**4.2.2 Description of cognitive task factors**

Cognitive task (CT) questionnaire is prepared to assess the cognitive task among the workers in (I.T.O power plant station, centrifugal pump industry WPIL India Limited ,and Shriram Piston & Rings Lt.) Industries. This is self-administered questionnaire consists of 55-items. The operators were asked to respond to each and every item of questionnaire by giving subjective opinions from strongly disagrees to strongly agree. The items of the questionnaire were classified into the following factors.

The first factor is skill discretion, described by (possibility of learning new things, repetitive nature of the work, creative thinking at work, and high level of skill, time span of activities and developmental nature of job). The second factor is decision authority, described by (lot of say on job, freedom to take own decisions while working, continual dependence on others). Third scale is organizational decision latitude, described by (influence over organizational changes, influence over work team's decisions, regular meeting's of work team, supervising people as a part of job, influence over policies of union). Fourth factor is psychological job demands described by (work hard, work fast, excessive work, enough time to finish the job, conflicting demands). Fifth factor is emotional demands described by (emotional demanding work, negotiation with others, suppressing genuine emotion, ability to take care, constant consultation with others).

Sixth factor is family/work stress, described by (responsibility for taking care of home, inference of family life and work). The seventh factor is perceived support which is the sum of three sub factors namely supervisor support, coworker support, organizational support and procedural justice, supervisor support is described by (concern of supervisor and helpful supervisor). Coworker support is described by (helpful coworkers and friendly coworkers). Organizational support is described by (organizational care about worker's opinions, care about well-being, consideration of goals and values, concern about workers). Procedural justice is described by (collecting accurate information for making decisions, providing opportunities to appeal the decisions, generating standards to take consistent decisions). Eighth factor is job insecurity (steady work, threat to job security, recent layoff, future layoff, valuable skills, hard to keep job for long duration). Ninth factor is physical job demands, described by (requires much physical effort, rapid physical activities, heavy load at work, awkward body positions and awkward upper body positions). The tenth factor is collective control, described by (sharing the hardships of t he job, possibility of helping the coworkers and unity among workers).

The eleventh factor is cognitive task type, described by (felt depressed, sleep was restless, enjoyed life, felt nervous while work, exceptionally tired in the morning and exhausted mentally and physically at the end of the day).

After data collections, data was analyzed and scores for each worker (noise level, age, cognitive task type and cognitive task efficiency), input/output parameters were categorized. The values of scores were used to establish the rules for optimum model. Neural fuzzy model under reference used three input and one output parameters. Questionnaire answers graded the cognitive task type into three categories (simple, moderate, and complex). Noise levels prevalent in the industries were graded as (low, medium, high), while workers were graded into three categories as (young, medium, and old age workers). Then noise levels and age are scaled from 40 dB(A) to 110 dB(A), and 15 to 65 years respectively, and Cognitive task type scaled from (1) strongly disagree to (5) strongly agree. While the output (cognitive task efficiency) classified as questionnaire answers weight (0%=strongly disagree, 25%=disagree, 50%=neutral, 75%=agree, and 100% =strongly agree). Model was constructed according to questionnaire form responses.

#### **4.2.3 Questionnaire studies (surveys) in the industry**

Data may be obtained either from the primary source or the secondary source. A primary source is one that itself collects the data; a secondary source is one that makes available data which were collected by some other agency. A primary source usually has more detailed information particularly on the procedures followed in collecting and compiling the data. Many methods for collecting the data such as direct personal interview, Mailed questionnaire method, indirect oral interviews schedule sent through enumerators, Information from correspondents etc.

So our data is direct personal interview method, under this method of collecting data , there is a face to face contact with the persons from whom the information is to be obtained (known as informants).

#### **4.2.4 Purpose of the questionnaire**

184 Fuzzy Inference System – Theory and Applications

Shriram Piston & Rings Ltd. is located at Ghaziabad, latitude (28°41׳07 (at longitude

Cognitive task (CT) questionnaire is prepared to assess the cognitive task among the workers in (I.T.O power plant station, centrifugal pump industry WPIL India Limited ,and Shriram Piston & Rings Lt.) Industries. This is self-administered questionnaire consists of 55-items. The operators were asked to respond to each and every item of questionnaire by giving subjective opinions from strongly disagrees to strongly agree. The items of the

The first factor is skill discretion, described by (possibility of learning new things, repetitive nature of the work, creative thinking at work, and high level of skill, time span of activities and developmental nature of job). The second factor is decision authority, described by (lot of say on job, freedom to take own decisions while working, continual dependence on others). Third scale is organizational decision latitude, described by (influence over organizational changes, influence over work team's decisions, regular meeting's of work team, supervising people as a part of job, influence over policies of union). Fourth factor is psychological job demands described by (work hard, work fast, excessive work, enough time to finish the job, conflicting demands). Fifth factor is emotional demands described by (emotional demanding work, negotiation with others, suppressing genuine emotion, ability

