates can be disposed on each of the facing ends of air gap 3130 and another pair of electrically isolated plates can be disposed on the facing ends of air gap 3140.

[0077] Referring to FIG. 3B, in a second example current measuring device configuration, the current measuring device 3200 (e.g., a clamp meter) includes a first magnetic core 3210 (first member) which is slidable relative to a second magnetic core 3220 (second member). The first magnetic core 3210 may overlap with the second magnetic core 3220 by a distance L. The current measuring device 3200 may include a plurality of air gaps 3230, 3240. Each of the plurality of air gaps 3230, 3240 have a gap with a distance D, for example. For example, the first magnetic core 3210 and second magnetic core 3220 may each have a high magnetic permeability. A material with high magnetic permeability is more easily magnetized in the presence of an applied magnetic field than a material with low magnetic permeability. The plurality of air gaps 3230, 3240 have a low magnetic permeability. At the locations of the plurality of air gaps 3230, 3240 the magnetic flux will exhibit fringing (e.g., as shown in FIG. 3B). In some implementations, the current measuring device 3200 may be configured to measure the capacitance across the air gap 3240 by implementing an electric circuit 3250 which is configured to apply a voltage (e.g., an AC voltage) across the air gap 3240. The current measuring device 3200 may be configured to output a current value determined based on a magnetic flux measured by a magnetic flux sensor (which can be disposed at the air gap 3240) and a correction factor determined according to the capacitance information associated with the air gap 3240 which is obtained based on the voltage applied across the air gap 3240. Similarly, the cunent measuring device 3200 may be configured to output another current value determined based on a magnetic flux measured by a magnetic flux sensor (which can be disposed at the air gap 3230) and a correction factor determined according to the capacitance information associated with the air gap 3230 which can also be obtained based on a voltage applied across the air gap 3230 by implementing an electric circuit (not shown) which is configured to apply a voltage (e.g., an AC voltage) across the air gap 3230. Therefore, the current measuring device 3200 may have a more flexible design and configuration compared to existing clamp meters.

[0078] Referring to FIG. 4A, in a third example current measuring device configuration, the current measuring device 4100 (e.g., a clamp meter) includes a magnetic core 4110 through which magnetic flux 4120 flows, where the magnetic core 4110 include a plurality of air gaps 4130, 4140. For example, the magnetic core 4110 may have a high magnetic permeability. A material with high magnetic permeability is more easily magnetized in the presence of an applied magnetic field than a material with low magnetic permeability. The plurality of air gaps 4130, 4140 have a low magnetic permeability. At the locations of the plurality of air gaps 4130, 4140 the magnetic flux will exhibit fringing (e.g., as shown in FIG.4A). Each of the plurality of air gaps 4130, 4140 have a gap with a distance D, for example. In some implementations, the current measuring device 4100 may be configured to measure a first capacitance across the air gap 4130 by implementing a first electric circuit 4150 which is configured to apply a first voltage (e.g., an AC voltage) across the air gap 4130 and to measure a second capacitance across the air gap 4140 by implementing a second electric circuit 4160 which is configured to apply a second voltage (e.g., an AC voltage) across the air gap 4140. The current measuring device 4100 may be configured to output a current value determined based on magnetic flux values measured by magnetic flux sensors (which can be disposed at the air gaps 4130, 4140) and a correction factor determined according to the capacitance information associated with the air gaps 4130, 4140 which are obtained based on the voltages applied across the air gaps 4130, 4140. In some implementations, the same voltage and / or a same frequency (e g., 100 kHz sinusoid) may be utilized with respect to the first electric circuit 4150 and the second electric circuit 4160. In some implementations, a different voltage and / or a different frequency may be utilized with respect to the first electric circuit 4150 and the second electric circuit 4160. In some implementations, the capacitance information from each of the first electric circuit 4150 and the second electric circuit 4160 may be measured at the same time. In some implementations, the capacitance information from the first electric circuit 4150 may be measured for a first duration of time and the capacitance information from the second electric circuit 4160 may be measured for a second duration of time (e.g., in an alternating manner). The current measuring device 4100 may be configured to output a corrected current value determined based on one or more magnetic flux measurements measured by one or more magnetic flux sensors (which can be disposed at the air gaps 4130, 4140) and one or more correction factors determined according to the first and second capacitance information associated with the air gaps 4130, 4140.

[0079] Referring to FIG. 4B, in a fourth example current measuring device configuration, the current measuring device 4200 (e.g., a clamp meter) includes a first magnetic core 4210 which is slidable relative to a second magnetic core 4220. The first magnetic core 4210 may overlap with the second magnetic core 4220 by a distance L. The current measuring device 4200 may include a plurality of air gaps 4230, 4240, Each of the plurality of air gaps 4230, 4240 have a gap with a distance D, for example. For example, the first magnetic core 4210 and second magnetic core 4220 may each have a high magnetic permeability. A material with high magnetic permeability is more easily magnetized in the presence of an applied magnetic field than a material with low magnetic permeability. The plurality of air gaps 4230, 4240 have a low magnetic permeability. At the locations of theplurality of air gaps 4230, 4240 the magnetic flux will exhibit fringing (e.g., as shown in FIG. 4B). In some implementations, the current measuring device 4200 may be configured to measure the capacitance across the air gap 4230 by implementing a first electric circuit 4250 which is configured to apply a first voltage (e.g., an AC voltage) across the air gap 4230. In some implementations, the current measuring device 4200 may be configured to measure the capacitance across the air gap 4240 by implementing a second electric circuit 4260 which is configured to apply a second voltage (e.g., an AC voltage) across the air gap 4240. In some implementations, the same voltage and / or a same frequency (e.g., 100 kHz sinusoid) may be utilized with respect to the first electric circuit 4250 and the second electric circuit 4260. In some implementations, a different voltage and / or a different frequency may be utilized with respect to the first electric circuit 4250 and the second electric circuit 4260. In some implementations, the capacitance information from each of the first electric circuit 4250 and the second electric circuit 4260 may be measured at the same time. In some implementations, the capacitance information from the first electric circuit 4250 may be measured for a first duration of time and the capacitance information from the second electric circuit 4260 may be measured for a second duration of time (e.g., in an alternating manner). The current measuring device 4200 may be configured to output a corrected current value determined based on one or more magnetic flux measurements measured by one or more magnetic flux sensors (which can be disposed at the air gaps 4230, 4240) and one or more correction factors determined according to the first and second capacitance information associated with the air gaps 4230, 4240. Therefore, the current measuring device 4200 may- have a more flexible design and configuration compared to existing clamp meters.

