Electronic device for obtaining data having changed sampling period, and control method therefor

Abstract

An electronic device for obtaining data having a changed sampling period, and a control method therefor are disclosed. The electronic device according to one embodiment of the present document comprises at least one sensor, a memory and at least one processor, wherein the memory can include instructions that, when executed, cause the at least one processor to: generate second data including information about an attribute of data corresponding to a first time point in first data acquired during a first time interval through the at least one sensor in a first sampling period; interpolate the generated second data so as to generate third data on the basis of a second sampling period of which the sampling period differs from that of the first sampling period; and apply a weight to data corresponding to at least one time point to be reconstructed of the generated third data so as to generate fourth data on the basis of the second sampling period.

Description

Electronic device for acquiring data with changed sampling cycle and control method thereof

[0001] This document relates to an electronic device for acquiring data with a changed sampling period and a control method thereof.

[0002] The variety of services and additional features offered through electronic devices, such as smartphones, is steadily increasing. To enhance the utility of these devices and satisfy the diverse needs of users, telecommunications service providers and electronic device manufacturers are competitively developing electronic devices that offer a variety of features and differentiate themselves from competitors. Consequently, the various functions offered through electronic devices are also becoming increasingly sophisticated.

[0003] Time series classification is actively being utilized in tasks such as activity recognition, power usage monitoring, and defect classification in manufacturing. Recently, massive amounts of time series data are being generated and acquired in real time, but there are limitations to continuously storing high-resolution data in the hardware (e.g., memory) of electronic devices. For example, in the healthcare field, when storing user medical data on electronic devices, the sensor's storage cycle may be shortened due to storage capacity limitations. Consequently, accurate time series classification can be difficult.

[0004] According to one embodiment of the present document, an electronic device can be provided that can improve the performance of a time series classification model by performing time series classification using a small amount of data acquired and stored according to a high sampling period and most of the data acquired and stored according to a low sampling period, for example, in a state where some data according to a high sampling period is stored in the electronic device.

[0005] According to one embodiment of the present document, a control method of an electronic device can be provided that can improve the performance of a time series classification model by performing time series classification using a small amount of data acquired and stored according to a high sampling period and most of the data acquired and stored according to a low sampling period, for example, in a state where some data according to a high sampling period is stored in the electronic device.

[0006] An electronic device according to one embodiment of the present document includes at least one sensor, a memory, and at least one processor, wherein the memory includes instructions that, when executed, cause the at least one processor to generate second data including information on properties of data corresponding to a first point in time among first data acquired during a first time interval through the at least one sensor at a first sampling period, the first point in time being a point in time included in the first time interval, interpolate the generated second data to generate third data based on a second sampling period having a different sampling period from the first sampling period, and apply a weight to data corresponding to at least one restoration target point in time among the generated third data to generate fourth data based on the second sampling period, wherein the weight is determined based on a correlation between a property of the data corresponding to the at least one restoration target point in time and the first data acquired during the first time interval, and wherein the data corresponding to the at least one restoration target point in time may include data that is not acquired by the at least one sensor.

[0007] A method for controlling an electronic device according to one embodiment of the present document includes: generating second data including information on properties of data corresponding to a first point in time among first data acquired during a first time interval through at least one sensor of the electronic device with one sampling period; generating third data based on a second sampling period having a different sampling period from the first sampling period by interpolating the generated second data; and generating fourth data based on the second sampling period by applying a weight to data corresponding to at least one restoration target time interval among the generated third data, wherein the weight is determined based on a correlation between a property of the data corresponding to the restoration target time interval and the first data acquired during the first time interval, and the data corresponding to the restoration target time interval may include data not acquired by the at least one sensor.

[0008] FIG. 1 is a block diagram of an electronic device within a network environment according to various embodiments of the present document.

[0009] FIG. 2 is an exemplary diagram for explaining a function or operation of acquiring data (e.g., fourth data) with a changed sampling period, according to one embodiment of the present document.

[0010] FIG. 3A is an exemplary diagram illustrating a function or operation of training a reconstructor and / or a classification module using data acquired at a high sampling rate, according to one embodiment of the present document.

[0011] FIG. 3b is an exemplary diagram for explaining a function or operation of acquiring data with a changed sampling period and classifying a class of data with a changed sampling period using a reconstructor and / or a classification module according to one embodiment of the present document.

[0012] FIG. 4 is an exemplary flowchart illustrating a process for performing the function or operation described in FIG. 3a.

[0013] FIGS. 5 and 6 are exemplary drawings for explaining the function or operation of an up-sampler included in a reconstruction module according to one embodiment of the present document.

