2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications最新文献_第2页

Analysis of the effects of bearings-only sensors on the performance of the neural extended kalman filter tracking system 纯轴承传感器对神经扩展卡尔曼滤波跟踪系统性能的影响分析

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595832

S. Stubberud, K. Kramer, J. A. Geremia

{"title":"Analysis of the effects of bearings-only sensors on the performance of the neural extended kalman filter tracking system","authors":"S. Stubberud, K. Kramer, J. A. Geremia","doi":"10.1109/CIMSA.2008.4595832","DOIUrl":"https://doi.org/10.1109/CIMSA.2008.4595832","url":null,"abstract":"The neural extended Kalman filter (NEKF) has proven to be a quality maneuver target tracking system when the sensors provide a fully observable measurement, such as a radarpsilas range-bearing measurement or a position report. As with any state estimation technique, the NEKF requires observability in order to estimate the target track states. Observability is needed as well to train the weights of the neural network, since the neural network training paradigm is coupled to the target states. Passive sensor systems, such as electronic surveillance measures and passive sonar arrays, provide an angle-only measurement. Such bearings-only measurements make the tracking system an unobservable system. For a Kalman filter estimator, this will result in the eigenvalues of the error covariance matrix to grow without bound. For the NEKF, since both the target state and the weights of the neural network are affected by the lack of observability, the results could be more pronounced. In this paper, the application of the NEKF in bearings-only tracking problems is analyzed to determine the effects on performance. The analyzed cases look at a single sensor platform in four important scenarios: a stationary platform and straight-line target, a stationary platform and a maneuvering target, a maneuvering platform and a straight-line target, and a maneuvering platform and a maneuvering target.","PeriodicalId":302812,"journal":{"name":"2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"11 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":"123053935","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: 3

Improvements to the bacterial memetic algorithm used for fuzzy rule base extraction 对用于模糊规则库提取的细菌模因算法的改进

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595829

L. Gál, János Botzheim, L. Kóczy

引用次数: 33

Uncertainty mesurement in video and infrared cameras system 视频和红外摄像机系统中的不确定度测量

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595821

B. Cornel, S. Mircea

引用次数: 0

Multiscale windowed denoising and segmentation of hyperspectral images 高光谱图像的多尺度加窗去噪与分割

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595828

G. Bilgin, S. Erturk, T. Yıldırım

引用次数: 3

Coupling Kernel Principal Component Analysis with ANN for improving analysis accuracy of seven-component alkane gaseous mixture 核主成分与神经网络耦合分析提高七组份烷烃气体混合物分析精度

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595823

Hui-min Hao, Jianan Cao, Hongliang Wang, Zhiqiang Yu, Junhua Liu

{"title":"Coupling Kernel Principal Component Analysis with ANN for improving analysis accuracy of seven-component alkane gaseous mixture","authors":"Hui-min Hao, Jianan Cao, Hongliang Wang, Zhiqiang Yu, Junhua Liu","doi":"10.1109/CIMSA.2008.4595823","DOIUrl":"https://doi.org/10.1109/CIMSA.2008.4595823","url":null,"abstract":"To further improving the analysis accuracy of Artificial Neural Networks (ANN) model for quantitative analysis of seven-component alkane gaseous mixtures composed of methane, ethane, propane, isobutane, n-butane, isopentane, and n-pentane, the Kernel Principal Component Analysis (KPCA) technique was proposed to couple with it. The gaseous mixtures were measured by a novel Acousto-Optic Tunable Filter Near Infrared (AOTF-NIR) spectrometer. KPCA mapped the NIR spectral data of gaseous mixtures by a Gaussian kernel to a high-dimensional feature space and implemented feature extraction in it. As input variables, the extracted features were fed into a three-layered ANN to create quantitative analysis model of above-mentioned seven component gases. The performance of KPCA-NN model was assessed by Root Mean Square Error of Prediction (RMSEP) of testing set. The RMSEP of seven components by KPCA-ANN were less than 0.361%. Comparing with the ANN model without KPCA feature extraction, the KPCA-ANN model obtained the less RMSEP values. The research results indicated that the KPCA-NN model shows higher analysis accuracy than ANN model.","PeriodicalId":302812,"journal":{"name":"2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"48 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":"133569701","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: 0

