Patent Publication Number: US-2022221839-A1

Title: Information processing apparatus, information processing method, and program

Description:
TECHNICAL FIELD 
     The present invention relates to an analysis technique using a smell sensor. 
     BACKGROUND ART 
     A technique for acquiring information relating to gas by measuring the gas with a sensor has been developed. PTL 1 below discloses a technique for determining a kind of sample gas by utilizing a signal (time-series data of a detection value) acquired by measuring the sample gas with a nanomechanical sensor. Specifically, PTL 1 discloses that, since a diffusion time constant of sample gas relative to a receptor of a sensor is determined by a combination of a kind of receptor and a kind of sample gas, the kind of sample gas can be determined based on the diffusion time constant acquired from a signal, and the kind of receptor. 
     CITATION LIST 
     Patent Literature 
     [PTL 1] Japanese Patent Application Publication No. 2017-156254 
     SUMMARY OF INVENTION 
     Technical Problem 
     A technique of PTL 1 performs an analysis of a kind of gas using a feature value acquired from output data of a sensor. However, output data (an output waveform) of a sensor that senses smell is essentially a high-order feature value, and performing an analysis with a high degree of accuracy is difficult. When a dynamic characteristic of a smell sensor can be successfully extracted as a feature value converted to a low order, performing an analysis using a smell sensor with a high degree of accuracy becomes easy. 
     The present invention has been made in view of the problem described above. One object of the present invention is to provide a technique for improving accuracy of an analysis using a smell sensor. 
     Solution to Problem 
     An information processing apparatus according to the present invention includes: 
     a model generating unit that generates an Auto-Regressive with eXogenous input (ARX) model of a smell sensor by use of input data controlling an input operation of gas including a smell component being a measurement target, and output data being acquired by inputting the gas to the smell sensor, based on the input data; and 
     a feature value computation unit that computes a transfer function of the smell sensor relating to the smell component by subjecting the ARX model to Z-Transform, and further computes a first-order lag transfer function feature value of the smell sensor relating to the smell component by subjecting the transfer function to partial fraction decomposition. 
     An information processing method performed by a computer according to the present invention includes: 
     generating an Auto-Regressive with eXogenous input (ARX) model of a smell sensor by use of input data controlling an input operation of gas including a smell component being a measurement target, and output data being acquired by inputting the gas to the smell sensor, based on the input data; and 
     computing a transfer function of the smell sensor relating to the smell component by subjecting the ARX model to Z-Transform, and further computing a first-order lag transfer function feature value of the smell sensor relating to the smell component by subjecting the transfer function to partial fraction decomposition. 
     A program according to the present invention causes a computer to execute the above-described information processing method. 
     Advantageous Effects of Invention 
     According to the present invention, a technique for generating a feature value easy to handle in an analysis using a smell sensor is provided. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The above-described object, the other objects, features, and advantages will become more apparent from a suitable example embodiment described below and the following accompanying drawings. 
         FIG. 1  is a diagram illustrating a functional configuration of an information processing apparatus according to a first example embodiment. 
         FIG. 2  is a diagram illustrating a sensor for acquiring smell data. 
         FIG. 3  is a diagram illustrating a computer for achieving the information processing apparatus. 
         FIG. 4  is a diagram illustrating a flow of processing executed by the information processing apparatus according to the first example embodiment. 
         FIG. 5  is a diagram illustrating an example of extracting a plurality of pieces of partial input data and a plurality of pieces of partial output data by use of a plurality of windows. 
         FIG. 6  is a diagram illustrating a flow of processing executed by an information processing apparatus according to a second example embodiment. 
         FIG. 7  is a diagram illustrating a result of “biK/biL” acquired by measuring sample gas including a smell component under differing environments a plurality of times. 
         FIG. 8  is a diagram illustrating a functional configuration of an information processing apparatus according to a third example embodiment. 
         FIG. 9  is a diagram illustrating one example of a database constructed in the third example embodiment. 
         FIG. 10  is a diagram illustrating a flow in which an output unit outputs information to a display apparatus. 
         FIG. 11  is a diagram illustrating a flow in which the output unit outputs information to the display apparatus. 
         FIG. 12  is a diagram illustrating a flow in which the output unit outputs information to the display apparatus. 
         FIG. 13  is a diagram illustrating a flow in which the output unit outputs information to the display apparatus. 
         FIG. 14  is a diagram illustrating a flow in which the output unit outputs information to the display apparatus. 
         FIG. 15  is a diagram illustrating a flow in which the output unit outputs information to the display apparatus. 
         FIG. 16  is a diagram illustrating a flow in which the output unit outputs information to the display apparatus. 
         FIG. 17  is a diagram illustrating a functional configuration of an information processing apparatus according to a fourth example embodiment. 
         FIG. 18  is a diagram illustrating a flow of processing executed by the information processing apparatus according to the fourth example embodiment. 
         FIG. 19  is a diagram illustrating a functional configuration of an information processing apparatus according to a fifth example embodiment. 
         FIG. 20  is a diagram illustrating a flow of processing executed by the information processing apparatus according to the fifth example embodiment. 
     
