Learning data acquisition device and learning data acquisition method

Abstract

This method is suitable for using data obtained from sensors as training data in edge devices, ensuring that necessary features are not lost. [Solution] A learning data acquisition device that obtains learning data to be used to create a machine learning model for detecting the occurrence of an event by creating a digital signal from analog physical signals obtained from sensors 101 provided at one or more locations, comprises a filter means 201 that limits the waveform range of the analog physical signal obtained from the sensor to the waveform range of the analog physical signal when the event does not occur, from the waveform range of the analog physical signal when the event does occur, and an AD converter 202 that converts the output signal of the filter means 201 into a digital signal of a predetermined number of bits.

Description

Technical Field

[0001] This invention relates to a learning data acquisition device and a learning data acquisition method.

Background Art

[0002] In particular, when obtaining learning data used for creating a machine learning model in AI mounted on an edge device, it is necessary to provide high-precision data as low-precision data.

[0003] Conventionally, for example, regarding failure detection by abnormal sounds and weather detection techniques by analysis of electromagnetic waves, etc., usually a plurality of observation points are used, and high-precision data using a high-precision AD converter is used as a learning model. However, in an edge device, resources such as memory and arithmetic circuits for AI are limited, and a method of performing analysis with reduced accuracy internally is applied for optimization of the AI model.

[0004] As a specific circuit configuration, as shown in FIG. 1, a device that uses microphones 11 and 12 as sensors and captures an audio signal as an analog physical signal can be cited. In this device, the outputs of microphones 11 and 12 are captured by an amplifier configured with operational amplifiers 13 and 14 as the core, amplified, and given to high-precision AD converters 15 and 16 for digitization. The high-precision AD converters 15 and 16 output analog inputs as digital data of, for example, 32 high-order bits to a microcomputer 20, which is an edge device, as input data for a learning model. However, the input data of this learning model remains high-precision data at present, and there is a problem that when handling data of a plurality of observation points (for example, sound sources) as an edge device, the load is large and it cannot be processed.

[0005] On the other hand, a conventional method of creating learning data is disclosed in Patent Document 1. Patent Document 1 shows that filtering processing and diffusion processing are used.

[0006] Patent Document 2 describes the learning process for an internal combustion engine as follows: When the angle range is changed to the advance side, the injection mode is synchronized with the change in the angle range, thereby allowing for a favorable understanding of the combustion state during transient operation. Furthermore, by variably setting the angle range depending on whether or not the output characteristics of the in-cylinder pressure sensor 26 are learned, it is possible to achieve both appropriate understanding of the combustion state and appropriate learning. Moreover, when the output characteristics of the in-cylinder pressure sensor 26 are learned, the learning process can be performed appropriately by setting the angle range to the advance side compared to when learning is not performed.

[0007] Patent Document 3 discloses a fast magnetic resonance imaging (MRCA) reconstruction method. In this method, a learning unit 22 performs learning using training data. The learning unit 22 performs deep learning to train an MRCNN 21 using training data 50 of k-space data from a high-resolution image 25 and k-space data 15 generated by downsampling the training data 50, optimizing various parameters of the MRCNN 21. In this deep learning step, the training data 50 is downsampled using an undersampling filter 40 that retains all data for frequencies lower than a predetermined spatial frequency and randomly retains data for frequencies higher than a predetermined spatial frequency, thereby generating k-space data 15. Furthermore, in this deep learning step, learning is performed using backpropagation based on the error between the training data 50 and the k-space data corresponding to the high-resolution image 25 reconstructed using the MRCNN 21.

[0008] Next, the data acquisition unit 10 operates according to the above sequence and acquires k-space data 15 based on the nuclear magnetic resonance signal (original signal) observed by magnetic resonance. This acquires k-space data 15 for obtaining a tomographic image of the subject. In this data acquisition, the number of iterations of the sequence or the number of slice positions Z to be observed is determined according to the undersampling filter 40. That is, for low-frequency components in k-space, the data is sampled without decimation, and for high-frequency components, the data is sampled in a randomly decimated state, thereby shortening the data acquisition time (imaging time).