[0080] Referring to FIG. 5A, in a fifth example current measuring device configuration, the current measuring device 5100 (e.g., a clamp meter) includes a housing 5110 which can accommodate a first magnetic core 5120 (first member) and a second magnetic core 5130 (second member) which may be partially accommodated in the housing 5110. As depicted in FIG. 5 A, the current measuring device 5100 is shown in a first position where a conductor 5150 can initially be disposed outside an interior portion located between the first magnetic core 5120 and the second magnetic core 5130, and a first end of the second magnetic core 5130 is spaced apart from a first end of the first magnetic core 5120.

[0081] Referring to FIG. 5B, the fifth example current measuring device configuration is shown in a second position, where the second magnetic core 5130 is moved (slid) in a direction 5190 such that the second magnetic core 5130 is further accommodated in the housing 5110 and is disposed closer to the first magnetic core 5120. As depicted in FIG. 5Bthe conductor 5150 has been moved in a direction 5160 so that the conductor 5150 is disposed inside the interior portion between the first magnetic core 5120 and the second magnetic core 5130. In some implementations, the second magnetic core 5130 may also be movable (e.g., slidable) relative to the first magnetic core 5120 in the direction 5160. The first magnetic core 5120 may overlap with the second magnetic core 5130 such that the first end of the first magnetic core 5120 overlaps with the first end of the second magnetic core 5130. An overlap amount between the first end of the second magnetic core 5130 and the first end of the first magnetic core 5120 may be variable. The current measuring device 5100’ (in the second position) may include a plurality of air gaps 5170, 5180. Each of the plurality of air gaps 5170, 5180 have a gap with a certain distance. For example, the first magnetic core 5120 and second magnetic core 5130 may each have a high magnetic permeability. A material with high magnetic permeability is more easily magnetized in the presence of an applied magnetic field than a material with low magnetic permeability. The plurality of air gaps 5170, 5180 have a low magnetic permeability. At the locations of the plurality of air gaps 5170, 5180 the magnetic flux will exhibit fringing. In some implementations, the current measuring device 5100’ may be configured to measure the capacitance across the plurality of air gaps 5170, 5180 (e.g., as described with respect to FIGS. 3B and 4B) by implementing one or more electric circuits (not shown in FIGS. 5A- 5B). In some implementations, the same voltage and / or a same frequency (e.g., 100 kHz sinusoid) may be utilized with respect to the one or more electric circuits. In some implementations, a different voltage and / or a different frequency may be utilized with respect to the one or more electric circuits. In some implementations, the capacitance information from each of the one or more electric circuits may be measured at the same time. In some implementations, the capacitance information from a first electric circuit may be measured for a first duration of time and the capacitance information from a second electric circuit may be measured for a second duration of time (e g., in an alternating manner). The current measuring device 5100’ may be configured to output a corrected current value determined based on one or more magnetic flux measurements measured by one or more magnetic flux sensors (which can be disposed at the plurality of air gaps 5170, 5180) and one or more correction factors determined according to the capacitance information associated with the plurality of air gaps 5170, 5180. Therefore, the current measuring device 5100’ may have a more flexible design and configuration compared to existing clamp meters. For example, the current measuring device 5100’ can have varying magnetic air gaps, but by correlating thecapacitance of the air gaps to the magnetic reluctance, novel and flexible core geometries can be implemented without negative impact to the accuracy of the current calculation.

[0082] Referring to FIG. 6, in a sixth example current measuring device configuration, the current measuring device 6100 (e.g., a clamp meter) includes a housing 6110 which can accommodate a first magnetic core 6120 and a second magnetic core 6130. The first magnetic core 6120 may be partially accommodated in the housing 6110 while the second magnetic core 6130 may be disposed outside of the housing 6110. In some implementations, the first magnetic core 6120 may be movable (e.g., slidable, rotatable, etc.) relative to the second magnetic core 6130 in a direction 6170. As depicted in FIG. 6, a conductor 6150 maybe disposed in an interior portion 6180 of the current measuring device 6100. For example, the first magnetic core 6120 can be moved (slid or rotated) in the direction 6170 such that an end of the first magnetic core 6120 is disposed adjacent to an end of the second magnetic core 6130 where an air gap 6160 is formed between the two ends. In some implementations, the first magnetic core 6120 may overlap with a portion of the second magnetic core 6130. In some implementations, the cunent measuring device 6100 may include a plurality of air gaps. For example, a second air gap 6140 can be provided at another portion inside of the housing 6110. In some implementations, the second magnetic core 6130 may be comprised of a rigid high magnetic permeability material that is less flexible (more rigid) than the high magnetic permeability' material from which the first magnetic core 6120 is formed. In some implementations, the current measuring device 6100 may be configured to measure the capacitance across the plurality of air gaps (e.g., as described with respect to FIGS. 3B and 4B) by implementing one or more electric circuits (not shown in FIG. 6). In some implementations, the same voltage and / or a same frequency (e.g., 100 kHz sinusoid) may be utilized with respect to the one or more electric circuits. In some implementations, a different voltage and / or a different frequency may be utilized with respect to the one or more electric circuits. In some implementations, the capacitance information from each of the one or more electric circuits may be measured at the same time. In some implementations, the capacitance information from a first electric circuit may be measured for a first duration of time and the capacitance information from a second electric circuit may be measured for a second duration of time (e.g., in an alternating manner). The current measuring device 6100 may be configured to output a corrected current value determined based on one or more magnetic flux measurements measured by one or more magnetic flux sensors (which can be disposed at the plurality of air gaps) and one or more correction factors determined according to the capacitance information associated with the plurality of air gaps. Therefore, the currentmeasuring device 6100 may have a more flexible design and configuration compared to existing clamp meters. For example, the current measuring device 6100 can have varying magnetic air gaps (e.g., with different distances), but by correlating the capacitance of the air gaps to the magnetic reluctance, novel and flexible core geometries can be implemented without negative impact to the accuracy of the current calculation.

