Patent Publication Number: US-2023147767-A1

Title: State detection system

Description:
TECHNICAL FIELD 
     The present disclosure relates to a state detection system. 
     BACKGROUND ART 
     A state detection system that detects a state change of an object or an environment using an electromagnetic wave is known. 
     This type of state detection system generally includes a sensor that includes a resonator and a reader that transmits and receives electromagnetic waves. In this type of state detection system, a method is used in which the reader detects a state change of the sensor by receiving a reflected wave from the sensor when the reader transmits an electromagnetic wave of a predetermined frequency to the sensor. 
     Since this type of state detection system can detect a state of an object in a non-contact manner, application to various uses such as article management is expected. 
     From such a background, for example, Patent Literature 1 discloses a state detection system in which an LC resonance tag is attached to a diaper or a urine absorbing pad, and in which a state change (dirt) of the object is detected from a change in the resonance frequency of the LC resonance tag due to absorption of a discharge by the diaper or the urine absorbing pad. At this time, Patent Literature 1 employs a method of periodically identifying the resonance frequency of the LC resonance tag to capture a change in the resonance frequency of the LC resonance tag, thereby detecting a state change (dirt) of the object (also referred to as a peak pick method). 
     CITATION LIST 
     Patent Literature 
     Patent Literature 1: JP 2001-134726 A 
     SUMMARY OF INVENTION 
     Technical Problem 
     Meanwhile, in this type of state detection system, generally, there is a problem in which it is difficult to secure the intensity of the reflected wave from the sensor and in which it is difficult to secure a good SN ratio. Therefore, a reflected wave spectrum of the sensor to be acquired (the spectrum hereinbelow represents a frequency spectrum of the reflected wave) is a spectrum on which many noises are superimposed, and it is often difficult to clearly identify the resonance frequency of the sensor. 
     Hence, a method (peak pick method) of detecting a state change (moisture content of the urine absorbing pad) of the detection target by identifying a change in the resonance frequency of the LC resonance tag before and after the change in the moisture amount around the LC resonance tag as in the state detection system in Patent Literature 1 has a problem in which erroneous detection caused by noise is likely to occur. In other words, with such a method, it is difficult to detect the state with high accuracy. 
     The present disclosure has been made in view of such problems, and an object thereof is to provide a state detection system capable of detecting a state change of an object or an environment with high accuracy. 
     Solution to Problem 
     The present main disclosure for solving the aforementioned problems relates to a state detection system including a sensor that includes an electromagnetic wave reflecting material and a resonator disposed adjacent to or integrally with the electromagnetic wave reflecting material, and that detects a state change of a surrounding object or surrounding environment as a change in its own electromagnetic wave reflection characteristic, a reader that transmits an electromagnetic wave to the sensor and receives a reflected wave of the electromagnetic wave, and that acquires reflected wave spectrum information of the sensor, and an analysis device that estimates a current state of a detection target of the sensor by applying information regarding reflected wave intensities at a plurality of frequency positions of the reflected wave spectrum to a learning model generated in advance on a basis of training data of a reflected wave spectrum for each state of the sensor. 
     Advantageous Effects of Invention 
     With a state detection system according to the present disclosure, it is possible to detect a state change of an object or an environment with high accuracy. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a diagram illustrating an example of a configuration of a state detection system. 
         FIG.  2    is a diagram illustrating an example of a configuration of a sensor. 
         FIG.  3    is a diagram illustrating an example of a reflected wave spectrum (frequency spectrum of a reflected wave) of the sensor acquired by a reader. 
       Each of  FIGS.  4 A,  4 B, and  4 C  is a diagram illustrating an example of a change in a reflected wave spectrum of the sensor caused by a state change of a detection target of the sensor. 
         FIG.  5    is a diagram illustrating a more preferable form of the sensor. 
         FIG.  6    is a diagram illustrating a mode in which the sensor detects an expansion/contraction state of an object to be detected. 
         FIG.  7    is a diagram illustrating a mode in which the sensor detects a change in the thickness of an object to be detected. 
         FIG.  8    is a diagram illustrating a mode in which the sensor detects a positional displacement state of an object to be detected. 
         FIG.  9    is a diagram illustrating a mode in which the sensor detects a change in the moisture content of an object to be detected. 
         FIG.  10    is a diagram illustrating a mode in which the sensor detects a change in the temperature of a surrounding environment. 
         FIG.  11    is a diagram illustrating a mode in which the sensor detects a change in the gas concentration of a surrounding environment. 
         FIG.  12    is a diagram illustrating a mode in which the sensor detects the degree of oxidation of an object to be detected. 
         FIG.  13    is a diagram illustrating a modification example of the configuration of the sensor. 
         FIG.  14    is an example of a flowchart when learning processing is performed on a learning model. 
         FIG.  15    is an example of a flowchart of processing of estimating a state of a detection target. 
         FIG.  16    is a diagram illustrating a configuration of Example 1. 
         FIG.  17    is a diagram illustrating a representative reflected wave spectrum obtained each time of estimation of the moisture absorption amount in a diaper. 
         FIG.  18    is a diagram illustrating a configuration of Example 2. 
         FIG.  19    is a diagram illustrating a representative reflected wave spectrum obtained for each cup position of the paper cup. 
         FIG.  20    is a diagram illustrating a configuration of Example 3. 
         FIG.  21    is a diagram illustrating representative reflected wave spectra in respective states where a substrate is not stretched, 1% stretched, 2% stretched, and 3% stretched. 
         FIG.  22    is a diagram illustrating a configuration of Example 4. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinbelow, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Note that, in the present description and the drawings, components having substantially the same function are labeled with the same reference signs, and redundant description is omitted. 
     [Basic Configuration of State Detection System] 
     First, a basic configuration of a state detection system according to an embodiment will be described with reference to  FIGS.  1  and  2   . 
       FIG.  1    is a diagram illustrating an example of a configuration of a state detection system U. 
     The state detection system U includes a sensor  10 , a reader  20 , and an analysis device  30 . 
     Here, the sensor  10  includes an electromagnetic wave reflecting material and a resonator disposed adjacent to or integrally with the electromagnetic wave reflecting material, and detects a state change of a surrounding object or surrounding environment around itself as a change in its own electromagnetic wave reflection characteristic (hereinbelow referred to as “a reflection characteristic of the sensor  10 ” or “a reflected wave spectrum of the sensor  10 ). The reader  20  transmits an electromagnetic wave to the sensor  10  while changing the transmission frequency, receives a reflected wave thereof, and acquires data of a current reflected wave spectrum of the sensor  10 . The analysis device  30  estimates a current state of a detection target of the sensor  10  using a pre-trained learning model  30 D on the basis of the data of the current reflected wave spectrum of the sensor  10 . 
     In such a configuration, the state detection system U can estimate the current state of the detection target of the sensor  10  with high accuracy from the change in the reflection characteristic of the sensor  10 . 
     [Configuration of Sensor] 
       FIG.  2    is a diagram illustrating an example of a configuration of the sensor  10 . 
       FIG.  3    is a diagram illustrating an example of a reflected wave spectrum (frequency spectrum of a reflected wave) of the sensor  10  acquired by the reader  20 . Note that the plotted points in  FIG.  3    are data of the reflected wave intensity at each transmission frequency acquired by the reader  20 . 
       FIG.  4    is a diagram illustrating an example of a change in a reflected wave spectrum of the sensor  10  caused by a state change of a detection target of the sensor  10 . 
     The sensor  10  includes a resonator  11 , an isolation layer  12 , and an electromagnetic wave reflecting material  13  in order from the upper surface side (the side opposed to the reader  20 ). Note that, hereinbelow, for convenience of description, the side opposed to the reader  20  is referred to as an upper side, and the side opposed to the reader  20  is referred to as a lower side. 
     The resonator  11  is, for example, a metal pattern formed on a substrate  11 B. The resonator  11  is formed in a strip shape by, for example, a metal pattern, and has a resonance structure that resonates when irradiated with an electromagnetic wave of a predetermined frequency. Then, for example, the resonator  11  absorbs an electromagnetic wave having a frequency matching its own resonance frequency (in  FIGS.  1  and  3   , when the frequency of the electromagnetic wave is f0), and reflects an electromagnetic wave having a frequency other than the above frequency when irradiated with the electromagnetic wave. 
     The resonance frequency of the resonator  11  is determined by the shape (mainly the length) of the metal pattern forming the resonator  11 . In general, when the maximum length of the resonator  11  is ½λ of the frequency of the electromagnetic wave, the resonator  11  resonates and exhibits an absorption peak at which the intensity of the reflected wave at the frequency corresponding to the resonator length is low. 
     The resonator  11  may include only one resonator as illustrated in  FIG.  2   , but may include a plurality of resonators in order to heighten the intensity of the reflected wave. In addition, from the viewpoint of diversifying the patterns of the reflected wave spectrum, the resonator  11  may include, for example, a plurality of resonators having different lengths so that the sensor  10  has a plurality of resonance frequencies. 
     As a method of forming the resonator  11  on the substrate  11 B, any method such as a printing method and pattern etching may be used. Also, as a material for the resonator  11 , a metal material such as copper, silver, gold, and aluminum is used. Note that, to cause the resonator  11  to have elasticity, it is preferable to use a metal material containing a binder or the like as a material for the resonator  11 . 