[0009] Furthermore, the data reconstruction unit 20 uses a multi-resolution convolutional neural network (MRCNN21) to reconstruct a high-resolution image 25 from k-space data 15 based on the original signal observed by magnetic resonance. Then, MRCNN21 uses SRCNN35 to reconstruct images obtained by inverse Fourier transform from the k-space data 15, 1 / 2 data, and 1 / 4 data, respectively. Furthermore, MRCNN21 reconstructs the high-resolution image 25 by upsampling the reconstructed data, that is, by matching the data size and superimposing them.

[0010] In the invention described in Reference 3, a tomographic image of a subject is obtained from a degraded tomographic image of the subject using high-speed magnetic resonance imaging (MRI). High-speed MRI restores the tomographic image of the subject with high resolution.

[0011] Furthermore, Patent Document 4 discloses a method for reducing the amount of data in an image data frame by masking or dropping a predetermined number of bits from the least significant bit of a digital image sensor data word, and Patent Documents 5 and 6 disclose techniques related to resolution control.

[0012] The inventions described in Patent Documents 1 to 3 above involve complex processing methods and are by no means suitable for edge devices. Furthermore, while the inventions described in Patent Documents 4 to 6 can be described as techniques for reducing data accuracy, they are unsuitable as training data because they are not created by extracting the necessary features for use in training data or analysis. [Prior art documents] [Patent Documents]

[0013] [Patent Document 1] International Publication No. 2021-025092 [Patent Document 2] Japanese Patent Publication No. 2008-138581 [Patent Document 3] Japanese Patent Publication No. 2020-146449 [Patent Document 4] Special Publication No. 2006-502673 [Patent Document 5] Japanese Patent Application Publication No. 8-331543 [Patent Document 6] Japanese Patent Publication No. 2014-135992 [Overview of the project] [Problems that the invention aims to solve]

[0014] The present invention has been made in view of the above-mentioned circumstances, and its purpose is to provide a learning data acquisition device and a learning data acquisition method that are suitable for using data obtained from sensors as learning data in edge devices and the like, without losing necessary features. [Means for solving the problem]

[0015] A learning data acquisition device according to an embodiment of the present invention is a learning data acquisition device that obtains learning data to be used to create a machine learning model for detecting the occurrence of an event, using digital signals created from analog physical signals obtained from sensors installed at one or more locations. When the first waveform range is defined by the level or frequency of the signal that should be considered an event occurrence by the machine learning model, and the second waveform range is defined by the level or frequency of the signal that should be considered an event occurrence by the machine learning model,The waveform range of the analog physical signal obtained from the sensor From the first waveform range to the second waveform range characterized by comprising filter means for restricting the waveform range of the analog physical signal obtained from the sensor, and an AD converter for converting the output signal of the filter means into a digital signal of a predetermined number of bits.

[0016] A learning data acquisition method according to an embodiment of the present invention is a learning data acquisition method for obtaining learning data used for creating a machine learning model that detects the occurrence of an event by a digital signal created from an analog physical signal obtained from sensors provided at one or more locations. Corresponding to the fact that the first waveform range is defined by the level or frequency of the signal that does not cause the event required by the machine learning model, which is the "waveform range of the analog physical signal when the event does not occur", and the second waveform range is defined by the level or frequency of the signal that should cause the event required by the machine learning model, Input the analog physical signal obtained from the sensors provided at the one or more locations to the filter means and, as stated above The waveform range of the analog physical signal obtained from the sensor From the first waveform range to the second waveform range 'characterized by comprising a range restriction step of restricting the waveform range of the analog physical signal obtained from the sensor, and an AD conversion step of converting the output signal of the filter means into a digital signal of a predetermined number of bits using an AD converter.

Brief Description of the Drawings

[0017] [Figure 1] Functional block diagram of an event occurrence detection system configured using a learning data acquisition device according to a conventional example. [Figure 2] Functional block diagram of an event occurrence detection system configured using a learning data acquisition device according to a first embodiment of the present invention. [Figure 3] Functional block diagram of an event occurrence detection system configured using a learning data acquisition device according to a second embodiment of the present invention.

Embodiments for Carrying Out the Invention

[0018] Hereinafter, a learning data acquisition device and a learning data acquisition method according to an embodiment of the present invention will be described with reference to the accompanying drawings. In each figure, the same components are denoted by the same reference numerals and redundant descriptions are omitted. FIG. 2 shows a functional block diagram of an event occurrence detection system configured using a learning data acquisition device according to a first embodiment of the present invention.