[0083] Referring to FIG. 7A, in a seventh example current measuring device configuration, the current measuring device 7100 (e.g., a clamp meter) includes a housing 7110 which can accommodate a magnetic core 7120. The magnetic core 7120 may be partially accommodated in the housing 7110. In some implementations, the magnetic core 7120 may be formed of a rigid high magnetic permeability material. As depicted in FIG. 7A, a conductor 7150 may be disposed in an interior portion 7180 of the current measuring device 7100. For example, the current measuring device 7100 may further include a flexible member comprising a flexible material 7130 (e.g., formed of polyimide) with a thin high magnetic permeability material 7140 (e.g., formed of a permalloy deposited as a thin film) which extends from one end of the magnetic core 7120 and is spaced apart from another end of the magnetic core 7120 to form an air gap 7160. The flexible member is configured to bend to allow the conductor 7150 to enter interior portion 7180 that is enclosed by the rigid magnetic core 7120. The flexible member is configured to return to proximate to the rigid magnetic core 7120 after being bent to allow the conductor 7150 to enter the interior portion 7180.

[0084] In some implementations, the current measuring device 7100 may include a plurality of air gaps. For example, a second air gap can be provided at another portion inside of the housing 7110. In some implementations, the current measuring device 7100 may be configured to measure the capacitance across the plurality of air gaps (e.g., as described with respect to FIGS. 3B and 4B) by implementing one or more electric circuits (not shown in FIG. 7A). In some implementations, the same voltage and / or a same frequency (e.g., 100 kHz sinusoid) may be utilized with respect to the one or more electric circuits. In some implementations, a different voltage and / or a different frequency may be utilized with respect to the one or more electric circuits. In some implementations, the capacitance information from each of the one or more electric circuits may be measured at the same time. In some implementations, the capacitance information from a first electric circuit may be measured for a first duration of time and the capacitance information from a second electric circuit may be measured for a second duration of time (e g., in an alternating manner). The current measuring device 7100 may be configured to output a corrected current value determinedbased on one or more magnetic flux measurements measured by one or more magnetic flux sensors (which can be disposed at the plurality of air gaps) and one or more correction factors determined according to the capacitance information associated with the plurality of air gaps. Therefore, the current measuring device 7100 may have a more flexible design and configuration compared to existing clamp meters. For example, the current measuring device 7100 can have varying magnetic air gaps (e.g., with different distances), but by correlating the capacitance of the air gaps to the magnetic reluctance, novel and flexible core geometries can be implemented without negative impact to the accuracy of the current calculation.

[0085] Referring to FIG. 7B, in an eighth example current measuring device configuration, the current measuring device 7200 (e.g., a clamp meter) includes a housing 7210 which can accommodate a magnetic core 7220. The magnetic core 7220 may be partially accommodated in the housing 7210. In some implementations, the magnetic core 7220 may be formed of a rigid high magnetic permeability material. As depicted in FIG. 7B, a conductor 7250 may be disposed in an interior portion 7290 of the current measuring device 7200. For example, the current measuring device 7200 may further include a first flexible member comprising a first flexible material 7230 (e.g., formed of polyimide) with a first thin high magnetic permeability material 7240 (e.g., formed of a permalloy deposited as a thin film) which extends from one end of the magnetic core 7220. For example, the current measuring device 7200 may further include a second flexible member comprising a second flexible material 7280 (e.g., formed of polyimide) with a second thin high magnetic permeability material 7270 (e.g., formed of a permalloy deposited as a thin film) which extends from another end of the magnetic core 7220. In some implementations, the first thin high magnetic permeability material 7240 may be disposed to face the second thin high magnetic permeability material 7270 such that an air gap 7260 is formed between the first thin high magnetic permeability material 7240 and the second thin high magnetic permeability material 7270. The first flexible member and the second flexible member may be configured to bend to allow the conductor 7250 to enter interior portion 7290 that is enclosed by the rigid magnetic core 7220. The first flexible member and the second flexible member may be configured to return to a position in which they are proximate to each other after being bent to allow the conductor 7250 to enter the interior portion 7290.

[0086] In some implementations, the current measuring device 7200 may include a plurality of air gaps. For example, a second air gap can be provided at another portion inside of the housing 7210. In some implementations, the current measuring device 7200 may be configured to measure the capacitance across the plurality of air gaps (e.g., as described withrespect to FIGS. 3B and 4B) by implementing one or more electric circuits (not shown in FIG. 7B). In some implementations, the same voltage and / or a same frequency (e.g., 100 kHz sinusoid) may be utilized with respect to the one or more electric circuits. In some implementations, a different voltage and / or a different frequency may be utilized with respect to the one or more electric circuits. In some implementations, the capacitance information from each of the one or more electric circuits may be measured at the same time. In some implementations, the capacitance information from a first electric circuit may be measured for a first duration of time and the capacitance information from a second electric circuit may be measured for a second duration of time (e g., in an alternating manner). The current measuring device 7200 may be configured to output a corrected current value determined based on one or more magnetic flux measurements measured by one or more magnetic flux sensors (which can be disposed at the plurality of air gaps) and one or more correction factors determined according to the capacitance information associated with the plurality of air gaps. Therefore, the current measuring device 7200 may have a more flexible design and configuration compared to existing clamp meters. For example, the current measuring device 7200 can have varying magnetic air gaps (e.g., with different distances), but by correlating the capacitance of the air gaps to the magnetic reluctance, novel and flexible core geometries can be implemented without negative impact to the accuracy of the current calculation.