     As the substrate  11 B on which the resonator  11  is formed, a material having electromagnetic wave permeability such as paper and resin is used. However, the form of the substrate  11 B is not limited to a plate shape, and may be a curved shape, a cylindrical shape, or the like. In other words, the resonator  11  may be formed directly on an article, such as a packaging material and a container. Furthermore, the substrate  11 B may be an object itself to be detected by the sensor  10 . 
     The isolation layer  12  is a space in which an insulating material or no object is arranged, is disposed between the resonator  11  and the electromagnetic wave reflecting material  13 , and insulates the resonator  11  and the electromagnetic wave reflecting material  13  from each other. 
     In a case where a space in which no object is arranged (filled with air) or a material having a low dielectric constant such as a foamed resin is used as the isolation layer  12 , a resonance phenomenon occurring in the resonator  11  is further amplified. Also, the isolation layer  12  is adjacent to the resonator  11 . Hence, when the dielectric constant of the isolation layer  12  changes, the resonance frequency of the resonator  11  changes due to the wavelength shortening effect of the dielectric. That is, the change in the dielectric constant of the isolation layer  12  appears as a change in the resonance frequency of the resonator  11  in the reflection characteristic of the sensor  10 . 
     The electromagnetic wave reflecting material  13  is disposed to be opposed to the resonator  11  with the isolation layer  12  interposed therebetween, and reflects the electromagnetic wave emitted from the reader  20  to the sensor  10 . The electromagnetic wave reflecting material  13  is, for example, a metal plate (for example, an aluminum plate) disposed in parallel with the substrate  11 B on which the resonator  11  is formed. The electromagnetic wave reflecting material  13  is disposed over a larger region than a region where the resonator  11  is formed at a position opposed to the resonator  11  in a planar view. 
     In addition, the electromagnetic wave reflecting material  13  also functions to amplify a resonance phenomenon occurring in the resonator  11 . Specifically, in a case where the electromagnetic wave reflecting material  13  is present, a resonance phenomenon occurring in the resonator  11  also occurs between the resonator  11  and the electromagnetic wave reflecting material  13 , and the resonance phenomenon is amplified. That is, the electromagnetic wave reflecting material  13  makes the resonance peak (absorption peak) significant in a case where the resonance phenomenon occurs in the resonator  11 . In this manner, by disposing the electromagnetic wave reflecting material  13  on the back surface of the resonator  11 , it is possible to enhance the contrast of the intensity of the reflected wave between the reflected wave generated in the sensor  10  at the time of resonance and the reflected wave generated in the sensor  10  at the time of non-resonance. 
     Note that, since the electromagnetic wave reflecting material  13  reflects the electromagnetic wave toward the reader  20  regardless of whether or not the frequency of the electromagnetic wave emitted from the reader  20  coincides with the resonance frequency of the resonator  11 , in the reflected wave spectrum of the sensor  10 , the intensity of the reflected wave from the electromagnetic wave reflecting material  13  appears as the reflection intensity in the baseband region (hereinbelow, the region other than the resonance peak). Also, the electromagnetic wave reflecting material  13  acts to make the resonance peak of the resonator  11  significant. Hence, in a case where the area of the region of the electromagnetic wave reflecting material  13  opposed to the resonator  11  changes, the change appears in the reflected wave spectrum of the sensor  10  as lowering of the reflection intensity in the baseband region and lowering of the peak intensity of the resonance peak (see  FIGS.  4 B and  4 C ). 
     In such a mode, the sensor  10  is configured such that at least any of the states of the resonator  11 , the isolation layer  12 , and the electromagnetic wave reflecting material  13  is interlocked with a state change of a detection target (described below with reference to  FIGS.  6  to  12   ). Then, the sensor  10  causes the reader  20  to detect the state change of the detection target by the change in the reflection characteristic of the reflected wave generated when the reader  20  emits the electromagnetic wave. 
     The state change to be detected by the sensor  10  is, for example, any of a change in the position of the surrounding object around the sensor  10 , a change in the form of the surrounding object around the sensor  10 , a change in the moisture content of the surrounding object around the sensor  10 , a change in the humidity of the surrounding environment around the sensor  10 , a change in the temperature of the surrounding environment around the sensor  10 , a change in the gas concentration of the surrounding environment around the sensor  10 , a change in the light illuminance of the surrounding environment around the sensor  10 , a change in the pH of the surrounding environment around the sensor  10 , a change in the magnetic field of the surrounding environment around the sensor  10 , and a change in the degree of oxidation of the surrounding object around the sensor  10 . 
     At this time, the state of the detection target is typically detected as a change in the resonance peak position (that is, the resonance frequency) (see  FIG.  4 A ), a change in the peak intensity of the resonance peak (see  FIG.  4 B ), or a change in the reflection intensity in the baseband region (see  FIG.  4 C ) in the reflected wave spectrum of the sensor  10 . 
     However, as described above, in practice, it is often difficult to accurately identify the resonance frequency of the sensor  10  from the reflected wave spectrum of the sensor  10 , and there is a possibility that the state change of the detection target cannot accurately be captured by the peak pick method as in the conventional technique in Patent Literature 1. Under such circumstances, in the state change system U according to the present embodiment, the state of the sensor  10  (that is, the state of the detection target) is identified from the entire pattern of the reflected wave spectrum of the sensor  10  without performing the processing of identifying the resonance frequency from the reflected wave spectrum of the sensor  10 . Note that the “entire pattern of the reflected wave spectrum of the sensor  10 ” mentioned here means the reflection intensities at a plurality of frequency positions in the reflected wave spectrum of the sensor  10 . In the state change system U (analysis device  30  to be described below) according to the present embodiment, in order to identify the state of the sensor  10 , information regarding the reflection intensities at least at three frequency positions (for example, three frequency positions across the resonance frequency determined from the design information of the resonator  11 ) is referred to. 
       FIG.  5    is a diagram illustrating a more preferable form of the sensor  10 . 
     As illustrated in  FIG.  5   , the sensor  10  preferably includes a sensitizer  14  that has sensitivity to a state change of a detection target of the sensor  10  and changes a reflection characteristic of the sensor  10  in accordance with the state change of the detection target of the sensor  10 . 
     The sensitizer  14  is made of a material corresponding to a detection target of the sensor  10 . For example, in a case where the detection target of the sensor  10  is the moisture content of the surrounding object or the humidity of the surrounding environment, a moisture absorbing material is used as the sensitizer  14 . Also, in a case where the detection target of the sensor  10  is the light amount of the surrounding environment, a material having photoresponsiveness is used as the sensitizer  14 . Also, in a case where the detection target of the sensor  10  is the magnetic intensity of the surrounding environment, a magnetic fluid is used as the sensitizer  14 . Also, in a case where the detection target of the sensor  10  is the degree of oxidation of the surrounding object, a metal having different corrosiveness from that of the detection target of the sensor  10  is used as the sensitizer  14 . Also, in a case where the detection target of the sensor  10  is the temperature of the surrounding environment, a material having thermal expansion characteristics is used as the sensitizer  14 . Also, in a case where the detection target of the sensor  10  is the form of the surrounding object, a pressure-sensitive material is used as the sensitizer  14 . Also, in a case where the detection target of the sensor  10  is the pH of the surrounding environment, a chemical substance absorbing material is used as the sensitizer  14 . 
     In the sensor  10 , the function of the sensitizer  14  is exhibited by affecting the dielectric constant of the proximity region of the resonator  11 , the tanδ of the proximity region of the resonator  11 , the conductivity of the electromagnetic wave reflecting material  13 , or the magnetic constant of the proximity region of the resonator  11 . That is, the sensitizer  14  amplifies a change in the reflected wave spectrum of the sensor  10  (for example, the frequency shift of the resonance frequency, the peak intensity of the resonance peak, or the reflection intensity in the baseband region) when the state of the detection target of the sensor  10  changes. 
     As illustrated in  FIG.  5   , the sensitizer  14  is preferably disposed in the isolation layer  12  of the sensor  10 . With such a disposing position, it is possible to more effectively change the reflected wave spectrum of the sensor  10  when the state of the detection target of the sensor  10  changes. 
     However, the disposing position of the sensitizer  14  may be any position as long as the function of sensitization is exhibited. For example, the sensitizer  14  does not have to be disposed in the isolation layer  12 , and may be disposed so as to cover the upper surface of the resonator  11  or may be disposed at the lateral portion of the resonator  11 . Also, the sensitizer  14  may be disposed on the lower surface side of the electromagnetic wave reflecting material  13 , or may be disposed apart from the resonator  11  and the electromagnetic wave reflecting material  13 . 
       FIGS.  6  to  12    illustrate various examples of a configuration for detecting a state change of a surrounding object or a surrounding environment by the sensor  10 . 
       FIG.  6    is a diagram illustrating a mode in which the sensor  10  detects an expansion/contraction state of an object to be detected. In this mode, for example, the resonator  11  of the sensor  10  is made of a member that can expand and contract in the longitudinal direction. The sensor  10  then detects the expansion/contraction state of the object to be detected as a change in the length of the resonator  11 . Note that the change in the length of the resonator  11  appears as a change in the resonance frequency in the reflected wave spectrum of the sensor  10 . 