    
    
     EXAMPLE EMBODIMENT 
     Example embodiments according to the present invention are described below by use of the drawings. Note that, a similar reference sign is assigned to a similar component in all the drawings, and description is not repeated where appropriate. Further, unless otherwise specially described, each block represents, in each block diagram, not a configuration on a hardware basis but a configuration on a function basis. Moreover, a direction of an arrow in the drawings serves for easy understanding of flow of information, and does not limit a direction of communication (one-way communication/two-way communication) unless otherwise specially described. 
     First Example Embodiment 
     &lt;Functional Configuration&gt; 
       FIG. 1  is a diagram illustrating a functional configuration of an information processing apparatus  20  according to a first example embodiment. The information processing apparatus  20  computes information indicating a dynamic characteristic of a sensor  10  by use of an input signal  12  to the sensor  10 , and an output signal  14  output from the sensor  10  in association with the input signal  12 . 
     Herein, the sensor  10  is a sensor having a receptor to which a molecule (a smell component) included in gas being a measurement target adheres, as illustrated in  FIG. 2 , and changing in detection value (output) according to adhesion and separation of a molecule in the receptor.  FIG. 2  is a diagram illustrating the sensor  10  for acquiring smell data. The sensor  10  is, for example, a Membrane-type Surface stress Sensor (MSS). The MSS has, as a receptor, a sensory membrane to which a molecule adheres. Then, stress generated in a support member of the sensory membrane changes due to the adhesion and separation of a molecule to and from the sensory membrane. The MSS outputs a detection value based on a change of the stress. Note that, the sensor  10  is not limited to the MSS. The sensor  10  may be a sensor that outputs a detection value, based on a change of a physical amount being related to viscoelasticity or a dynamic characteristic (mass, moment of inertia, or the like) of a member of the sensor  10  resulting according to adhesion and separation of a molecule to and from a receptor. For example, various types of sensors such as cantilever-type, membrane-type, optical, piezoelectric, vibration-response sensors can be applied as the sensor  10 . 
     A detection value (the output signal  14 ) of the sensor  10  changes due to an operation of exposing gas being a measurement target to the sensor  10  (hereinafter, this is also referred to as a “sampling operation”), and an operation of removing gas being a measurement target from the sensor  10  (hereinafter, this is also referred to as a “purge operation”). For example, a non-illustrated pump mechanism sucks in gas being a measurement target (a sampling operation) in a rising period of the input signal  12  (a period in which a signal level is High). Moreover, the non-illustrated pump mechanism removes gas being a measurement target from the sensor  10  by use of impurity gas (air, or the like) or the like (a purge operation) in a falling period of the input signal  12  (a period in which a signal level is Low). A detection value (the output signal  14 ) of the sensor  10  varies by control of the sampling operation or the purge operation in response to a value of the input signal  12 . In other words, it can be said that an input signal for controlling the sampling operation and the purge operation is equivalent to input to the sensor  10  in a system called the sensor  10 . In the following description, as needed, the input signal  12  and the output signal  14  are also referred to as U and Y, respectively. Moreover, a value of the input signal  12  at a time t and a value of the output signal  14  at a time t are also referred to as u(t) and y(t), respectively. U becomes a matrix in which u(t)s are enumerated. Y becomes a matrix in which y(t)s are enumerated. 
     Returning to  FIG. 1 , the functional configuration of the information processing apparatus  20  is described. As illustrated, the information processing apparatus  20  according to the present example embodiment includes a model generating unit  210  and a feature value computation unit  220 . 
     The model generating unit  210  learns an input/output relation of the sensor  10  by use of input data of the sensor  10  and output data of the sensor  10 , and generates an Auto-Regressive with eXogenous input (ARX) model indicating the input/output relation of the sensor  10 . Herein, the input data of the sensor  10  are data that control an input operation (sampling operation/purge operation) of gas including a smell component being a measurement target. As to the example of  FIG. 2 , the input signal  12  is equivalent to input data. Moreover, the output data of the sensor  10  are data acquired by inputting gas to the sensor  10 , based on the input data. As to the example of  FIG. 2 , the output signal  14  is equivalent to output data. 
     Herein, an ARX model of the sensor  10  is represented by an equation (1) below. In the equation (1), y(t) is an output of the sensor  10  at a time t, u(t) is an input to the sensor  10  at a time t, a i  (with underline) is an autoregressive coefficient, and b i  (with underline) is an exogenous input coefficient. The model generating unit  210  can learn (generate) an input/output relation of the sensor  10  as an ARX model indicated by the equation (1) below, from, for example, the input signal  12  and the output signal  14  as illustrated in  FIG. 2 . 
     
       
         
           
             
               
                 
                   [ 
                   
                     Mathematical 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     1 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     y 
                     ⁡ 
                     
                       ( 
                       t 
                       ) 
                     
                   
                   = 
                   
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                       
                       m 
                     
                     ⁢ 
                     
                       { 
                       
                         
                           
                             
                               a 
                               ¯ 
                             
                             i 
                           
                           ⁢ 
                           
                             y 
                             ⁡ 
                             
                               ( 
                               
                                 t 
                                 - 
                                 i 
                               
                               ) 
                             
                           
                         
                         + 
                         
                           
                             
                               b 
                               ¯ 
                             
                             i 
                           
                           ⁢ 
                           
                             u 
                             ⁡ 
                             
                               ( 
                               
                                 t 
                                 - 
                                 i 
                               
                               ) 
                             
                           
                         
                       
                       } 
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     Next, the feature value computation unit  220  generates a feature value indicating a characteristic of a sensor by use of the ARX model generated by the model generating unit  210 . First, the feature value computation unit  220  performs Z-Transform on the ARX model generated by the model generating unit  210 . Further, the feature value computation unit  220  computes a transfer function of a first-order lag system by subjecting, to partial fraction decomposition, a result of subjecting the ARX model to Z-Transform. 
     The feature value computation unit  220  first acquires an equation (2) below by subjecting, to Z-Transform, the ARX model indicated by the equation (1). In the equation (2) below, Y(z)/U(z) is a ratio of Z-Transform of an output Y to an input U of a sensor (i.e., a transfer function in a Z-area). 
     