[0087] The example current measuring devices of FIGS. 3A-5B may be appropriate and useful for environments in which a panel box is crowded as rigid magnetic core materials can keep one conductor (e.g., a cable) away from other conductors (other cables) to help the current measuring device shielded from extraneous electrical fields. The example current measuring devices of FIGS. 6-7B may be appropriate and useful for environments in which quick readings are needed, as the flexible members can allow for the conductor to easily enter the interior portion for measurements of the current.

[0088] In some implementations, a current measuring device (e.g., user computing system 100, computing system 1200, etc.) can be configured to provide error indications under certain conditions, flag increased error tolerance, and / or output a confidence level, associated with a current measurement. For example, if the air gap increases to a distance beyond some threshold level, the capacitance measurement may be less than an acceptable threshold level. In response to the capacitance measurement being less than the acceptable threshold level, the current measuring device (e.g., current sensing application 1210) may be configured to provide the error condition, flag increased error tolerance, and / or output a confidence level, associated with the current measurement. For example, if the air gapincreases to a distance beyond some threshold level, the distance may not have been calibrated for and a corresponding capacitance measurement may be out of range such that a correction factor has not been determined.

[0089] In some implementations, a current measuring device (e.g., user computing system 100, computing system 1200, etc.) can be configured to generate, and provide for presentation to a user, an indication of the confidence level of the calibrated value of the current in the conductor. In various embodiments, the confidence level may be based on a value reflecting the capacitance information of one or more air gaps of the magnetic core. Further, the current measuring device may be configured to generate, and provide for presentation to a user, a notification, alert, message, etc., to a user associated with the current measuring device in response to the confidence level of the corrected current value of the current in the conductor being less than a threshold confidence value.

[0090] In some implementations, the current measuring device (e.g., user computing system 100, computing system 1200, etc.) can be configured to generate, and provide for presentation to a user, other notifications, alerts, messages, etc., regarding the operation of the current measuring device. For example, the current measuring device can be configured to provide an output alerting a user associated with the cunent measuring device in response to a measured capacitance value being less than a threshold capacitance value (e.g., when the air gap becomes too large). For example, the current measuring device can be configured to provide an output alerting a user associated with the cunent measuring device in response to a difference between the corrected electric cunent value and the uncorrected electric cunent value (e.g., a cunent value of the conductor indicated by the magnetic flux measured by the magnetic flux sensor) being greater than a threshold difference value.

[0091] Refernng now to FIG. 8, depicted is a flowchart diagram of an example method 8100 for training one or more machine-learned models. In some implementations, one or more machine-learned models may be implemented by the user computing system 100 and / or server computing system 300 to implement one or more operations of the methods described herein (e.g., method 2100 and / or method 2200). For example, one or more machine-learned models may be trained to predict or estimate a correction factor for correcting a cunent value based on inputs including magnetic flux information and capacitance information. The method 8100 can facilitate the iterative process of refining a machine-learned model to improve its performance in generating predictions based on input data. The method 8100 can be implemented by a computing system that includes one or more computing devices, whichcan execute the steps of the method 8100 using one or more machine-learned models stored in memory.

[0092] At 8110, the method 8100 can include obtaining a training example. This training example can be a piece of data or a set of data used to train the machine-learned model. Training examples can be sourced from various datasets, such as a training dataset, a validation dataset, or a testing dataset, and can be labeled or unlabeled, depending on the learning paradigm employed (e.g., supervised, unsupervised, semi-supervised, or reinforcement learning).

[0093] At 8120, the method 8100 can include processing the training example to generate a prediction. This step can involve using one or more machine-learned models to analyze the training example and produce an output. The output can be a direct prediction from the machine-learned model or can result from a sequence of processing operations that include the model’s output. The processing can leverage various machine learning techniques, such as neural networks, decision trees, or support vector machines, to interpret the training example and derive a prediction.

[0094] At 8130, the method 8100 can include receiving an evaluation signal (e.g., loss signal) associated with the prediction. The loss signal can be computed using a loss function that measures the discrepancy between the predicted output and the ground truth or expected output. Different types of loss functions can be utilized, such as mean squared error for regression tasks or cross-entropy loss for classification tasks. The loss signal provides feedback on the accuracy of the prediction, which can be used to adjust the parameters of the machine-learned model.

[0095] At 8140, the method 8100 can include updating the machine-learned model using the loss signal. This operation can involve adjusting the values of the model’s parameters to minimize the loss signal, thereby improving the model’s predictive capability. Techniques such as gradient descent or backpropagation can be employed to iteratively update the parameters based on the gradient of the loss signal with respect to the parameter values. This updating process can be performed over multiple training iterations, with the objective of converging to a set of parameter values that yield the best performance of the model on the training data.

[0096] The method 8100 can be part of a larger training procedure that includes pretraining, fine-tuning, and potentially refining the model with user feedback. Pre-training can involve training the model on a large-scale dataset to establish a broad performance base, while fine-tuning can focus on smaller-scale training on higher-quality data. User feedbackcan further refine the model’s performance by incorporating real-world usage data and human evaluations.