Sixth factor is family/work stress, described by (responsibility for taking care of home, inference of family life and work). The seventh factor is perceived support which is the sum of three sub factors namely supervisor support, coworker support, organizational support and procedural justice, supervisor support is described by (concern of supervisor and helpful supervisor). Coworker support is described by (helpful coworkers and friendly coworkers). Organizational support is described by (organizational care about worker's opinions, care about well-being, consideration of goals and values, concern about workers). Procedural justice is described by (collecting accurate information for making decisions, providing opportunities to appeal the decisions, generating standards to take consistent decisions). Eighth factor is job insecurity (steady work, threat to job security, recent layoff, future layoff, valuable skills, hard to keep job for long duration). Ninth factor is physical job demands, described by (requires much physical effort, rapid physical activities, heavy load at work, awkward body positions and awkward upper body positions). The tenth factor is collective control, described by (sharing the hardships of t he job, possibility of helping the

The eleventh factor is cognitive task type, described by (felt depressed, sleep was restless, enjoyed life, felt nervous while work, exceptionally tired in the morning and exhausted

After data collections, data was analyzed and scores for each worker (noise level, age, cognitive task type and cognitive task efficiency), input/output parameters were categorized. The values of scores were used to establish the rules for optimum model. Neural fuzzy model under reference used three input and one output parameters. Questionnaire answers graded the cognitive task type into three categories (simple,

(77°26׳06 (as shown in Figure 4.4.

**4.2.2 Description of cognitive task factors** 

questionnaire were classified into the following factors.

to take care, constant consultation with others).

coworkers and unity among workers).

mentally and physically at the end of the day).


#### **4.2.5 Why we used the questionnaire survey?**

This method suitable for this study, the explanations are following:


To obtain occupants opinions on the industrial/cognitive task, questionnaire was administered. This questionnaire was consisting of 55 questions related to cognitive work effects on industrial worker performance in different noise level environment. The objective of the detailed survey was to confirm and clarify the results obtained from the short-form

Some Studies on Noise and Its Effects on Industrial/Cognitive Task Performance and Modeling 187

a variety of instruments and software to analyze their measurements. The choice of a particular instrument and approach for measuring and analyzing occupational noise depends on many factors, not the least of which will be the purpose for the measurement and the environment in which the measurement will be made. In general, measurement methods should conform to the American National Standard Measurement of Occupational

A noise survey or mapping takes noise measurements throughout an entire plant or section to identify noisy areas. Noise surveys provide very useful information which enables us to

Areas where employees are likely to be exposed to harmful levels of noise and personal

Noise survey is conducted in areas where noise exposure is likely to be hazardous. Noise level refers to the level of sound. A noise survey involves measuring noise level at selected locations throughout an entire plant or sections to identify noisy areas. This is usually done with a sound level meter (SLM). A reasonably accurate sketch showing the locations of workers and noisy machines is drawn. Noise level measurements are taken at a suitable number of positions around the area and are marked on the sketch. The more measurements taken were more accurate the survey. A noise map can be produced by drawing lines on the sketch between points of equal sound level. Noise survey maps; provide very useful

The following sections briefly explain the theory of sound and the contours estimation procedure. Theory Two basic formulae play an important role in estimating the noise level. Herein, the terms 'sound' and 'noise' are used interchangeably. These formulae convert sound power to sound intensity, and sound intensity to sound pressure level respectively.

> <sup>2</sup> 4 *<sup>P</sup> <sup>I</sup>*

10log *<sup>I</sup> <sup>L</sup>*

By knowing the noise level (L), in dB (A), of a given noise source, its noise level can be estimated at any distance (d) from the source. This can be achieved by initially converting

0

*I* 

*<sup>d</sup>* (4.1)

(4.2)

 Machines and equipment which generate harmful levels of noise, Employees who might be exposed to unacceptable noise levels, and

information by clearly identifying areas where there are noise hazards.

Noise Exposure, ANSI S12.19-1997 [ANSI 1996a].

Noise control options to reduce noise exposure.

P is the sound power (W) of the noise source

d the distance (m) from the noise source, L is the sound pressure level (dB (A)), I0 is the reference sound intensity.

I the sound intensity (W/m2),

**4.4 Noise mapping** 

dosimeter may be needed,

identify:

Where:

survey. Questions corresponding to the statements in the short-form questionnaire were used. A total 155 questionnaire were distributed among the workers of automobile industries. Responses were made using likert scale 5-point scales instead of simple choices.


An example of the procedure used to calculate the value required is shown below:-

Sample survey response shows the procedure adopted for response collection of workers, all responses rating adding and divided by the number of questions to find out the ratio of the performance.


Output Performance ratio (η) =50%/5=10%

Similarly we have found the output performance ratio at different noise levels. Respectively and see the corresponding value of "reduction in cognitive work efficiency (η)" from Table 4.2, the detailed procedure for calculation has been described in Appendix-B.