       FIG.  7    is a diagram illustrating a mode in which the sensor  10  detects a change in the thickness of an object to be detected. In this mode, for example, the sensor  10  is configured such that the thickness of the isolation layer  12  (that is, the distance between the resonator  11  and the electromagnetic wave reflecting material  13 ) changes in conjunction with the thickness of the object to be detected. The sensor  10  then detects the thickness of the object to be detected as a change in the thickness of the isolation layer  12 . Note that, in the structure of the sensor  10  illustrated in  FIG.  2   , in general, the intensity of the reflected wave from the sensor  10  is the most significant when the distance between the resonator  11  and the electromagnetic wave reflecting material  13  is a predetermined distance, and is less significant as the distance between the resonator  11  and the electromagnetic wave reflecting material  13  is away from the predetermined distance. That is, the change in the thickness of the isolation layer  12  appears as a change in the peak intensity of the resonance peak in the reflected wave spectrum of the sensor  10 . 
       FIG.  8    is a diagram illustrating a mode in which the sensor  10  detects a positional displacement state of an object to be detected. In this mode, for example, the sensor  10  includes a first resonator  11 X attached to a first object  11 BX and a second resonator  11 Y attached to a second object  11 BY so as to be opposed to the first resonator  11 X, and is configured such that the positional relationship between the first resonator  11 X and the second resonator  11 Y can be changed in response to the positional displacement between the first object  11 BX and the second object  11 BY. The sensor  10  then detects the positional displacement state between the first object  11 BX and the second object  11 BY as a change in the area in which the first resonator  11 X and the second resonator  11 Y are opposed. The change in the area in which the first resonator  11 X and the second resonator  11 Y are opposed appears as a change in the peak intensity of the resonance peak or a change in the resonance frequency in the reflected wave spectrum of the sensor  10 . 
       FIG.  9    is a diagram illustrating a mode in which the sensor  10  detects a change in the moisture content of an object to be detected. In this mode, for example, the sensor  10  has a structure in which a part of the object to be detected is arranged in the isolation layer  12 , and is configured such that a liquid N3 can enter the isolation layer  12  from the periphery. The sensor  10  then detects a change in the moisture content of the object to be detected as a change in the dielectric constant of the isolation layer  12 . Note that the change in the dielectric constant change of the isolation layer  12  appears as a change in the resonance frequency of the resonator  11  in the reflected wave spectrum of the sensor  10 . 
       FIG.  10    is a diagram illustrating a mode in which the sensor  10  detects a change in the temperature of a surrounding environment. In this mode, for example, the sensor  10  includes a thermal expansion material as the sensitizer  14  in the isolation layer  12 , and is configured such that surrounding air can enter the isolation layer  12 . The sensor  10  then detects a change in the temperature of the surrounding environment as a change in the thickness of the isolation layer  12 . Note that the change in the thickness of the isolation layer  12  appears as a change in the peak intensity of the resonance peak in the reflected wave spectrum of the sensor  10 . 
       FIG.  11    is a diagram illustrating a mode in which the sensor  10  detects a change in the gas concentration of a surrounding environment. In this mode, for example, the isolation layer  12  of the sensor  10  is formed as a space in a pipe through which a gas flows. The sensor  10  then detects a change in the gas concentration as a change in the dielectric constant of the isolation layer  12 . Note that the change in the dielectric constant change of the isolation layer  12  appears as a change in the resonance frequency of the resonator  11  in the reflected wave spectrum of the sensor  10 . 
       FIG.  12    is a diagram illustrating a mode in which the sensor  10  detects the degree of oxidation of an object to be detected. In this mode, for example, the electromagnetic wave reflecting material  13  of the sensor  10  is configured as a part of the object to be detected. In the sensor  10 , a metal material having a higher corrosion rate than the object to be detected (for example, the ionization tendency thereof is higher than that of the object to be detected) is disposed as the sensitizer  14  at a position opposed to the resonator  11 . The sensor  10  then detects the degree of oxidation of the object to be detected as a change in the conductivity of the electromagnetic wave reflecting material  13 . Note that the change in the conductivity of the electromagnetic wave reflecting material  13  appears as a change in the peak intensity of the resonance peak and a change in the reflection intensity in the baseband region in the reflected wave spectrum of the sensor  10 . 
     Modification Example of Sensor 
     The sensor  10  usable in the state detection system U according to the present disclosure is not limited to one having the structure in  FIG.  2    as long as the sensor  10  has a resonator structure capable of resonating in response to the transmitted electromagnetic wave. 
       FIG.  13    is a diagram illustrating a modification example of the configuration of the sensor  10 . The sensor  10  according to the modification example includes an electromagnetic wave reflecting material  113  and a slot-type resonator  111  formed in the electromagnetic wave reflecting material  113 . The electromagnetic wave reflecting material  113  is formed on a substrate  111 B, for example. 
     The electromagnetic wave reflecting material  113  is, for example, a conductor pattern layer formed on the substrate  111 B, and is made of a conductive material such as an aluminum material and a copper material. The conductor pattern layer of the electromagnetic wave reflecting material  113  has a rectangular slot formed by partially cutting out the solid conductor layer, and the resonator  111  is formed by the slot. Typically, the resonator  111  resonates when the length of the slot corresponds to about λ/2 of the wavelength of the emitted electromagnetic wave. 
     The reflected wave spectrum of the sensor  10  according to the modification example shows a spectrum similar to that in  FIG.  3   . That is, when the resonator  111  resonates, an absorption peak appears, and in a frequency band other than the resonance frequency of the resonator  111 , intensity information caused by a reflected wave from the electromagnetic wave reflecting material  113  appears in the baseband region. 
     Note that the sensor  10  according to the modification example is also preferably provided with the sensitizer  14 . Note that the resonator structure in  FIG.  13    may be applied as the resonator  11  in  FIG.  2   . 
     [Configuration of Reader] 
     The reader  20  includes a transmission unit  21 , a reception unit  22 , and a control unit  23 . Note that, for example, the reader  20  is disposed at a position away from the sensor  10  by several cm to several m so as to be opposed to the upper surface of the sensor  10 . 
     The transmission unit  21  transmits an electromagnetic wave of a predetermined frequency to the sensor  10 . The transmission unit  21  includes, for example, a transmission antenna, an oscillator, and the like. 
     The transmission unit  21  transmits, for example, a sinusoidal electromagnetic wave having a peak intensity at a single frequency. Then, the transmission unit  21  temporally changes the transmission frequency of the electromagnetic wave to be transmitted from the transmission antenna, and performs a frequency sweep within a preset predetermined frequency band. Alternatively, the transmission unit  21  may collectively emit at a time an electromagnetic wave having a specific intensity profile in a predetermined frequency band (that is, the impulse method). 
     The frequency band in which the reflected wave spectrum is acquired is, for example, an HF band, a UHF band, a UWB frequency band (3.1 GHz to 10.6 GHz), a 24 GHz band, a millimeter wave band, or the like. Then, the transmission frequency of the transmission unit  21  is set stepwise at every bandwidth of at least 500 MHz or less, preferably at every bandwidth of 10 MHz, within the frequency band. Note that the frequency band of the electromagnetic wave transmitted by the transmission unit  21  is set to include the resonance frequency of the resonator  11  of the sensor  10 . 
     The reception unit  22  receives a reflected wave from the sensor  10  generated when the transmission unit  21  transmits an electromagnetic wave. The reception unit  22  includes, for example, a reception antenna and a reception signal processing circuit that detects the intensity and phase of the reflected wave on the basis of the reception signal of the reflected wave acquired by the reception antenna. Then, the reception unit  22  generates reflected wave spectrum information (frequency spectrum data) of the sensor  10  from the intensity of the reflected wave detected at each transmission frequency of the electromagnetic wave, for example. Note that, when generating the reflected wave spectrum information of the sensor  10 , the reception unit  22  may use the intensity itself of the reflected wave or may use the intensity ratio between the intensity of the transmission wave and the intensity of the reflected wave. Furthermore, the reflected wave spectrum information may include phase characteristic information in addition to amplitude characteristic information for each frequency. 
     Note that the transmission unit  21  and the signal processing circuit of the reception unit  22  may be configured integrally by a vector network analyzer. 
     The control unit  23  comprehensively controls the reader  20 . Note that, for example, in order to sequentially monitor the state of the object to be detected, the control unit  23  causes the transmission unit  21  and the reception unit  22  to execute the above-described processing at predetermined time intervals. 
     Note that, in order to improve the sensitivity of the sensitive material (for example, the sensitizer  14 ) built in the sensor  10 , the reader  20  may collect the reflected wave spectrum information of the sensor  10  while applying an external stimulus different, in nature, from the state change of the detection target to the sensor  10 . For example, the reader  20  may collect the reflected wave spectrum information of the sensor  10  while applying light, heat, or ultrasonic waves to the sensor  10 . As a result, for example, the moisture absorption property, photoresponsiveness, thermal expansion property, chemical substance absorption property, or the like of the sensitive material (for example, the sensitizer  14 ) can temporarily be increased, and a change from the reference state of the reflected wave spectrum of the sensor  10  can be captured more easily. 
     [Configuration of Analysis Device] 
     The analysis device  30  acquires current reflected wave spectrum information of the sensor  10  from the reader  20 , and estimates a current state of a detection target of the sensor  10  on the basis of the current reflected wave spectrum information of the sensor  10 . Note that the analysis device  30  is, for example, a computer including a central processing unit (CPU), a read only memory (ROM), a random access memory (RAM), an input port, an output port, and the like, and is configured to be able to perform data communication with the reader  20 . 