[0097] In some implementations, the method 8100 can be adapted to various stages of model training, allowing for flexibility in the training process. For instance, certain portions of the machine-learned model can be “frozen” during fine-tuning to retain information learned from broader domains, or the method 8100 can be implemented for specific tasks such as online training or reinforcement learning based on runtime inferences.

[0098] Overall, the method 8100 provides a structured approach to training machine- learned models, enabling the iterative improvement of model performance through the acquisition of training examples, processing to generate predictions, receiving loss signals, and updating the model parameters. This structured training process can enhance the ability of machine-learned models to accurately predict outcomes and generalize to new, unseen data.

[0099] Referring now to FIG. 9, a block diagram illustrates an example implementation of a sequence processing model(s) 4 configured to process sequences of information. The sequence processing model(s) 4 can receive input(s) 2, which may include a variety of data types such as text, images, audio, or other forms of data. These input(s) 2 are then processed to obtain an input sequence 5, which is a representation of the data in a format understood by the sequence processing model(s) 4.

[0100] Sequence processing model(s) 4 can include one or multiple machine-learned model components configured to ingest, generate, or otherwise reason over sequences of information. For example, some example sequence processing models in the text domain are referred to as “Large Language Models,” or LLMs. Other example sequence processing models can operate in other domains, such as image domains, see, e.g., Dosovitskiy et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, ARXIV:2010.11929v2 (Jun. 3, 2021), audio domains, biochemical domains, , by way of example. Sequence processing model(s) 4 can process one or multiple types of data simultaneously. Sequence processing model(s) 4 can include relatively large models (e.g., more parameters, computationally expensive, etc ), relatively small models (e.g., fewer parameters, computationally lightweight, etc.), or both.

[0101] The input sequence 5 comprises a senes of input elements, denoted as element 5- 1, element 5-2, ..., through element 5-M, where M represents the total number of elements in the input sequence 5. Each element within input sequence 5 can represent discrete orcontinuously distributed data points within an embedding space, capturing the essential information conveyed by input(s) 2.

[0102] The sequence processing model(s) 4 further include prediction layer(s) 6, which can process the input sequence 5 to generate an output sequence 7. The prediction layer(s) 6 are composed of one or more machine-learned model architectures that manipulate and transform the input elements to extract higher-order meaning and relationships between them. This transformation allows for the prediction of new output elements based on the context provided by the input sequence 5.

[0103] A transformer is an example architecture that can be used in prediction layer(s) 6. See, e.g., Vaswani et al., Attention Is All You Need, ARXIV: 1706.03762v7 (Aug. 2, 2023). A transformer is an example of a machine-learned model architecture that uses an attention mechanism to compute associations between items within a context window. The context window can include a sequence that contains input sequence 5 and potentially one or more output element(s) 7-1, 7-2, . . . , 7-N. A transformer block can include one or more attention layer(s) and one or more post-attention layer(s) (e.g., feedforward layer(s), such as a multilayer perceptron).

[0104] Output sequence 7 is generated from prediction layer(s) 6 and includes a series of output elements, labeled as element 7-1, element 7-2, ..., through element 7-N, where N represents the total number of elements in the output sequence 7. These output elements can be the result of autoregressive generation, where each likely next output element is sampled and added to the context window for subsequent predictions. Alternatively, output sequence 7 can be generated non-autoregressively, predicting multiple output elements together without sequential conditioning.

[0105] The system can then generate output(s) 3 based on the output sequence 7. These output(s) 3 can be utilized in various applications, such as content generation, classification, or instruction implementation, depending on the nature of the input(s) 2 and the configuration of the sequence processing model(s) 4.

[0106] In some implementations, sequence processing model(s) 4 can be adapted for specific tasks or data domains. For instance, sequence processing model(s) 4 can be configured to process textual input for natural language understanding tasks or image-based input for visual recognition tasks. The flexibility of sequence processing model(s) 4 allows them to handle multimodal input sequences, facilitating information extraction and reasoning across diverse data modalities.

[0107] Alternative implementations may include sequence processing model (s) 4 with different configurations of prediction layer(s) 6, such as transformer-based architectures, recurrent neural networks (RNNs), long short-term memory (LSTM) models, or convolutional neural networks (CNNs). These various architectures can enable sequence processing model(s) 4 to understand or generate sequences of information that are tailored to the specific requirements of the application at hand.

[0108] Referring now to FIG. 10, a block diagram illustrates an example implementation of a multi-modal sequence processing model that can populate an input sequence 8. Central to this implementation is the task indicator 9, which can provide a signal to the model(s) processing the input sequence 8, indicating the specific task being performed. This task indicator 9 can include a model or component configured to identify a particular task and inject a corresponding input value into the input sequence 8, represented by element 8-0. The input value can be a learned representation within a continuous embedding space, signaling to the model(s) the nature of the task at hand.

[0109] The system further includes various input modalities, such as input modality 10-1, input modality 10-2, and input modality 10-3, each associated with different data types. For instance, input modality 10-1 might represent textual data, input modality 10-2 might represent image data, and input modality 10-3 might represent audio data. Each input modality can provide a unique set of data that contributes to the multimodal nature of the input sequence 8.

[0110] Data-to-sequence models, specifically data-to-sequence model(s) 11-1, data-to- sequence model(s) 11-2, and data-to-sequence model(s) 11-3, are adapted to project data from their respective input modalities into a format compatible with the input sequence 8. These models can transform the data to obtain elements 8-1, 8-2, 8-3, etc., for input modality 10-1; elements 8-4, 8-5, 8-6, etc., for input modality 10-2; and elements 8-7, 8-8, 8-9, etc., for input modality 10-3. The elements within input sequence 8 can indicate specific locations within a multidimensional embedding space, mapping to discrete or continuously distributed locations depending on the nature of the data.

