33 results about "Diagnostic modalities" patented technology

The invention provides a TBOX-based vehicle fault diagnosis system, which is characterized in that a TBOX is installed in a vehicle and is used for acquiring vehicle data and judging whether the vehicle has a fault based on the vehicle data, and when the vehicle has the fault, the TBOX enters a diagnosis and maintenance mode, and the diagnosis and maintenance mode comprises a field diagnosis mode and a remote diagnosis mode; the diagnosis end is used for establishing a data communication channel with the TBOX in a diagnosis maintenance mode; and the TBOX is also used for transmitting the vehicle fault data to the diagnosis end through the data communication channel, receiving a vehicle configuration instruction issued by the diagnosis end, and issuing the vehicle configuration instruction to a target ECU unit of the vehicle to realize vehicle fault diagnosis full-link diagnosis operation. According to the scheme, a traditional fault diagnosis instrument is omitted, hardware cost of vehicle service diagnosis is reduced, usability and convenience of vehicle diagnosis are provided, and transmission safety and reliability between the TBOX and a platform or a terminal are guaranteed through a self-developed data transmission protocol.

Method for predicting a heating effect of an imaging sequence of a second imaging modality on a detector of a first modality (12) of a combined imaging device (10) as a function of a reference imaging sequence of the second imaging modality, wherein the first modality (12) is designed to acquire diagnostic data of an object of investigation (15) and the second imaging modality comprises a magnetic resonance imaging device (11) designed to acquire magnetic resonance image data of the object of investigation (15), wherein the first modality (12) comprises a temperature compensation unit (59) designed to compensate for a temperature change of the detector, which comprises the following steps: • Performing (S2) a reference imaging sequence with the second imaging modality, wherein in the reference imaging sequence a gradient magnetic field and / or a high-frequency electromagnetic field is applied to the image acquisition area of ​​the combined imaging device (10) and wherein the reference imaging sequence has several imaging parameters that determine a property of the gradient magnetic field and / or the high-frequency electromagnetic field, • Detection (S3) of at least one temperature-dependent parameter of the detector of the first modality (12), • Determining (S4) the heating effect on the detector of the first modality (12) as a function of several imaging parameters of the reference imaging sequence and the at least one temperature-dependent parameter of the detector, and • Prediction (S7) of the heating effect of the imaging sequence on the detector using a smart algorithm, wherein the smart algorithm is designed to predict the heating effect depending on several imaging parameters of the imaging sequence and data acquired by the reference imaging sequence.

An apparatus and method for optical skin treatment procedures and in-treatment monitoring thereof. The apparatus is operable in two modes: a treatment mode for delivery of treatment light to an area of a patient's skin abutted to a light guide of the apparatus; and a diagnostic mode for acquiring one or more images of the abutted area of skin. Switching between the two modes may be made in a short time, facilitating monitoring during treatment. In exemplary embodiments, the apparatus sends image data to an analyzer, which analyzes images and computes an optimal treatment course, at least optimal parameters (intensity, pulse width, repetition rate) for the next delivery, and sends the optimal treatment course to the controller for implementation by the apparatus in real time.

Apparatuses and methods for recovering solid tissue and dislodged cells ("D-cells") from a biopsy are disclosed herein. In an embodiment, a biopsy container apparatus for recovering solid tissue and D-cells from a biopsy includes a receiving container and a sieve container. The receiving container has a receiving chamber with an upper opening and a watertight bottom surface. The sieve container is configured to be located at least partially within the receiving chamber and removed from the receiving chamber through the upper opening. The sieve container has a bottom sieve surface including a plurality of apertures configured to pass the D-cells from the biopsy but not the solid tissue from the biopsy into the receiving chamber after the solid tissue and the D-cells from the biopsy are placed into the sieve container.