[0014] FIG. 7a is an exemplary drawing for explaining a comparative example in which data at a restoration target point in time restored according to a conventional technique does not match data that should have actually been measured.

[0015] FIG. 7b is an exemplary drawing illustrating an embodiment in which data at a point in time of restoration that has been restored according to one embodiment of the present document substantially matches data that should have actually been measured.

[0016] FIG. 1 is a block diagram of an electronic device (101) within a network environment (100) according to various embodiments.

[0017] Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (104) or a server (108) via a second network (199) (e.g., a long-range wireless communication network). In one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0018] The processor (120) may, for example, execute software (e.g., a program (140)) to control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) and perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operations, the processor (120) may store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the commands or data stored in the volatile memory (132), and store result data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or an auxiliary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together with the main processor (121). For example, when the electronic device (101) includes the main processor (121) and the auxiliary processor (123), the auxiliary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a given function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0019] The auxiliary processor (123) may control at least a portion of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0020] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0021] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0022] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0023] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0024] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0025] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0026] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0027] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0028] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0029] The haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. According to one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0030] The camera module (180) can capture still images and moving images. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0031] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0032] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0033] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0034] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) can support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0035] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). In one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). In one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas, for example, by the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device via the at least one selected antenna. In some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0036] According to various embodiments, the antenna module (197) may form a mmWave antenna module. In one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0037] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0038] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0039] FIG. 2 is an exemplary diagram for explaining a function or operation of acquiring data (e.g., fourth data) with a changed sampling period according to one embodiment of the present document. FIG. 3b is an exemplary diagram for explaining a function or operation of acquiring data with a changed sampling period and classifying a class of the data with a changed sampling period using a reconstructor module and / or a classification module according to one embodiment of the present document. FIG. 5 and FIG. 6 are exemplary diagrams for explaining a function or operation of an up sampler (314) included in a reconstructor module (310) according to one embodiment of the present document.

[0040] Referring to FIGS. 2 and 3B, an electronic device (101) (e.g., a processor (120)) according to an embodiment of the present document may, in operation 210, generate second data including information on an attribute of data corresponding to a first point in time (e.g., heart rate data of a user) acquired during a first time interval (e.g., 30 minutes) through at least one sensor (e.g., a sensor module (176) of FIG. 1) with a first sampling period (e.g., a low sampling period, for example, 1 minute) (e.g., a low sampling period, for example, 1 minute). The first data according to an embodiment of the present document may be, for example, data illustrated in FIG. 6. , data , or data It can correspond to at least one data. The second data according to one embodiment of the present document is, for example, the data shown in FIG. 6. may correspond to. The attribute according to one embodiment of the present document may include, for example, trend information (e.g., information on whether the data value increases or decreases) of the first data during a time interval specified with respect to a first point in time (e.g., an interval 10 minutes before and after 40 minutes in the center), phase information (e.g., whether it is a sine function waveform) of the first data during a time interval specified with respect to a first point in time (e.g., an interval 10 minutes before and after 40 minutes in the center), and / or information on data determined to be unnecessary for upsampling among data acquired during a time interval specified with respect to the first point in time (e.g., an interval 10 minutes before and after 40 minutes in the center), (e.g., information on noise). The function or operation of converting the first data into second data according to one embodiment of the present document may be performed by the encoder (312) included in the reconstruction module (310) according to one embodiment of the present document. An encoder (312) according to one embodiment of the present document may generate second data including information related to attributes of first data. An encoder (312) according to one embodiment of the present document may optionally include at least one attention layer and / or a convolution layer. A sampling period of the first data and a sampling period of the second data according to one embodiment of the present document may have substantially the same sampling period (e.g., 1 minute).

[0041] An electronic device (101) (e.g., processor (120)) according to an embodiment of the present document may, in operation 220, interpolate the second data generated according to operation 210 to generate third data based on a second sampling period (e.g., 10 seconds) having a different sampling period from the first sampling period (e.g., 1 minute). Operation 220 according to an embodiment of the present document may be performed, for example, by a reconstruction module (310) included in the processor (120). The third data according to an embodiment of the present document may be, for example, data illustrated in FIG. 6. The operation 220 according to one embodiment of the present document may be performed by an encoder (312) and / or an up-sampler (314) included in the processor (120).

