2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA.最新文献_第3页

A comparison of neural networks architectures for geometric modelling of 3D objects 三维物体几何建模的神经网络体系结构比较

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397253

A. Crétu, E. Petriu, G. Patry

引用次数: 1

CFAR intrusion detection method based on support vector machine prediction 基于支持向量机预测的CFAR入侵检测方法

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397219

D. He, H. Leung

引用次数: 7

Blood pressure estimation using neural networks 神经网络血压估计

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397222

S. Colak, C. Isik

引用次数: 23

A DLSI approach for content-based image classification 基于内容的图像分类DLSI方法

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397250

S. Nilufar, L. Chen, H. K. Kwan

引用次数: 2

Fast and accurate approximation of the long wave radiation parameterization in a GCM using neural networks: evaluation of computational performance and accuracy of approximation in the NCAR CAM-2 基于神经网络的GCM长波辐射参数化快速精确逼近:NCAR CAM-2计算性能和逼近精度评价

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397230

V. Krasnopolsky, M. Fox-Rabinovitz, D. Chalikov

{"title":"Fast and accurate approximation of the long wave radiation parameterization in a GCM using neural networks: evaluation of computational performance and accuracy of approximation in the NCAR CAM-2","authors":"V. Krasnopolsky, M. Fox-Rabinovitz, D. Chalikov","doi":"10.1109/CIMSA.2004.1397230","DOIUrl":"https://doi.org/10.1109/CIMSA.2004.1397230","url":null,"abstract":"A new approach based on a synergetic combination of statistical/machine learning and deterministic modeling within atmospheric models is presented. The approach uses neural networks as a statistical or machine learning technique for an accurate and fast emulation or statistical approximation of model physics parameterizations. It is applied to the development of an accurate and fast approximation of an atmospheric long wave radiation parameterization for the NCAR Community Atmospheric Model, which is the most time consuming component of model physics. The developed neural network emulation is two orders of magnitude, 50-80 times, faster than the original parameterization. A comparison of the parallel 10-year climate simulations performed with the original parameterization and its neural network emulations, confirmed that these simulations produce almost identical results. The obtained results show the conceptual and practical possibility of an efficient synergetic combination of deterministic and statistical learning components within an atmospheric climate or forecast model.","PeriodicalId":102405,"journal":{"name":"2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114349017","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: 1

Segmentation of connected Arabic characters using hidden Markov models 使用隐马尔可夫模型分割连接的阿拉伯字符

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397244

A. M. Gouda, M. Rashwan

引用次数: 22

Real-time data analysis of action potentials 动作电位的实时数据分析

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397223

R. Schrott, A. Keuer, J. Taube, D. Schmuck, H. Beikirch, W. Baumann, E. Schreiber

{"title":"Real-time data analysis of action potentials","authors":"R. Schrott, A. Keuer, J. Taube, D. Schmuck, H. Beikirch, W. Baumann, E. Schreiber","doi":"10.1109/CIMSA.2004.1397223","DOIUrl":"https://doi.org/10.1109/CIMSA.2004.1397223","url":null,"abstract":"In this paper an automated approach for the measurement of the electrical activity of a biological neural network is proposed. This method can be applied in the drug development process to verify the lead compounds of the high throughput screening with cell-based assays and there with reducing animal experiments. This verification is also called high content screening. To be able to detect and to evaluate action potentials, which mainly represent the electrical cell activity, neurons are cultured on a silicon sensor chip with integrated electronics and a multielectrode array (MEA). Due to the high parallelism of the measurement efficient and flexible algorithms are needed to assess and to classify the acquired data in real time. A system, consisting of a field programmable gate array (FPGA) and a digital signal processor (DSP) provide the required implementation platform. Filtering based on the discrete wavelet transform removes superimposed noise and low frequency disturbances from the neural signal. This analysis offers also a method to compute an adaptive threshold, which is essential for the detection process. Subsequently the measured data is classified to provide the user with a feedback of the experiment. First promising evaluation results from simulations and proof of concept hardware implementations can be presented.","PeriodicalId":102405,"journal":{"name":"2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA.","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127790321","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: 1

Characterizing semiconductor devices using computational intelligence techniques with semiconductor automatic test system (ATE) 利用计算智能技术和半导体自动测试系统(ATE)表征半导体器件

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397232

E. Liau, D. Schmitt-Landsiedel

{"title":"Characterizing semiconductor devices using computational intelligence techniques with semiconductor automatic test system (ATE)","authors":"E. Liau, D. Schmitt-Landsiedel","doi":"10.1109/CIMSA.2004.1397232","DOIUrl":"https://doi.org/10.1109/CIMSA.2004.1397232","url":null,"abstract":"Characterization of semiconductor devices is used to gather as much data about the device as possible to determine weaknesses in design or trends in the manufacturing process. This is done by varying the device specification parameters with respect to set of predefined tests and determining where the part passes or fails. The key to this process is discovering the single trip (fig. 1. pass/fail) point as accurately as possible. However, this approach cannot guarantee the robustness of device performance variation vs specification based on only a single trip point and single test analysis. This means device could still violate the specification while passing all characterization tests. In this paper, we propose a novel multiple trip point characterization concept to overcome the constraint of single trip point concept in device characterization phase. In addition, we use computational intelligence techniques to further manipulate these sets of multiple trip point values and tests based on semiconductor ATE, such that characterization trip point values with respect to different tests can be learned by neural network and fuzzy system, then performing classification task of worst case variation of device's performance vs specification. At last, the final worst-case variation can be further detected by genetic algorithm. Our experimental results demonstrate an excellent design parameter variation analysis in device characterization phase, as well as detection of a set of worst-case tests that can provoke the worst-case variation, while traditional approach was not capable of detecting them.","PeriodicalId":102405,"journal":{"name":"2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131979361","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

Morphological classification of blood leucocytes by microscope images 显微镜下血液白细胞的形态分类

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397242

V. Piuri, F. Scotti

引用次数: 223

Artificial neural networks for meteorological nowcast 面向气象预报的人工神经网络

2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. Pub Date : 2004-07-14 DOI: 10.1109/CIMSA.2004.1397226

E. Pasero, W. Moniaci

引用次数: 17