The home appliance of the present invention controls the operation of a plurality of loads based on diagnostic control information when a command to execute a first diagnostic mode is selected through an input unit, controls a communication unit to transmit control information of the plurality of loads and detection information detected by at least one sensor to a server while the first diagnostic mode is executed, and controls an output unit to output diagnostic result information received from the server. A home appliance according to one aspect controls a communication unit to transmit control information of at least one load and detection information detected by at least one sensor to a server when a command to execute a second diagnostic mode is received through an input unit while at least one of the plurality of loads is operating.

The invention relates to the field of charging cable detection, and discloses a DP-DN wire for checking the internal resistance of a wire rod, which comprises an intelligent control module arranged in a charging adapter and an intelligent management module arranged in electronic equipment. The two modules communicate and negotiate through a data wire core to determine a basic internal resistance test mode or an advanced diagnosis mode. In the basic mode, DC resistance measurement is cooperatively performed. In the advanced diagnosis mode, the intelligent control module injects an alternating current detection signal and collects an adapter end phasor; the intelligent management module collects the phasor of the electronic equipment end and transmits the phasor back through the load modulation unit. A central control unit of the intelligent control module calculates complex impedance according to phasors at the two ends, compares a real-time impedance spectrum with a fault feature library through a diagnosis and matching engine, and determines a state identifier. And the self-adaptive charging strategy controller adjusts charging parameters accordingly. According to the invention, the cable state can be accurately diagnosed, and self-adaptive safe charging is realized.

Systems and methods include an Information Handling System (IHS) that is adapted to provide diagnostic boot cycles. Once an IHS is powered, a boot sequence is initiated. During or after booting of the IHS, a request is detected for a diagnostic boot cycle by a requestor, such as by the operating system of the IHS. When a diagnostic boot cycle has been requested, an EC (Embedded Controller) of the IHS is booted in a diagnostic mode to perform a diagnostic test on one or more hardware components of the IHS. The results of the diagnostic test are stored to the NVRAM (Non-Volatile Random-Access Memory) that stores the boot code and that is accessible to the requestor of the diagnostic test once the IHS has been rebooted to a non-diagnostic mode.

A device comprises a scan chain and a device circuit. The scan chain includes a plurality of sequential elements and a diagnostic hardware circuit. The sequential elements are connected serially and grouped into a plurality of segments. The diagnostic hardware circuit includes a plurality of circuit components, each connected to a sequential element of a respective segment. The diagnostic hardware circuit identifies a faulty segment of the scan chain during a diagnostic mode. Test pattern data is serially shifted through the scan chain to isolate the faulty sequential element of the defective segment during the diagnostic mode. The device circuit is connected to the scan chain, performs one or more circuit functions, and generates functional data that is sequentially shifted through the scan chain during a functional mode.

A method for heating, ventilation, and air conditioning (HVAC) system diagnostics includes, in response to determining that a triggering event has occurred, entering a filter diagnostics mode. A first instruction is sent to a thermostat to shut down an HVAC system. A user is instructed to locate and remove a filter. The filter is classified as acceptable or dirty. In response to classifying the filter as acceptable, the user is instructed to the turn on the HVAC system. In response to determining based on the triggering event that a desired mode is a cooling mode, a first value of a room temperature is determined. The user is instructed to set a temperature setpoint below the first value. A second value of the room temperature is determined. In response to determining that the second value is less than the first value, it is determined that the HVAC system operates properly.

This invention discloses a subject data acquisition system based on multi-mode interaction. The system includes an edge computing gateway, and a flexible piezoelectric thin film array, a millimeter-wave radar module, and a directional microphone array, all connected to the edge computing gateway. The edge computing gateway is configured to control the millimeter-wave radar module to operate at a low duty cycle and extract the compression profile in basic scanning mode. When the piezoelectric signal envelope variance exceeds the limit, it triggers entry into a directional diagnostic mode, calculating the geometric centroid coordinates and extracting the main peak of ventricular ejection. Based on these geometric centroid coordinates, the beamforming weight vector of the directional microphone array is updated to extract the audio signal sequence. The cross-correlation function between the main peak and the Doppler phase signal trough of the chest wall displacement is calculated to obtain the time delay parameter. After sliding window compensation alignment and feature vector extraction, the results are input into the model to output the state confidence assessment result. This application can achieve high signal-to-noise ratio and low false alarm rate for accurate spatiotemporal alignment of multi-source heterogeneous physiological signals and imperceptible monitoring of abnormal states.

