JPH10160665A - Method and device for evaluating washing degree utilizing fuzzy principle - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a washing degree evaluation method which utilizes fuzzy principle which is washing-degree measurable by functional inspection, by introducing mathematical function which uses fuzzy principle, and introducing relation ship between a surface reflectivity measurement value of an artificial contaminated fabric and a measurement value by functional inspection. SOLUTION: Relating to the washing-degree evaluation method, a special panel is selected through a specified discrimination process to train and control the panel during specified period (310 and 320). Further, an existing machine value and a sensitivity value measured with the panel are compared to each other for analysis, for comparison and analysis between a reference machine value and washing degree (330 and 340), and according to the comparison result analyzed through the comparison/analysis step, a concentration change graph is generated to know a washing-degree recognition capability (350 and 360). Then, with the use of sensitivity value measured with the special panel, a machine value is anticipated (370) and a sensitivity value is anticipated with the machine value (380).

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and an apparatus for evaluating the degree of cleaning using the fuzzy principle, and more particularly to the evaluation of the degree of cleaning as an index for improving the performance of washing by a washing machine or the like using the fuzzy principle. The present invention relates to a cleaning degree evaluation method and a device using a suitable fuzzy principle.

[0002]

2. Description of the Related Art FIG. 1 is a view showing a general apparatus for evaluating washing performance through image analysis of an artificially stained cloth according to the prior art. Referring to FIG. 1, an operation process for evaluating the washing performance through image analysis of the artificially stained cloth will be described. First,
The surface reflectance before and after the contamination of the artificially contaminated cloth 100 is measured through the spectrophotometer 102, and the measured artificially contaminated cloth 100 is measured.
The washing performance, that is, the cleaning degree D, is calculated through the computer 104 on the basis of the surface reflectance.

More specifically, first, the surface reflectance R of the original cloth is passed through a spectrophotometer 102 before the artificial contamination cloth 100 is contaminated.
is measured, and then the surface reflectance Rs of the contaminated artificially stained cloth 100 before washing is measured through a spectrophotometer 102 to evaluate the washing performance. Thereafter, a washing experiment is performed according to an experimental method specified under predetermined conditions, and the surface reflectance Rw of the washed artificially stained cloth 100 after washing is measured.

The degree of cleaning D is calculated by the following mathematical formula 1 based on the surface reflectances Ro, Rs, and Rw measured through the above-described measurement process.

[0005]

(Equation 1) The washing performance is evaluated based on the cleaning degree D.

[0006]

However, when the degree of cleaning is evaluated using the surface reflectance, it is difficult to set the wavelength of the spectrophotometer 102, and when the artificially stained cloth 100 is a colored cloth, There is a problem that the cleaning degree evaluation method using the surface reflectance cannot be applied. In addition, the need to develop products in response to consumer demands has led to a situation in which sensory evaluation using human sensibility as a sensor should be introduced in the measurement of cleanliness.

[0007] In other words, there is a difference between the evaluation of the degree of cleaning perceived by the consumer's sensibility (ie, visual sense) and the evaluation of the degree of cleaning based on the measurement of the mechanical value for the artificially contaminated cloth, ie, the surface reflectance. For example, if it can be classified only when the evaluation value of the cleaning degree is 2 or more due to the visual limit of the consumer, the evaluation value of the cleaning degree based on the mechanical value (the cleaning degree based on the surface reflectance) is classified in one unit. In such a case, since the consumer cannot perceive the change of one unit sensibly, there is no point in improving the mechanical strength.

SUMMARY OF THE INVENTION The present invention has been made to solve the above various problems, and an object of the present invention is to introduce a mathematical function using the fuzzy principle to measure the surface reflectance of an artificially stained cloth. It is an object of the present invention to provide a cleaning degree evaluation method using the fuzzy principle and a device therefor that can measure the cleaning degree by the sensory inspection by deriving the association between the value and the measurement value obtained by the sensory inspection.