Table 4.2. Rating ratio for reduction in cognitive task efficiency.

For Linguistic rules in Fuzzy logic Toolbox @ MATLAB software require 27 rules. Questionnaires were selected randomly from the given set of questionnaire depicted as linguistic rule.

#### **4.3 Noise measurement**

No single method or process exists for measuring occupational noise. Hearing safety and health professionals can use a variety of instruments to measure noise and can choose from a variety of instruments and software to analyze their measurements. The choice of a particular instrument and approach for measuring and analyzing occupational noise depends on many factors, not the least of which will be the purpose for the measurement and the environment in which the measurement will be made. In general, measurement methods should conform to the American National Standard Measurement of Occupational Noise Exposure, ANSI S12.19-1997 [ANSI 1996a].

#### **4.4 Noise mapping**

186 Fuzzy Inference System – Theory and Applications

survey. Questions corresponding to the statements in the short-form questionnaire were used. A total 155 questionnaire were distributed among the workers of automobile industries. Responses were made using likert scale 5-point scales instead of simple choices. Along with the questionnaire the demographic data like age, noise level, gender etc.

The operators or workers were asked to respond to the self administered questionnaire

These responses were transferred to a five point likert scale by assigning the rating from

 Not to cause any work loss in the general industry, the questionnaire forms were distributed during the day shift and collected the next day while it has been done on a

Sample survey response shows the procedure adopted for response collection of workers, all responses rating adding and divided by the number of questions to find out the ratio of the

 Addition the input response (answers) = 2+4+2+3+1+3+3+2+2+2+3+2+3+3+2+3+2+3+ 3+2+2+2+3+1+3+4+3+4+1+3+2+2+4+1+1+2+2+2+4+3+3+3+2+2+3+3+2= 116

Similarly we have found the output performance ratio at different noise levels. Respectively and see the corresponding value of "reduction in cognitive work efficiency (η)" from Table

For Linguistic rules in Fuzzy logic Toolbox @ MATLAB software require 27 rules. Questionnaires were selected randomly from the given set of questionnaire depicted as

No single method or process exists for measuring occupational noise. Hearing safety and health professionals can use a variety of instruments to measure noise and can choose from

All responses were collected and calculated the performance rating of the workers.

An example of the procedure used to calculate the value required is shown below:-

Each choice filled through the worker or operator at the time of working.

Addition the output response (answers) =25%+0%+0%+0%+25%=50%

4.2, the detailed procedure for calculation has been described in Appendix-B.

100% Strongly agree (Very high)

25% Disagree (Low) 50% Neutral (moderate) 75% Agree (High)

Table 4.2. Rating ratio for reduction in cognitive task efficiency.

Ratio value (η) Reduction in cognitive task efficiency 0.00% Strongly disagree (None)

were also collected.

1 to 5.

performance.

linguistic rule.

**4.3 Noise measurement** 

by giving their objective opinions.

one-to-one basis during the night shift.

Input Performance ratio (x) = 116/ 47 = 2.4

Output Performance ratio (η) =50%/5=10%

A noise survey or mapping takes noise measurements throughout an entire plant or section to identify noisy areas. Noise surveys provide very useful information which enables us to identify:


Noise survey is conducted in areas where noise exposure is likely to be hazardous. Noise level refers to the level of sound. A noise survey involves measuring noise level at selected locations throughout an entire plant or sections to identify noisy areas. This is usually done with a sound level meter (SLM). A reasonably accurate sketch showing the locations of workers and noisy machines is drawn. Noise level measurements are taken at a suitable number of positions around the area and are marked on the sketch. The more measurements taken were more accurate the survey. A noise map can be produced by drawing lines on the sketch between points of equal sound level. Noise survey maps; provide very useful information by clearly identifying areas where there are noise hazards.

The following sections briefly explain the theory of sound and the contours estimation procedure. Theory Two basic formulae play an important role in estimating the noise level. Herein, the terms 'sound' and 'noise' are used interchangeably. These formulae convert sound power to sound intensity, and sound intensity to sound pressure level respectively.

$$I = \frac{P}{4\prod d^2} \tag{4.1}$$

$$L = 10\log\left[\frac{I}{I\_0}\right] \tag{4.2}$$

Where:

P is the sound power (W) of the noise source

I the sound intensity (W/m2),

d the distance (m) from the noise source,

L is the sound pressure level (dB (A)),

I0 is the reference sound intensity.