A tool to create virtual lesions inside a 3D lung tumor image. The image can be an MRI or CT image of a patient's lung to be treated. This virtual pre procedural ablation allows the operator to identify optimal ablation sites and select the optimal ablation parameters for the given anatomy. Also, the actual procedure will be performed with the lung parenchyma surrounding the tumor preventing ultrasound to penetrate beyond the tumor walls. A diagnostic mode will guide the operator to optimize ablation parameters. This virtual pre ablation, the parenchymal ultrasound barrier and the diagnostic catheter operation should enable the operator to conduct the therapeutic procedure safe, fast and effective.

The abnormality diagnosis device for the wire saw (1) includes a diagnosis mode execution unit (51), a data set acquisition unit (52), a deviation information calculation unit (54), and a determination unit (55). The diagnosis mode execution unit (51) executes the following diagnosis modes before cutting with the wire saw (1): a first diagnosis mode where the processing roller rotates at a constant speed, a second diagnosis mode where the winding spool rotates at a constant speed, and a third diagnosis mode where the winding spool rotates at a constant speed. The data set acquisition unit (52) acquires first to third data sets for multiple data items using the first to third diagnosis modes. The deviation information calculation unit (54) calculates deviation information related to the deviation obtained by comparing the first to third data sets with the first to third reference data sets under normal conditions. The determination unit (55) determines whether the wire saw (1) has any abnormalities based on the deviation information. Thus, the operating status of the wire saw before cutting is fully monitored, and abnormality diagnosis is accurately performed.

A pressure sensor includes a microelectromechanical system (MEMS) device having a first pressure sensitive capacitor element, a second pressure sensitive capacitor element, a first reference capacitive element, and a second reference capacitive element arranged in a bridge configuration, a first output pad, and a second output pad; and an application-specific integrated circuit (ASIC) in electrical communication with the MEMS device having a first input pad and a second input pad, a measurement interface including a first input and a second input, a first switch coupled between the first input pad and the second input pad, and a second switch coupled between the second input of the measurement interface and the second input pad of the ASIC.

Disclosed herein are devices and methods for recovering solid tissue and exfoliated cells ("D-cells") from biopsy. In an embodiment, a biopsy container device includes a sample collection container, a basket screen, and a buffer container. The sample collection container includes a reagent chamber. The basket screen is configured for at least partially removable attachment within the sample collection container, and the basket screen includes a screen surface configured to permit passage of exfoliated cells from the biopsy but not permit passage of solid tissue from the biopsy. The buffer container is configured for removable attachment to a sample collection container, and the buffer container includes a buffer chamber.

The embodiment of the invention relates to the technical field of air conditioners, and discloses a fluorine deficiency diagnosis method and fluorine deficiency diagnosis of a multi-connected air conditioner and electronic equipment, the multi-connected air conditioner comprises at least one outdoor unit and a plurality of indoor units, and the fluorine deficiency diagnosis method comprises the steps that first operation is received; in response to the first operation, indicating that the fluorine lack diagnosis mode selected by the user is a local diagnosis mode or a cloud diagnosis mode; in response to the fact that the first operation indicates that the fluorine lack diagnosis mode is the local diagnosis mode, the multi-connected air conditioner is controlled to operate according to a first strategy, and the fluorine lack condition is determined based on operation data; the first operation indicates that the fluorine deficiency diagnosis mode is the cloud diagnosis mode, first information is sent, the first information indicates that a fluorine deficiency diagnosis model is matched according to the refrigerating capacity of the outdoor unit and the refrigerating capacity of the indoor unit, and the fluorine deficiency condition is determined based on the successfully matched fluorine deficiency diagnosis model.

