JPH0210993B2 - - Google Patents

Description

[Detailed Description of the Invention] [Summary] Development of a paper sheet validating machine that collects data on paper sheets including banknotes and performs various processes in developing a validating machine for the paper sheets. In order to improve the time and accuracy required for creating the discrimination logic in the device, a dictionary for the discriminant functions is created from a database, and a frequency table for each group of the discriminant functions is created from the database. , it is disclosed that a threshold value is determined based on this table, thereby automatically creating a classification logic having a desired classification rate.

[Industrial Application Field] The present invention relates to a paper sheet identification machine development device, and in particular, to a paper sheet identification machine development device, which collects arbitrary data to create a paper sheet database, and automatically performs processing based on the data on the database. The present invention relates to a paper sheet validator development device used in a sheet validator development device that configures and evaluates a classification logic.

[Conventional technology]

Conventionally, recognition machines for paper sheets including banknotes have been developed. When examining the classification rate by the classification machine, in the case of conventional technology,
As shown in Figure 2, 100 paper sheets to be identified
Data similar to that used for discrimination by the discrimination machine under development is sent to a database 103 via a data collector 101 and a data editor 102.
Collect a large amount of paper sheets above, then create a group-based dictionary table 104, and use the discrimination logic and threshold table 105 corresponding to the classification machine under development to determine how accurately the paper sheet can be classified by the classification machine under development. The determination unit 106 was designed to check this.

To describe the above matter more specifically, for example, the following judgments were made.

(i) First, for a “randomly sampled sample”, the squares (zones/
data α (color, amount of reflection by color,
A dictionary table for each group (average and variance for each group) is created based on the category (denomination, direction of injection, etc.) determined for each specimen.

(ii) The values of the group-specific dictionary table created as above are used as "predetermined group-specific discriminant functions"
Calculate the coefficients of the discriminant function.

(iii) Read the square data α for each banknote to be classified, calculate the discriminant function value corresponding to the group according to the category at that time, find the frequency of appearance, and calculate the classification rate using the threshold determined according to the frequency distribution. Measure whether the product is good or bad.

That is, a method has often been adopted in which a preferable discrimination mode is set "in advance," so to speak, and it is checked to what extent the discrimination mode can be correctly identified.
In other words, a decision logic is created in advance using an arbitrary small sample, and a large amount of collected data is passed through this logic to measure the classification rate/misclassification rate, confirm the performance, and trace the cause of performance deterioration. It was on. Furthermore, the second
In the figure, 110 corresponds to a data processing device.

[Problem that the invention seeks to solve]

Conventional discrimination logic is created by deriving a discriminant function from a small sample randomly selected from a database, creating a frequency table using this for the entire database, and calculating the desired discrimination rate from this frequency table. The discrimination logic was created using the value as the threshold. That is, as mentioned above, a dictionary table for each group is created using randomly selected samples, the coefficients of the discriminant function are calculated based on the contents of the table, and the good/bad classification rate is measured based on the coefficients. Because I was
If the results of the classification rate are unsatisfactory, it is necessary to re-extract the sample (of course, the dictionary table is updated and the group-by-group discriminant function needs to be recalculated), or the threshold value must be changed. I had no choice but to repeat trial and error. Therefore, the creation of a small sample, the derivation of a discriminant function, and the determination of a threshold value are independent tasks, resulting in a time-consuming problem. Furthermore, in order to derive a discriminant function using a small sample, a small sample is used that is not guaranteed to have the same accuracy as the database, and therefore an inaccurate discriminant function is derived. A problem arose.

The inventor of the present application has previously disclosed, for example, Japanese Patent Application Laid-Open No. 60-180086
No., Special Application No. 1987-180087, Special Application No. 181446,
When developing a paper sheet recognition machine in Japanese Patent Application No. 60-181447, Japanese Patent Application No. 60-230702, etc., we considered the possibility of simulating the processing operations of various recognition machines. We are proposing a paper sheet identification machine development system.

The paper sheet identification machine development system collects a large amount of data at each surface position on paper sheets,
Data with undesired values are automatically excluded from the collected data, and a data group that will be read by the discriminator to be developed is extracted and generated, allowing various simulations to be performed.

Even in the above-mentioned paper sheet discriminator development system, it is desirable to solve the above-mentioned inaccurate discriminant function and the undesirable problem of group division.