By knowing the noise level (L), in dB (A), of a given noise source, its noise level can be estimated at any distance (d) from the source. This can be achieved by initially converting

Some Studies on Noise and Its Effects on Industrial/Cognitive Task Performance and Modeling 189

From the noise level in dB (A) of machine k, Lk, convert it to its sound power, Pk, using

120 /10 4 10 *Lk Pk*

Next, a set of locations (points of interest) on the floor must be identified where the combined noise levels will be estimated. These points are expressed as (Xi, Yi), i =1 to n, where n is the number of points. Conventionally, the factory floor layout is divided into grids. The grid dimension depends on the size of the factory floor and the required degree of accuracy of the noise contours. If the size of the factory is large and/or high degree of accuracy is required, the number of grids will be large (i.e., the grid size will be small). However, the larger the number

The noise intensity of machine k at location i, Ii, k, can be estimated using the following steps.

<sup>2</sup> 2 2 [( ) ( ) ] *ik i k i k d xx yy* (4.7)

The combined machine noise intensity at location i, CDCZ can be determined by adding all

1 4 *<sup>m</sup> <sup>k</sup> k ik <sup>P</sup> <sup>I</sup>*

The effect of the ambient noise level must be accounted for by adding Iab to Equitation (4.9).

*<sup>P</sup> I I*

1

*i ab*

*i ab*

*I I*

1 4 *<sup>m</sup> <sup>k</sup>*

By substituting Equation (4.8) into Equation (4.12), both the terms Pk and 4π disappear.

*k ik*

*d*

 2

*k ik L*

*d*

120 /10 *<sup>m</sup> <sup>k</sup>*

2

Repeat Equitation (4.8) for k=1 to m; Where m denotes the number of machines

**4. Determining the locations where the combined noise levels will be estimated:** 

(4.6)

<sup>2</sup> / 4 *<sup>k</sup> ik IP d* (4.8)

*<sup>d</sup>* (4.9)

(4.10)

(4.11)

Equations (4.1) and (4.2), and by assuming that d =1 m.

of grids implies the longer time to construct the noise contours.

**2. Combining all machine noise intensities:** 

2

The combined noise intensity at location i now become:

machine noise intensities Ii, k, k=1 to m.

**3. Adding the ambient noise intensity:** 

Thus, Equation (4.10) can be written as:

**1. Computing the machine noise intensity at the specified location:** 

Initially, the Euclidean distance, dik, between points i and k must be determined.

Then, the machine noise intensity at location i is computed using Equation (4.3).

**4.4.2 Computation steps** 

the noise level (dB (A)) of the noise source into its sound power (watt) using Eq. (4.1) and Eq. (4.2) and by assuming that the noise level is measured at 1 m from the source (i.e., d=1). From the inverse square law, the sound intensity at a distance d from the noise source is then attenuated by Eq. (4.1). In case there are n noise sources, the combined noise level ( *L* ) at any given location can be estimated using the following formula:

$$\overline{L} = 10 \sum\_{i=1}^{n} \log \frac{I}{I\_0} \tag{4.3}$$

For the ease of computation, Eq. (4.2) can be rewritten as follows:

$$I = 10^{(L \cdot 120/10)} \tag{4.4}$$

Then, the combined sound intensity (I) can be directly computed from

$$\overline{I} = \sum\_{i=1}^{n} 10 \, \frac{(\, \! \_i - 120)}{10} \, \tag{4.5}$$

#### **4.4.1 Construction of a noise contour map**

The procedure for constructing a noise contour map of the workplace can be described as Follows:

#### **Initialization steps:-**

#### **1. Determining (x, y) coordinates of machine locations:**

The layout of the factory floor must be obtained and all machines (or noise sources) must be plotted on the layout map. Since the computation requires an assumption of a pointed noise source, the machine location must be represented by a point on the X-Y plane. By selecting one corner of the factory floor as the reference origin (usually the lower left corner), the machine location can be expressed as a pair of X and Y coordinates which are measured from that reference point. That is, the location of machine k is expressed as, (Xk, Yk)

#### **2. Determining the ambient noise intensity:**

The ambient noise level (dB (A)) must be either measured or estimated. For a direct measurement, the ambient noise is measured when none of the machines are operating. To obtain reliable data, several measurements should be taken from different locations and different times. Then the average noise level is calculated and used as the ambient noise level of the factory floor. It must be converted to the ambient noise intensity, Iab, using Eq. (4.4).

#### **3. Determining the sound power of the machine:**

The machine noise level may be difficult to determine since it is impossible to isolate the machine and measure its noise level without any noise interference from others. If applicable, each machine can be operated and measurement taken correspondingly. Otherwise, the machine manufacturer can be contacted to obtain information (specifications) about the noise level generated by the machine. Similarly, the noise level of machine (k) must be expressed as the sound power (Pk), using the following conversion.

From the noise level in dB (A) of machine k, Lk, convert it to its sound power, Pk, using Equations (4.1) and (4.2), and by assuming that d =1 m.

$$P\_k = 4\prod 10^{(l\_k - 120)/10} \tag{4.6}$$

Repeat Equitation (4.8) for k=1 to m; Where m denotes the number of machines

#### **4. Determining the locations where the combined noise levels will be estimated:**

Next, a set of locations (points of interest) on the floor must be identified where the combined noise levels will be estimated. These points are expressed as (Xi, Yi), i =1 to n, where n is the number of points. Conventionally, the factory floor layout is divided into grids. The grid dimension depends on the size of the factory floor and the required degree of accuracy of the noise contours. If the size of the factory is large and/or high degree of accuracy is required, the number of grids will be large (i.e., the grid size will be small). However, the larger the number of grids implies the longer time to construct the noise contours.