A vehicle diagnostic system (100) for a vehicle (102) includes an event data buffer (130) and an event data recorder (124) configured to record event data (126, 128) received from one or more components of the vehicle in the event data buffer while the vehicle is operating. The system also includes an input interface (110) configured to capture audio data (112) representing an utterance (106) spoken by an operator of the vehicle, and a network transceiver (120) configured to communicate via a network (170). The system also includes a vehicle electronic control unit (114) configured to determine, based on the audio data, that an operator triggers a diagnostic mode session, and to communicate diagnostic data to a remote service center server (180) based on determining that the operator triggers the diagnostic mode session. The diagnostic data includes related portions of the event data and information representing utterances.

The application relates to a diagnosis method and device of a height sensor, a vehicle and a storage medium, the diagnosis method comprising: in the case of determining that the height sensor needs to be diagnosed, controlling the working of an air spring in an electrically-controlled air suspension according to a preset rule; in the working process of the air spring, acquiring charging data corresponding to the height sensor in the electrically-controlled air suspension; according to the charging data, determining a diagnosis mode corresponding to the height sensor; and performing a control operation corresponding to the diagnosis mode. Thus, before the height sensor in the electrically-controlled air suspension is calibrated, the charging data corresponding to the height sensor acquired in the working process of the air spring can be used to diagnose the current mode of the height sensor, and according to the diagnosed mode, a corresponding control operation is performed, so that the accuracy of the installation of the height sensor is ensured, the input of manpower and material resources and the complexity of the calibration process of the height sensor are reduced.

The present application relates to the technical fields of energy storage system battery cluster level protection and control, more specifically, the present application relates to a kind of energy storage cluster controller integrated with SSCB, including main control module, data acquisition module, communication module and main circuit, main circuit is equipped with B+, B- and P+, P-, fuse Fuse and current detection unit HA are connected in series at B+ side, and solid-state circuit breaker SSCB is used to replace charge-discharge contactor and parallel diode network.The SSCB includes Kz, Kcd, Kp and the pre-charge / limiting branch formed by Rp, and the buffer branch formed by Rc and Cp.Main control module realizes two-stage pre-charge input based on current I and voltage parameters, and realizes energy controlled release through adaptive threshold Ix1 / Ix2 and soft shutdown control, reduces input impact and shutdown overvoltage;At the same time, in the diagnostic mode, Kp-Rp small current channel is used to carry out online self-checking and health assessment on Kz / Kp / Kcd, Rp and Rc / Cp, and report through CAN.

A device includes a scan chain and a device circuit. The scan chain includes a plurality of sequential elements and a diagnostic hardware circuit. The sequential elements are connected in series and grouped into a plurality of segments. The diagnostic hardware circuit includes a plurality of circuit components, each connected to one sequential element of a respective segment. The diagnostic hardware circuit identifies a defective segment of the scan chain during a diagnostic mode. Test pattern data is serially shifted through the scan chain to isolate the faulty sequential element of the defective segment during the diagnostic mode. The device circuit is connected to the scan chain, performs one or more circuit functions, and generates functional data that is sequentially shifted through the scan chain during a functional mode.

The invention relates to the technical field of therapeutic instruments, and provides a multi-protocol adaptive modular therapeutic instrument and a control method thereof, and the therapeutic instrument comprises a therapeutic instrument host and an external therapeutic accessory. The external treatment accessory comprises a gynecological probe and a body surface electrode plate; the therapeutic apparatus host comprises: a host housing; the main control module is arranged in the host shell; the body fat detection device comprises an energy generation and conversion module, an output interface module, a sensor module, a body fat detection module and a man-machine interaction assembly, and is characterized in that the energy generation and conversion module comprises an electric spectrum generator, a spectrum generator and a radio frequency generator, and an intelligent energy switching matrix is designed; various treatment functions such as low-frequency / intermediate-frequency electrical stimulation, phototherapy and radio-frequency heating are integrated into one host, and the electrotherapy pole piece and the phototherapy lamp piece which can be quickly replaced are combined, so that a user can seamlessly switch different treatment and diagnosis modes in the same treatment period without purchasing multiple devices or replacing the whole probe, and the utilization rate of the devices is improved.