       
         
           
             
               
                 
                   [ 
                   
                     Mathematical 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     2 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     
                       Y 
                       ⁡ 
                       
                         ( 
                         z 
                         ) 
                       
                     
                     
                       U 
                       ⁡ 
                       
                         ( 
                         z 
                         ) 
                       
                     
                   
                   = 
                   
                     
                       
                         
                           ∑ 
                           
                             i 
                             = 
                             1 
                           
                         
                         m 
                       
                       ⁢ 
                       
                         
                           
                             b 
                             ¯ 
                           
                           i 
                         
                         ⁢ 
                         
                           z 
                           
                             - 
                             i 
                           
                         
                       
                     
                     
                       1 
                       - 
                       
                         
                           
                             ∑ 
                             
                               i 
                               = 
                               1 
                             
                           
                           m 
                         
                         ⁢ 
                         
                           
                             
                               a 
                               ¯ 
                             
                             i 
                           
                           ⁢ 
                           
                             z 
                             
                               - 
                               i 
                             
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     Further, the feature value computation unit  220  acquires a following equation by subjecting a right side of the equation (2) to partial fraction decomposition. 
     
       
         
           
             
               
                 
                   [ 
                   
                     Mathematical 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     3 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     
                       Y 
                       ⁡ 
                       
                         ( 
                         z 
                         ) 
                       
                     
                     
                       U 
                       ⁡ 
                       
                         ( 
                         z 
                         ) 
                       
                     
                   
                   = 
                   
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                       
                       m 
                     
                     ⁢ 
                     
                       
                         
                           b 
                           ˜ 
                         
                         i 
                       
                       
                         z 
                         - 
                         
                           a 
                           i 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
     In the equation (3), a i  indicates a feature value relating to a desorption rate of a smell component i, and b i  (with tilde) indicates a feature value relating to an adsorption rate of the smell component i. Note that, b i  with tilde is also referred to briefly as “b i ” in the following description. The feature value computation unit  220  acquires a pair of a i  and b i  as a first-order lag transfer function feature value, as indicated in an equation (4) below. 
       {a i ,{tilde over (b)} i } i=1   m    [Mathematical 4]
 
