**2.4 Studies on the fuzzy logic and their application**

Lah, et al. (2005) [20] Developed an experimental model for found that the controlled dynamic thermal and illumination response of human-built environment in real-time conditions. He was designing an experimental test chamber for thermal and illumination response on a human performance. The time-dependent outside conditions as external system disturbances, the air temperatures and the solar radiation oscillation are also included as input data, this input data controlled by fuzzy logic toolbox. After the many experiments they were found that outside conditions as sun light & temperature was highly effects on human performance.

Zaheeruddin, et al. (2006) [21] Developed a model (system) for predicting the effects of sleep disturbance by noise on humans as a function of noise level, age, and duration of its occurrence. The modeling technique is based on the concept of fuzzy logic, which offers a convenient way of representing the relationships between the inputs and outputs of a system in the form of IF-THEN rules. They were taken the three input variables; such as noise level, duration of sleep and age of the person and one output variable is noise effect (sleep disturbance). In this model they was decided the range of the variables & fluctuated these ranges in fuzzy logic model. After fluctuation they were found the many output variables. They were concluded that the middle-aged people have more probability of sleep disruption than the young people at the same noise levels. However, very little difference is found in sleep disturbance due to noise between young and old people. In addition, the duration of occurrence of noise is an important factor in determining the sleep disturbance over the limited range from few seconds to few minutes. Finally, authors have compared our model results with some of the findings of researchers reported in International Journals.

Zaheeruddin (2006) [22] Studied that noise effects on industrial worker performance. From the literature survey, they observed that the three most important factors influencing human work efficiency are noise level, type of task, and exposure time. Therefore they was developed a model on neuro-fuzzy system. According his model they were taken three input variables (noise level, type of task & exposure time) and one output variables (reduction in work efficiency). All variables apply in neuro-fuzzy models and collect the results. He was concluded that the main thrust of the present work has been to develop a neuro-fuzzy model for the prediction of work efficiency as a function of noise level, type of tasks and exposure times. It is evident from the graph that the work efficiency, for the same exposure time, depends to a large extent upon the noise level and type of task. It has also been verified that simple tasks are not affected even at very high noise level while complex tasks get significantly affected at much lower noise level.

Aluclu, et al. (2008) [23] They described noise-human response and a fuzzy logic model developed by comprehensive field studies on noise measurements (including atmospheric parameters) and control measures. The model has two subsystems constructed on noise reduction quantity in dB. The first subsystem of the fuzzy model depending on 549 linguistic rules comprises acoustical features of all materials used in any workplace. Totally 984 patterns were used, 503 patterns for model development and the rest 481 patterns for testing the model. The second subsystem deals with atmospheric parameter interactions with noise and has 52 linguistic rules. Similarly, 94 field patterns were obtained; 68 patterns were used for training stage of the model and the rest 26 patterns for testing the model. These rules were determined by taking into consideration formal standards, experiences of specialists and the measurements patterns. They were found that the model was compared with various statistics (correlation coefficients, max-min, standard deviation, average and coefficient of skewers) and error modes (root mean square Error and relative error). The correlation coefficients were significantly high, error modes were quite low and the other statistics were very close to the data.

Zaheeruddin, et al. (2008) [24] They developed an expert system using fuzzy approach to investigate the effects of noise pollution on speech interference. The speech interference measured in terms of speech intelligibility is considered to be a function of noise level, distance between speaker and listener, and the age of the listener. The main source of model development is the reports of World Health Organization (WHO) and field surveys conducted by various researchers. It is implemented on Fuzzy Logic Toolbox of MATLAB using both Mamdani and Sugeno techniques. They were found his result from fuzzy logic model & comparison of the results from World Health Organization (WHO) and U.S. Environmental Protection Agency (EPA). After comparison they were concluded that the model has been implemented on Fuzzy Logic Toolbox of MATLAB the results obtained from the proposed model are in good agreement with the findings of field surveys conducted in different parts of the world. The present effort also establishes the usefulness of the Fuzzy technique in studying the environmental problems where the cause-effect relationships are inherently fuzzy in nature.

Mamdani, et al. (1975) [25] Studied after an experiment on the "linguistic" synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. They developed 24 rules for controlling stem engine. The experiment was initiated to investigate the possibility of human interaction with a learning controller. However, the control strategy set up linguistically proved to be far better than expected in its own right, and the basic experiment of linguistic control synthesis in a non-learning controller is reported here.

Ross (2009) [26] Presented their approach to introduce some applications of fuzzy logic, introduced the basic concept of fuzziness and distinguish uncertainty from other form of uncertainty. It also introduce the fundamental idea of set membership, thereby laying the foundation for all material that follows, and presents membership functions as the format used from expressing set membership. Chapters discussed the fuzzification of scalar and the deffuzification of membership functions & various forms of the implication operation and the composition operation provided.