#### **4.4.2 Computation steps**

188 Fuzzy Inference System – Theory and Applications

the noise level (dB (A)) of the noise source into its sound power (watt) using Eq. (4.1) and Eq. (4.2) and by assuming that the noise level is measured at 1 m from the source (i.e., d=1). From the inverse square law, the sound intensity at a distance d from the noise source is then attenuated by Eq. (4.1). In case there are n noise sources, the combined noise level ( *L* )

> 1 0 10 log *n*

> > 120

10 *i <sup>n</sup> <sup>L</sup>*

(4.3)

(4.5)

*i <sup>I</sup> <sup>L</sup> I*

> 1 10

The procedure for constructing a noise contour map of the workplace can be described as

The layout of the factory floor must be obtained and all machines (or noise sources) must be plotted on the layout map. Since the computation requires an assumption of a pointed noise source, the machine location must be represented by a point on the X-Y plane. By selecting one corner of the factory floor as the reference origin (usually the lower left corner), the machine location can be expressed as a pair of X and Y coordinates which are measured

The ambient noise level (dB (A)) must be either measured or estimated. For a direct measurement, the ambient noise is measured when none of the machines are operating. To obtain reliable data, several measurements should be taken from different locations and different times. Then the average noise level is calculated and used as the ambient noise level of the factory floor. It must be converted to the ambient noise intensity, Iab, using Eq. (4.4).

The machine noise level may be difficult to determine since it is impossible to isolate the machine and measure its noise level without any noise interference from others. If applicable, each machine can be operated and measurement taken correspondingly. Otherwise, the machine manufacturer can be contacted to obtain information (specifications) about the noise level generated by the machine. Similarly, the noise level of machine (k)

must be expressed as the sound power (Pk), using the following conversion.

from that reference point. That is, the location of machine k is expressed as, (Xk, Yk)

*i <sup>I</sup>* 

 *I* = 10*(L-120)/10* (4.4)

at any given location can be estimated using the following formula:

For the ease of computation, Eq. (4.2) can be rewritten as follows:

**4.4.1 Construction of a noise contour map** 

**2. Determining the ambient noise intensity:** 

**3. Determining the sound power of the machine:** 

**1. Determining (x, y) coordinates of machine locations:** 

Follows:

**Initialization steps:-** 

Then, the combined sound intensity (I) can be directly computed from

#### **1. Computing the machine noise intensity at the specified location:**

The noise intensity of machine k at location i, Ii, k, can be estimated using the following steps. Initially, the Euclidean distance, dik, between points i and k must be determined.

$$d\_{ik} = [(\mathbf{x}\_i - \mathbf{x}\_k)^2 + (y\_i - y\_k)^2]^2 \tag{4.7}$$

Then, the machine noise intensity at location i is computed using Equation (4.3).

$$I = P\_k \nmid 4 \prod d^2\_{\ \ ik} \tag{4.8}$$

#### **2. Combining all machine noise intensities:**

The combined machine noise intensity at location i, CDCZ can be determined by adding all machine noise intensities Ii, k, k=1 to m.

$$\overline{I} = \sum\_{k=1}^{m} \frac{P\_k}{4 \prod d^2\_{\ ik}} \tag{4.9}$$

#### **3. Adding the ambient noise intensity:**

The effect of the ambient noise level must be accounted for by adding Iab to Equitation (4.9). The combined noise intensity at location i now become:

$$\overline{I}\_i = I\_{ab} + \sum\_{k=1}^m \frac{P\_k}{4\prod d^2\_{\ ik}} \tag{4.10}$$

By substituting Equation (4.8) into Equation (4.12), both the terms Pk and 4π disappear. Thus, Equation (4.10) can be written as:

$$\overline{I}\_i = I\_{ab} + \sum\_{k=1}^m \frac{\left(L\_k - 120\right) / 10}{d^2\_{\ ik}} \tag{4.11}$$

Some Studies on Noise and Its Effects on Industrial/Cognitive Task Performance and Modeling 191

Table 4.3. (b) Employees location and dosage calculation.

Fig. 4.6. WPIL India Limited, Ghaziabad Noise map (noiseatwork V1.31).

Table 4.4. (a) (X, Y) Coordinates of noise measurement and noise levels.

#### **4. Converting the combined noise intensity into its noise level (dB (A)):**

Finally, the combined noise intensity at location i is converted into the combined Noise level in dB (A), Li, using Equation (4.2).