[0042] An electronic device (101) (e.g., processor (120)) according to one embodiment of the present document, in operation 230, generates third data (e.g., data) generated according to operation 220. ) can generate fourth data based on the second sampling period by applying a weight to data corresponding to at least one restoration target point in time (e.g., a point in time when 40 minutes and 30 seconds have passed since data acquisition) (e.g., data at the restoration target point in time that is not acquired by at least one sensor (e.g., sensor module (176))). The fourth data according to one embodiment of the present document may be, for example, data illustrated in FIG. 6. The function or operation of the up sampler (314) according to one embodiment of the present document to generate the fourth data by applying weights will be described in more detail with reference to FIGS. 5 and 6.

[0043] Referring to FIG. 5, an electronic device (101) (e.g., up-sampler (314)) according to an embodiment of the present document may, in operation 510, acquire trend and phase information for a restoration target point in time (e.g., 40 minutes 10 seconds, 40 minutes 20 seconds, 40 minutes 30 seconds, 40 minutes 40 seconds, and 40 minutes 50 seconds after the time when the data was first acquired) among interpolated data (e.g., second data). The trend and phase information according to an embodiment of the present document may be input as an input (e.g., Q) of at least one attention layer (314b) included in the up-sampler (314). An electronic device (101) (e.g., up-sampler (314)) according to an embodiment of the present document may, in operation 520, acquire interpolated data (e.g., data ) can obtain trend and phase information for the entire time interval (e.g., 50 minutes). The trend and phase information according to one embodiment of the present document can be input as an input (e.g., K) of at least one attention layer (314b) included in the up sampler (314). The electronic device (101) (e.g., up sampler (314)) according to one embodiment of the present document, in operation 530, interpolated data (e.g., data ) can be obtained. Interpolated data (e.g., data) according to one embodiment of this document ) information can be input as an input (e.g., V) of at least one attention layer (314b) included in the up sampler (314). The electronic device (101) (e.g., up sampler (314)) according to one embodiment of the present document, in operation 540, compares the trend and phase information acquired according to operation 510 and operation 520 to interpolate data (e.g., data ) can be weighted. For example, for each point in time to be restored (e.g., 40 minutes 10 seconds, 40 minutes 20 seconds, 40 minutes 30 seconds, 40 minutes 40 seconds, and 40 minutes 50 seconds from the time when the data was first acquired, respectively), trend and phase information for each point in time has already been acquired and interpolated (e.g., data ) is determined to be related to trend and phase information in a specific time section (e.g., when the trend and phase information are determined to be similar to each other within an error range), the electronic device (101) according to one embodiment of the present document may generate (e.g., restore) data corresponding to the restoration target time point by assigning a high weight to the data corresponding to the restoration target time point (e.g., the time point at which 40 minutes 10 seconds, 40 minutes 20 seconds, 40 minutes 30 seconds, 40 minutes 40 seconds, and 40 minutes 50 seconds have passed from the time point at which the data was first acquired). The up-sampler (314) according to one embodiment of the present document may include at least one Fully Connected Layer (314a), at least one Attention Layer (314b), and / or at least one Convolution Layer (314c).

[0044] An electronic device (101) (e.g., processor (120)) according to one embodiment of the present document, upsampled data (e.g., data ) can be used to perform time series classification. Time series classification according to one embodiment of the present document can be performed by a classification module (320) included in the processor (120). Referring again to FIG. 3b, the electronic device (101) (e.g., processor (120)) according to one embodiment of the present document can input first data (e.g., data) as input to the reconstruction module (310). , and / or data ) can be input into the reconstruction module (310). According to one embodiment of this document, first data (e.g., data ) is, data The electronic device (101) (e.g., processor (120)) according to one embodiment of the present document may input the output data that has passed through the reconstruction module (310) into the classification module (320). The electronic device (101) (e.g., processor (120)) according to one embodiment of the present document may classify the class of the data input to the classification module (320). The electronic device (101) (e.g., processor (120)) according to one embodiment of the present document may control the classification module (320) so that, when there are two or more types of data input to the classification module (320) (e.g., when data having different sampling periods and / or phases are input to the classification module (320), the data having different sampling periods and / or phases input to the classification module (320) are classified into the same class by correcting the error of the data input to the classification module (320) using a consistency regulation technique. The classification module (320) according to one embodiment of this document can learn a class for a specific cycle and / or phase through such a function or operation, and the result of such learning can be applied to data input to the classification module (320) thereafter.

[0045] FIG. 3A is an exemplary diagram illustrating a function or operation of training a reconstruction module and / or a classification module using data acquired at a high sampling rate, according to one embodiment of the present document. FIG. 4 is an exemplary flowchart illustrating a process for performing the function or operation described in FIG. 3A.