#### **2.5 Studies on the noise survey**

178 Fuzzy Inference System – Theory and Applications

Lah, et al. (2005) [20] Developed an experimental model for found that the controlled dynamic thermal and illumination response of human-built environment in real-time conditions. He was designing an experimental test chamber for thermal and illumination response on a human performance. The time-dependent outside conditions as external system disturbances, the air temperatures and the solar radiation oscillation are also included as input data, this input data controlled by fuzzy logic toolbox. After the many experiments they were found that outside conditions as sun light & temperature was highly

Zaheeruddin, et al. (2006) [21] Developed a model (system) for predicting the effects of sleep disturbance by noise on humans as a function of noise level, age, and duration of its occurrence. The modeling technique is based on the concept of fuzzy logic, which offers a convenient way of representing the relationships between the inputs and outputs of a system in the form of IF-THEN rules. They were taken the three input variables; such as noise level, duration of sleep and age of the person and one output variable is noise effect (sleep disturbance). In this model they was decided the range of the variables & fluctuated these ranges in fuzzy logic model. After fluctuation they were found the many output variables. They were concluded that the middle-aged people have more probability of sleep disruption than the young people at the same noise levels. However, very little difference is found in sleep disturbance due to noise between young and old people. In addition, the duration of occurrence of noise is an important factor in determining the sleep disturbance over the limited range from few seconds to few minutes. Finally, authors have compared our model results with some of the findings of researchers

Zaheeruddin (2006) [22] Studied that noise effects on industrial worker performance. From the literature survey, they observed that the three most important factors influencing human work efficiency are noise level, type of task, and exposure time. Therefore they was developed a model on neuro-fuzzy system. According his model they were taken three input variables (noise level, type of task & exposure time) and one output variables (reduction in work efficiency). All variables apply in neuro-fuzzy models and collect the results. He was concluded that the main thrust of the present work has been to develop a neuro-fuzzy model for the prediction of work efficiency as a function of noise level, type of tasks and exposure times. It is evident from the graph that the work efficiency, for the same exposure time, depends to a large extent upon the noise level and type of task. It has also been verified that simple tasks are not affected even at very high noise level while complex

Aluclu, et al. (2008) [23] They described noise-human response and a fuzzy logic model developed by comprehensive field studies on noise measurements (including atmospheric parameters) and control measures. The model has two subsystems constructed on noise reduction quantity in dB. The first subsystem of the fuzzy model depending on 549 linguistic rules comprises acoustical features of all materials used in any workplace. Totally 984 patterns were used, 503 patterns for model development and the rest 481 patterns for testing the model. The second subsystem deals with atmospheric parameter interactions with noise and has 52 linguistic rules. Similarly, 94 field patterns were obtained; 68 patterns were used for training stage of the model and the rest 26 patterns for testing the model.

**2.4 Studies on the fuzzy logic and their application** 

effects on human performance.

reported in International Journals.

tasks get significantly affected at much lower noise level.

Nanthavanij, et al. (1999) [27] Developed noise contours by two procedures: 1) Analytical and 2) Graphical. The graphical procedure requires input data: ambient noise level, noise levels generated by individual machines, and the (x, y) coordinates of the machine locations. When draw the noise contours in work shop floor, a set of mathematical formulae is also developed to estimate the combined noise levels at predetermined locations (or points) of the workplace floor. Contour lines are then drawn to connect points having an equal noise level. The analytical nature of the procedure also enables engineers to quickly construct the noise contour map and revise the map when changes occur in noise levels due to a workplace re-layout or an addition of a new noise source.

Some Studies on Noise and Its Effects on Industrial/Cognitive Task Performance and Modeling 181

of industrial workers have been instituted in many places. For example, in the U.S., the Occupational Noise Exposure Regulation states that industrial employers must limit noise

Based on the literature surveyed as presented in previous section, it was observed that a great majority of people working in industry are exposed to noise with different cognitive task type. In this study, attempt has been made to find out the combined effects of noise level and cognitive task type on industrial worker's performance. Attempt has also been made in present study to identify the noisy industries located in Delhi and around Delhi. Different industries with or without noise were categorized based on measured sound

Sound pressure level for industries clearly shown in Appendix-A. In this context, measurement the sound pressure level and cognitive task type, questionnaire studies have been conducted at automobile, power plant and steel textile industries in and around Delhi and also noise counters has been drawn for noisy industries. Assuming that the working environment (Temperature, Humidity, illumination level, other facilities), are same in the industries under reference; categorization has been made as presented in the

Limited, Ghaziabad 45 – 95 Low noise

Table 4.1. (a) Industries name & their category with reference to noise level.

Ghaziabad 63 – 102 Medium noise

Delhi 75-116 High noise

2. WPIL India Limited, Ghaziabad 10 12 16

In addition to this, the questionnaire data was segregated based on various sections of above-mentioned industries. Performance rating was obtained based on questionnaire survey for different noise levels and type of cognitive task (simple, moderate, and complex). On the collected performance rating data, we have implemented our model using Sugeno technique (Fuzzy Logic Tool box) of MATLAB. It is a three input-one output system. The input variables are noise level, Age of the worker or operator, and cognitive task type and the reduction in cognitive task efficiency is taken as the output variable. The whole

46 up

Ghaziabad 8 18 18

Delhi 14 22 37

(dB (A)) Category workers

Medium age 31-45

level <sup>44</sup>

level <sup>38</sup>

level <sup>73</sup>

number

Young age 15-30

exposure of their employees to 85 dB (A) for 8 hr period.

S.No. Industry Noise level

S.No. Industry Old age

Table 4.1. (b) Industries name with reference to workers age groups.

1. Shriram Piston and Rings Limited,

3. I.T.O power plant station New

methodology shown in Figure 4.1

1. Shriram Piston and Rings

3. I.T.O power plant station New

2. WPIL India Limited,

pressure level.

Table 4.1(a) and 4.1(b).

Kumar (2008) [28] Studied the case of high level of noise in rice mills and to examine the response of the workers towards noise. They was done a noise survey was conducted in eight renowned rice mills of the north-eastern region of India. They were following the guidelines of CCOHS for noise survey. Their model as same like above author model. But they was taking the size of grid is 1m X 1m.