#### **4.5 Industrial noise surveys**

#### **4.5.1 NoiseAtWorkV1.31**

Software for mapping and analysis of noise at workplaces for health and safety representatives (NoiseAtWorkV1.31) [29] is software for mapping and analysis of noise levels at places where people work. Based on measured noise levels and working times of employees, noise contours and Leq. 8hr values are calculated by the software. The software is used by health and safety representatives for the management of occupational noise risks.

Fig. 4.5. Shriram Piston & Rings Lt. Ghaziabad Noise map (noiseatwork V1.31).


Table 4.3. (a) (X, Y) Coordinates of noise measurement and noise levels.


Table 4.3. (b) Employees location and dosage calculation.

Finally, the combined noise intensity at location i is converted into the combined Noise level

Software for mapping and analysis of noise at workplaces for health and safety representatives (NoiseAtWorkV1.31) [29] is software for mapping and analysis of noise levels at places where people work. Based on measured noise levels and working times of employees, noise contours and Leq. 8hr values are calculated by the software. The software is used by health and safety representatives for the management of occupational noise risks.

**4. Converting the combined noise intensity into its noise level (dB (A)):** 

Fig. 4.5. Shriram Piston & Rings Lt. Ghaziabad Noise map (noiseatwork V1.31).

Table 4.3. (a) (X, Y) Coordinates of noise measurement and noise levels.

in dB (A), Li, using Equation (4.2).

**4.5 Industrial noise surveys 4.5.1 NoiseAtWorkV1.31** 

Fig. 4.6. WPIL India Limited, Ghaziabad Noise map (noiseatwork V1.31).


Table 4.4. (a) (X, Y) Coordinates of noise measurement and noise levels.

Some Studies on Noise and Its Effects on Industrial/Cognitive Task Performance and Modeling 193

While fuzzy system are shown to be universal approximations to algebraic functions, it is not attribute that actually makes them valuable to us in understanding new or evolving problems. Rather, the primary benefit of fuzzy system theory is to approximate system behavior where analytical functions or numerical relations do not exist. Hence, fuzzy systems have a high potential to understand the very system that red void of analytic formulations: complex system. Complex system can be new systems that have not been tested, they can be system involved with the human condition such as biological or medical system, or they can be social, economic, or political systems, where the vast arrays of input and output could not all possibly be captured analytically or controlled in any conventional sense. Moreover the relationship between the cause and effects of these systems is generally

Alternatively, fuzzy system theory can have utility in assessing some of our more conventional, less complex system. For example, for some problems exact solutions are not always necessary. An approximate but fast, solution can be useful in making preliminary design decisions or as an initial estimation in a more accurate numerical technique to save computational costs or in the myriad of situations where the inputs to a problem are vague,

Fuzzy models in a broad sense are of two types. The first category of the model proposed by Mamdani is based on the collections of IF-THEN rules with both fuzzy-antecedent and consequent predicates. The advantage of this model is that the rule base is generally provided by an expert, and hence, to a certain degree, it is transparent to interpretation and analysis. The second category of the fuzzy model is based on the Takagi-Sugeno-Kang (TSK)

For this study we have establishment of the Sugeno type Fuzzy models under the recommendations of Occupational Safety and Health Administration (OSHA)[30], 90 dB (A) for 8 hr. duration, as shown in Figure 4.8, because of the adaptive data Surgeon's model is the proper method to build the model. Fuzzy Logic Toolbox is a collection of functions built on the MATLAB® numeric computing environment. Fuzzy logic has two different meanings. In a narrow sense, fuzzy logic is a logical system, which is an extension of multi valued logic. However, in a wider sense fuzzy logic (FL) is almost synonymous with the theory of fuzzy sets, a theory which relates to classes of objects with unsharp boundaries in

Table 4.5. (b) Employees location and dosage calculation.

**4.6 Building systems with fuzzy logic toolbox** 

not understood, but often can be observed.

ambiguous, or not known at all.

which membership is a matter of degree.

method of reasoning.


Table 4.4. (b) Employees location and dosage calculation.

Fig. 4.7. (I.T.O) power plant station New Delhi Noise map (noiseatwork V1.31).


Table 4.5. (a) (X, Y) Coordinates of noise measurement and noise levels.


Table 4.5. (b) Employees location and dosage calculation.

### **4.6 Building systems with fuzzy logic toolbox**

192 Fuzzy Inference System – Theory and Applications

Table 4.4. (b) Employees location and dosage calculation.

Fig. 4.7. (I.T.O) power plant station New Delhi Noise map (noiseatwork V1.31).

Table 4.5. (a) (X, Y) Coordinates of noise measurement and noise levels.