[0009]

SUMMARY OF THE INVENTION In order to achieve the above-mentioned object, a cleaning degree evaluation method using the fuzzy principle according to the present invention is a cleaning degree evaluation method for evaluating the cleaning degree using the fuzzy principle. Selecting a specialized panel through a predetermined identification process, training and managing the panel for a predetermined period of time, and comparing and analyzing existing mechanical values and sensitivity values measured by the panel to obtain reference mechanical values. And a step of comparing and analyzing the degree of cleansing, a step of creating a concentration change table according to the comparison result analyzed through the comparative analysis step, and grasping the ability to recognize the degree of cleansing, at least the sensitivity value measured by the specialized panel And estimating a sensibility value using the machine value. .

Here, the mechanical value predicting step includes the steps of measuring a sensitivity value of the degree of artificial contamination through the selected specialized panel, and measuring a mechanical value of the artificially stained cloth through mechanical value measuring means; Constructing a defuzzification function using the fuzzy principle after setting the sensitivity value measured through the step as input data and setting the machine value measured through the mechanical value measurement means as output data, respectively, Predicting the machine value using the calculated defuzzification function, and digitizing the sensitivity value by comparing and analyzing the predicted machine value and the measured machine value. The sensitivity value predicting step may include measuring a sensitivity value of the degree of artificial contamination through the selected specialized panel, measuring a mechanical value of the artificially-contaminated cloth through mechanical value measuring means, and measuring the mechanical value through the mechanical value measuring means. Using the obtained mechanical values as input data, setting the sensitivity values measured through the measuring step as output data, and constructing a defuzzification function using the fuzzy principle; and Estimating the machine value as a sensitivity value using a function.

Further, the computer readable storage medium of the present invention is a computer readable storage medium for storing a cleaning degree evaluation program for evaluating a cleaning degree using a fuzzy principle, wherein the cleaning degree evaluation program is an existing one. By comparing and analyzing the mechanical value of the and the sensitivity value measured by the specialized panel,
A step of comparing and analyzing a reference mechanical value and a degree of cleansing, a step of creating a concentration change table according to the comparison result analyzed through the comparative analysis step, and a step of grasping the ability to recognize the degree of cleansing, at least measured by the specialized panel And estimating a mechanical value using the obtained sensitivity value, and predicting a sensitivity value using the mechanical value.

Further, the present invention is a computer-readable storage medium for storing a cleaning degree evaluation program for evaluating the degree of cleaning by using the fuzzy principle, wherein a comparison analysis between existing mechanical values and sensitivity values measured by a specialized panel is performed. Having a concentration change table created according to the result, wherein the cleaning degree evaluation program predicts at least a mechanical value using a sensitivity value measured by the specialized panel and a sensitivity value using the mechanical value Is predicted.

A computer-readable storage medium for storing a cleaning degree evaluation program for evaluating the degree of cleaning using the fuzzy principle, wherein the sensitivity value of the artificial contamination degree measured through a specialized panel and the mechanical value measuring means are measured. And at least one defuzzification function constructed using a fuzzy principle based on the measured mechanical value of the artificially-contaminated cloth, wherein the cleaning degree evaluation program executes the machine-based processing of the artificially-contaminated cloth through mechanical value measuring means. Measuring a value, a step of predicting a machine value using the constructed defuzzification function, and comparing and analyzing the predicted machine value and the measured machine value to obtain a sensitivity value. The method includes a step of digitizing and a step of predicting a machine value as a sensitivity value using the constructed defuzzification function.

Further, the apparatus of the present invention is an apparatus for evaluating the degree of cleaning using the fuzzy principle, and compares and analyzes existing mechanical values with sensitivity values measured by a specialized panel to obtain reference mechanical values. And a cleansing degree are compared and analyzed, a concentration change table is created according to the comparison result, and a machine value is predicted using a cognitive ability grasping means for grasping the cleansing cognitive ability and a sensitivity value measured by the specialized panel. And / or a prediction unit for predicting a sensitivity value using the mechanical value.

An apparatus for evaluating the degree of cleaning using the fuzzy principle stores a concentration change table created according to the result of a comparative analysis between existing mechanical values and sensitivity values measured by a specialized panel. Storage means, input means for inputting the sensitivity value of the artificial contamination degree measured through the specialized panel and the mechanical value of the artificial contamination cloth measured through the mechanical value measurement means, and based on the sensitivity value and the mechanical value A defuzzified function constructing means for constructing a defuzzified function using a fuzzy principle, and a machine value is predicted using the constructed defuzzified function, and the predicted machine value and the measured machine are used. A first predictor for numerically converting the sensitivity value by comparing and analyzing the value, and a second predictor for predicting the machine value as a sensitivity value using the constructed defuzzification function. Characterized in that it comprises.