[0111] In some implementations, the data-to-sequence models 11-1, 11-2, and 11-3 can be trained jointly or independently from the machine-learned sequence processing model(s) 4 to facilitate end-to-end training. These models can form part of the machine-learned sequence processing model(s) 4 illustrated in FIG. 9, enhancing the system’s ability to extract and reason over information from diverse data modalities.

[0112] The input sequence 8, as depicted in FIG. 10, represents a multimodal input sequence that contains elements from different data modalities using a common dimensional representation. This enables the system to process and reason over a variety of data types, facilitating complex tasks such as information extraction, content generation, or decisionmaking processes. The elements within input sequence 8, such as elements 8-0 through 8-9, can be processed by subsequent components of the system. For example, with reference to FIG. 9, the input sequence 8 from FIG. 10 can be processed with the prediction layer(s) 6, to generate an output sequence that can drive the generation of output(s) 3 based on the processed multimodal data.

[0113] Referring now to FIG. 11, an example training flow for training a machine-learned model is depicted. The training flow illustrates the progression of a model through various stages, beginning with an initialized model 21 and culminating in a refined model 27 that can be output to downstream system(s) 28 for deployment or further development.

[0114] The initialized model 21 represents the starting point of the training process. This model can be in an initial state, with weight values that are either randomly assigned or based on an initialization schema. In some cases, the initial weight values can be derived from prior pre-training for the same or different models, providing a foundation upon which further training can be built.

[0115] The pre-training stage 22 signifies the initial phase of training, where the initialized model 21 undergoes large-scale training over potentially noisy data to achieve a broad base of performance levels across various tasks or data types. Pre-training stage 22 can be implemented using one or more pre-training pipelines that operate over data from dataset(s). This stage can be omitted if the initialized model 21 is already pre-trained, such as when the model contains, is, or is based on a pre-trained foundational model or an expert model.

[0116] Upon completion of the pre-training stage 22, the model transitions to a pretrained model 23. This model can then undergo fine-tuning in the fine-tuning stage 24, where smaller-scale training is conducted on higher-quality data, such as labeled or curated datasets. Fine-tuning stage 24 can be facilitated by one or more fine-tuning pipelines, which refine the performance of pre-trained model 23 to meet specific performance criteria or to adapt to a narrower domain present in the fine-tuning dataset(s).

[0117] The fine-tuned model 25 represents the outcome of fine-tuning stage 24, which can then be subjected to refinement with user feedback 26. This stage involves incorporating feedback from human users to further enhance the model’s performance. Refinement withuser feedback 26 can include reinforcement learning techniques and can be based on human feedback on the model’s performance during use.

[0118] The refined model 27 emerges from the refinement with user feedback 26 as an updated version of development model 16, which can then be output to downstream system(s) 28. Downstream system(s) 28 can be any system or platform where the refined model 27 is deployed for practical application or further development.

[0119] Overall, FIG. 11 provides a structured representation of the training flow for a machine-learned model, outlining the systematic approach to evolving the model from an initial state to a fully refined state ready for practical application or further development.

[0120] Referring now to FIG. 12, a block diagram is presented that illustrates an example networked computing system capable of performing various aspects of the disclosed machine learning-based current sensing techniques (e.g., associating capacitance information with magnetic reluctance information, associating magnetic flux information with current information, associating magnetic flux information with the first correction factor, associating capacitance information with the second correction factor, estimating or predicting a corrected current value based on inputs including magnetic flux information and capacitance information, etc.). The system includes a plurality of computing devices and systems that are interconnected via a network, facilitating cooperative interactions to execute the disclosed methods.

[0121] The example computing device 50 can represent a diverse range of computing devices, such as personal computing devices, mobile devices, or server computing devices. Computing device 50 includes one or more processors 51, which can be any suitable processing device such as a microprocessor or a controller. These processors 51 can execute data 53 and instructions 54 stored in memory 52 to perform operations that implement features of the present disclosure. Memory 52 can be a non-transitory computer-readable storage medium, such as RAM or flash memory devices.

[0122] Computing device 50 can also include machine-learned models 55, which can be loaded into memory 52 and utilized by processors 51 for various tasks, such as estimating or correlating a magnetic reluctance to capacitance, determining correction factors, generated corrected readings of electrical current, etc. These machine-learned models 55 can be developed locally on computing device 50 or received from other systems like server computing system(s) 60.

[0123] Server computing system(s) 60 can mirror the structure of computing device 50, comprising processors 61 and memory 62 that store data 63 and instructions 64. Servercomputing system(s) 60 can also include machine-learned models 65, which can be the same as or different from machine-learned models 55 on computing device 50. These machine- learned models 65 can be used to host or serve model inferences for client devices, potentially implementing machine-learned models in a client-server relationship.

[0124] Network 49 serves as the communication medium that enables data exchange between computing device 50 and server computing system(s) 60. Network 49 can be any type of communications network, such as the internet, and can support various communication protocols and encodings to facilitate secure and efficient data transmission.

[0125] In some implementations, computing device 50 and server computing system(s) 60 can operate in a distributed computing environment, where server computing system(s) 60 manage the implementation of machine-learned models 65, and computing device 50 acts as a client device accessing the services provided by server computing system(s) 60. This configuration can allow for remote performance of inference and training operations, with outputs returned to computing device 50 for further use or analysis.

[0126] The server computing system(s) 60 and / or the computing device 50 can include and collaboratively operate to implement a current sensing application as described herein. For example, some or all of the aspects of the current sensing applications described herein can be implemented by the server computing system(s) 60 (e.g., shown at current sensing application 66). For example, the server computing system(s) 60 can implement the current sensing application 66 as a web application or software as a service. Additionally, or alternatively, some or all of the aspects of the current sensing application applications described herein can be implemented by the computing device 50 (e.g., shown at current sensing application 56). For example, the computing device 50 can implement the current sensing application 56 using locally stored and executed computer code (e.g., software).Thus, server computing system(s) 60 and / or the computing device 50 can include and execute computer instructions stored on computer-readable media to implement the systems and methods described herein.

