**7. Appendix**

### **7.1 Appendix-A**

Department of labor occupational noise exposure standard

Fig. A.1. Permitted daily exposure time [30]


Table A.1. Permissible Noise Exposures


#### **7.2 Appendix-B**

208 Fuzzy Inference System – Theory and Applications

3. The input & output variables range may also be converted into small ranges such as

4. Input/output data must be categorized and scaled to set the optimum number and shape of membership functions, by increasing the probability (membership functions)

5. Workers subjected under high cognitive task must be working on low noise level

6. This study proved the questionnaire studies it's easy to simulate and programming using neural-fuzzy model to give as approximately solution for several case steadies. 7. The problem of noise should be taken into consideration during their establishment

phases (construction of the building, allocation of the machinery, etc.). 8. It's possible to modify the present model (FIS) to be a part of control system.

**0 Duration per day (Hrs.) Sound level dBA, slow response** 

1 8 90 2 6 92 3 4 95 4 3 97 5 2 100 6 1-1/2 102 7 1 105 8 ½ 110 9 1/4 or less 115

extremely low, very low, low, medium low, medium etc.

model performance will be improved.

environment to keep their performance.

Department of labor occupational noise exposure standard

Fig. A.1. Permitted daily exposure time [30]

Table A.1. Permissible Noise Exposures

**7. Appendix 7.1 Appendix-A** 

Some Studies on Noise and Its Effects on Industrial/Cognitive Task Performance and Modeling 211

38. Your work is steady A B C D E

40. You are lay off recently A B C D E

42. You have valuable skills A B C D E

 Feeling depressed A B C D E Sleep restless A B C D E Do not Enjoy life A B C D E

Table B.1. Cognitive task (CT) questionnaire form for industrial applications [2]

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

37. You need generated standards to take consistent decision

39. You are threat to job

41. You have future lay

43. Hard to keep job for long

44. Much physical effort is

45. Have rapid physical

46. Have heavy loads at

47. You feel awkward body

48. You feel awkward upper body position

49. Sharing the hardship of

50 Have possibility to help the coworkers and a

> unity among workers

 Feel nervous while working

 Exceptionally tired in the morning and exhausted mentally and physically at end of the day

security

duration

required

activities

work

position

the job

off



A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

A B C D E

19. Have excessive work A B C D E

25. Highly care for your job A B C D E

20. Time is enough to finish

your work

others

24. You suppressing genuine emotion

26. Your consultation is constant with others

27. You don't have high responsibility to taking

> interference between family life and your job

29. You get the concern and the help of your supervisor

31. Have organizational care about workers opinions

33. Have high consideration of goals and values

accurate information to

34. you concern about workers

35. You need to collect

make decision

the decision

36. You need providing opportunities to appeal

30. Have friendly and helpful coworkers

32. You care about we-being

care for home

28. You have high

21. There is conflicting demands on your job

22. Have high emotional demands to work

23. You are negotiation with


Table B.1. Cognitive task (CT) questionnaire form for industrial applications [2]

Some Studies on Noise and Its Effects on Industrial/Cognitive Task Performance and Modeling 213

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**8. References** 


**10** 

*Italy* 

**Fuzzy Inference System for** 

*Scuola Superiore Sant'Anna, TeCIP Institute, PERCRO, Pisa* 

Silvia Cateni and Valentina Colla

**Data Processing in Industrial Applications** 

In the last years Fuzzy Inference Systems (FIS) have been used in several industrial applications in the field of automatic control, data classification, decision analysis, expert

The large use of FIS in the industrial field is mainly due to the nature of real data, that are often incomplete, noisy and inconsistent, and to the complexity of several processes, where the application of mathematical models can be impractical or even impossible, due to the lack of information on the mechanisms ruling the phenomena under consideration. Fuzzy theory is in fact essential and applicable to many complex systems and the linguistic formulation of its rule basis provides an optimal, very suitable and intuitive tool to

In real world database anomalous data (often called *outliers*) can be frequently found, which are due to several causes, such as erroneous measurements or anomalous process conditions. Outliers elimination is a necessary step, for instance, when building a training database for tuning a model of the process under consideration in standard operating conditions. On the other hand, in many applications, such as medical diagnosis, network intrusion or fraud detection, rare events are more interesting than the common samples. The rarity of certain patterns combined to their low separability from the rest of data makes difficult their identification. This is the case, for instance, of classification problems when the patterns are not equally distributed among the classes (the so-called *imbalanced dataset*  (Vannucci et al., 2011)). In many real problems, such as document filtering and fraud detection, a binary classification problem must be faced, where the data belonging from the "most interesting" class are far less frequent than the data belonging to the second class, which corresponds to normal situations. The main problem with imbalanced dataset is that the standard learners are biased towards the common samples and tend to reduce the error

In this chapter a preliminary brief review of traditional outlier detection techniques and classification algorithms suitable for imbalanced dataset is presented. Moreover some recent practical applications of FIS that are capable to outperform the widely adopted traditional

systems, time series prediction, and pattern recognition.

formalise the relationships between input and output variables.

rate without taking the data distribution into account.

methods for detection of rare data are presented and discussed.

**1. Introduction** 