The above and other objects and various advantages of the present invention will become more apparent to those skilled in the art through the embodiments of the present invention described below with reference to the accompanying drawings.

[0017]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment according to the present invention will be described below in detail with reference to the accompanying drawings. <Example of Configuration of Cleaning Degree Evaluation Apparatus According to this Embodiment> FIG. 2 is a schematic block diagram of hardware for performing a cleaning degree evaluation method using a fuzzy principle according to this embodiment.

In FIG. 2, an input unit 210 includes a sensory evaluation value evaluated by the sensitivity of a predetermined number of specialized panels selected through a predetermined process (an inspector selected for evaluation of the sensitivity value), and a sensory evaluation value. This is for inputting the surface reflectance measured through a reflectance measuring device (for example, a spectrophotometer). In addition, the control unit 220 controls the input unit 2
A defuzzification function is constructed and provided to the fuzzy logic system 230 using each sensory evaluation value according to the sensitivity input through 10 and the surface reflectance measurement value measured by the surface reflectance measurement device. The analysis result analyzed through the fuzzy logic system 230, that is,
A control signal for displaying a predicted surface reflectance value established by the association between the sensory evaluation value and the surface reflectance or a predicted sensory evaluation value predicted by the surface reflectance is generated.

Further, the fuzzy logic system 230
The surface reflectance value and the sensory evaluation value are predicted using the defuzzification function provided from the control unit 220, and the prediction result is provided to the control unit 220. The monitor 240 displays a result predicted through the fuzzy logic system 230 according to a display control signal from the control unit 220.

The present apparatus can be realized by a general-purpose personal computer in which a processor including a CPU, a RAM, and a ROM is connected to input / output devices such as a display, a keyboard, a drive for an external storage medium, and a communication device. In this case, each of the above components corresponds to a program module executed by the processor, and these programs or necessary data are stored in a RAM in a personal computer via a drive from a floppy disk, CD-ROM, MO, or the like.
And may be executed. <Operation Example of Cleaning Degree Evaluation Apparatus of the Present Embodiment> The operation process of the cleaning degree evaluation method using the fuzzy principle according to the present embodiment through the hardware including the above components will be described with reference to FIGS. This will be described in more detail.

First, the basic steps for establishing the sensory evaluation of the cleanliness are as follows. First, based on the consistency coefficient using the fair test of Scheffe, the inspector for the cleanliness evaluation, that is, the specialized panel, is used. Select (Step 31
0). (Explanation of Consistency Coefficient) Here, the consistency coefficient is a method of giving an index of discrimination ability using the number of linear triangles, and will be described below with reference to FIG.

As shown in FIG. 6A, when there are three objects A, B, and C, B is superior to A and B
If it is determined that C is superior to C, then C over A
Of course is determined to be superior. However, as shown in FIG. 6 (B), even though it is determined that B is superior to A and C is superior to B, it is determined that A is superior to C. When it is issued, it is called a normal triangle.

Therefore, as a result of the fair test of the Shafey for the panel, if the number of the above-mentioned straight triangles is equal to or less than the predetermined number, it can be determined that the corresponding panel has consistency in the discriminating ability. The mathematical formula for determining the consistency coefficient ζ is shown in the following mathematical formula 2.

[0024]

(Equation 2) Here, K indicates the number of individuals to be compared, and d indicates the number of linear triangles. At this time, if there is no linear triangle, the consistency coefficient に な る becomes 1. Next, while training and managing the specialized panels (ie, discriminating inspectors) and the like selected through step 310 for a predetermined period of time, the reproducibility of judgment, consistency with standards, and consistency between panels Is evaluated to determine whether to participate in the experiment (step 32).
0).

Further, the existing reflectance is compared with a sensory evaluation value evaluated by a specialized panel or the like (Step 3).
30) The measured value based on the surface reflectance determined to be most consistent with the sensory evaluation is set as a reference scale, and then the degree of cleaning is compared and analyzed (step 340). For more information,
A concentration change table is prepared for comparing and analyzing the measured values associated with the sensory evaluation (step 350). For example, the state of the artificially stained cloth that has not been washed at all is 0%, and the state of the artificially stained cloth that has been completely washed is 0%. The state is set as 100%, the intervals are divided into 10%, and a total of 11 levels of density change tables are created, and then, the ability of recognizing the degree of cleaning for various artificially contaminated cloths is grasped (step 360).

