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Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm : A Field Study

Zare Sajad, Ghotbiravandi Mohammad Reza, Elahishirvan Hossein, Ahsaeed Mostafa Ghazizadeh, Rostami Mina, Esmaeili Reza

Archives of Acoustics

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2020

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Vol. 45, No. 2

303--311

EN

The aim of the study study was to model, with the use of a neural network algorithm, the significance of a variety of factors influencing the development of hearing loss among industry workers. The workers were categorized into three groups, according to the A-weighted equivalent sound pressure level of noise exposure: Group 1 (LAeq < 70 dB), Group 2 (LAeq 70-80 dB), and Group 3 (LAeq > 85 dB). The results obtained for Group 1 indicate that the hearing thresholds at the frequencies of 8 kHz and 1 kHz had the maximum effect on the development of hearing loss. In Group 2, the factors with maximum weight were the hearing threshold at 4 kHz and the worker’s age. In Group 3, maximum weight was found for the factors of hearing threshold at a frequency of 4 kHz and duration of work experience. The article also reports the results of hearing loss modeling on combined data from the three groups. The study shows that neural data mining classification algorithms can be an effective tool for the identification of hearing hazards and greatly help in designing and conducting hearing conservation programs in the industry.

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Ustojčivost’ bašennyh kranov ot oprokidyvaniâ pri slučajnom vozdejstvii poryvov vetra

Sorokin P., Mishin A.

TTS Technika Transportu Szynowego

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2012

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R. 19, nr 9

881--887, CD

RU

В статье рассматриваются вопросы проектирования системы управления устойчивостью башенных кранов при воздействии на них ветровой нагрузки. Способ сохранения устойчивости кранов основан на использовании искусственной нейронной сети. Целью предлагаемой системы является корректировка положения стрелы крана в зависимости от порывов ветра.

EN

The article refers to the control system stability designing of tower cranes under the influence of wind loading. The method to ensure the overturning stability is based on using of neural network algorithm. The purpose is to adjust the beam position depending on the gust loading.

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Air motor system modelling using extended neural network algorithms

Marumo R., Tokhi M. O.

Systems Science

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2005

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Vol. 31, no 3

87-101

EN

This paper investigates the development of neuro-modelling approaches for a highly non-linear system. The work is motivated by the fact that the response of a pneumatic drive is very slow, which leads to inability of the system to attain set points due to high hysteresis. Also the dynamic model of the pneumatic system is highly non-linear, which greatly complicates controller design and development. To address these problem areas, two streams of research efforts have evolved. These are: using conventional methods to develop a modelling and control strategy and adopting a strategy that does not require mathematical model of the system. This paper presents an investigation into the modelling of an air motor incorporating a pneumatic equivalent of the electric H-bridge. The pneumatic H-bridge has been devised for speed and direction control of the motor. The system characteristics are divided into three main regions, namely low speed, medium speed and high speed. The system is highly non-linear in the low speed region and hence a neuro-modelling approach is proposed.