[Means for solving problems]

The present invention solves the above-mentioned problems, and while obtaining a database, automatically and appropriately selects a group and achieves a predetermined classification rate without adding any artificial judgment or operation. By performing processing to obtain the desired discriminant function, the desired discriminant function can be derived and the threshold value can be determined.

FIG. 1 shows a principle diagram of the present invention. Reference numeral 1 in the figure represents paper sheets including banknotes. Reference numeral 2 is a data collector, which has a function of reading information about set paper sheets using a plurality of sensors. 3 is a data editor that stores information that cannot be read by the sensor of the data collector (setting direction, type, lot, surrounding environment, etc. when paper sheets are set in the data collector 2). It has a function to add data to the data read. Reference numeral 4 denotes a database in which data of paper sheets to be processed by the data processing device 110 is stored.

5 is a primitive dictionary creation unit, which has a function of creating a primitive dictionary based on sensor data for each additional information based on the information added to paper sheet data on the database. ing. The original dictionary will be described later in FIG. 7, and stores information corresponding to individual meshes on paper sheets as a result of collecting information on a large amount of paper sheets.

Reference numeral 7 is a group conversion table for deriving the group to which the sheet belongs based on the added information (setting direction, type, etc. for paper sheets).

Reference numeral 6 denotes a group dictionary creation unit, which has a function of synthesizing a group dictionary from the original dictionary created by the original dictionary creation unit 5 using the group conversion table 7.

Note that according to 5, 6, and 7 above, the dictionary creation unit 120
is configured.

Reference numeral 8 denotes a discriminant function frequency table creation unit, which has a function of creating a frequency table of each discriminant function for each group to which paper sheets on the database belong. 10 is a set value and indicates a desired classification rate. 9 is
It is a threshold value creation unit and has a function of deriving a threshold value that achieves the classification rate of a set value. Note that the threshold value creation processing unit 130 is constituted by the above-mentioned 8 and 9.

Reference numeral 11 denotes a threshold table in which thresholds of discriminant functions corresponding to each classification rate for each group are stored.

[Effect]

In the data processing device 110, all data
A dictionary is created using the base (contents of 4), and then the threshold value creation of the discriminant function corresponding to each classification rate for each group is instructed and determined by setting value 10. For this reason, we have solved the conventional problem of creating differential logic based on small specimens, and
Continuous operation reduces processing time.

〔Example〕

Examples will be explained below, and the classification rate and misclassification rate in the explanation are classification rate = (number of groups identified as group x) / (group x
misidentification rate = (number of groups identified as groups other than group x)/(total number of groups x) (where x: any group).

FIG. 3 shows the configuration of an embodiment of the banknote data collector, in which FIG. 3A is a side view, FIG. 3B is a plan view, and FIG.
Figure C shows a configuration diagram of the roller. Reference numeral 14 in the figure is a feeding section, which supplies the banknote 18 to the data collection section 15 when the banknote 18 is inserted. The data collection unit 15 includes a roller 19 as shown in FIGS. 3B and 3C.
is generated, and the banknote 18' is conveyed on the conveyance path 20 as shown by the arrow. The conveyance path 20 is shown in FIG. 3B.
As shown, it has a width that is sufficiently large compared to the distance in the longitudinal direction of the banknote 18'. On the conveyance path 20, banknote entry detection sensors S ₁ , S ₂ , 21 and passage detection sensors 22 are provided, and data collection sensors 23 are arranged symmetrically with respect to the vertical center line shown in FIG. 3B. has been done. A track detection sensor 24 is also provided as shown.

As described above, since the width of the conveyance path 20 is sufficiently large compared to the longitudinal length of the banknote 18', the upper end of the banknote 18' is at the upper end of the conveyance path 20 shown in FIG. 3B. The banknotes 18' can be placed in any desired position, from a state in which the banknotes 18' are set and transported so that they touch each other, to a state in which the banknotes 18' are set and transported in such a way that the lower end of the banknotes 18' touches the lower end of the transport path 20 shown in FIG. It is possible to set Corresponding to these set positions, which position on the banknote 18' is sensed by the data collection sensor 23 is determined.