     The analysis device  30  includes a learning unit  31  that performs learning processing on the learning model  30 D using training data, and an estimation unit  32  that estimates a current state of a detection target of the sensor  10  by applying information regarding reflected wave intensities at a plurality of frequency positions of a current reflected wave spectrum of the sensor  10  to the learning model  30 D. 
     That is, the analysis device  30  uses the learning model  30 D to detect the state of the sensor  10  from the pattern of the reflected wave spectrum of the sensor  10  instead of the mode of detecting the state of the sensor  10  using the peak pick method as in the conventional technique in Patent Literature 1. 
     Here, as the learning model  30 D used by the analysis device  30 , a model optimized by machine learning is typically used. As the learning model  30 D, for example, support vector machine (SVM), k-nearest neighbor, logistic regression, Lasso regression, ridge regression, elastic net regression, support vector regression, a decision tree, or the like is used. Note that the configuration of the learning model  30 D is similar to that of a known model, and thus description thereof is omitted here. 
     This type of learning model  30 D is subjected to learning processing to extract features of the pattern to be identified, and is autonomously optimized so that the pattern to be identified can accurately be identified even from data on which noise or the like is superimposed. In this regard, the reflected wave spectrum of the sensor  10  draws a unique pattern around the resonance peak position of the resonator  11  for each state of the detection target. That is, the learning model  30 D of this type is subjected to learning processing, using the reflected wave spectrum information, with the state of the detection target as a label, as training data. Hence, when certain reflected wave spectrum information is input, the learning model  30 D can identify the state of the most similar reflected wave spectrum information among pieces of reflected wave spectrum information for respective states used as the training data. In particular, at this time, training data is also prepared for various pieces of reflected wave spectrum information, assuming various situation changes having different positional relationships with the reader  20 , and the learning processing is performed using these various pieces of reflected wave spectrum information, whereby the learning model  30 D obtains a high generalization ability. 
     The hyperparameter of the learning model  30 D is optimized by a known learning algorithm, and as a method of the optimization, grid search may be used, for example. Furthermore, as the learning algorithm for the learning model  30 D, for example, ensemble learning using at least one of SVM, k-nearest neighbor, logistic regression, Lasso regression, ridge regression, elastic net regression, support vector regression, and a decision tree may be used. 
     For example, the learning model  30 D has a configuration in which reflected wave intensities at a plurality of frequency positions of a reflected wave spectrum (for example, respective plotted points in  FIG.  3   ) are input, and in which a state of a detection target is output. 
     Here, each of the frequency positions of the reflected wave intensities to be input into the learning model  30 D is set at every bandwidth of at least 500 MHz, preferably at every bandwidth of 10 MHz within the frequency band in which the reflected wave spectrum information is acquired. The frequency positions preferably include a resonance frequency in a standard state of the resonator  11  of the sensor  10  or a frequency in the vicinity thereof. Note that the reflected wave spectrum information input into the learning model  30 D may include phase characteristic information in addition to amplitude characteristic information for each frequency. 
     Furthermore, the state of the detection target output by the learning model  30 D may be the state of the detection target (for example, a moisture content value, or a gas concentration value) expressed quantitatively by a numerical value, or may be one by which the level of the change amount from the reference state of the detection target can be identified, such as “change amount: large”, “change amount: medium”, and “change amount: small” (hereinbelow, collectively referred to as a “state change amount”.). In other words, the learning model  30 D may be configured as a regression learning model or may be configured as a classification learning model. 
     However, as the learning model  30 D used by the analysis device  30 , a model optimized by multiple regression analysis may be used instead of the model optimized by machine learning. 
     For example, the learning unit  31  performs learning processing on the learning model  30 D using, as training data, reflected wave spectrum information for each state (that is, for each state of the detection target) of the sensor  10  obtained by actual measurement or simulation. That is, the learning unit  31  performs learning processing on the learning model  30 D using, as training data, reflected wave spectrum information of the sensor  10  in each of various states to which a label corresponding to each state change amount of the detection target is assigned as true data. As a result, the learning model  30 D is optimized so as to be able to output the state change amount of the detection target in a case where the reflected wave spectrum having a pattern conforming to the input reflected wave spectrum is generated. 
     Note that, in a case where the learning model  30 D is, for example, a classification learning model, the reflected wave spectrum information of the sensor  10  used as the training data is reflected wave spectrum information for each state of the candidate to be classified. On the other hand, in a case where the learning model  30 D is a regression learning model, the data may be pieces of reflected wave spectrum information for respective arbitrary state change amounts as long as pieces of reflected wave spectrum information for a plurality of states are included. 
     The estimation unit  32  inputs information of reflected wave intensities at a plurality of frequency positions of current reflected wave spectrum information of the sensor  10  into the learning model  30 D on which the learning processing has been performed by the learning unit  31  (see the plotted points in  FIG.  3   ), and estimates a current state of a detection target of the sensor  10  from an output result of the learning model  30 D. Note that the reflected wave intensities at the plurality of frequency positions of the current reflected wave spectrum information of the sensor  10  that the estimation unit  32  inputs into the learning model  30 D are typically reflected wave intensities at the same frequency positions as the frequency positions referred to when the learning processing is performed on the learning model  30 D. 
       FIG.  14    is an example of a flowchart when learning processing is performed on the learning model  30 D. Note that, here, the reader  20  and the analysis device  30  execute each step in accordance with a command from the developer. 
     In this flowchart, a reflected wave spectrum of the sensor  10  is repeatedly acquired in the loop processing in step S 11   a  and the loop processing in step S 11   b  (step S 12 ). 
     Here, the loop processing in step S 11   a  is processing of acquiring a reflected wave spectrum of the sensor  10  for each state of the detection target of the sensor  10 . In this loop processing, for example, in a case where there are four types of state to be identified by the sensor  10 , a reflected wave spectrum of the sensor  10  is acquired for each of the four types of state. Note that, at this time, the state of the detection target of the sensor  10  may be changed manually by the developer, or may be changed by changing the surrounding environment of the sensor  10  by means of an external device. 
     Also, the loop processing in step S 11   b  is processing of acquiring a reflected wave spectrum of the sensor  10  a predetermined number of times while changing the orientation of the sensor  10  and the object around the sensor  10 . 
     The loop processing in step S 11   b  is data acquisition processing for making the learning model  30 D a model with higher robustness. Even if the state change amounts of the detection target are the same, the reflected wave spectra may slightly differ depending on the orientation of the sensor  10  or the object around the sensor  10 . From such a viewpoint, pieces of reflected wave spectrum information are acquired under various conditions where the orientations of the sensor  10  and the objects around the sensor  10  are different as training data pieces, and machine learning is performed on the learning model  30 D using the training data pieces. Note that, for the training data pieces acquired at this time, true values of the same state change amounts will be set. 
     After the loop processing in step S 11   a  and the loop processing in step S 11   b  are completed, the analysis device  30  (learning unit  31 ) executes learning processing on the learning model  30 D using a known machine learning algorithm with these pieces of reflected wave spectrum information as training data (step S 13 ). 
       FIG.  15    is an example of a flowchart of processing of estimating a state of a detection target. 
     In step S 21 , the reader  20  transmits an electromagnetic wave from the transmission antenna to the sensor  10  while changing the transmission frequency, and receives a reflected wave from the sensor  10  by means of the reception antenna. As a result, the reader  20  acquires current reflected wave spectrum information of the sensor  10 . 
     In step S 22 , the analysis device  30  (estimation unit  32 ) inputs the current reflected wave spectrum information of the sensor  10  into the pre-trained learning model  30 D, and calculates a state change amount of the detection target using the learning model  30 D. 
     In step S 23 , the analysis device  30  (estimation unit  32 ) displays the state change amount of the detection target calculated in step S 22  on a display unit (not illustrated). 
     [Verification Experiment] 
     Next, a result of verifying the accuracy of the estimation processing of the state detection system U according to the present disclosure will be shown. 
     Example 1 
     In Example 1, the state detection system U according to the present disclosure is applied to estimation of the moisture absorption amount of a diaper. 
       FIG.  16    is a diagram illustrating a configuration of Example 1. Example 1 verified, when the sensor  10  was attached to a diaper, and a predetermined amount of water was absorbed in the diaper, whether or not the moisture absorption amount could be identified accurately. 
       FIG.  17    is a diagram illustrating a representative reflected wave spectrum obtained each time of estimation of the moisture absorption amount in a diaper. Here, reflected wave spectra were acquired when 100 ml, 200 ml, 300 ml, and 400 ml of water were absorbed in a diaper. Note that, in each learning processing on the learning model to be described below, the reflected wave spectrum information in each state in  FIG.  17    was used. 
     Table 1 is a table illustrating various estimation methods used in Example 1 and evaluation results thereof. 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                   
                 Estimation 
                   