[0127] The disclosed networked computing system exemplifies a versatile and scalable platform for implementing the advanced machine learning techniques described herein. By leveraging the interconnected nature of the computing devices and systems, the disclosed methods can be executed in a manner that optimizes resource utilization and maximizes the capabilities of the machine-learned models.Additional Disclosure

[0128] The technology discussed herein makes reference to servers, databases, software applications, and other computer-based systems, as well as actions taken and information sent to and from such systems. The inherent flexibility of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. For instance, processes discussed herein can be implemented using a single device or component or multiple devices or components working in combination. Databases and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel.

[0129] Aspects of the disclosure have been described in terms of illustrative embodiments thereof. Any and all features in the following claims can be combined or rearranged in any way possible, including combinations of claims not explicitly enumerated in combination together, as the example claim dependencies listed herein should not be read as limiting the scope of possible combinations of features disclosed herein. Accordingly, the scope of the disclosure is by way of example rather than by way of limitation, and the subject disclosure does not preclude inclusion of such modifications, variations or additions to the disclosure as would be readily apparent to one of ordinary skill in the art. Moreover, terms are described herein using lists of example elements joined by conjunctions such as “and,” “or,” “but,” etc. It should be understood that such conjunctions are provided for explanatory purposes only. Clauses and other sequences of items joined by a particular conjunction such as “or,” for example, can refer to “and / or,” “at least one of’, “any combination of’ example elements listed therein, etc. Terms such as “based on” should be understood as “based at least in part on.”

[0130] Terms used herein are used to describe the example embodiments and are not intended to limit and / or restrict the disclosure. The singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. In this disclosure, terms such as "including", "having", “comprising”, and the like are used to specify features, numbers, steps, operations, elements, components, or combinations thereof, but do not preclude the presence or addition of one or more of the features, numbers, steps, operations, elements, components, or combinations thereof.

[0131] The term "and / or" includes a combination of a plurality of related listed items or any item of the plurality' of related listed items. For example, the scope of the expression or phrase "A and / or B" includes the item "A", the item "B", and the combination of items "A and B”.

[0132] In addition, the scope of the expression or phrase "at least one of A or B" is intended to include all of the following: (1) at least one of A, (2) at least one of B, and (3) at least one of A and at least one of B. Likewise, the scope of the expression or phrase "at least one of A, B, or C" is intended to include all of the following: (1) at least one of A, (2) at least one of B, (3) at least one of C, (4) at least one of A and at least one of B, (5) at least one of A and at least one of C, (6) at least one of B and at least one of C, and (7) at least one of A, at least one of B, and at least one of C.

[0133] It will be understood that, although the terms first, second, third, etc., may be used herein to describe various elements, the elements are not limited by these terms.Instead, these terms are used to distinguish one element from another element. For example, without departing from the scope of the disclosure, a first element may be termed as a second element, and a second element may be termed as a first element.

[0134] The term “can” should be understood as referring to a possibility of a feature in various implementations and not as prescribing an ability that is necessarily present in every implementation. For example, the phrase “X can perform Y” should be understood as indicating that, in various implementations, X has the potential to be configured to perform Y, and not as indicating that in every instance X must always be able to perform Y. It should be understood that, in various implementations, X might be unable to perform Y and remain within the scope of the disclosure.

[0135] The term “may” should be understood as referring to a possibility of a feature in various implementations and not as prescribing an ability that is necessarily present in every implementation. For example, the phrase “X may perform Y” should be understood as indicating that, in various implementations, X has the potential to be configured to perform Y, and not as indicating that in every instance X must always be able to perform Y. It should be understood that, in various implementations, X might be unable to perform Y and remain within the scope of the disclosure.

[0136] To the extent terms including "module", and "unit," and the like are used herein, these terms may refer to, but are not limited to, a software or hardware component or device,such as a Field Programmable Gate Array (FPGA) or Application Specific Integrated Circuit (ASIC), which performs certain tasks. A module or unit may be configured to reside on an addressable storage medium and configured to execute on one or more processors. Thus, a module or unit may include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. The functionality provided for in the components and modules / units may be combined into fewer components and modules / units or further separated into additional components and modules.

[0137] Aspects of the above-described example embodiments may be recorded in non- transitory computer-readable media including program instructions to implement various operations embodied by a computer. The media may also include, alone or in combination with the program instructions, data files, data structures, and the like. Examples of non- transitory computer-readable media include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD ROM disks, Blu-Ray disks, and DVDs; magneto-optical media such as optical discs; and other hardware devices that are specially configured to store and perform program instructions, such as semiconductor memory , readonly memory (ROM), random access memory (RAM), flash memory, USB memory, and the like. Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter. The program instructions may be executed by one or more processors. The described hardware devices may be configured to act as one or more software modules in order to perform the operations of the above-described embodiments, or vice versa. In addition, a non-transitory computer-readable storage medium may be distributed among computer systems connected through a network and computer-readable codes or program instructions may be stored and executed in a decentralized manner. In addition, the non- transitory computer-readable storage media may also be embodied in at least one application specific integrated circuit (ASIC) or Field Programmable Gate Array (FPGA).

[0138] Each block of the flowchart illustrations may represent a unit, module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks may occur out of order. For example, two blocks shown in succession may in fact be executed substantially concurrently(simultaneously) or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

[0139] While the disclosure has been described with respect to various example embodiments, each example is provided by way of explanation, not limitation of the disclosure. Those skilled in the art, upon attaining an understanding of the foregoing, can readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the disclosure does not preclude inclusion of such modifications, variations and / or additions to the disclosed subject matter as would be readily apparent to one of ordinary skill in the art. For example, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the disclosure covers such alterations, variations, and equivalents.