[0046] Referring to FIGS. 3A and 4, an electronic device (101) (e.g., a processor (120)) according to one embodiment of the present document, in operation 410, acquires data (e.g., ) can be used to train the reconstruction module (310) and / or the classification module (320). The electronic device (101) (e.g., processor (120)) according to one embodiment of the present document can, in operation 420, generate and / or classify restored data using the result of the learning according to operation 410. The electronic device (101) according to one embodiment of the present document can generate and / or classify data obtained based on a high sampling period (e.g., ) by downsampling one or more downsampled data (e.g. , ) can be generated. An electronic device (101) according to one embodiment of the present document generates one or more down-sampled data (e.g., , ) is input to the reconstruction module (310), and the output data (e.g. , ) can be obtained. An electronic device (101) according to one embodiment of the present document can obtain data (e.g., data obtained based on a high sampling period) ) and output data (e.g. ) can be compared with each other. An electronic device (101) (e.g., a reconstruction module (310)) according to one embodiment of the present document can obtain data (e.g., ) and output data (e.g. ) can be learned using the results of comparing the data (e.g., the degree of similarity of data). For example, if the degree of similarity of data is within a pre-specified error range, it can be learned (e.g., determined) that upsampling is being performed appropriately. Alternatively, if the degree of similarity of data exceeds a pre-specified error range, the electronic device (101) according to one embodiment of the present document may acquire data (e.g., ) and output data (e.g. ) can be trained to reduce the mean square error between the two. The electronic device (101) (e.g., processor (120)) according to one embodiment of the present document can classify the class of data input to the classification module (320). The electronic device (101) (e.g., processor (120)) according to one embodiment of the present document can classify the class of data input to the classification module (320) when the type of data input to the classification module (320) is two or more (e.g., , When input), the classification module (320) can be controlled so that data having different sampling periods and / or phases input to the classification module (320) are classified into the same class by correcting the error of the data input to the classification module (320) using consistency regulation technology. The classification module (320) according to one embodiment of the present document can learn a class for a specific period and / or phase through such a function or operation, and the result of such learning can be applied to data input to the classification module (320) thereafter.

[0047] FIG. 7a is an exemplary diagram illustrating a comparative example in which data at a restoration target point in time (730) restored according to a conventional technique does not match data (710) that should have actually been measured. FIG. 7b is an exemplary diagram illustrating an embodiment in which data at a restoration target point in time (730) restored according to one embodiment of the present document substantially matches data (710) that should have actually been measured.

[0048] Referring to FIG. 7a, a comparative example is illustrated in which, in a conventional technique, data at a restored restoration target point in time (730) does not match the data (710) that should have actually been measured, and thus accurate class classification cannot be performed. However, referring to FIG. 7b, an embodiment is illustrated in which, according to one embodiment of the present document, data at a restored restoration target point in time (730) restored based on data (720) acquired with a low sampling cycle substantially matches the data (710) that should have actually been measured. In this way, according to various embodiments of the present document, accurate time-series classification can be performed using restored data.

[0049] An electronic device (e.g., the electronic device (101) of FIG. 1) according to one embodiment of the present document includes at least one sensor (e.g., the sensor module (176) of FIG. 1), a memory, and at least one processor (e.g., the processor (120) of FIG. 1), wherein the memory includes instructions that, when executed, cause the at least one processor to generate second data including information on properties of data corresponding to a first point in time among first data acquired during a first time interval through the at least one sensor at a first sampling period, the first point in time being a point in time included in the first time interval, interpolate the generated second data to generate third data based on a second sampling period having a sampling period different from the first sampling period, and apply a weight to data corresponding to at least one restoration target point in time among the generated third data to generate fourth data based on the second sampling period, wherein the weight is based on a correlation between a property of data corresponding to the at least one restoration target point in time and the first data acquired during the first time interval. The data determined and corresponding to the at least one restoration target point in time may include data not acquired by the at least one sensor.

[0050] A method for controlling an electronic device according to one embodiment of the present document includes: generating second data including information on properties of data corresponding to a first point in time among first data acquired during a first time interval through at least one sensor of the electronic device with one sampling period; generating third data based on a second sampling period having a different sampling period from the first sampling period by interpolating the generated second data; and generating fourth data based on the second sampling period by applying a weight to data corresponding to at least one restoration target time interval among the generated third data, wherein the weight is determined based on a correlation between a property of the data corresponding to the restoration target time interval and the first data acquired during the first time interval, and the data corresponding to the restoration target time interval may include data not acquired by the at least one sensor.

