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• Tool Development for Human Audible Spectrum Compensation

  1-6.

  Francisco Gonçalves Filomena Soares

  Vítor Carvalho

  José Machado

  Views:

  105

  Communication relies on good understanding. Humans relate to each other through visual, audible and tactile communication. It is imperative that the audible communication message reaches the receiver in good conditions, in order to keep a healthy, smooth and understandable speech. There are some disturbances in human speech and communication when hearing damage is present. Nowadays, hearing loss is a frequent injury, caused by noise pollution, daily stress or noisy workplaces. Yet, it can be treated by several ways. This project consists in developing a tool that captures the emitter's voice audible spectrum, filters the noise and other frequencies, and compensates the message, enabling the listener/receiver understanding. The purpose of this research is not aimed to substitute nor compete with hearing aids in the market, which are well-developed, certified and prescribed by Otorhinolaryngology clinicians. The focus of this study is to identify the issues of human hearing loss and to develop an algorithm for hearing compensation by using filtering techniques in a simulated environment applied to a hearing model.

• Noise and Performances Analysis of Commerical Aircrafts using Artificial Neural Networks

  1-6.

  Şahin Yildirim

  Views:

  92

  Commerical aircrafts are very important part for airway travelling. In spite of high technology on aircrafts, there is still fatality accidents in the world. Because of this reason, it is very important criteria to analyse noises of main elements of the air-craft systems. In tis study, an aircraft’s main disturbances are analysed with proposed neural networks. Firstly, the noises of the jet, turbine and fan were measured from the aircraft. Secondly, the measured parameter values were predicted the proposed neural networks. The results of the proposed neuarl approaches were shown that this type of neural predictors will be employed to predict aircrafts unpredicted disturbances in real time applications.

• System Identification and Model Validation of Recursive Least Squares Algorithm for Box–Jenkins Systems

  1-6.

  Nasar Aldian Ambark Shashoa

  Abdurrezag S. Elmezughi

  Ibrahim N. Jleta

  Nasser B. Ekreem

  Views:

  216

  In this paper, a new-type recursive least squares algorithm is proposed for identifying the system model parameters and the noise model parameters of Box–Jenkins Systems. The basic idea is based on replacing the unmeasurable variables in the information vectors with their estimates. The proposed algorithm has high computational efficiency because the dimensions of the involved covariance matrices in each subsystem become small. Validation of the model is evaluated using some statistical methods, Which, best-fit criterion and Histogram. Simulation results are presented to illustrate the effectiveness of the proposed algorithm.

• Vibration Analysis and Optimal Design of Pneumatic Circuits Using Artificial Neural Networks

  1-9.

  Şahin Yildirim

  Asli Durmuşoğlu

  Views:

  150

  Pneumatic systems are commonly used in industry processes and applications of automotion. These systems are make attractive power transmission with the compressed air, because of they are economic, clear, safe and simple structured.So in this systems, as noise and vibration effects, it’ s undesirable situations both human health and system performances yield and working life. In this study, vibration and noise datas are obtained from two type of pneumatic systems prototype which are classify metal and non-metal materials and performed an analysis with help of this datas and used neural network which has adaptive and quick contruction.