The invention discloses an automobile rearview mirror assembly line abnormity diagnosis system based on cloud computing, and relates to the professional technical field of cloud computing abnormity diagnosis, and the system specifically comprises a sensor network deployment module, a diagnosis mode intelligent selection module, a cloud computing module, a cloud computing module, a cloud computing module, a cloud computing module, a cloud computing module, a cloud computing module and a cloud computing module, and the sensor network deployment module is used for synchronously collecting assembly line data through a multi-source sensing network deployed at an assembly line key station. By judging different abnormities, defining sudden faults and composite faults, selecting different diagnosis modes for different faults, specifically implementing modules for the diagnosis modes, and using different diagnosis modes for different faults, the two diagnosis modes realize intelligent interaction through cloud collaboration of cloud computing. According to the method, through collaborative analysis of vibration, temperature and visual three-mode data, the composite fault recognition accuracy is improved, spatial-temporal feature coupling and knowledge graph dual-mode diagnosis are adopted, and the early-stage discovery rate of progressive faults can be improved.

Disclosed is a power device system comprising: a power device and a controller, the power device comprising: a power switch; and control end drivers, and wherein the power device is configured to operate in both a ramp-up diagnostic mode and a ramp-down diagnostic mode, where the control end drivers are respectively configured to: apply a test current at a first node of a conductive channel of the power switch; applying a first driver voltage less than or greater than a threshold voltage at a control terminal of the power switch; respectively increasing or decreasing the first driver voltage until a voltage drop or a voltage increase is respectively detected; and measuring the first driver voltage when the voltage drop is detected as a ramp-up threshold voltage or a ramp-down threshold voltage, respectively, wherein the controller is configured to determine a health state of the power switch based on the ramp-up threshold voltage and the ramp-down threshold voltage.

An integrated circuit device includes a scan chain and a device circuit. The scan chain includes a plurality of sequential elements and a diagnostic hardware circuit. The sequential elements are connected in series and grouped into a plurality of sections. The diagnostic hardware circuit includes a plurality of circuit components, each connected to one sequential element of a respective section. The diagnostic hardware circuit identifies a defective section of the scan chain during a diagnostic mode. During the diagnostic mode, test pattern data is serially shifted through the scan chain to isolate a faulty sequential element in the defective section. The device circuit is connected to the scan chain, performs one or more circuit functions during a functional mode, and generates functional data that is sequentially shifted through the scan chain. Embodiments of the present application also disclose methods for diagnosing faults in an integrated circuit device.

The invention provides a multi-scale multi-working-condition optimization diagnosis method and system for an electric pile based on an agent model, and relates to the technical field of fuel cells, and the method comprises the steps: constructing a parameterized input vector comprising a structure variable subset and an operation variable subset; establishing a multi-physics field dynamic coupling flow resistance network model, solving a balance equation set of fluid distribution in the electric pile, and obtaining an original data set reflecting a flow distribution uniformity index and a system total pressure drop; training and constructing a nonlinear global performance agent model based on a response surface method; the performance agent model comprises a multi-order interaction operator; executing a design optimization mode or a performance diagnosis mode based on the performance agent model; in a design optimization mode, reversely screening out a structure parameter combination with the highest robustness score in the interval; and in the performance diagnosis mode, outputting a diagnosis report containing the performance bottleneck. According to the method, the top speed of multi-scale-all-working-condition coupling optimization of the electric pile can be realized, and meanwhile, the method has second-level precise design and diagnosis capability.