       WHERE  a   i :=(1−Δ tβ   i ),  {tilde over (b)}   i :=Δtγ i a i ρ i ,
 
     Note that, in the equation (4) above, a i  indicates an adsorption rate of the smell component i, β i  indicates a desorption rate of the smell component i, γ i  indicates a proportionality constant of the number of molecules adhering to a sensor receptor relating to the smell component i and a sensor output generated thereby, ρ i  indicates density of the smell component i, and Δt indicates a time interval in a discrete-time system. The first-order lag transfer function feature value represented by the equation (4) above can be utilized as a feature value representing a combination of a sensory membrane being set in the sensor  10  and the smell component i. Moreover, dynamics of the sensor  10  is physically interpretable for each smell component i from the above-described relational expression of a i  and b i . 
     A first-order lag transfer function feature value acquired by the present example embodiment is a feature value of an order lower than output data of the sensor  10 . Accuracy of a discrimination analysis or a regression analysis can be improved by using the first-order lag transfer function feature value converted to a low order in this way. 
     &lt;Hardware Configuration of Information Processing Apparatus  20 &gt; 
     Each functional configuration unit of the information processing apparatus  20  may be achieved by hardware (example: a hard-wired electronic circuit, or the like) that achieves each functional configuration unit, or may be achieved by a combination of hardware and software (example: a combination of an electronic circuit and a program controlling the electronic circuit, or the like). A case where each functional configuration unit of the information processing apparatus  20  is achieved by a combination of hardware and software is further described below. 
       FIG. 3  is a diagram illustrating a computer  1000  for achieving the information processing apparatus  20 . The computer  1000  is any computer. For example, the computer  1000  is a stationary computer such as a personal computer (PC) or a server machine. Alternatively, for example, the computer  1000  is a portable computer such as a smartphone or a tablet terminal. The computer  1000  may be a dedicated computer designed to achieve the information processing apparatus  20 , or may be a general-purpose computer. 
     The computer  1000  includes a bus  1020 , a processor  1040 , a memory  1060 , a storage device  1080 , an input/output interface  1100 , and a network interface  1120 . The bus  1020  is a data transmission path through which the processor  1040 , the memory  1060 , the storage device  1080 , the input/output interface  1100 , and the network interface  1120  transmit/receive data to/from each other. However, a method of mutually connecting the processor  1040  and the like is not limited to bus connection. 
     The processor  1040  includes various types of processors such as a central processing unit (CPU), a graphics processing unit (GPU), and a field-programmable gate array (FPGA). The memory  1060  is a main storage apparatus achieved by use of a random access memory (RAM) or the like. The storage device  1080  is an auxiliary storage apparatus achieved by use of a hard disk, a solid state drive (SSD), a memory card, a read only memory (ROM), or the like. 
     The input/output interface  1100  is an interface for connecting the computer  1000  and an input/output device. For example, an input apparatus such as a keyboard or a touch panel, and an output apparatus such as a display device or a speaker are connected to the input/output interface  1100 . 
     The network interface  1120  is an interface for connecting the computer  1000  to a communication network. The communication network is, for example, a local area network (LAN) or a wide area network (WAN). A method of connecting the network interface  1120  to the communication network may be wireless connection or may be wired connection. 
     The storage device  1080  stores a program module that achieves each functional configuration unit (the model generating unit  210 , the feature value computation unit  220 , and the like) of the information processing apparatus  20 . The processor  1040  reads each of the program modules onto the memory  1060 , executes the read program module, and thereby achieves a function being associated with each of the program modules. 
     &lt;Flow of Processing&gt; 
       FIG. 4  is a diagram illustrating a flow of processing executed by the information processing apparatus  20  according to the first example embodiment. In the example of  FIG. 4 , the sensor  10  has two sensory membranes (a sensory membrane K and a sensory membrane L). Moreover, in the example of  FIG. 4 , sample gas having the smell component i is input to the sensor  10  in response to an input signal U (e.g., an M-sequence signal) having a random sampling period. The output Y of the sensor  10  in this case is a sum of an output of the sensory membrane K and an output of the sensory membrane L. 
     First, the model generating unit  210  acquires input/output data for each sensory membrane (S 102 ). For example, the model generating unit  210  acquires input data U to the sensor  10  and output data Y K  of the sensory membrane K as input/output data of the sensory membrane K. Moreover, the model generating unit  210  acquires input data U to the sensor  10  and output data Y L  of the sensory membrane L as input/output data of the sensory membrane L. Then, the model generating unit  210  generates an ARX model for each sensory membrane, based on input/output data for each sensory membrane (S 104 ). For example, the model generating unit  210  generates an ARX model regarding the sensory membrane K, based on the input data U to the sensor  10  and the output data Y K  of the sensory membrane K. Moreover, the model generating unit  210  generates an ARX model regarding the sensory membrane L, based on the input data U to the sensor  10  and the output data Y L  of the sensory membrane L. Then, the feature value computation unit  220  performs Z-Transform on the ARX model generated for each sensory membrane (S 106 ). Then, the feature value computation unit  220  subjects, to partial fraction decomposition, each result of subjecting each ARX model to Z-Transform, and computes a first-order lag transfer function feature value for each sensory membrane (S 108 ). 
     &lt;Modification Example&gt; 
     A model generating unit  210  may be configured in such a way as to extract a plurality of pieces of partial input data and a plurality of pieces of partial output data by use of a plurality of windows, and generate a plurality of ARX models. 
       FIG. 5  is a diagram illustrating an example of extracting a plurality of pieces of partial input data and a plurality of pieces of partial output data by use of a plurality of windows. In the example illustrated in  FIG. 5 , n pairs of partial input data and partial output data are extracted from input data u(t) and output data y(t) by use of a plurality of windows W 1  to Wn. A width of one window is set as a such a width that a frequency component equal to or more than a predetermined reference is included in the input data u(t). For example, the model generating unit  210  can determine a continuous time area covering a frequency component equal to or more than the reference by subjecting the input data u(t) to Fourier transform, and determine a width of the area as a width of one window. Note that, as illustrated in  FIG. 5 , the model generating unit  210  may determine a position of each window in such a way that two adjacent windows (example: W 1  and W 2 ) partially overlap each other. 