[0019] This embodiment is a learning data acquisition device 200 that obtains learning data used to create a machine learning model for detecting the occurrence of an event from a digital signal created from an analog physical signal obtained from a sensor 101 provided at one location. The learning data acquisition device 200 includes a filter means 201 and an AD converter 202.

[0020] The filter means 201 restricts the waveform range of the analog physical signal obtained from the sensor 101 from the waveform range of the analog physical signal when the event does not occur to the waveform range of the analog physical signal when the event occurs. The AD converter 202 converts the output signal of the filter means 201 into a digital signal of a predetermined number of bits.

[0021] The AD converter 202 can be configured to convert the signal into a digital signal of a predetermined number of bits from 4 bits to 8 bits. Also, the waveform range is not limited to being defined only by the signal level. That is, when the frequency of the analog physical signal obtained from the sensor 101 varies from the waveform range when the event does not occur to the waveform range when the event occurs, the filter means 201 can use a band-pass filter, a high-pass filter, or a low-pass filter.

[0022] The digital signal created as described above is provided to an event occurrence device 300 composed of a microcomputer or the like as an edge device, and is used as learning data for creating a machine learning model for detecting the occurrence of an event.

[0023] The generated digital signal is filtered by the filter means 201 to limit the waveform range of the analog physical signal obtained from the sensor 101 from the waveform range of the analog physical signal when no event occurs to the waveform range of the analog physical signal when the event occurs. Therefore, even if the low-precision (low-bit count) digital signal generated above is used as input data for a learning model in an edge device such as a microcomputer, the problem of excessive load and inability to process it will not occur.

[0024] Furthermore, the generated digital signal, filtered by the filter means 201, has a waveform range of the analog physical signal obtained from the sensor 101 that ranges from the waveform range of the analog physical signal when no event occurs to the waveform range of the analog physical signal when the event occurs, and includes the characteristic range necessary for analysis. Therefore, even though it is a low-precision (low-bit) digital signal, it can be said to be suitable data as input data for a learning model.

[0025] Figure 3 shows a functional block diagram of an event occurrence detection system configured using a learning data acquisition device according to the second embodiment of the present invention. This system uses microphones 11 and 12 as sensors to capture audio signals as analog physical signals. The outputs of microphones 11 and 12 are captured and amplified by an amplifier configured with operational amplifiers 13 and 14 at its core, and then supplied to a filtering means 210.

[0026] The filter means 210 is configured as a differential amplifier that receives two sets of analog physical signals, which are audio signals, obtained from microphones 11 and 12, which are sensors installed at two locations, and outputs a spike-shaped waveform when noise occurs. With a filter means 210 configured in this way, training data can be obtained to create a machine learning model that recognizes the occurrence of a malfunction due to abnormal noise as "when the event occurred".

[0027] This filtering means 210 limits the waveform range of the audio signal, which is an analog physical signal obtained from the microphones 11 and 12, to the range of the analog physical signal when the event does not occur, from the waveform range of the analog physical signal when the event does occur.

[0028] The AD converter 220 converts the output signal of the filter means 210 into a digital signal of a predetermined number of bits. The AD converter 220 can convert the signal into a digital signal of a predetermined number of bits, from 4 bits to 8 bits.

[0029] The above-mentioned filter means 210 and AD converter 220 constitute a training data acquisition device, and the output of the AD converter 220 is output to the microcomputer 230, which is an edge device, as input data for the training model.

[0030] In this embodiment as well, the filter means 210 limits the waveform range of the analog physical signals obtained from the microphones 11 and 12, which are sensors, from the waveform range of the analog physical signals when no event occurs to the waveform range of the analog physical signals when the event occurs. Therefore, even if the low-precision (low-bit) digital signals created above are used as input data for the learning model in the microcomputer 230, the problem of high load and inability to process them does not occur.

[0031] Furthermore, the digital signal created by this embodiment, filtered by the filter means 210, has a waveform range of the analog physical signal obtained from the microphones 11 and 12 that extends from the waveform range of the analog physical signal when no event occurs to the waveform range of the analog physical signal when the event occurs, and includes the characteristic range necessary for analysis. Therefore, even though it is a low-precision (low-bit) digital signal, it can be said to be suitable data as input data for a learning model.

