2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications最新文献

Artificial neural networks for real-time optical hand posture recognition using a color-coded glove 使用颜色编码手套进行实时光学手部姿势识别的人工神经网络

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595842

F. Malric, A. El Saddik, N. Georganas

引用次数: 8

Developing a neural network model for magnetic yoke structure 建立了磁轭结构的神经网络模型

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595836

H. Ravanbod, E. Norouzi

{"title":"Developing a neural network model for magnetic yoke structure","authors":"H. Ravanbod, E. Norouzi","doi":"10.1109/CIMSA.2008.4595836","DOIUrl":"https://doi.org/10.1109/CIMSA.2008.4595836","url":null,"abstract":"Magnetic flux leakage technique is used extensively to detect and characterize defects in natural gas and oil transmission pipelines. The amount of magnetic flux introduced into the test sample is an important factor in the resolution of flaw detection. It depends on the power of permanent magnets and the geometrical design of the magnetic yoke. Finite element method (FEM) is the most widely used method of analyzing magnetic yoke due to its power, accuracy and straightforwardness. On the other hand its calculations are so complicated and time consuming, and every single modification in the parameters of the problem requires a new run. In this paper, we present an innovative method to overcome the problem of heavy calculations. In this method an artificial neural network (ANN) is trained to simulate the behavior of the magnetic yoke for different design parameters with an acceptable error. Afterwards the trained ANN calculates the desired output (usually generated flux) for a new design of the yoke by generalization of the already seen samples. This new method has got two advantages over the traditional FEM. First it is very fast and second it is flexible due to modifications in parameters.","PeriodicalId":302812,"journal":{"name":"2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116149068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Radial basis function networks with quantized parameters 具有量化参数的径向基函数网络

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595826

M. B. Lucks, N. Oki

引用次数: 1

Urban pollution monitoring through opportunistic mobile sensor networks based on public transport 通过基于公共交通的机会移动传感器网络进行城市污染监测

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595835

F. Gil-Castiñeira, F. López-Peña

引用次数: 28

A DCT based nonlinear predictive coding for feature extraction in speech recognition systems 基于DCT的非线性预测编码在语音识别系统中的特征提取

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595825

M. Azar, F. Razzazi

引用次数: 11

Image processing for granulometry analysis via neural networks 图像处理的粒度分析通过神经网络

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595827

S. Ferrari, V. Piuri, F. Scotti

{"title":"Image processing for granulometry analysis via neural networks","authors":"S. Ferrari, V. Piuri, F. Scotti","doi":"10.1109/CIMSA.2008.4595827","DOIUrl":"https://doi.org/10.1109/CIMSA.2008.4595827","url":null,"abstract":"The analysis of granulometry of substances is relevant in a great variety of the research and industrial applications as such as the pharmaceutical sector, the food sector, the basic materials production and in the concrete and wood panel industries. This analysis is important since many relevant properties of the materials can depend on the distribution of the particles sizes/shapes during the production. In this work we present an innovative method capable to estimate the particles size distribution in an image without the use of segmentation techniques by using neural networks. The paper contribution is twofold. The proposed method presents a set of techniques based on wavelet analysis and image processing techniques suitable to extract relevant features for the granulometry analysis. Then, the extracted set of features is used as input to neural networks in order to achieve the classification of each single pixel accordingly to the probability to belong to a specific class of particles size (a single band in the histogram of the distribution of the particles size). The produced outputs have been used to perform the estimation of the particle granulometry contained in the image. Results are encouraging and show the effectiveness of the proposed method.","PeriodicalId":302812,"journal":{"name":"2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115021145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Mobile robot navigation using particle swarm optimization and noisy RFID communication 基于粒子群优化和噪声RFID通信的移动机器人导航

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595843

W. Gueaieb, M. S. Miah

引用次数: 11

Towards artificial intelligence based automatic adaptive response analyzer for high frequency analog BIST 基于人工智能的高频模拟BIST自动自适应响应分析仪研究

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595841

E. Petlenkov, A. Jutman, S. Nõmm, R. Ubar

引用次数: 1

Application of reinforcement learning to improve control performance of plant 应用强化学习提高装置的控制性能

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595837

M. Shadi, Mahdi Sargolzaei

引用次数: 0

Genetic algorithm implementation for distributed security systems optimization 分布式安全系统优化的遗传算法实现

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595845

P. Bykovyy, Y. Pigovsky, V. Kochan, A. Sachenko, G. Markowsky, S. Aksoy

引用次数: 5