                 Number of 
                 Learning 
                 Ensemble 
                 Accuracy 
               
               
                 Example No. 
                 method 
                 Sensitizer 
                 analysis points 
                 model 
                 learning 
                 rate 
               
               
                   
               
             
            
               
                 Comparative 
                 Peak 
                 Not 
                 1 
                 Single 
                 — 
                 Estimation 
               
               
                 Example 1-1 
                 pick 
                 provided 
                   
                 regression 
                   
                 impossible 
               
               
                   
                   
                   
                   
                 model 
               
               
                 Comparative 
                 Single 
                 Not 
                 1 
                 Single 
                 — 
                 14% 
               
               
                 Example 1-2 
                 regression 
                 provided 
                   
                 regression 
               
               
                   
                 analysis 
                   
                   
                 model 
               
               
                 Example 1-1 
                 Multiple 
                 Not 
                 3 
                 Multiple 
                 — 
                 60% 
               
               
                   
                 regression 
                 provided 
                   
                 regression 
               
               
                   
                 analysis 
                   
                   
                 model 
               
               
                 Example 1-2 
                 Machine 
                 Not 
                 3 
                 SVM 
                 — 
                 72% 
               
               
                   
                 learning 
                 provided 
               
               
                 Example 1-3 
                 Machine 
                 Not 
                 1 at every 
                 SVM 
                 — 
                 83% 
               
               
                   
                 learning 
                 provided 
                 10 MHz 
               
               
                 Example 1-4 
                 Machine 
                 Not 
                 1 at every 
                 k-nearest 
                 — 
                 82% 
               
               
                   
                 learning 
                 provided 
                 10 MHz 
                 neighbor 
               
               
                 Example 1-5 
                 Machine 
                 Not 
                 1 at every 
                 SVM 
                 ◯ 
                 92% 
               
               
                   
                 learning 
                 provided 
                 10 MHz 
               
               
                 Example 1-6 
                 Multiple 
                 Provided 
                 3 
                 Multiple 
                 — 
                 67% 
               
               
                   
                 regression 
                   
                   
                 regression 
               
               
                   
                 analysis 
                   
                   
                 model 
               
               
                 Example 1-7 
                 Machine 
                 Provided 
                 1 at every 
                 SVM 
                 ◯ 
                 98% 
               
               
                   
                 learning 
                   
                 10 MHz 
               
               
                   
               
            
           
         
       
     
     Here, the conditions regarding the type of estimation method, whether or not the sensitizer  14  was attached to the sensor  10 , the number of analysis points (that is, frequency positions) when the learning processing was performed, the type of the learning model  30 D, and whether or not ensemble learning was performed were variously changed, and the accuracy rate of estimation of the moisture absorption amount under each condition was calculated. Here, the accuracy rate is a rate at which it could be determined that the water absorption amount was within the range of 100 to 200 ml when reflected wave spectra of 100 diapers in which 150 ml of water was absorbed (100 reflected wave spectra acquired while changing the orientation and position of the reader  20 ) were acquired, and when detection of the state of each diaper was conducted for each of the reflected wave spectra using the pre-trained learning model  30 D. 
     In the peak pick method in Comparative Example 1-1, a calibration curve for regression analysis was created using as variables the intensity ratio and the water absorption amount at the peak frequency of the reflected wave spectrum serving as the training data, and estimation of the moisture absorption amount was attempted using the calibration curve. However, in this method, a resonance peak could not be detected in the reflected wave spectrum of a diaper having water absorption of 100 ml or more, and a calibration curve for regression analysis could not be created. 
     In the single regression analysis in Comparative Example 1-2, a calibration curve for regression analysis was created using as variables the intensity ratio and the water absorption amount at 9.8 GHzGHz (here, the frequency corresponds to a frequency near the resonance frequency of the sensor  10 ) of the reflected wave spectrum serving as the training data, and estimation of the moisture absorption amount was attempted using the calibration curve. However, with this method, only a 14% accuracy rate was obtained. 
     In the multiple regression analysis in Example 1-1, a calibration curve for regression analysis was created setting the number of analysis points of the reflected wave spectrum serving as the training data to three points (intensity ratios at 9.0, 9.8 GHz, and 10.5 GHz) and using the water absorption amount as a variable, and estimation of the moisture absorption amount was attempted using the calibration curve. In this method, a 60% accuracy rate was obtained. 
     In the machine learning method in Example 1-2, a learning model was generated by means of the SVM using training data with a slightly wet state of a diaper having water absorption of 100 ml or 200 ml as a label, and training data with a heavily wet state of a diaper having water absorption of 300 ml or 400 ml as a label. At this time, as analysis points, the intensity ratios at 9.0 GHz, 9.8 GHz, and 10.5 GHz of the reflected wave spectrum were taken. Then, when the detection of the state of the diaper was conducted using the generated pre-trained learning model on the reflected wave spectra of the 100 diapers in which 150 ml of water was absorbed, the number of diapers that were identified as being in a slightly wet state (a state where the water absorption amount was within the range of 100 to 200 ml) was 72 diapers (accuracy rate: 72%). 
     In the machine learning method in Example 1-3, a similar operation to that in Example 1-2 was performed except that the number of analysis points was 150 points with each point set at every 10 MHz in the range of 9 GHz to 10.5 GHz. Then, when the detection of the state of the diaper was conducted using the generated pre-trained model on the reflected wave spectra of the 100 diapers in which 150 ml of water was absorbed, the number of diapers that were identified as being in a slightly wet state (a state where the water absorption amount was within the range of 100 to 200 ml) was 83 diapers (accuracy rate: 83%). 
     In the machine learning method in Example 1-4, a similar operation to that in Example 1-3 was performed except that the SVM was changed to the k-nearest neighbor. Then, when the detection of the state of the diaper was conducted using the generated pre-trained model on the reflected wave spectra of the 100 diapers in which 150 ml of water was absorbed, the number of diapers that were identified as being in a slightly wet state (a state where the water absorption amount was within the range of 100 to 200 ml) was 82 diapers (accuracy rate: 82%). 
     In the machine learning method in Example 1-5, a similar operation to that in Example 1-3 was performed except that a plurality of learning models were generated by means of the SVM, and that a pre-trained model was generated by means of ensemble learning thereof. Then, when the detection of the state of the diaper was conducted using the generated pre-trained model on the reflected wave spectra of the 100 diapers in which 150 ml of water was absorbed, the number of diapers that were identified as being in a slightly wet state (a state where the water absorption amount was within the range of 100 to 200 ml) was 92 diapers (accuracy rate: 92%). 
     In the multiple regression analysis in Example 1-6, similar learning processing and estimation processing to those in Example 1-2 were performed using the sensor  10  provided with the sensitizer  14  (here, a moisture absorbing material made of polyvinyl alcohol). As a result, the number of diapers that were identified as being in a slightly wet state (a state where the water absorption amount was within the range of 100 to 200 ml) was 67 diapers (accuracy rate: 67%). 
     In the machine learning method in Example 1-7, similar learning processing and estimation processing to those in Example 1-3 were performed using the sensor  10  provided with the sensitizer  14  (here, a moisture absorbing material made of polyvinyl alcohol). As a result, the number of diapers that were identified as being in a slightly wet state (a state where the water absorption amount was within the range of 100 to 200 ml) was 98 diapers (accuracy rate: 98%). 
     In the above manner, it has been found that, by estimating the moisture absorption amount using the learning model  30 D generated by means of the multiple regression analysis or the machine learning method, it is possible to accurately estimate the moisture absorption amount as compared with the mode in which the moisture absorption amount is estimated in a conventional method such as the peak pick method. 
     Example 2 
     In Example 2, the state detection system U according to the present disclosure is applied to estimation of the position of a paper cup. 
       FIG.  18    is a diagram illustrating a configuration of Example 2. In Example 2, the sensor  10  was attached to the bottom of a paper cup, and the reflected wave spectrum of the reflected wave from the sensor  10  was acquired by means of the reader  20  disposed below the paper cup. Then, this example verified whether or not the situation when the paper cup was displaced to the side by 0.5 cm from the opposed position to the reader  20  could be estimated accurately. 
       FIG.  19    is a diagram illustrating a representative reflected wave spectrum obtained for each cup position of the paper cup.  FIG.  19    illustrates a reflected wave spectrum obtained at each position when the paper cup is displaced to the side by 0.0 cm, 0.5 cm, 1.0 cm, and 1.5 cm from the opposed position to the reader  20 . In each learning processing on the learning model, the reflected wave spectrum information in each state in  FIG.  19    was used. 
     Table 2 is a table illustrating various estimation methods used in Example 2 and evaluation results thereof. 
     