     The model generating unit  210  generates an ARX model by use of one pair of partial input data and partial output data extracted for each window as the above-described input data and output data. In other words, the model generating unit  210  generates a plurality of ARX models by use of a plurality of pieces of partial input data and a plurality of pieces of partial output data extracted by use of a plurality of windows. 
     Then, a feature value computation unit  220  computes a plurality of first-order lag transfer function feature values by subjecting the plurality of ARX models to Z-change and partial fraction decomposition. Then, the feature value computation unit  220  executes machine learning by use of the plurality of computed first-order lag transfer function feature values as learning data, and determines a first-order lag transfer function feature value of the sensor  10 . Moreover, the feature value computation unit  220  may be configured in such a way as to determine a first-order lag transfer function feature value of the sensor  10  after performing statistical processing such as abnormal value removal on the plurality of computed first-order lag transfer function feature values. 
     The configuration according to the present modification example allows acquisition of a first-order lag transfer function feature value having a higher degree of accuracy as compared with a case of computing a first-order lag transfer function feature value (a feature value indicating a dynamic characteristic of the sensor  10 ) by use of one ARX model. 
     Second Example Embodiment 
     In the present example embodiment, one example of application of a first-order lag transfer function feature value is described. When two or more sensory membranes differing in kind from each other are set in a sensor  10 , an information processing apparatus  20  can generate a feature value being robust against a change of a measurement environment by use of a first-order lag transfer function feature value for each sensory membrane. 
       FIG. 6  is a diagram illustrating a flow of processing executed by the information processing apparatus  20  according to a second example embodiment. In the example of  FIG. 6 , the sensor  10  has two sensory membranes (a sensory membrane K and a sensory membrane L) differing in kind from each other. Moreover, in the example of  FIG. 6 , sample gas having a smell component i is input to the sensor  10  in response to an input signal U (e.g., an M-sequence signal) having a random sampling period. An output Y of the sensor  10  in this case is a sum of an output of the sensory membrane K and an output of the sensory membrane L. 
     First, a model generating unit  210  acquires input/output data for each sensory membrane, in response to measurement of the sample gas including a smell component i (S 202 ). Then, the model generating unit  210  generates an ARX model for each sensory membrane, based on input/output data for each sensory membrane (S 204 ). Then, the feature value computation unit  220  subjects the ARX model for each sensory membrane to Z-Transform (S 206 ). Further, the feature value computation unit  220  computes a first-order lag transfer function feature value (a i , b i ) for each sensory membrane by subjecting, to partial fraction decomposition, a result of subjecting the ARX model to Z-Transform (S 208 ). The processing in S 202  to S 208  is similar to the processing in S 102  to S 108  in  FIG. 4 . Then, the feature value computation unit  220  computes a ratio of the first-order lag transfer function feature values b i s related to an adsorption rate of the smell component i, with regard to the sensory membrane K and the sensory membrane L (S 210 ). Note that, in the following description, a ratio between a first-order lag transfer function feature value b iK  and a first-order lag transfer function feature value b iL  is also referred to as “b iK /b iL ”. 
     Herein, an output of the sensor  10  can vary in response to not only a kind of smell component being a measurement target, but also an environment (e.g., temperature, humidity, or the like at measurement) in which the smell component is measured. Note that, an output of the sensor  10  varies depending on a change of stress generated in a support member by a smell component adhering to a sensory membrane set in the sensor  10 , as described by use of  FIG. 2 . In other words, when “an output of the sensor  10  changes due to a measurement environment”, it can be considered that “an adsorption rate of a sensory membrane set in the sensor  10  varies in response to a change of the measurement environment”. Further, it is assumed that an adsorption rate of a sensory membrane can be expressed by a product of a parameter varying depending on a measurement environment and a parameter specific for each kind of sensory membrane. Based on this assumption, a parameter part depending on a change of a measurement environment is cancelled by performing processing (processing of computing a ratio of first-order lag transfer function feature values related to an adsorption rate of a smell component) in S 202 . In other words, a ratio (b iK /b iL ) between a first-order lag transfer function feature value acquired with regard to the sensory membrane K and a first-order lag transfer function feature value acquired with regard to the sensory membrane L can be utilized for an analysis of the smell component i as a feature value being robust against a change of a measurement environment. 
     Note that, the above-described assumption has an ideal that a parameter specific for each kind of sensory membrane is always a constant value regardless of a measurement environment. However, in reality, it can hardly be said that a parameter specific for each membrane kind is not at all affected by a change of a measurement environment. Thus, the information processing apparatus  20  may be configured in such a way as to perform the following processing. 
     First, the sensor  10  measures sample gas including the smell component i under environments differing from each other a plurality of times. For each single measurement, the model generating unit  210  generates each of an ARX model regarding the sensory membrane K and an ARX model regarding the sensory membrane L, and the feature value computation unit  220  computes, based on the ARX models of the sensory membranes K and L, the first-order lag transfer function feature values “b iK ” and “b iL ” related to an adsorption rate of the smell component i, respectively. Moreover, the feature value computation unit  220  acquires the ratio “b iK /b iL ” of the first-order lag transfer function feature values “b iK ” and “b iL ” for each single measurement. Then, the feature value computation unit  220  determines, based on a plurality of “b iK /b iL ” acquired by a plurality of times of measurements, a range of a measurement environment in which “b iK /b iL ” becomes constant (an inclination is 0). Then, the feature value computation unit  220  stores, in a storage area such as a storage device  1080 , information indicating the determined range of the measurement environment, in association with information indicating a kind of smell component and information indicating a kind of sensory membrane (a combination of sensory membranes). 
     For example, it is assumed that a result as illustrated in  FIG. 7  is acquired with regard to “b iK /b iL ” when the sensor  10  having the sensory membrane K and the sensory membrane L measures sample gas including the smell component i under differing environments a plurality of times.  FIG. 7  is a diagram illustrating a result of “b iK /b iL ” acquired by measuring sample gas including the smell component i under differing environments a plurality of times. In  FIG. 7 , a horizontal axis indicates a “measurement environment (temperature)”, and a vertical axis indicates the “ratio (b iK /b iL ) of the first-order lag transfer function feature value b iK  when the first-order lag transfer function feature value b iL  is determined as a reference. 