While fuzzy system are shown to be universal approximations to algebraic functions, it is not attribute that actually makes them valuable to us in understanding new or evolving problems. Rather, the primary benefit of fuzzy system theory is to approximate system behavior where analytical functions or numerical relations do not exist. Hence, fuzzy systems have a high potential to understand the very system that red void of analytic formulations: complex system. Complex system can be new systems that have not been tested, they can be system involved with the human condition such as biological or medical system, or they can be social, economic, or political systems, where the vast arrays of input and output could not all possibly be captured analytically or controlled in any conventional sense. Moreover the relationship between the cause and effects of these systems is generally not understood, but often can be observed.

Alternatively, fuzzy system theory can have utility in assessing some of our more conventional, less complex system. For example, for some problems exact solutions are not always necessary. An approximate but fast, solution can be useful in making preliminary design decisions or as an initial estimation in a more accurate numerical technique to save computational costs or in the myriad of situations where the inputs to a problem are vague, ambiguous, or not known at all.

Fuzzy models in a broad sense are of two types. The first category of the model proposed by Mamdani is based on the collections of IF-THEN rules with both fuzzy-antecedent and consequent predicates. The advantage of this model is that the rule base is generally provided by an expert, and hence, to a certain degree, it is transparent to interpretation and analysis. The second category of the fuzzy model is based on the Takagi-Sugeno-Kang (TSK) method of reasoning.

For this study we have establishment of the Sugeno type Fuzzy models under the recommendations of Occupational Safety and Health Administration (OSHA)[30], 90 dB (A) for 8 hr. duration, as shown in Figure 4.8, because of the adaptive data Surgeon's model is the proper method to build the model. Fuzzy Logic Toolbox is a collection of functions built on the MATLAB® numeric computing environment. Fuzzy logic has two different meanings. In a narrow sense, fuzzy logic is a logical system, which is an extension of multi valued logic. However, in a wider sense fuzzy logic (FL) is almost synonymous with the theory of fuzzy sets, a theory which relates to classes of objects with unsharp boundaries in which membership is a matter of degree.

Some Studies on Noise and Its Effects on Industrial/Cognitive Task Performance and Modeling 195

The modeling of imprecise and qualitative knowledge as well as the transmission of uncertainty is possible though the use of fuzzy logic. Besides these generic advantages, the neuro-fuzzy approach also provides the corresponding application specific merits [31-32] some of the neuro-fuzzy systems are popular by their shorts names. For example ANFIS

Our present model is based on adaptive neuro-fuzzy interface system (ANFS) an ANFIS is a fuzzy interface system implement in framework of adaptive neural networks. ANFIS either uses input/output data sets to construct a fuzzy interface system whose membership functions are tuned using a learning algorithm or an expert may be specify a fuzzy interface system and then the system is trained with the data pairs by an adaptive network . The conceptual diagram of ANFIS based on latter approach shown in figure 4.9. Is consists of two major components namely fuzzy interface system and adaptive neural network. A fuzzy interface system has five functional blocks. A fuzzifier converts real numbers of input into fuzzy sets. This functional unit essentially transforms the crisp inputs into a degree of match with linguistic values. The database (or dictionary) contains the Membership functions of fuzzy sets. The membership function provide flexibility to the fuzzy sets in modeling commonly used linguistic expressions such as "the noise level is low "or "person is young." A rule base consist of a set of linguistic statements of the form, if *x* is *A* then *y* is *B*, where *A* and *B* are labels of fuzzy sets on universes of discourse characterized by appropriate membership function of database . An interface engine perform s the interface operations on the rules to infer the output by a fuzzy reasoning method. Defuzzifier

[33], DENFIS [34], SANFIS [35] and FLEXNFIS [36], etc.

Fig. 4.9. Conceptual diagram of ANFIS.

Fig. 4.8. Flow diagram for model structure.

#### **4.6.1 Algorithm**


#### **4.6.2 Neuro-fuzzy computing**

Neuro-fuzzy computing is a judicious integration of the merits of neural and fuzzy approaches. This incorporates the generic advantages of artificial neural networks like massive parallelism, robustness, and learning in data-rich environments into the system.

Fig. 4.8. Flow diagram for model structure.

1. Selection of the input and output variables.

2. Determination of the ranges of input and output variables.

6. Check model validity by using 20% of input/output pairs.

3. Determination of the membership functions for various input and output variables. 4. Formation of the set of linguistic rules that represent the relationships between the

5. Selection of the appropriate reasoning mechanism for the formalization of the neural

7. Evaluation of the model adequacy;if the model does not produce the desired results,

Neuro-fuzzy computing is a judicious integration of the merits of neural and fuzzy approaches. This incorporates the generic advantages of artificial neural networks like massive parallelism, robustness, and learning in data-rich environments into the system.

**4.6.1 Algorithm** 

system variables;

modify the rules in step 4.

**4.6.2 Neuro-fuzzy computing** 

fuzzy model.

The modeling of imprecise and qualitative knowledge as well as the transmission of uncertainty is possible though the use of fuzzy logic. Besides these generic advantages, the neuro-fuzzy approach also provides the corresponding application specific merits [31-32] some of the neuro-fuzzy systems are popular by their shorts names. For example ANFIS [33], DENFIS [34], SANFIS [35] and FLEXNFIS [36], etc.