       
         
           
               
               
               
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                   
                   
                 Number of 
                   
                 Ensemble 
                   
               
               
                 Example No. 
                 Estimation method 
                 analysis points 
                 Learning model 
                 learning 
                 Accuracy rate 
               
               
                   
               
             
            
               
                 Comparative 
                 Peak pick 
                 1 
                 Single regression 
                 — 
                 Estimation 
               
               
                 Example 2-1 
                   
                   
                 model 
                   
                 impossible 
               
               
                 Example 2-1 
                 Multiple regression 
                 3 
                 Multiple regression 
                 — 
                 60% 
               
               
                   
                 analysis 
                   
                 model 
                   
                   
               
               
                 Example 2-2 
                 Machine learning 
                 3 
                 SVM 
                 — 
                 72% 
               
               
                 Example 2-3 
                 Machine learning 
                 1 at every 10 MHz 
                 SVM 
                 — 
                 83% 
               
               
                 Example 2-4 
                 Machine learning 
                 1 at every 10 MHz 
                 SVM 
                 ◯ 
                 92% 
               
               
                   
               
            
           
         
       
     
     Here, the conditions regarding the type of estimation method, whether or not the sensitizer  14  was attached to the sensor  10 , the number of analysis points (that is, frequency positions) when the learning processing was performed, the type of the learning model  30 D, and whether or not ensemble learning was performed were variously changed, and the accuracy rate of estimation of the cup position under each condition was calculated. Here, the accuracy rate is a rate at which it could be determined that the position of the paper cup was within the range of 0.5±0.1 cm when the paper cup was installed at the position of 0.5 cm, when 100 reflected wave spectra (100 reflected wave spectra acquired while changing the orientation and position of the reader  20 ) were acquired, and when estimation of the position of the paper cup was conducted for each of the reflected wave spectra using the pre-trained learning model  30 D, using the pre-trained learning model  30 D. 
     In the peak pick method in Comparative Example 2-1, creation of a calibration curve for regression analysis was attempted by picking up the peak frequency of the reflected wave spectrum serving as the training data and using as variables the intensity ratio and the side movement position at the frequency. However, a peak could not be detected in any of the spectra, and a calibration curve could not be created. In the peak pick method, the installation position of the paper cup could not be clarified. 
     In the multiple regression analysis in Example 2-1, multiple regression analysis was performed using, as variables, the intensity ratio and the side movement position at each of 8.0 GHz, 9.0 GHz, and 10.0 GHz taken as the analysis points of the reflected wave spectrum serving as the training data. Then, the paper cup to which the sensor  10  was attached was installed again at a position of 0.5 cm, the reflected wave spectrum was measured with n=100, and the position of the paper cup was estimated from the intensity ratios at the analysis points of the reflected wave spectrum. As a result, the number of cases where the position was identified as a position within the range of 0.5±0.1 cm was 60 cases (accuracy rate: 60%). 
     In the machine learning method in Example 2-2, a pre-trained model was generated by means of the SVM using training data of the intensity ratios at 8.0 GHz, 9.0 GHz, and 10.0 GHz with each of the positions of the paper cup as a label. Then, the paper cup to which the sensor was attached was installed again at a position of 0.5 cm, and the reflected wave spectrum was measured with n=100. The number of cases where the position of the paper cup was identified as a position of 0.5 cm from the intensity ratios at the analysis points of the reflected wave spectrum using the generated pre-trained model was 72 cases (accuracy rate: 72%). 
     In the machine learning method in Example 2-3, a similar operation to that in Example 2-2 was performed except that the number of analysis points was 200 points with each point set at every 10 MHz in the range of 8 to 10 GHz. Then, the paper cup to which the sensor was attached was installed again at a position of 0.5 cm, the reflected wave spectrum was measured with n=100, and the position of the paper cup was identified from the intensity ratios at the analysis points of the reflected wave spectrum using the generated pre-trained model. As a result, the number of cases where the position was identified as a position of 0.5 cm was 83 cases (accuracy rate: 83%). 
     In the machine learning method in Example 2-4, a similar operation to that in Example 2-3 was performed except that a plurality of learning models were generated by means of the SVM, and that a pre-trained model was generated by means of ensemble learning thereof. Then, the paper cup to which the sensor was attached was installed again at a position of 0.5 cm, the reflected wave spectrum was measured with n=100, and the position of the paper cup was identified from the intensity ratios at the analysis points of the reflected wave spectrum using the generated pre-trained model. As a result, the number of cases where the position was identified as a position of 0.5 cm was 92 cases (accuracy rate: 92%). 
     In the above manner, it has been found that, by estimating the cup position using the learning model  30 D generated by means of the multiple regression analysis or the machine learning method, it is possible to accurately estimate the cup position as compared with the mode in which the cup position is estimated in a conventional method such as the peak pick method. 
     Example 3 
     In Example 3, the state detection system U according to the present disclosure is applied to estimation of the stretched state of a substrate. 
       FIG.  20    is a diagram illustrating a configuration of Example 3. In Example 3, a sample made of a polyethylene terephthalate substrate to which the sensor  10  was attached was installed in a tensile strength tester, a reflected wave spectrum of a reflected wave from the sensor  10  when the substrate was stretched was detected, and estimation of the stretched state of the substrate was conducted. 
       FIG.  21    is a diagram illustrating representative reflected wave spectra in respective states where the substrate is not stretched, 1% stretched, 2% stretched, and 3% stretched. Note that, as illustrated in  FIG.  21   , in a case where the sample is stretched by 3%, a resonance peak is not observed due to the breakage of the sensor  10 . In each learning processing on the learning model to be described below, the reflected wave spectrum information in each state in  FIG.  21    was used. 
     Table 3 is a table illustrating various estimation methods used in Example 3 and evaluation results thereof. 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                   
                 Estimation 
                   
                 Number of 
                 Learning 
                 Ensemble 
                 Accuracy 
               
               
                 Example No. 
                 method 
                 Sensitizer 
                 analysis points 
                 model 
                 learning 
                 rate 
               
               
                   
               
             
            
               
                 Comparative 
                 Peak 
                 Not 
                 1 
                 Single 
                 — 
                 Estimation 
               
               
                 Example 3-1 
                 pick 
                 provided 
                   
                 regression 
                   
                 impossible 
               
               
                   
                   
                   
                   
                 model 
               
               
                 Comparative 
                 Single 
                 Not 
                 1 
                 Single 
                 — 
                 23% 
               
               
                 Example 3-2 
                 regression 
                 provided 
                   
                 regression 
               
               
                   
                 analysis 
                   
                   
                 model 
               
               
                 Example 3-1 
                 Multiple 
                 Not 
                 3 
                 Multiple 
                 — 
                 62% 
               
               
                   
                 regression 
                 provided 
                   
                 regression 
               
               
                   
                 analysis 
                   
                   
                 model 
               
               
                 Example 3-2 
                 Machine 
                 Not 
                 3 
                 SVM 
                 — 
                 75% 
               
               
                   
                 learning 
                 provided 
               
               
                 Example 3-3 
                 Machine 
                 Not 
                 1 at every 
                 SVM 
                 — 
                 82% 
               
               
                   
                 learning 
                 provided 
                 10 MHz 
               
               
                 Example 3-4 
                 Machine 
                 Not 
                 1 at every 
                 k-nearest 
                 — 
                 81% 
               
               
                   
                 learning 
                 provided 
                 10 MHz 
                 neighbor 
               
               
                 Example 3-5 
                 Machine 
                 Not 
                 1 at every 
                 SVM 
                 ◯ 
                 94% 
               
               
                   
                 learning 
                 provided 
                 10 MHz 
               
               
                 Example 3-6 
                 Multiple 
                 Provided 
                 3 
                 Multiple 
                 — 
                 69% 
               
               
                   
                 regression 
                   
                   
                 regression 
               
               
                   
                 analysis 
                   
                   
                 model 
               
               
                 Example 3-7 
                 Machine 
                 Provided 
                 1 at every 
                 SVM 
                 — 
                 96% 
               
               
                   
                 learning 
                   
                 10 MHz 
               
               
                   
               
            
           
         
       
     