     In the example of  FIG. 7 , “b iK /b iL ” is constant in a temperature range of T1 to T2, and varies in value in response to a change of a measurement environment in other ranges. In this case, the feature value computation unit  220  determines a temperature range of T1 to T2 in which a value of “b iK /b iL ” is constant (i.e., an inclination is 0). Note that, the feature value computation unit  220  may be configured in such a way as to determine a range in which an inclination satisfies a predetermined reference (e.g., −0.05 or more and 0.05 or less). Then, the feature value computation unit  220  stores, in a storage area such as the storage device  1080 , information indicating a temperature range of T1 to T2, in association with information indicating the smell component i and information indicating a combination of the sensory membrane K and the sensory membrane L. Herein, information stored in the storage device  1080  is information indicating a condition in which a feature value being robust against a change of a measurement environment can be acquired. 
     The configuration according to the present example embodiment allows generation of a database that accumulates a condition (a combination of a kind of smell component, a kind of sensory membrane, and a measurement environment) in which a feature value being robust against a change of a measurement environment can be acquired. The database can be utilized, for example, as described in a third example embodiment. 
     Third Example Embodiment 
     &lt;Functional Configuration&gt; 
       FIG. 8  is a diagram illustrating a functional configuration of an information processing apparatus  20  according to a third example embodiment. The information processing apparatus  20  according to the present example embodiment is configured in such a way as to further include a processing unit (an output unit  230 ) that utilizes information accumulated in a storage area such as a storage device  1080  in the second example embodiment. 
     As one example, the output unit  230  can output information indicating a recommended measurement environment for each kind of smell component, with input of information indicating a configuration of a sensor  10  (a kind of sensory membrane being set in the sensor  10 ). As a specific example, it is assumed that the output unit  230  acquires input information indicating a combination of a sensory membrane K and a sensory membrane L in a state where information as illustrated in  FIG. 9  is stored in the storage area.  FIG. 9  is a diagram illustrating one example of a database constructed in the third example embodiment. In this case, the output unit  230  determines, based on the input information, information indicating a “smell component i” and a “temperature range of T1 to T2” and information indicating a “smell component j” and a “temperature range of T5 to T6”, from the information illustrated in  FIG. 9 . Then, the output unit  230  outputs the determined information (information indicating a recommended measurement environment for each kind of smell component) to a display apparatus connected to the information processing apparatus  20  (example:  FIG. 10 ).  FIG. 10  is a diagram illustrating a flow in which the output unit  230  outputs information to the display apparatus. 
     In the present example, when a configuration of the sensor  10  is determined, information indicating a recommended measurement environment is output for each kind of smell component, by inputting information indicating the configuration of the sensor  10 . With such information, a user of the sensor  10  can easily determine how to utilize the sensor  10  having the determined configuration (under what environment and for what smell component measurement is performed). 
     As another example, the output unit  230  can output information indicating a recommended configuration of the sensor  10  (a combination of sensory membranes) for each kind of smell component, with input of information indicating a measurement environment (an environment in which the sensor  10  is placed) of the smell component. As a specific example, it is assumed that the output unit  230  acquires input information indicating that temperature of a measurement environment is in a range of T1 to T2 in a state where information as illustrated in  FIG. 9  is stored in the storage area. In this case, the output unit  230  determines, based on the input information, information indicating the “smell component i” and a “combination of the sensory membrane K and the sensory membrane L”, and information indicating the “smell component j” and a “combination of a sensory membrane N and a sensory membrane O”, from the information illustrated in  FIG. 9 . Then, the output unit  230  outputs the determined information (information indicating a recommended configuration of the sensor  10  for each kind of smell component) to a display apparatus connected to the information processing apparatus  20  (example:  FIG. 11 ).  FIG. 11  is a diagram illustrating a flow in which the output unit  230  outputs information to the display apparatus. 
     In the present example, when a measurement environment (an environment in which the sensor  10  is placed) is determined, information indicating a recommended configuration of the sensor  10  is output for each kind of smell component, by inputting information on the measurement environment. With such information, a user can be notified of what configuration of a sensor may be used for what smell in the determined measurement environment to enable stable measurement. 
     As another example, the output unit  230  can output information indicating a recommended configuration of the sensor  10  (a combination of sensory membranes) and a recommended measurement environment (a range of temperature or humidity, or the like), with input of information indicating a kind of smell component being a measurement target. As a specific example, it is assumed that the output unit  230  acquires input information indicating the “smell component i” as information on a smell component being a measurement target in a state where information as illustrated in  FIG. 9  is stored in the storage area. In this case, the output unit  230  determines, based on the input information, information indicating a “combination of the sensory membrane K and the sensory membrane L” and a “temperature range of T1 to T2”, and information indicating a “combination of the sensory membrane K and the sensory membrane M” and a “temperature range of T3 to T4”, from the information illustrated in  FIG. 9 . Then, the output unit  230  outputs the determined information (information indicating a recommended configuration of the sensor  10  and a recommended measurement environment) to a display apparatus or the like connected to the information processing apparatus  20  (example:  FIG. 12 ).  FIG. 12  is a diagram illustrating a flow in which the output unit  230  outputs information to the display apparatus. 
     In the present example, when a smell component to be a measurement target is determined, information indicating a configuration of the sensor  10  and a measurement environment that are suited to measurement of the smell component is output, by inputting information on the smell component. With such information, a user becomes able to easily determine “what configuration of a smell sensor to prepare and under what environment the smell sensor is operated in order to accurately perform a discrimination analysis of a smell component being a target”. 