[0051] Electronic devices according to the various embodiments disclosed in this document may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to the embodiments of this document are not limited to the aforementioned devices.

[0052] The various embodiments of this document and the terminology used therein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among those phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0053] The term "module" used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0054] Various embodiments of the present document may be implemented as software (e.g., a program (2540)) including one or more instructions stored in a storage medium (e.g., an internal memory (2536) or an external memory (2538)) readable by a machine (e.g., an electronic device (2501)). For example, a processor of the machine (e.g., an electronic device (2501)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0055] According to one embodiment, the method according to various embodiments disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) via an application store (e.g., Play Store™) or directly between two user devices (e.g., smart phones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0056] According to one embodiment of the present document, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to one embodiment of the present document, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to one embodiment of the present document, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In electronic devices, At least one sensor, memory, and comprising at least one processor, The above memory, when executed by the at least one processor, causes the electronic device to: Generating second data including information on properties of data corresponding to a first point in time among first data acquired during a first time interval through at least one sensor with a first sampling period, wherein the first point in time is a point in time included in the first time interval; By interpolating the second data generated above, third data based on a second sampling period having a different sampling period from the first sampling period is generated, Includes instructions for applying a weight to data corresponding to at least one restoration target point in time among the generated third data to generate fourth data based on the second sampling period, The weight is determined based on the correlation between the attribute of the data corresponding to the at least one restoration target point in time and the first data acquired during the first time interval, and An electronic device, characterized in that the data corresponding to at least one restoration target point in time is data not acquired by the at least one sensor.

2. In paragraph 1, An electronic device, characterized in that the above property includes trend information and / or periodicity information of data corresponding to the first point in time.

3. In paragraph 1 or 2, The above memory, when executed, causes at least one processor to: An electronic device, characterized in that it further includes instructions for performing learning by down-sampling data based on a third sampling period having a shorter sampling period than the first sampling period.

4. In any one of paragraphs 1 to 3, The electronic device further includes a classification module set to classify the class of the fourth data, The above memory, when executed, causes at least one processor to: An electronic device characterized in that the down-sampled data further includes instructions for training the classification module by correcting errors in results classified by the classification module.

5. In any one of paragraphs 1 to 4, An electronic device, characterized in that the relevance is determined based on the properties of data corresponding to the restoration target time point and the result of the first data acquired during the first time interval being output through an attention layer.

6. In any one of paragraphs 1 to 5, An electronic device, characterized in that the second sampling period has a shorter sampling period than the first sampling period.

7. In any one of paragraphs 1 to 6, An electronic device, characterized in that the restoration target point in time is a point in time included in the first time cycle.

8. In any one of paragraphs 1 to 7, The above memory, when executed, causes at least one processor to: An electronic device, characterized in that it further includes instructions to convolve the third data with the result of the attention layer to generate the fourth data.

9. In any one of paragraphs 1 to 8, The above memory, when executed, causes at least one processor to: An electronic device, characterized in that it further includes an instruction for classifying a class of the fourth data using the classification module.

10. In any one of paragraphs 1 to 9, The electronic device further comprises an encoder that generates the second data, An electronic device, characterized in that the encoder comprises an attention layer and a convolution layer.

11. In the method, An operation of generating second data including information on properties of data corresponding to a first point in time among first data acquired during a first time interval through at least one sensor of an electronic device with a first sampling period, wherein the first point in time is a point in time included in the first time interval; An operation of interpolating the generated second data to generate third data based on a second sampling period having a different sampling period from the first sampling period; Including an operation of generating fourth data based on the second sampling period by applying a weight to data corresponding to at least one restoration target time point among the generated third data, The above weight is determined based on the correlation between the attribute of the data corresponding to the restoration target time point and the first data acquired during the first time interval, and A method, characterized in that the data corresponding to the restoration target point in time is data not acquired by the at least one sensor.

12. In paragraph 11, A method, characterized in that the above properties include trend information and / or periodicity information of data corresponding to the first point in time.

13. In paragraph 11 or 12, A method further characterized by including an operation of performing learning by down-sampling data based on a third sampling period having a shorter sampling period than the first sampling period.

14. In any one of paragraphs 11 to 13, The electronic device further includes a classification module set to classify the class of the fourth data, A method for controlling the electronic device, characterized in that it further includes an operation of training the classification module by correcting errors in results classified by the classification module using the down-sampled data.

15. In any one of paragraphs 11 to 14, A method characterized in that the above relevance is determined based on the properties of data corresponding to the restoration target time point and the result outputted through the attention layer of the first data acquired during the first time interval.

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