Further, a surface reflectance value is predicted using the sensory evaluation value (step 370), and a sensory evaluation value is predicted using the surface reflectance value (step 380). (Example of Predicting Surface Reflectance Value) Next, the operation process of predicting the surface reflectance value using the sensory evaluation value in step 370 will be described in detail with reference to FIG.

First, a predetermined number (for example, 618 artificially stained cloths) of each of the artificially stained cloths subjected to a sensory evaluation by a predetermined number of expertly trained (for example, 10) expert panels are used. The evaluation value is measured (step 371), and the surface reflectance of the same artificially contaminated cloth is measured through the surface reflectance measuring device (step 37).
2).

Next, the sensory evaluation value and the surface reflectance measured through steps 371 and 372 are input through the input unit 210. At this time, the sensory evaluation value measured by the panel is used as input data, and the surface reflectance is calculated. The surface reflectance measured through the measuring device is set and input as output data (step 373). As an example, Table 1 below shows that a predetermined number (618 sheets) of
It shows the correspondence between each sensory evaluation value measured by a specialized panel or the like of a person (X ₁ , X ₂ ,..., X ₁₀ ) and the surface reflectance (Y) measured by the surface reflectance measuring device.

[0029]

[Table 1] Hereinafter, the values of the sensory evaluation value and the surface reflectance in Table 1 above are represented by (X ₁ ⁽¹⁾ , X ₂ ⁽¹⁾ ,..., X _n ⁽¹⁾ ; Y ⁽¹⁾ ), (X ₁
⁽²⁾ , X ₂ ⁽²⁾ ,..., X _n ⁽²⁾ ; Y ⁽²⁾ ). Here, the input data is a predetermined number of artificially contaminated cloth (for example, 6
The sensory evaluation value for each of the 18 artificially stained cloths), and the output data is the measured surface reflectance.

Next, the control unit 220 constructs a defuzzification function using the input data and the output data inputted through the input unit 210 (step 374), and converts the defuzzification function constructed through step 374. Provided to the fuzzy logic system 230. (Example of constructing a defuzzification function) Here, an operation process of constructing a defuzzification function through the control unit 220 will be described in detail.

First, after arbitrarily dividing between the minimum value and the maximum value of each variable and assigning a fitness function, a fuzzy set indicating the maximum fitness value for a given input is used. To form one rule. Inconsistent rules (for example, the same rule for the former but a different rule for the latter) and the like exist between the rules determined through such a method, but the former is used to select only one of them. A rule is used in which the square of the fitness value determined by the above and the fitness value of the latter is the maximum.

The mapping from the input data X to the output data Y can be obtained by using the above rules. The defuzzification function used at this time is as shown in the following mathematical formula 3.

[0033]

(Equation 3) If the fitness function is defined as a Gaussian function, the defuzzification function of Equation 3 is expressed as Equation 4 below.

[0034]

(Equation 4) Here, / x ^l _i and σ ^l _i indicate the average value and standard deviation of the Gaussian function used in the former for the i-th variable of the l-th rule, respectively. Can be adjusted. / y ⁱ is defined as the median of the fuzzy set defined in the latter.

On the other hand, due to contradictory rules, etc.
Only a few rules are derived from the obtained data pair,
Data may not be fully reflected
However, in order to prevent such a case, 1 rule-1 data
Use the law. The 1 rule-1 data pair method is the number
A non-fuzzified function such as shown in Equation 4 is used for input / output nonlinear functions.
As a system variable that determines the number relationship, the Gaussian function
Mean and median of the latter function (/ x^l _i, / Y^l )instead of
Given input / output data (x ^l _i, Y^l ), I = 1,
By substituting 2, ..., n, l = 1,2, ..., N
It can be expressed as in Mathematical Formula 5.

[0036]

(Equation 5) The above method has an advantage that all information including a pair for deriving a function can be used, since one input / output data pair forms a rule while having a median value. Although having disadvantages that are involved, such disadvantages are solved using adaptive optimal fuzzy logic described below.