     As another example, the output unit  230  can output information indicating a recommended measurement environment (a range of temperature or humidity, or the like), with input of information indicating a configuration of the sensor  10  (a combination of sensory membranes) and a kind of smell component being a measurement target. As a specific example, it is assumed that the output unit  230  acquires input information indicating a combination of the sensory membrane K and the sensory membrane L and the smell component i in a state where information as illustrated in  FIG. 9  is stored in the storage area. In this case, the output unit  230  determines, based on the input information, information indicating a “temperature range of T1 to T2”, from among pieces of the information illustrated in  FIG. 9 . Then, the output unit  230  outputs the determined information (information indicating a recommended measurement environment) to a display apparatus or the like connected to the information processing apparatus  20  (example:  FIG. 13 ).  FIG. 13  is a diagram illustrating a flow in which the output unit  230  outputs information to the display apparatus. 
     In the present example, when a configuration of a smell sensor and a smell component to be measured with the smell sensor are determined, information indicating a recommended measurement environment is output, by inputting information on the configuration of the smell sensor and the smell component. With such information, a user of the sensor  10  can easily recognize an environment in which measurement can be performed with stable accuracy. 
     As another example, the output unit  230  can output information indicating a kind of smell component recommended as a measurement target, with input of information indicating a configuration of the sensor  10  (a combination of sensory membranes), and information indicating a measurement environment (an environment in which the sensor  10  is placed) of a smell component. As a specific example, it is assumed that the output unit  230  acquires input information indicating a combination of the sensory membrane K and the sensory membrane L and a temperature range of T1 to T2 in a state where information as illustrated in  FIG. 9  is stored in the storage area. In this case, the output unit  230  determines, based on the input information, information indicating the “smell component i”. Then, the output unit  230  outputs the determined information indicating a kind of smell component (the smell component i) to a display apparatus or the like connected to the information processing apparatus  20  (example:  FIG. 14 ).  FIG. 14  is a diagram illustrating a flow in which the output unit  230  outputs information to the display apparatus. 
     In the present example, when a configuration of a smell sensor and an environment in which the smell sensor is placed are already known, information indicating a smell component recommended as a measurement target is output, by inputting information on the configuration of the smell sensor and the environment. With such information, a user of the sensor  10  can easily recognize a smell component suited to measurement. 
     As another example, the output unit  230  can output information indicating a recommended configuration of the sensor  10  (a combination of sensory membranes), with input of information indicating a kind of smell component being a measurement target and a measurement environment (an environment in which the sensor  10  is placed) of the smell component. As a specific example, it is assumed that the output unit  230  acquires input information indicating the smell component i and a temperature range of T1 to T2 in a state where information as illustrated in  FIG. 9  is stored in the storage area. In this case, the output unit  230  determines, based on the input information, information indicating a “combination of the sensory membrane K and the sensory membrane L”, from among pieces of the information illustrated in  FIG. 9 . Then, the output unit  230  outputs the determined information (information indicating a recommended configuration of the sensor  10 ) to a display apparatus or the like connected to the information processing apparatus  20  (example:  FIG. 15 ).  FIG. 15  is a diagram illustrating a flow in which the output unit  230  outputs information to the display apparatus. 
     In the present example, when a smell component to be a measurement target and an environment in which the smell component is measured are already known, information indicating a recommended configuration of the sensor  10  (a combination of sensory membranes) is output, by inputting information on the smell component and the environment. With such information, even an inexperienced person can easily determine a sensory membrane to be set in the sensor  10 . 
     Moreover, when information indicating a plurality of smell components being measurement targets is acquired as an input together with information indicating a measurement environment, the output unit  230  can also determine a priority order of a configuration of the sensor  10 , based on information on the database acquired in the third example embodiment. Specifically, it is assumed that, in a range of a measurement environment indicated by input information, there exist a combination A of sensory membranes in which a ratio of first-order lag transfer function feature values becomes constant (an inclination is 0) with regard to all the smell components indicated by the input information, and a combination B of sensory membranes in which a ratio of first-order lag transfer function feature values does not become constant (an inclination is not 0) with regard to at least some of the smell components. In this case, the output unit  230  gives a higher priority to the combination A that enables an analysis with stable accuracy with regard to all the smell components than the combination B. Then, the output unit  230  outputs information indicating a priority for each combination to a display apparatus or the like connected to the information processing apparatus  20  (example:  FIG. 16 ).  FIG. 16  is a diagram illustrating a flow in which the output unit  230  outputs information to the display apparatus. In the example of  FIG. 16 , the output unit  230  outputs a number N indicating a priority in association with each configuration of the sensor  10 . With such information, a user can easily determine a configuration of the sensor  10  best suited to measurement. 
     Fourth Example Embodiment 
     In the present example embodiment, another example of application of a first-order lag transfer function feature value is described. When two or more sensory membranes of the same kind are set in a sensor  10 , an information processing apparatus  20  can inspect performance of each of the sensory membranes by use of a first-order lag transfer function feature value for each sensory membrane. 
     &lt;Functional Configuration&gt; 
       FIG. 17  is a diagram illustrating a functional configuration of the information processing apparatus  20  according to a fourth example embodiment. As illustrated in  FIG. 17 , the information processing apparatus  20  according to the present example embodiment further includes a product judgement unit  240 . The product judgement unit  240  judges whether a first sensory membrane is an acceptable product, by use of a ratio between a first-order lag transfer function feature value of the sensory membrane (first sensory membrane) being an inspection target and a first-order lag transfer function feature value of a sensory membrane (second sensory membrane) to be a reference. 
     &lt;Flow of Processing&gt; 