As shown in FIG. 4, the top of the banknote 18' is divided into m×n grid areas 25, and the _grid areas arranged in the horizontal direction as shown in _FIG . If the grid area groups arranged in the vertical direction in FIG. 4 are defined as zones Z ₁ , Z ₂ , Z ₃ . While the banknote 18' is being conveyed, the data collection sensor 23 collects data on the square areas on the two upper and lower tracks. And banknote 1
8' on the conveyance path 20, each of the square areas 25 within a predetermined range on the banknote 18' is
5 data can be collected. The data in the hatched square area 25 in FIG. 4 is extracted at the transport timing corresponding to zone Z ₂ while one of the sensors 23 is collecting data along the track T _i+1 . be done. At this time, the sensor 23
Which track corresponds to can be determined by which position of the track detection sensor 24 the end of the banknote 18' passes through.

The banknotes that have been conveyed through the data collection unit 15 are stored in the illustrated storage unit 16 as shown in the illustrated banknotes 18″.The data collected during the above-mentioned conveyance is sent from the control unit 17 to the banknotes illustrated in FIG. The data is transferred to the data editor 3.

FIG. 5 shows the configuration of an embodiment of the data editing device. Reference numeral 26 in the figure is a control unit (processor);
27 represents a data transmission means that receives data from the data collector. 28 is a data input section, which corresponds to each data transferred from the data collector 2, (i) data on what denomination the banknote 18' corresponds to;
(ii) Which direction does the data correspond to when the banknote 18' is set (there are two orientations for the front side and two orientations for the back side)? (iii) What environmental conditions were there at the time of data collection? (iv) Which banknote production lot does the banknote 18' correspond to? These additional data are added to the data input unit 2.
It is input from 8.

29 is a data display section, 30 is a ROM writer, 3
1 is the data store section (floppy disk 4
).

Data 32 transferred from data collector 2
As shown in the upper part of FIG. 6, (i) skew amount and setting direction information, (ii) length information of data 32, (iii) conveyance speed information of banknote 18', (iv) track position information, ( v) sense data, having a data format with . Then, additional data is added to the data 32 from the data input section 28 shown in FIG. The information is stored in the database 4 above.

Original dictionary creation unit 5 in data processing device 110
reads the paper sheet data on the database 4 one by one, classifies the data based on the additional information on the data and the track position information, and divides the data into the minimum division unit (unit... shown in Figure 4). Shaded area 2
5), the number N of data for each zone, the cumulative sum ΣX for each zone, and the cumulative sum of data for each zone = multiplicative sum ΣX ² are held on the table as shown in FIG.

The group-specific dictionary creation unit 6 uses a group conversion table 7 representing the correspondence between additional information and track position information and group numbers determined correspondingly, as shown in FIG. The average value M and the variance value V are calculated and stored in a group dictionary table as shown in FIG.
Incidentally, in assigning the group numbers G ₁ , G ₁ , . . . Gl in correspondence with the additional information and the track signal information as described above, it is performed as follows. That is, the seventh
Examine the contents of the original dictionary table shown in the figure. For example, the average value for each zone for the contents of the first table shown in FIG. 7 and the average value of the contents of the second table shown in FIG. If the average values for each zone tend to be similar across all zones, the first table and the second table above are treated as belonging to the same group _G1. Do it like this. When assigning group numbers in this way, it may be done manually by collecting those that are intuitively similar, or it may be done by manually collecting groups that are intuitively similar, or by creating a table with the approximate contents using an appropriately prepared discriminant function. They may be grouped together in the same group.

The average value M and variance value V at this time are derived as follows.