     Here, the conditions regarding the type of estimation method, whether or not the sensitizer  14  was attached to the sensor  10 , the number of analysis points (that is, frequency positions) when the learning processing was performed, the type of the learning model, and whether or not ensemble learning was performed were variously changed, and the accuracy rate of estimation of the stretch rate under each condition was calculated. Here, the accuracy rate is a rate at which it was determined that the stretch rate was 1.5% to 2.5% when reflected wave spectra of 100 samples that were stretched by 2% (100 reflected wave spectra acquired while changing the orientation and position of the reader  20 ) were acquired, and when detection of the state of each sample was conducted for each of the reflected wave spectra using the pre-trained learning model. 
     In the peak pick method in Comparative Example 3-1, a calibration curve for regression analysis was created using as variables the intensity ratio and the stretch rate (%) at the peak frequency of the reflected wave spectrum serving as the training data, and estimation of the stretch rate was attempted using the calibration curve. However, in this method, a resonance peak could not be detected at the time of 2% stretch and 3% stretch, and a calibration curve for regression analysis could not be created. 
     In the single regression analysis in Comparative Example 3-2, a reflected wave spectrum of a sample with each stretch rate (%) was measured, and regression analysis was performed using the intensity ratio and the stretch rate (%) at 9.4 GHz as variables to create a calibration curve of the stretch rate and the intensity ratio. Subsequently, 100 samples obtained by stretching the samples by 2% with a tensile strength tester were separately prepared, and the reflected wave spectra were measured. Then, when the stretch rate (%) was read from the intensity ratio at 9.4 GHz of each reflected wave spectrum with reference to the calibration curve described above, the number of samples that were detected as those having a stretch rate of about 2% (1.5% to 2.5%) was only 23 samples (accuracy rate: 23%). 
     In the multiple regression analysis in Example 3-1, using the reflected wave spectra of 100 samples stretched by 1%, 100 samples stretched by 2%, and 100 samples stretched by 3%, multiple regression analysis was performed using, as variables, the intensity ratio and the stretch rate (%) at each of 9.0, 9.4, and 9.8 GHzGHz taken as analysis points of the reflected wave spectrum of each stretch rate (%) sample. Subsequently, 100 samples in  FIG.  51    obtained by stretching the samples by 2% with a tensile strength tester were separately prepared, and the reflected wave spectra were measured. When the stretch rate was confirmed from each intensity ratio at each analysis point, the number of samples that were detected as those having a stretch rate of about 2% (1.5% to 2.5%) was only 62 samples (accuracy rate: 62%). 
     In the machine learning method in Example 3-2, using the reflected wave spectra of 100 samples stretched by 1%, 100 samples stretched by 2%, and 100 samples stretched by 3%, a pre-trained model was generated by means of the SVM using as training data the intensity ratio at each of 9.0, 9.4, and 9.8 GHzGHz taken as analysis points of the reflected wave spectrum of each stretch rate (%) sample with each stretch rate (%) as a label. Subsequently, 100 samples obtained by stretching the samples by 2% with a tensile strength tester were separately prepared, and the reflected wave spectra were measured. When it was confirmed using the generated learning model whether or not the stretch rate was identified as 2% from each intensity ratio at each analysis point, 75 samples out of 100 samples were detected as those having a stretch rate of 2% (accuracy rate: 75%). 
     In the machine learning method in Example 3-3, a similar operation to that in Example 3-2 was performed except that the number of analysis points at which the intensity ratio was taken was 120 points with each point set at every 10 MHz in the range of 8.8 to 10.0 GHz. Then, when 100 samples obtained by stretching the samples by 2% with a tensile strength tester were separately prepared, and it was confirmed using the generated learning model whether or not the stretch rate was identified as 2% from the intensity ratio at each analysis point of each of the reflected wave spectra, 82 samples out of 100 samples were detected as those having a stretch rate of 2% (accuracy rate: 82%). 
     In the machine learning method in Example 3-4, a similar operation to that in Example 3-3 was performed except that the SVM was changed to the k-nearest neighbor. Then, when the stretch rate of each sample was identified using the generated learning model for the reflected wave spectrum of each sample having a stretch rate of 2% generated separately, 81 samples out of 100 samples were detected as those having a stretch rate of 2% (accuracy rate: 81%). 
     In the machine learning method in Example 3-5, a similar operation to that in Example 3-4 was performed except that a plurality of learning models were generated by means of the SVM, and that a pre-trained model was generated by means of ensemble learning thereof. Then, when the stretch rate of each sample was identified using the generated learning model for the reflected wave spectrum of each sample having a stretch rate of 2% generated separately, 94 samples out of 100 samples were detected as those having a stretch rate of 2% (accuracy rate: 94%). 
     In the multiple regression analysis in Example 3-6, similar learning processing and estimation processing to those in Example 3-1 were performed using the sensor  10  provided with the sensitizer  14  (here, an ethylcellulose resin film disposed in the slot of the resonator  11  of the sensor  10 ). As a result, the number of samples detected as those having a stretch rate of 2% (1.5% to 2.5%) was 69 samples (accuracy rate: 69%). Note that the sensitizer  14  is a member in which fine cracks are generated and the dielectric constant changes when external stress is applied. The dielectric constant of the sensitizer  14  is, for example, about 2.1 before application of external stress, and changes to, for example, about 1.4 after application of external stress. 
     In the machine learning method in Example 3-7, similar learning processing and estimation processing to those in Example 3-3 were performed using the sensor  10  provided with the sensitizer  14  (here, an ethylcellulose resin film disposed in the slot of the resonator  11  of the sensor  10 ). As a result, the number of samples detected as those having a stretch rate of 2% (1.5% to 2.5%) was 96 samples (accuracy rate: 96%). 
     In the above manner, it has been found that, by estimating the degree of stretch of the substrate using the learning model  30 D generated by means of the multiple regression analysis or the machine learning method, it is possible to accurately estimate the degree of stretch as compared with the mode in which the degree of stretch is estimated in a conventional method such as the peak pick method. 
     Example 4 
     In Example 4, the state detection system U according to the present disclosure is applied to estimation of the ethylene gas concentration. 
       FIG.  22    is a diagram illustrating a configuration of Example 4. In Example 4, the sensor  10  has the sensor structure in  FIG.  2   , and the conductive material constituting the resonator  11  includes a material whose conductivity changes due to adhesion of ethylene. Here, a part of the conductive material constituting the resonator  11  includes a gas absorbing material made of carbon nanotubes. The sensor  10  is configured such that the reflected wave spectrum changes (here, the peak intensity of the resonance peak changes) in accordance with the adhesion amount of ethylene. 
     In Example 4, a reflected wave spectrum was measured while spraying ethylene gas at each concentration (0.1 ppm, 0.5 ppm, 1.0 ppm, and 1.5 ppm) on the sensor  10 , and training data was acquired (not illustrated). 
     Table 4 is a table illustrating various estimation methods used in Example 4 and evaluation results thereof. 
     
       
         
           
               
               
               
               
               
               
               
               
             
               
                 TABLE 4 
               
               
                   
               
               
                   
                 Estimation 
                   
                 Number of 
                 Learning 
                 Ensemble 
                 External 
                 Accuracy 
               
               
                 Example No. 
                 method 
                 Sensitizer 
                 analysis points 
                 model 
                 learning 
                 stimulus 
                 rate % 
               
               
                   
               
             
            
               
                 Comparative 
                 Peak 
                 Not 
                 1 
                 Single 
                 Not 
                 Not 
                 Estimation 
               
               
                 Example 4-1 
                 pick 
                 provided 
                   
                 regression 
                 performed 
                 applied 
                 impossible 
               
               
                   
                   
                   
                   
                 model 
               
               
                 Comparative 
                 Single 
                 Not 
                 1 
                 Single 
                 Not 
                 Not 
                  7% 
               
               
                 Example 4-2 
                 regression 
                 provided 
                   
                 regression 
                 performed 
                 applied 
               
               
                   
                 analysis 
                   
                   
                 model 
               
               
                 Example 4-1 
                 Multiple 
                 Not 
                 3 
                 Multiple 
                 Not 
                 Not 
                 42% 
               
               
                   
                 regression 
                 provided 
                   
                 regression 
                 performed 
                 applied 
               
               
                   
                 analysis 
                   
                   
                 model 
               
               
                 Example 4-2 
                 Multiple 
                 Not 
                 3 
                 Multiple 
                 Not 
                 Light 
                 52% 
               
               
                   
                 regression 
                 provided 
                   
                 regression 
                 performed 
                 stimulus 
               
               
                   
                 analysis 
                   
                   
                 model 
               
               
                 Example 4-3 
                 Multiple 
                 Provided 
                 3 
                 Multiple 
                 Not 
                 Not 
                 57% 
               
               
                   
                 regression 
                   
                   
                 regression 
                 performed 
                 applied 
               
               
                   
                 analysis 
                   
                   
                 model 
               
               
                 Example 4-4 
                 Multiple 
                 Provided 
                 3 
                 Multiple 
                 Not 
                 Light 
                 65% 
               
               
                   
                 regression 
                   
                   
                 regression 
                 performed 
                 stimulus 
               
               
                   
                 analysis 
                   
                   
                 model 
               
               
                 Example 4-5 
                 Machine 
                 Provided 
                 3 
                 SVM 
                 Not 
                 Not 
                 71% 
               
               
                   
                 learning 
                   
                   
                   
                 performed 
                 applied 
               
               
                 Example 4-6 
                 Machine 
                 Provided 
                 3 
                 SVM 
                 Not 
                 Light 
                 83% 
               
               
                   
                 learning 
                   
                   
                   
                 performed 
                 stimulus 
               
               
                 Example 4-7 
                 Machine 
                 Provided 
                 1 at every 
                 SVM 
                 Not 
                 Light 
                 92% 
               
               
                   
                 learning 
                   
                 10 MHz 
                   
                 performed 
                 stimulus 
               
               
                 Example 4-8 
                 Machine 
                 Provided 
                 1 at every 
                 SVM 
                 Performed 
                 Light 
                 95% 
               
               
                   
                 learning 
                   
                 10 MHz 
                   
                   
                 stimulus 
               
               
                   
               
            
           
         
       
     