       FIG. 18  is a diagram illustrating a flow of processing executed by the information processing apparatus  20  according to the fourth example embodiment. In the example of  FIG. 18 , the sensor  10  has two sensory membranes (a sensory membrane K and a sensory membrane L) of the same kind. The sensory membrane K is a sensory membrane being an inspection target, and the sensory membrane L is a sensory membrane having performance to be a reference. Moreover, in the example of  FIG. 18 , sample gas having a smell component i is input to the sensor  10  in response to an input signal U (e.g., an M-sequence signal) having a random sampling period. An output Y of the sensor  10  in this case is a sum of an output of the sensory membrane K and an output of the sensory membrane L. 
     First, a model generating unit  210  acquires input/output data for each sensory membrane, in response to measurement of the sample gas including a smell component i (S 302 ). Then, the model generating unit  210  generates an ARX model for each sensory membrane, based on input/output data for each sensory membrane (S 304 ). Then, the feature value computation unit  220  subjects the ARX model for each sensory membrane to Z-Transform (S 306 ). Further, the feature value computation unit  220  computes a first-order lag transfer function feature value (a i , b i ) for each sensory membrane by subjecting, to partial fraction decomposition, a result of subjecting the ARX model to Z-Transform (S 308 ). The processing in S 302  to S 308  is similar to the processing in S 102  to S 108  in  FIG. 4 . 
     Then, the feature value computation unit  220  computes a ratio (b iK /b iL ) of the first-order lag transfer function feature values b i s related to an adsorption rate of the smell component i, with regard to the sensory membrane K and the sensory membrane L (S 310 ). Herein, when the sensory membrane K and the sensory membrane L have the same performance, a first-order lag transfer function feature value of each of the sensory membranes becomes equal. In this case, a value of b iK /b iL  becomes 1. Thus, the product judgement unit  240  judges whether b iK /b iL  satisfies a predetermined reference (b iK /b iL  becomes 1 or a value close to 1) (S 312 ). When b iK /b iL  satisfies the predetermined reference (S 312 : YES), the product judgement unit  240  judges that the sensory membrane K being an inspection target is an “acceptable product having performance equal to the sensory membrane L being a reference product” (S 314 ). On the other hand, when b iK /b iL  does not satisfy the predetermined reference (S 312 : NO), the product judgement unit  240  judges that the sensory membrane K being an inspection target is a “rejected product that does not have performance equal to the sensory membrane L being a reference product” (S 316 ). 
     In consequence, the present example embodiment enables to determine whether performance of a sensory membrane is good/poor, by using a first-order lag transfer function feature value acquired by the method described in the first example embodiment. 
     Fifth Example Embodiment 
     In the present example embodiment, another example of application of a first-order lag transfer function feature value is described. When two or more sensory membranes of the same kind are set in a sensor  10 , an information processing apparatus  20  can correct an individual difference (a margin of error of output performance) between the sensory membranes by use of a first-order lag transfer function feature value for each sensory membrane. 
     &lt;Functional Configuration&gt; 
       FIG. 19  is a diagram illustrating a functional configuration of the information processing apparatus  20  according to a fifth example embodiment. As illustrated in  FIG. 19 , the information processing apparatus  20  according to the present example embodiment further includes an individual difference correction unit  250 . The individual difference correction unit  250  corrects an individual difference between two sensory membranes of the same kind, by use of a ratio of first-order lag transfer function feature values of the two sensory membranes. 
     &lt;Flow of Processing&gt; 
       FIG. 20  is a diagram illustrating a flow of processing executed by the information processing apparatus  20  according to the fifth example embodiment. In the example of  FIG. 20 , the sensor  10  has two sensory membranes (a sensory membrane K and a sensory membrane L) of the same kind. Moreover, in the example of  FIG. 20 , sample gas having a smell component i is input to the sensor  10  in response to an input signal U (e.g., an M-sequence signal) having a random sampling period. An output Y of the sensor  10  in this case is a sum of an output of the sensory membrane K and an output of the sensory membrane L. 
     First, a model generating unit  210  acquires input/output data for each sensory membrane, in response to measurement of the sample gas including a smell component i (S 402 ). Then, the model generating unit  210  generates an ARX model for each sensory membrane, based on input/output data for each sensory membrane (S 404 ). Then, the feature value computation unit  220  subjects the ARX model for each sensory membrane to Z-Transform (S 406 ). Further, the feature value computation unit  220  computes a first-order lag transfer function feature value (a i , b i ) for each sensory membrane by subjecting, to partial fraction decomposition, a result of subjecting the ARX model to Z-Transform (S 408 ). The processing in S 402  to S 408  is similar to the processing in S 102  to S 108  in  FIG. 4 . 
     Then, the feature value computation unit  220  computes a ratio (b iK /b iL ) of the first-order lag transfer function feature values b,s related to an adsorption rate of the smell component i, with regard to the sensory membrane K and the sensory membrane L (S 410 ). The individual difference correction unit  250  determines a correction coefficient for correcting an output value of either one of the sensory membrane K and the sensory membrane L, based on the ratio (b iK /b iL ) between a first-order lag transfer function feature value b iK  and a first-order lag transfer function feature value b iL  acquired by the processing in S 410  (S 412 ). Specifically, the individual difference correction unit  250  determines a reciprocal number of b iK /b iL  as a correction coefficient for the output value of the sensory membrane K, and stores the determined correction coefficient in a memory  1060  or the like. Alternatively, the individual difference correction unit  250  may determine b iK /b iL  as a correction coefficient for the output value of the sensory membrane L, and stores the determined correction coefficient in the memory  1060  or the like. Then, the individual difference correction unit  250  corrects the output value of the sensory membrane K or the sensory membrane L by use of the correction coefficient determined in S 412  (S 414 ). 
     In the present example embodiment, an individual difference for each sensory membrane is corrected by way of software. Variation in accuracy of an analysis of the sensor  10  can be prevented by equalizing performance of each sensory membrane. 
     While the example embodiments of the present invention have been described above with reference to the drawings, the present invention should not be limited to the example embodiments and interpreted accordingly, and various modifications, improvements, and the like can be made based on knowledge of a person skilled in the art without departing from the spirit of the present invention. Various inventions can be formed by a suitable combination of a plurality of components disclosed in the example embodiments. For example, some of all the components indicated in the example embodiments may be deleted, or components in differing example embodiments may be suitably combined. Moreover, in each example embodiment, an order of illustrated processes (steps) can be altered to the extent consistent with contents.