This invention provides a multi-scale, multi-condition optimization and diagnostic method and system for fuel cell stacks based on a surrogate model, relating to the field of fuel cell technology. The method includes: constructing a parameterized input vector containing subsets of structural variables and subsets of operational variables; establishing a multi-physics dynamically coupled flow resistance network model, solving the equilibrium equations for fluid distribution within the stack, and obtaining a raw dataset reflecting the flow distribution uniformity index and the total system pressure drop; training and constructing a nonlinear global performance surrogate model based on the response surface methodology; the performance surrogate model includes multi-order interaction operators; executing a design optimization mode or a performance diagnostic mode based on the performance surrogate model; in the design optimization mode, inversely selecting the combination of structural parameters with the highest robustness score within the specified interval; and in the performance diagnostic mode, outputting a diagnostic report containing performance bottlenecks. This invention enables extremely rapid multi-scale, multi-condition coupled optimization of fuel cell stacks, while possessing second-level accurate design and diagnostic capabilities.

The present application relates to a kind of gasoline car evaporative emission system leak diagnosis module and diagnosis method, the leak diagnosis module includes shell, gas pump and multiple phase analysis switching component.The first cavity and second cavity with main hole are arranged in the shell, the gas pump is arranged in shell and its outlet is communicated with main hole, it is provided with driving motor and current sensor on driving motor.The multiple phase analysis switching component has conversion valve with multiple reference holes in the first cavity and switching motor for switching with conversion valve outside the second cavity.The leak diagnosis module is set to when gas pump works, by controlling the on-off position of multiple phase analysis switching component, it is switched between different phase reference mode and diagnosis mode corresponding to multiple reference holes, and the leak quantitative analysis of evaporative emission system is carried out to improve the accuracy of leak diagnosis.

The invention relates to a urine image feature extraction and discrimination method based on deep learning. The method comprises the following steps: acquiring an original image of a urine sample to be detected through image acquisition equipment and transmitting the original image to a central processing system; preprocessing and feature extraction are carried out on the original urine image, corresponding analysis modes are determined, and the analysis modes comprise a conventional physicochemical feature mode, a visible component recognition mode and a multi-feature comprehensive diagnosis mode; and calling corresponding deep learning models according to different modes to carry out feature recognition and classification, and finally generating a urine component analysis result or comprehensive diagnosis prompt information. According to the method, multi-mode collaboration is combined with a deep learning model, so that automatic and high-precision analysis of multi-dimensional features such as urine color, transparency and visible components is realized, and the accuracy and diagnosis efficiency of urine detection are effectively improved.

The application discloses a valve fault diagnosis method of a power plant boiler system, which comprises the following steps: monitoring points are arranged on the periphery of each valve to acquire monitoring data; the sensors comprise temperature sensors, pressure sensors and flow sensors; all the sensors are connected to a wireless communication module to upload data; a basic database is established; after a diagnosis mode is started, a monitoring period is set, and basic comparison data is screened out; and alarm or safety operation is performed according to the diagnosis result. The application can greatly reduce the requirement of manual inspection by arranging sensors on the periphery of the valve equipment, comparing the diagnosis data with the basic database to judge the fault cause, and giving a fault solution scheme or operation instruction in time, can complete real-time monitoring of the instruments in the factory and rapid processing of faults, and can save a lot of labor cost.

The invention provides a transformer fault diagnosis method and system based on adaptive reasoning, and the method comprises the steps: S1, data receiving and preprocessing: collecting and fusing multi-source heterogeneous data from a transformer online monitoring system and a historical database in real time, carrying out the cleaning and normalization processing of the data, and constructing a time sequence feature; s2, using a transformer hybrid reasoning diagnosis model for diagnosis: inputting the processed data into the transformer hybrid reasoning diagnosis model, the transformer hybrid reasoning diagnosis model adaptively selecting a reasoning mode according to the complexity of the input data, the reasoning mode comprising a rapid diagnosis mode and a deep traceability mode; and S3, outputting a diagnosis report: outputting the corresponding diagnosis report according to the selected reasoning mode. The method can improve the diagnosis efficiency, reduces the calculation cost, and achieves the intellectualization and adaptability of transformer fault diagnosis.