Our present model is based on adaptive neuro-fuzzy interface system (ANFS) an ANFIS is a fuzzy interface system implement in framework of adaptive neural networks. ANFIS either uses input/output data sets to construct a fuzzy interface system whose membership functions are tuned using a learning algorithm or an expert may be specify a fuzzy interface system and then the system is trained with the data pairs by an adaptive network . The conceptual diagram of ANFIS based on latter approach shown in figure 4.9. Is consists of two major components namely fuzzy interface system and adaptive neural network. A fuzzy interface system has five functional blocks. A fuzzifier converts real numbers of input into fuzzy sets. This functional unit essentially transforms the crisp inputs into a degree of match with linguistic values. The database (or dictionary) contains the Membership functions of fuzzy sets. The membership function provide flexibility to the fuzzy sets in modeling commonly used linguistic expressions such as "the noise level is low "or "person is young." A rule base consist of a set of linguistic statements of the form, if *x* is *A* then *y* is *B*, where *A* and *B* are labels of fuzzy sets on universes of discourse characterized by appropriate membership function of database . An interface engine perform s the interface operations on the rules to infer the output by a fuzzy reasoning method. Defuzzifier

Fig. 4.9. Conceptual diagram of ANFIS.

Some Studies on Noise and Its Effects on Industrial/Cognitive Task Performance and Modeling 197

Once the input and output variables are identified, the neuro-fuzzy system is realized using a six-layered network as shown in Figure 4.10. The input, output and node functions of each

Each node in layer 1 represents the input variables of the model identified in step 1 this

The fuzzification layer describes the membership function of each input fuzzy set, membership functions are used to characterize fuzziness in fuzzy sets, the output of each

function. Its value on the unit interval (0, 1) measure the degree to which elements x belongs to the fuzzy set A, xi is the input to the node i and Ai is the linguistic label for each input

Each node in this layer is an adaptive node that is the output of each node depends on the parameters pertaining to these nodes. Thus the membership function for *A* can be any appropriate parameterized membership function. The most commonly used membership functions are triangular, trapezoidal, Gaussian, and bell shaped. Any of these choices may be used, the triangular and trapezoidal membership functions have been used extensively especially in real-time implementations due to their simple formulas and computational

In our original fuzzy model [40] we have used triangular membership functions however since these membership functions are composed of straight line segments they are not smooth at corner points specified by the parameters though the parameters of these membership functions can be optimized using direct search methods but they are less efficient and more time consuming, also the derivatives of the functions are not continuous so the powerful and more efficient gradient methods cannot be used for optimizing their parameters Gaussian and bell shaped membership functions are becoming increasingly popular for specifying fuzzy sets as they are non-linear and smooth and their derivatives are continuous gradient methods can be used easily for optimizing their design parameters . Thus in this model, we have replaced the triangular fuzzy memberships with bell shapes functions (Table 4.7). The bell or generalized bell (or gbell) shaped membership function is

> <sup>2</sup> 1

*xc a*

1 / *<sup>A</sup> <sup>b</sup> x*

The desired shape of gbell membership function can be obtained by proper selection of the parameters more specifically we can adjust *c* and *a* to vary the center and width of membership function, and *b* to control the slope at the crossover points. The parameter *b* gives gbell shaped membership function one more degree of freedom than the Gaussian membership function and allows adjusting the steepness at crossover points. The

*x* where the symbol

*<sup>A</sup> x* is the membership

(4.12)

layer simply transmits these input variables to the fuzzification layer.

Step 2. Determining the network structure

node i in this layer is given by *A i <sup>i</sup>*

specified by a set of three fitting parameters {*a,b,c*} as:

parameters in this layer are referred to as premise parameters.

variable associated with this node.

**Layer 1: Input layer** 

efficiency.

**Layer 2: Fuzzification layer** 

layer are explained in the subsequent paragraphs

converts the fuzzy outputs obtained by interface engine into a non-fuzzy output real number domain. In order to incorporate the capability of learning from input/output data sets in fuzzy interface systems, a corresponding adaptive neural network is generated. An adaptive network is a multi-layer feed-forward network consisting of nodes and directional links through which nodes are connected. As shown in Figure 4.10. Layer 1 is the input layer, layer 2 describes the membership functions of each fuzzy input, layer 3 is interface layer and normalizing is performed in layer 4. Layer 5 gives the output and layer 6 is the defuzzification layer. The layers consist of fixed and adaptive nodes, each adaptive node has asset of parameters and performs a particular function (node function) on incoming signals.

The learning model may consist of either back propagation or hybrid learning algorithm, the learning rules specifies how the parameter of adaptive node should be change to minimize a prescribed error measure [37]. The change in values of the parameters results in change in shape of membership functions associated with fuzzy interface system.