Claims

WHAT IS CLAIMED IS:

1. A current measuring device, comprising a magnetic core including a first air gap; a first magnetic flux sensor disposed at the first air gap; one or more memories configured to store instructions; and one or more processors configured to execute the instructions to perform operations, wherein, when a conductor is positioned within an interior portion of the magnetic core, the operations include: outputting a current value associated with current flowing through the conductor, wherein the current value is determined based on a magnetic flux measured by the first magnetic flux sensor and a first correction factor determined based on capacitance information associated with the first air gap.

2. The current measuring device of claim 1, wherein the operations further include: determining the capacitance information based on a measurement of a voltage applied across the first air gap; and determining the first correction factor based on the capacitance information and the magnetic flux.

3. The current measuring device of claim 2, wherein the operations further include: implementing an electric circuit to apply the voltage across the first air gap and to apply an alternating current to the electric circuit, and the capacitance information is determined based on the voltage applied across the first air gap and the alternating current.

4. The current measuring device of claim 2, wherein the operations further include: implementing an electric circuit to apply the voltage across the first air gap and to apply an alternating current to the electric circuit, and the capacitance information is determined based on relative changes in a ratio of the voltage applied across the first air gap and the alternating current.

5. The current measuring device of claim 1, wherein the operations further include:determining the first correction factor based on a relationship between the capacitance information and first calibration data associated with one or more air gap parameters associated with the first air gap.

6. The current measuring device of claim 5, wherein the one or more air gap parameters associated with the first air gap include at least one of an area of the first air gap, a distance of the first air gap, or overlapping surface areas around the first air gap.

7. The current measuring device of claim 5, wherein the operations further include: determining a second correction factor based on the magnetic flux measured by the first magnetic flux sensor, and the current value is determined based on the magnetic flux measured by the first magnetic flux sensor, the first correction factor determined based on the capacitance information associated with the first air gap, and the second correction factor determined based on the magnetic flux measured by the first magnetic flux sensor.

8. The current measuring device of claim 7, wherein determining the second correction factor based on the magnetic flux measured by the first magnetic flux sensor comprises comparing to second calibration data associated with one or more magnetic core parameters associated with the magnetic core.

9. The current measuring device of claim 8, wherein the one or more magnetic core parameters associated with the magnetic core include at least one of properties of the magnetic core or sensor sensitivity of the first magnetic flux sensor.

10. The current measuring device of claim 8, wherein the first calibration data and the second calibration data are based on empirically gathered data.

11. The current measuring device of claim 1 , wherein the magnetic core includes a first member and a second member, in a first position of the first member, a first end of the first member is spaced apart from a first end of the second member, andthe first member is configured to move from the first position to a second position via a sliding motion, the first end of the first member overlapping with the first end of the second member when the first member is in the second position.

12. The current measuring device of claim 11, wherein an overlap amount between the first end of the first member and the first end of the second member is variable.

13. The current measuring device of claim 1, further comprising: a first flexible member extending from a first end of the magnetic core, wherein an end of the first flexible member and a second end of the magnetic core are spaced apart to form the first air gap, and the first flexible member is configured to bend to permit the conductor to enter the interior portion.

14. The current measuring device of claim 1, further comprising: a first flexible member extending from a first end of the magnetic core; and a second flexible member extending from a second end of the magnetic core, wherein the first air gap is formed at a location in which the first flexible member overlaps with the second flexible member, and the first flexible member and the second flexible member are each configured to bend to permit the conductor to enter the interior portion.

15. The current measuring device of claim 1, wherein the magnetic core includes a second air gap, the current measuring device further comprises a second magnetic flux sensor disposed at the second air gap, and the current value is determined based on the magnetic flux measured by the first magnetic flux sensor, another magnetic flux measured by the second magnetic flux sensor, and the first correction factor determined based on the capacitance information associated with the first air gap and capacitance information associated with the second air gap.

16. The current measuring device of claim 15, wherein the operations further include: implementing a first electric circuit to apply, during a first time interval, a first voltage across the first air gap and a first alternating current to the first electric circuit; implementing a second electric circuit to apply, during a second time interval, a second voltage across the second air gap and a second alternating current to the second electric circuit; the capacitance information associated with the first air gap is determined based on the first voltage applied across the first air gap and the second alternating current; and the capacitance information associated with the second air gap is determined based on the second voltage applied across the second air gap and the second alternating current.

17. The current measuring device of claim 1, wherein the operations further comprise: providing an output alerting a user associated with the current measuring device in response to at least one of: the capacitance information indicating a capacitance across the first air gap is less than a threshold capacitance value, or a confidence level associated with the current value is less than a threshold confidence value.

18. The current measuring device of claim 1, wherein the current measuring device is a clamp meter or a current probe.

19. A computer-implemented method for a current measuring device including a magnetic flux sensor disposed at an air gap included in a magnetic core of the current measuring device, the method comprising: measuring, by the magnetic flux sensor when a conductor is positioned at an interior portion of the magnetic core, a magnetic flux; and outputting a current value associated with current flowing through the conductor, wherein the current value is determined based on the magnetic flux and a correction factor determined according to capacitance information associated with the air gap.

20. A non-transitory computer readable medium storing instructions which, when executed by one or more processors of a current measuring device including a magnetic fluxsensor and a magnetic core with an air gap, cause the one or more processors to perform operations, the operations comprising: measuring, by the magnetic flux sensor when a conductor is positioned at an interior portion of the magnetic core, a magnetic flux; and outputting a current value associated with current flowing through the conductor, wherein the current value is determined based on the magnetic flux and a correction factor determined according to capacitance information associated with the air gap.

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