N ₁ : Number of data when denomination 1, direction 1, track T ₁ , x ₁ : Data value when denomination 1, direction 1, track T ₁ , N ₁ ': Denomination 1, direction 1, track Number of data when T ₂ , x ₁ ′: Data value when denomination 1, direction 1, track T ₂ M ₁₁ = 1/N ₁ +N ₁ ′+……・( _N1 〓 X ₁ + _N1 ′ 〓 X ₁ ′+… ⁾ V ₁₁ =1/N ₁ + _{N 1} ′+… ・( _{N1 〓} X ₁ ^{2 +} N1 _′ 〓 Pre-prepared discriminant F _a (M _a1 , M _a2 ,..., M _an , V _a1 , V _a2 ,
..., V _an , X ₁ , X ₂ , ..., X _n ) by substituting the values of the group dictionary to create a group discriminant function. For example, the function of group G1
f ₁ is f ₁ =F ₁ (M ₁₁ , M ₁₂ ,..., M _1n , V ₁₁ , V ₁₂ ,...,
V _1n , X ₁ , X ₂ , ..., X _n ) (where X ₁ to _{X n} are sensor data of zones 1 to m). For example, when using the Mahalanobis distance, the form of the above function Fa is F = (X ₁ − M _a1 ) ² / (γV _a1 ) ² + (X ₂ − _{M a
2} ) ² / (γV _a2 ) ² +……+ (X _o −M _ao ) ² / (γV _ao ) ² −
It will be something like 1. Note that in the above equation, γ represents a threshold value. Moreover, when using Euclidean distance, it is easy to write F=(X ₁ −M _a1 ) ² /γ ² +(X ₂ −M _a2 ) ² /
It becomes something like γ ² +...+(X _o −M _ao ) ² /γ ² −1.

Next, as shown in FIG. 10, the data on the database 4 is read one by one and the groups shown in the group conversion table (FIG. 8) are
Calculate the discriminant function for the group, use the result to add 1 to the number of data for the value shown in the frequency table, and calculate the data.
A frequency table is created by performing the same processing on the entire base data.

The threshold creation unit 9 derives thresholds that satisfy the following conditions for each group from the frequency table for each discriminant function and the set value (S) that indicates the classification rate from the outside, and stores them in the threshold table 11.

The threshold value K ₁ of the discriminant function of group 1 in the case of the setting value S is determined as satisfying S( _K1 〓 ^k=1 f _1k )/( _h 〓 ^k=1 f _1k ).

With the above configuration, the discriminant function necessary for the discrimination logic and the threshold value for obtaining the desired discrimination rate are derived when creating the database.

〔Effect of the invention〕

According to the present invention, a highly reliable identification logic is obtained since it is based on the entire database. Furthermore, since the process up to the creation of the threshold value is a series of continuous operations, the classification logic can be created in a short time, and the classification logic can be obtained with a classification rate according to external settings.

[Brief explanation of drawings]

FIG. 1 is a diagram explaining the principle of the present invention, FIG. 2 is a diagram explaining a conventional example, and FIGS. 3 to 6 are diagrams showing an embodiment from data collection to creation of a database. Figure 7 is a diagram explaining the original dictionary table, Figure 8 is a diagram explaining the group conversion table, Figure 9 is a diagram explaining the group-specific dictionary table, and Figure 10 is a diagram explaining creating a frequency table. . In the figure, 1 is a paper sheet, 2 is a data collector, 3 is a data editor, 4 is a database, 5 is a primitive dictionary creator, 7 is a group conversion table, 8 is a discriminant function frequency table creator, 9 10 represents a threshold value creation unit, 10 represents a set value, and 11 represents a threshold value table.

Claims (1)

[Scope of Claims] 1. A data collector 2 that reads information about set paper sheets 1 including banknotes using a plurality of sensors; A data editing device 3 that adds additional data related to data, a database 4 that holds information edited by the data editing device 3, and an information processing function that processes information in the database 4. The information processing function section includes at least a data processing device 110 having a data processing function section;
In a paper sheet validating machine development device that performs a simulation corresponding to the operation of a paper sheet validating machine to be developed using the contents of For each of N groups G1, G2,... (N is an integer of 2 or more) to which the above-mentioned paper sheets belong,
It is equipped with a group dictionary creation unit 6 that creates a dictionary that retains the group features extracted for each group, and the data of each paper sheet in the database is divided into the N numbers based on the dictionary. a discriminant function frequency table creation unit 8 which has a discriminant function for determining which of the groups it belongs to, and which creates a frequency table of values taken by paper sheets of each group in the database of the discriminant function; An apparatus for developing a paper sheet discriminator, characterized in that the information processing function section calculates a threshold value of a discriminant function based on the grouped groups and performs the simulation. ”. 2. The information processing function section has a threshold creation section 9 that uses the frequency table created above to calculate a threshold value for each discriminant function that gives a predetermined classification rate, and automatically creates a classification logic that gives a predetermined classification rate. 2. A paper sheet discriminator development apparatus according to claim 1, wherein said paper sheet discriminator development apparatus creates a paper sheet discriminator.