That is, the adaptive optimal fuzzy logic is a method of clustering given input / output data using a diagram having a certain value r as a radius, thereby forming a data group 1
One as one rule. First, the median value x ^l ₀ / of one cluster is changed to x ^l / using the first input / output data, and the radius of the cluster is r
The newly introduced weight variables are defined as follows. That is, A ^l (1) = y ^l , B ^l (1) = 1.

Next, assuming that K (K = 2, 3,...) Data pairs are taken into account, x ₀ ¹ /, x ₀ ² /, x ₀ ³ /.
, X ₀ ^M / median clusters now exist, and the distance from the input data x ^k / to the median of such clusters is | x ^k / −x ₀ ¹ / |, I = 1,2, ...,
Calculate as M and determine the most recent cluster | x ^k / −x ₀ ^ik / |.

At this time, if | x ^k / −x ₀ ^ik / |> r, x ^k / is set as the median value of the new cluster. In other words, x ₀ ^{M + 1} /. Further, the weight value is set as in the following mathematical formula 6.

[0040]

(Equation 6) If | x ^k / −x ₀ ^ik / | ≦ r, it is as shown in the following mathematical formula 7.

[0041]

(Equation 7) Finally, the output of the k-th adaptive optimal fuzzy logic is calculated using the following mathematical formula 8.

[0042]

(Equation 8) Next, the fuzzy logic system 230 predicts the surface reflectance value using the defuzzification function (Equation 5) provided by the control unit 220 (step 375), and sends the prediction result to the control unit 220. provide.

Further, the control unit 220 compares and analyzes the surface reflectance predicted by the fuzzy logic system 230 with the surface reflectance measured by the surface reflectance measuring device (step 376), and displays the same. A control signal is generated, and a result of the comparison and analysis is displayed on the monitor 240 through the control unit 220 based on the control signal for display from the control unit 220.

Therefore, the fuzzy logic system 230
The analysis showed that the predicted surface reflectance measurements showed a predetermined pattern with increasing number of panels, and that such patterns were very close to the actual surface reflectance measurements. . As a result, it is possible to present a sensory evaluation that can replace the existing mechanical cleanliness measurement method (step 377).

When the sensory evaluation value is switched to the measurement value based on the surface reflectance by using the defuzzification function in the same manner as in the above-described method, the minimum surface that the consumer can feel sensuously (visually) is obtained. An increase in reflectivity can be sought, which means a substantial performance improvement from the consumer's perspective. If a formula for calculating the ripple effect of the uncertainty is used to express this as a mathematical expression, n _i (x _i ) is the range of the degree of cleaning that can be recognized in the sensory evaluation of the i-th panel. In this case, the recognizability by the following mathematical expression 9 is represented by the surface reflectance value f (x ₁ , x ₂ ,..., X) in the form represented by the mathematical expression 10.
_i ) is reflected.

[0046]

(Equation 9) As an example, under the three prediction conditions shown in FIGS. 7A, 7B, and 7C, it is assumed that only FIG. 7C is waiting for a performance improvement felt by consumers and the like. Can be.

On the other hand, unlike the case where the sensory evaluation value is switched to the surface reflectance value using the above-mentioned mathematical expression 5, the mathematical expression 8
When the surface reflectance is switched to the sensory evaluation value using
By substituting only the surface reflectance value into Equation 8,
Performance improvement can be evaluated from a sensory aspect. The above operation process is represented by the following equation (10).

[0048]

(Equation 10) Here, Rf is a surface reflectance value, δ is a standard deviation of the surface reflectance, and is a constant having a relationship independent of the surface reflectance. The meaning of the above mathematical expression 10 includes that development is meaningful only when the cleansing degree σ {δF (Rf) / δRf} after development is larger than the range of consumer recognition.

(Example of Predicting Sensory Evaluation Value) Next, step 3
The operation process of estimating the sensory evaluation value using the surface reflectance value of 80 will be described in detail with reference to FIG. 5, steps 381 and 382 correspond to steps 371 and 37 shown in FIG.
2 is actually performed in the same manner, and the overlapping description is omitted.