     Here, the conditions regarding the type of estimation method, whether or not the sensitizer  14  was attached to the sensor  10 , the number of analysis points (that is, frequency positions) when the learning processing was performed, the type of the learning model  30 D, whether or not ensemble learning was performed, and whether or not an external stimulus was applied were variously changed, and the accuracy rate of estimation of the gas concentration under each condition was calculated. Here, the accuracy rate is a rate at which it could be determined that the ethylene gas concentration was within the range of 0.5 to 1.0 ppm when a reflected wave spectrum was measured 100 times while spraying the ethylene gas of 0.8 ppm on the sensor  10 . 
     Note that, in Example 4, in the modes in which the sensitizer  14  is used (Examples 4-3 to 4-8), a copper complex is provided as a sensitizer to the carbon nanotube layer constituting the conductive member of the resonator  11 . The conductivity of the carbon nanotube layer greatly changes when ethylene gas adheres to the copper complex portion. That is, this increases the resonance current flowing through the resonator  11  and emphasizes the change in the resonance peak caused by the change in the gas concentration of the ethylene gas. 
     Also, in Example 4, in the modes in which an external stimulus was given to the sensor  10  (Example 4-2, Example 4-4, and Example 4-6 to Example 4-8), the reader  20  irradiated the sensor  10  with light. By irradiating the gas absorbing material made of the carbon nanotube layer (and the copper complex) with light at the time of acquiring the reflected wave spectrum of the sensor  10 , the gas absorbing amount in the carbon nanotube layer (and the copper complex) can be increased. That is, as a result, at the time of acquiring the reflected wave spectrum of the sensor  10 , the amount of change in conductivity of the carbon nanotube layer due to gas absorption was increased. 
     In the peak pick method in Comparative Example 4-1, creation of a calibration curve for regression analysis was attempted using as variables the intensity ratio and the gas concentration at the peak frequency of the reflected electromagnetic wave spectrum of the sensor  10  at each ethylene gas concentration. However, a peak could not be detected in the reflected electromagnetic wave spectrum at a concentration of 1.0 ppm or less, and a calibration curve could not be created. In the peak pick method, a state in which the ethylene gas concentration was 0.5 to 1.0 ppm could not be identified. 
     In the single regression analysis in Comparative Example 4-2, the measurement of the reflected electromagnetic wave spectrum of the sensor  10  was performed 100 times while spraying the gas containing ethylene at each concentration (0.1 ppm, 0.5 ppm, 1.0 ppm, and 1.5 ppm), and a calibration curve for regression analysis was created using the intensity ratio and the gas concentration at 8.0 GHz as variables. Subsequently, the reflected electromagnetic wave spectrum was measured 100 times while spraying 0.8 ppm of ethylene gas on the sensor  10 . The number of times where it could be determined that the ethylene gas concentration was within the range of 0.5 to 1.0 ppm from the intensity ratio at 8.0 GHz of the reflected electromagnetic wave spectrum with reference to the aforementioned calibration curve was only 7 times out of 100 times (accuracy rate: 7%). 
     In the multiple regression analysis in Example 4-1, the number of analysis points of the reflected wave spectrum serving as the training data was changed to 3 points (intensity ratios at 7.3 GHz, 8.0 GHz, and 8.7 GHz) from that of the single regression analysis in Comparative Example 4-2. Subsequently, the measurement of the reflected electromagnetic wave spectrum of the sensor  10  was performed 100 times while spraying 0.8 ppm ethylene gas, and detection of the ethylene gas concentration was conducted from the three analysis points of the spectrum by means of the multiple regression analysis. Then, the number of times where it could be determined that the ethylene gas concentration was within the range of 0.5 to 1.0 ppm was 42 times out of 100 times (accuracy rate: 42%). 
     In the machine learning method in Example 4-2, a similar operation to that in Example 4-1 was performed except that the measurement of the reflected electromagnetic wave spectrum of the sensor  10  was performed while emitting 100,000 lx of light as an external stimulus. The measurement of the reflected electromagnetic wave spectrum of the sensor  10  was performed 100 times while emitting 100,000 lx of light and spraying 0.8 ppm ethylene gas, and detection of the ethylene gas concentration was conducted from the three analysis points of the spectrum by means of the multiple regression analysis. Then, the number of times where it could be determined that the ethylene gas concentration was within the range of 0.5 to 1.0 ppm was 52 times out of 100 times (accuracy rate: 52%). 
     In the machine learning method in Example 4-3, a similar operation to that in Example 4-2 was performed except that the sensitizer  14  (here, the carbon nanotube layer) was used. The measurement of the reflected electromagnetic wave spectrum of the sensor  10  was performed 100 times while spraying 0.8 ppm ethylene gas, and the number of times where it could be determined by means of the multiple regression analysis that the ethylene gas concentration was within the range of 0.5 to 1.0 ppm was 57 times out of 100 times (accuracy rate: 57%). 
     In the machine learning method in Example 4-4, a similar operation to that in Example 4-2 was performed except that the measurement of the reflected electromagnetic wave spectrum was performed while giving the sensor  10  a light stimulus. The number of times where it could be determined by means of the multiple regression analysis that the ethylene gas concentration was within the range of 0.5 to 1.0 ppm was 65 times out of 100 times (accuracy rate: 65%). 
     In the machine learning method in Example 4-5, for three analysis points (intensity ratios at 7.3 GHz, 8.0 GHz, and 8.7 GHz) of each of 100 reflected electromagnetic wave spectra of the sensor  10  at each gas concentration, the learning processing was performed on the learning model  30 D by means of the SVM using the training data with a spectrum having a gas concentration of 0.1 ppm as the label of the immature state, the training data with a spectrum having a gas concentration of 0.5 or 1.0 ppm as the label of the state of the appropriate harvest time, and the training data with a spectrum having a gas concentration of 1.5 ppm as the label of the overmature state. Subsequently, the measurement of the reflected electromagnetic wave spectrum of the sensor  10  was performed 100 times while spraying 0.8 ppm ethylene gas, and detection of the state was conducted for each reflected electromagnetic wave spectrum using the generated learning model  30 D. Then, the number of times where it could be determined that the state showed the appropriate harvest time (gas concentration: 0.5 to 1.0 ppm) was 71 times (accuracy rate: 71%). 
     In the machine learning method in Example 4-6, a similar operation to that in Example 4-5 was performed except that the measurement of the reflected electromagnetic wave spectrum of the sensor  10  was performed while emitting 100,000 lx of light as an external stimulus, and learning processing was performed on the learning model  30 D. Subsequently, the measurement of the reflected electromagnetic wave spectrum of the sensor  10  was performed 100 times while emitting 100,000 lx of light as an external stimulus and spraying 0.8 ppm ethylene gas, and detection of the state was conducted for each reflected electromagnetic wave spectrum using the generated learning model  30 D. Then, the number of times where it could be determined that the gas concentration was 0.5 to 1.0 ppm was 83 times (accuracy rate: 83%). 
     In the machine learning method in Example 4-7, a similar operation to that in Example 4-6 was performed except that the number of analysis points was 301 points with each point set at every 10 MHz in the range of 7 to 10 GHz, and learning processing was performed on the learning model  30 D. The measurement of the reflected electromagnetic wave spectrum of the sensor  10  was performed 100 times while emitting 100,000 lx of light as an external stimulus and spraying 0.8 ppm ethylene gas, and detection of the state was conducted for each reflected electromagnetic wave spectrum using the generated learning model  30 D. Then, the number of times where it could be determined that the state showed the appropriate harvest time (gas concentration: 0.5 to 1.0 ppm) was 92 times (accuracy rate: 92%). 
     In the machine learning method in Example 4-8, a similar operation to that in Example 4-7 was performed except that a plurality of learning models  30 D were generated by means of the SVM, and that learning processing was performed on the learning model  30 D by means of ensemble learning thereof. The measurement of the reflected electromagnetic wave spectrum of the sensor  10  was performed 100 times while emitting 100,000 lx of light as an external stimulus and spraying 0.8 ppm ethylene gas, and detection of the state was conducted for each reflected electromagnetic wave spectrum using the generated learning model  30 D. Then, the number of times where it could be determined that the state showed the appropriate harvest time (gas concentration: 0.5 to 1.0 ppm) was 95 times (accuracy rate: 95%). 
     In the above manner, it has been found that, by estimating the gas concentration using the learning model  30 D generated by means of the multiple regression analysis or the machine learning method, it is possible to accurately estimate the cup position as compared with the mode in which the gas concentration is estimated in a conventional method such as the peak pick method. 
     [Effects] 
     As described above, with the state detection system U according to the present embodiment, even in a case where the resonance peak does not clearly appear in a reflected wave spectrum due to the influence of noise, it is possible to detect a state change of a detection target with high accuracy. 
     In particular, in a case where a model optimized by machine learning is used as the learning model  30 D, robustness is further improved against a change in the frequency spectrum due to noise or an environmental change at the time of use, and the state change of the detection target can be detected accurately. 
     Also, by introducing the sensitizer  14  into the sensor  10  and giving an external stimulus in combination, the state change of the detection target can be detected with higher accuracy. 
     Further, by causing the sensor  10  to have a structure of including a resonator in which the absorption peak appears clearly at the time of resonance, the reflected wave spectrum of the sensor  10  can be a reflected wave spectrum in which a change accompanying the state change of the detection target easily appears. This makes it possible to detect the state change of the detection target with high accuracy. 
     Although specific examples of the present invention have been described in detail above, these are illustrative only, and do not limit the scope of the claims. The technology described in the claims includes various modifications and changes of the specific examples illustrated above. 
     The entire disclosure of the specification, drawings, and abstract included in Japanese Patent Application No. 2020 077776 filed on Apr. 24, 2020 is incorporated herein by reference. 
     INDUSTRIAL APPLICABILITY 
     With a state detection system according to the present disclosure, it is possible to detect a state change of an object or an environmental change around an object with high accuracy. 
     REFERENCE SIGNS LIST 
     
         
         
           
             U State detection system 
               10  Sensor 
               11 ,  111  Resonator 
               11 B,  111 B Substrate 
               12  Isolation layer 
               13 ,  113  Electromagnetic wave reflecting material 
               14  Sensitizer 
               20  Reader 
               21  Transmission unit 
               22  Reception unit 
               23  Control unit 
               30  Analysis device 
               31  Learning unit 
               32  Identification unit 
               30 D Learning model