Reliability assessment under uncertainty using Dempster-Shafer and vague set theories 基于Dempster-Shafer和模糊集理论的不确定可靠性评估

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595847

S. Pashazadeh, M. Sharifi

{"title":"Reliability assessment under uncertainty using Dempster-Shafer and vague set theories","authors":"S. Pashazadeh, M. Sharifi","doi":"10.1109/CIMSA.2008.4595847","DOIUrl":"https://doi.org/10.1109/CIMSA.2008.4595847","url":null,"abstract":"Analyzing reliability of a system in design stage requires expertpsilas estimations and statistical data with various degrees of epistemic uncertainty and doing aggregation in a coherent framework. Dempster-Shafer (DS) theory is theorypotentially valuable tool for combination of evidence obtained from multiple different sources. One approach for fuzzy reliability assessment is using Vague set (VS) theory. DS theory has many similarities with VS theory. Uncertain raw data about the component reliability of a system can be combined using different combination methods of DS theory and can be represented in the form of triangular fuzzy vague number. Using the proper methods and equations, the fuzzy reliability of the system can be computed with triangular vague numbers of components reliability. Combining these two theories eliminates the gap between the representation of combined evidences and the way of representing the reliability of components in the VS theory for reliability assessment. Our proposed method eliminates this gap in very convenient form. Because of closed relevance of these two theories we can represent the output of DS combination in the form of vague triangular number in the VS theory. With this method we eliminate the loss of meaningful information in this conversion.","PeriodicalId":302812,"journal":{"name":"2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"138 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":"134484087","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: 8

Application of data approximation and classification in measurement systems - comparison of “neural network” and “Least Squares” approximation 数据逼近与分类在测量系统中的应用——“神经网络”与“最小二乘”逼近的比较

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595834

Amir Jabbari, R. Jedermann, W. Lang

{"title":"Application of data approximation and classification in measurement systems - comparison of “neural network” and “Least Squares” approximation","authors":"Amir Jabbari, R. Jedermann, W. Lang","doi":"10.1109/CIMSA.2008.4595834","DOIUrl":"https://doi.org/10.1109/CIMSA.2008.4595834","url":null,"abstract":"In measurement systems, environmental conditions are measured based on predefined scenarios. Measured data are then processed in either a decentralized or centralized manner. In advanced systems (especially for distributed data processing), taking artificial intelligence features into consideration could improve measurement performance and reliability. It is assumed as autonomy in measurement system which leads to distributed ldquointelligent data measurement and processingrdquo. In this paper, two different methodologies for ldquotemperature predictionrdquo are compared. A discussion concerning the classification of recorded data is then presented. Both a mathematical approach, the so-called ldquoleast squaresrdquo approach, and a model-free approach, called back-propagation, are applied and compared for temperature approximation. After approximation, the predicted temperature values are compared with real temperature records for classification purposes. The ldquoclassification mechanismrdquo includes signal processing features for improving performance.","PeriodicalId":302812,"journal":{"name":"2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"73 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133652200","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: 5

Categorization of power quality transients using higher-order statistics and competitive layers-based neural networks 基于高阶统计和基于竞争层的神经网络的电能质量暂态分类

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595838

J.-J.G. de-la-Rosa, A. M. Muñoz

引用次数: 3

An occupancy grid based SLAM method 基于占用网格的SLAM方法

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595844

Ozan Ozisik, Sirma Yavuz

引用次数: 15

A quality of performance model for evaluating post-stroke patients 评价脑卒中后患者的行为质量模型

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2008-07-14 DOI: 10.1109/CIMSA.2008.4595824

A. Alamri, M. Eid, A. El Saddik

引用次数: 5