Next, the sensory evaluation value and the surface reflectance measured through steps 381 and 382 are input through the input unit 210. At this time, the surface reflectance measured through the surface reflectance measuring device is used as input data. Then, an average of the sensory evaluation values measured by the panel is set and input as output data (step 383). Thereafter, the control unit 220 constructs a defuzzification function using the input data and the output data input through the input unit 210 (step 384), and converts the defuzzification function constructed through step 374 into a fuzzy logic system. 230
To provide.

Here, the operation process in which the defuzzification function is constructed through the control unit 220 is substantially the same as the operation process described above, and will not be described here. Next, the fuzzy logic system 230 uses the defuzzification function provided from the control unit 220 to predict the sensory evaluation (step 385), and provides the sensory evaluation to the control unit 220. According to the control signal for displaying, the prediction result provided from the fuzzy logic system 230 through the monitor 240 is displayed.

For example, of the three cases shown in FIGS. 8A, 8B, and 8C, only the case of FIG. 8C shows that the consumer feels the improvement of the washing performance with the skin. Thus, the surface reflectance value can be switched to the sensory evaluation value (step 386). In this way, since the sensory evaluation value of the initial specialized panel can be used as a database, it is not necessary to perform the measurement by the specialized panel every time the sensory evaluation value is measured.

As described above, the sensory evaluation value measured by the specialized panel through the input unit 210 is predicted as the surface reflectance through the fuzzy logic system 230, and the surface reflectance predicted through the fuzzy logic system 230 is calculated by the control unit. Displayed on the monitor 240 according to the control signal for display from 220. Also, when the surface reflectance is input through the input unit 210, the sensory evaluation value measured by the above-mentioned specialized panel is made into a database, and is predicted as the sensory evaluation value through the fuzzy logic system 230 and provided to the control unit 220. According to the control signal for display from the unit 220, the sensory evaluation value predicted through the fuzzy logic system 230 is displayed on the monitor 240.

[0054]

As described above, when the present invention is used, not only can the correlation between the surface reflectance and the sensory evaluation value be investigated, but also the functional relationship for predicting the sensory evaluation value due to human sensitivity as the surface reflectance value. Is obtained, the sensitivity is controlled through this, and there is an effect of maximizing the sensitivity.

Also, by clustering the data, it is possible to prevent duplication of information and to give consistency to the data. If information on sensory evaluation values measured by a specialized panel is stored in a database, simple It has the effect of predicting human sensitivity evaluation with mechanical measurements.

[Brief description of the drawings]

FIG. 1 is a schematic configuration diagram of a system for evaluating washing performance through image analysis of an artificially stained cloth according to the related art.

FIG. 2 is a schematic block configuration diagram of hardware for performing a cleaning degree evaluation method using a fuzzy principle according to the present embodiment.

FIG. 3 is a flowchart for explaining an operation process of determining a cleaning degree using a fuzzy principle according to the present embodiment.

FIG. 4 is a flowchart for explaining an operation process for digitizing sensibility according to the present embodiment.

FIG. 5 is a flowchart for explaining an operation process of sensitizing a numerical value according to the present embodiment.

FIG. 6 is a diagram for explaining a consistency coefficient for evaluating the discriminating ability of the artificially stained cloth using the number of straight triangles.

FIG. 7 is a diagram for improving the cleaning degree in a mechanical sense.

FIG. 8 is a diagram for improving the cleansing degree in the sense of sensibility.

[Explanation of symbols]

 210 input unit 220 control unit 230 fuzzy logic system 240 monitor

Continuing on the front page (72) Inventor Anmi Ei 282, Moka 2-dong, Mapo-gu, Seoul, Republic of Korea 17/10 (72) Inventor Lee Seung-sang Hyundai Apartment 202-803, Hseon-seon-gu, Suwon-si, Gyeonggi-do, Republic of Korea

Claims (8)