[0032] In this embodiment, the filter means 210 may be configured using a differential amplifier that receives two time-series analog physical signals obtained from a sensor installed at one location and outputs a spike-shaped waveform when noise occurs. Alternatively, analog physical signals obtained from sensors installed at three or more locations may be input, and two different combinations of these signals may be directed to the filter means 210 using differential amplifiers to obtain training data to be used in creating a machine learning model.

[0033] As described above, each embodiment is suitable for cases where data obtained from sensors is used as training data in edge devices, etc., and provides a training data acquisition device and training data acquisition method that do not lose necessary features.

[0034] Furthermore, if the filtering means of this embodiment ensures that the waveform range of the analog physical signal obtained from the sensor falls between the waveform range of the analog physical signal when no event occurs and the waveform range of the analog physical signal when the event occurs, then the digital data after digital conversion will include the characteristic range necessary for analysis, and therefore, a limiter circuit can be used as the filtering means. [Explanation of Symbols]

[0035] 11, 12 Microphone 13, 14 Op-amps 101 Sensor 200 Training data acquisition device 201, 210 Filtering means 202, 220 AD converters 230 Microcomputers 300 Event Occurrence Detection Device

Claims

1. In a learning data acquisition device that obtains learning data to be used in creating a machine learning model for detecting the occurrence of an event, using digital signals created from analog physical signals obtained from sensors installed at one or more locations, When the "waveform range of the analog physical signal when the event does not occur," defined by the level or frequency of the signal that does not result in an event required by the machine learning model, is a first waveform range, and the "waveform range of the analog physical signal when the event occurs," defined by the level or frequency of the signal that should result in an event required by the machine learning model, is a second waveform range, a filter means for restricting the waveform range of the analog physical signal obtained from the sensor from the first waveform range to the second waveform range, A converter that converts the output signal of the filter means into a digital signal of a predetermined number of bits. A learning data acquisition device characterized by comprising the following:

2. The learning data acquisition device according to claim 1, characterized in that the filtering means is configured with a differential amplifier that receives two time-series analog physical signals obtained in time series from a sensor provided at one location, or two sets of analog physical signals obtained from sensors provided at two locations, and outputs a spike-shaped waveform when noise occurs.

3. The learning data acquisition device according to claim 1, characterized in that the filtering means is a limiter circuit.

4. The learning data acquisition device according to claim 1, characterized in that the filtering means is a bandpass filter.

5. The learning data acquisition device according to claim 1, characterized in that the filtering means is a high-pass filter.

6. The learning data acquisition device according to claim 1, characterized in that the filtering means is a low-pass filter.

7. The learning data acquisition device according to claim 1, characterized in that the AD converter converts the data into a digital signal of a predetermined number of bits, from 4 bits to 8 bits.

8. In a method for acquiring training data to obtain training data for creating a machine learning model that detects the occurrence of an event, using digital signals created from analog physical signals obtained from sensors installed at one or more locations, Corresponding to the fact that the first waveform range is defined by the level or frequency of the signal that does not cause the event required by the machine learning model, which is the "waveform range of the analog physical signal when the event does not occur", and the second waveform range is defined by the level or frequency of the signal that should cause the event required by the machine learning model, A range limiting step involves inputting the analog physical signals obtained from sensors provided at one or more locations to a filter means and limiting the waveform range of the analog physical signals obtained from the sensors from the first waveform range to the second waveform range, The AD conversion step involves using an AD converter to convert the output signal of the filter means into a digital signal of a predetermined number of bits. A method for acquiring learning data, characterized by comprising the following:

9. The learning data acquisition method according to claim 8, characterized in that the filtering means inputs two time-series analog physical signals obtained in a time series from a sensor provided at one location, or two sets of analog physical signals obtained from sensors provided at two locations, to a differential amplifier, and outputs a spike-shaped waveform when noise occurs.

10. The learning data acquisition method according to claim 8, characterized in that the filtering means uses a limiter circuit.

11. The method for acquiring training data according to claim 8, characterized in that the filtering means uses a bandpass filter.

12. The method for acquiring training data according to claim 8, characterized in that the filtering means uses a high-pass filter.

13. The method for acquiring training data according to claim 8, characterized in that the filtering means uses a low-pass filter.

14. The learning data acquisition method according to claim 8, characterized in that the AD converter converts the data into a digital signal of a predetermined number of bits, from 4 bits to 8 bits.

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