[Claims] 1. A cleaning degree evaluation method for evaluating a cleaning degree using a fuzzy principle, comprising the steps of selecting a specialized panel through a predetermined identification process, training and managing the panel for a predetermined time, Comparing and analyzing the mechanical value of the panel and the sensitivity value measured by the panel, and comparing and analyzing the reference mechanical value and the degree of cleansing; and creating a concentration change table according to the comparison result analyzed through the comparative analysis step. Grasping the washing level cognitive ability, at least one of the step of predicting the mechanical value using the sensitivity value measured by the specialized panel and the step of predicting the sensitivity value using the machine value And a cleaning degree evaluation method using the fuzzy principle. 2. The step of predicting the mechanical value comprises: measuring a sensitivity value of the degree of artificial contamination through the selected specialized panel; and measuring a mechanical value of the artificially stained cloth through mechanical value measuring means. Setting the sensitivity value measured through the above as input data, and setting the mechanical value measured through the mechanical value measuring means as the output data, and then constructing a defuzzification function using the fuzzy principle. Predicting the machine value using the defuzzified function, and comparing and analyzing the predicted machine value and the measured machine value, thereby digitizing the sensitivity value. The cleaning degree evaluation method using the fuzzy principle according to claim 1. 3. The sensitivity value predicting step includes: measuring a sensitivity value of the degree of artificial contamination through the selected specialized panel; and measuring a mechanical value of the artificially stained cloth through a mechanical value measuring unit; Using the mechanical value measured through the means as input data, and setting the kansei value measured through the measuring step as output data, and then constructing a defuzzification function using the fuzzy principle; And estimating the mechanical value as a sensitivity value using a defuzzification function. 4. A computer-readable storage medium storing a cleaning degree evaluation program for evaluating a cleaning degree using a fuzzy principle, wherein the cleaning degree evaluation program is measured by an existing machine value and a specialized panel. A step of comparing and analyzing the sensitivity value and comparing and analyzing the reference mechanical value and the cleaning degree; and a step of creating a concentration change table according to the comparison result analyzed through the comparative analysis step and grasping the cleaning degree recognition ability. A computer comprising: at least one of a step of predicting a machine value using a sensitivity value measured by the specialized panel and a step of predicting a sensitivity value using the machine value. A readable storage medium. 5. A computer-readable storage medium for storing a cleaning degree evaluation program for evaluating a cleaning degree using the fuzzy principle, wherein the computer-readable storage medium is used for comparing and analyzing existing mechanical values and sensitivity values measured by a specialized panel. Having a concentration change table created according to the result, wherein the cleaning degree evaluation program at least predicts a mechanical value using a sensitivity value measured by the specialized panel and a sensitivity value using the mechanical value A computer-readable storage medium comprising any one of the steps of: 6. A computer-readable storage medium for storing a cleaning degree evaluation program for evaluating the degree of cleaning using the fuzzy principle, wherein the sensitivity value of the artificial pollution degree measured through a specialized panel and the mechanical value measurement means are provided. The apparatus has at least one defuzzification function constructed using a fuzzy principle based on the measured mechanical value of the artificially-contaminated cloth, and the cleaning degree evaluation program includes: Measuring a value, predicting a machine value using the constructed defuzzification function, and comparing and analyzing the predicted machine value and the measured machine value to obtain a sensitivity value. A numerical value; and a step of predicting a machine value as a sensitivity value using the constructed defuzzification function. Data readable storage medium. 7. An apparatus for evaluating the degree of cleansing using the fuzzy principle, wherein a comparison is made between an existing mechanical value and a sensitivity value measured by a specialized panel, and a reference mechanical value is compared with the cleansing degree. Analyze,
A concentration change table is created in accordance with the comparison result, and a cognitive ability grasping means for grasping the cleansing degree recognizing ability, and a machine value is predicted using the sensitivity value measured by the specialized panel, and / or the machine value is used. An estimating means for estimating the sensitivity value. 8. An apparatus for evaluating a degree of cleaning using a fuzzy principle, wherein a concentration change table created according to a result of a comparative analysis between existing mechanical values and sensitivity values measured by a specialized panel is stored. Storage means, input means for inputting the sensibility value of the degree of artificial contamination measured through the specialized panel and the mechanical value of the artificially stained cloth measured through the mechanical value measurement means, and based on the sensibility value and the mechanical value Defuzzification function construction means for constructing a defuzzification function using a fuzzy principle, and a machine value is predicted using the constructed defuzzification function, and the predicted machine value and the measured machine are used. A first predictor for quantifying the sensibility value by comparing and analyzing the value, a second predictor for predicting the machine value as a sensibility value using the constructed defuzzification function; Device, characterized in that it comprises.