2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第5页

Adaptive feature extraction based on Stacked Denoising Auto-encoders for asynchronous motor fault diagnosis 基于叠置去噪自编码器的异步电动机故障诊断自适应特征提取

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2016-10-01 DOI: 10.1109/CISP-BMEI.2016.7852830

Na Xiao, Dan Liu, Ailing Luo, Xiangwei Kong, Tianshe Yang, Nan Xing, Fangzheng Li

{"title":"Adaptive feature extraction based on Stacked Denoising Auto-encoders for asynchronous motor fault diagnosis","authors":"Na Xiao, Dan Liu, Ailing Luo, Xiangwei Kong, Tianshe Yang, Nan Xing, Fangzheng Li","doi":"10.1109/CISP-BMEI.2016.7852830","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852830","url":null,"abstract":"As the important power equipment in the mechanical system, fault diagnosis for asynchronous motor is helpful to monitor working status and prevent failure causing unnecessary loss. In the fault diagnosis domain, feature extraction is the key step which is related to the performance of diagnosis results. For the asynchronous motor, the motor current signature analysis (MCSA) is one of the most powerful diagnosis method with stator-current signals. However, MCSA has some shortcomings, which degrade performance and accuracy of a motor-diagnosis system. Therefore, advanced feature extraction algorithm of current signal using Stacked Denoising Auto-encoders (SDAE) is proposed in this paper. The method of SDAE and application in motor are discussed in detail. Then, the features learned from the SDAE is displayed and a softmax regression model is used to verify the discriminability of the features. The experiments show that SDAE is an effective feature extraction technique for asynchronous motor fault diagnosis.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"315 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122025506","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

Local mean decomposition algorithm improved by de-correlation 局部均值分解算法的去相关改进

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2016-10-01 DOI: 10.1109/CISP-BMEI.2016.7852898

Ying Xiao, Yu-Hua Dong

引用次数: 1

Protein sub-cellular localization based on noise-intensity-weighted linear discriminant analysis and an improved k-nearest-neighbor classifier 基于噪声强度加权线性判别分析和改进k近邻分类器的蛋白质亚细胞定位

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2016-10-01 DOI: 10.1109/CISP-BMEI.2016.7853022

Zhenfeng Lei, Shunfang Wang, Dongshu Xu

{"title":"Protein sub-cellular localization based on noise-intensity-weighted linear discriminant analysis and an improved k-nearest-neighbor classifier","authors":"Zhenfeng Lei, Shunfang Wang, Dongshu Xu","doi":"10.1109/CISP-BMEI.2016.7853022","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7853022","url":null,"abstract":"Data dimension reduction and classification are the key steps in protein sub-cellular localization. With the rapid development of biological science and technology, a plenty of high dimensional biological data have generated, accompanied by certain noise. How to express high dimensional data in low dimension space and achieve better classification effect have become one of the significant tasks for researchers in the application of protein sub-cellular localization. Both the traditional dimension reduction algorithm of linear discriminant analysis (LDA) and the popular classifier of k-nearest neighbor (KNN) cannot meet the needs of the current application well if they are simply used without improvements. The aim of LDA is to seek out a projecting line at certain direction letting the projection of samples as far away as possible. However, noise jamming expands the within-class distance and makes the classes uneasily separated even by LDA. Besides, KNN has not taken samples' inequality into consideration primely. Therefore, this paper first uses the noise intensity as a kind of weight in LDA, then improves KNN algorithm by considering the inequality of samples from different classes with a within-class KNN method. Experimental results show that the proposed method by combining the above two improvements gets ideal feasibility and effectiveness in classification through the verification of Jackknife.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124027766","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

Camera calibration and its application of binocular stereo vision based on artificial neural network 基于人工神经网络的双目立体视觉摄像机标定及其应用

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2016-10-01 DOI: 10.1109/CISP-BMEI.2016.7852811

J. Sun, Yuzhong Ma, Han Yang, Xinglong Zhu

引用次数: 7

Feature selection algorithm for evoked EEG signal due to RGB colors RGB颜色诱发脑电信号的特征选择算法

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2016-10-01 DOI: 10.1109/CISP-BMEI.2016.7852955

Eman T. Alharbi, Saim Rasheed, S. Buhari

{"title":"Feature selection algorithm for evoked EEG signal due to RGB colors","authors":"Eman T. Alharbi, Saim Rasheed, S. Buhari","doi":"10.1109/CISP-BMEI.2016.7852955","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852955","url":null,"abstract":"In this paper, a single trial classification is introduced for the Electroencephalography (EEG) signals evoked by RGB colors. The effectiveness of a single trial classification is an important step towards online classification of EEG signals. Signals are analyzed by Empirical Mode Decomposition (EMD) technique, and the last decomposition is used in the feature extraction stage. We investigate different feature extraction methods in order to find out the best method which can be used with colors dataset. These methods are: Event-Related Spectral Perturbations (ERSP), Target mean, AutoRegressive and EMD residual. In addition, we propose a new feature selection algorithm, which focuses on selecting the best features by studying the behavior of EEG components that appear due to the introduced color. We introduced a comparison between the classification results of using all extracted features, the results of using the selected features by the proposed algorithm and the results of using the selected features by recursive feature elimination algorithm, which is used by similar study. The proposed algorithm is proved with all the investigated feature extraction methods as the classification accuracies are increased. Support Vector Machine (SVM) is used in the classification process. We found that the execution time of using color's stimulus is only 0.23s, which is much less than the time which was required by any other stimulus such as imagery and spelling word presented in the previous researches. The best feature extraction method that gives the highest classification accuracy and can be used with real time BCI systems are Target Mean and EMD residual, as their accuracies are high and the computation time is very low.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128438465","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: 7

Recognition algorithm for huge number of very similar objects 大量非常相似物体的识别算法

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2016-10-01 DOI: 10.1109/CISP-BMEI.2016.7852768

Lingfeng Kong, Qingxiang Wu

引用次数: 1

Two methods for estimating noise amplitude spectral in non-stationary environments 非平稳环境下噪声振幅谱的两种估计方法

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2016-10-01 DOI: 10.1109/CISP-BMEI.2016.7852852

S. Ou, W. Liu, Suojin Shen, Ying Gao

引用次数: 1

A kernel fuzzy clustering infrared image segmentation algorithm based on histogram and spatial restraint 基于直方图和空间约束的核模糊聚类红外图像分割算法

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2016-10-01 DOI: 10.1109/CISP-BMEI.2016.7852728

Shaoyi Li, Jun Ma

{"title":"A kernel fuzzy clustering infrared image segmentation algorithm based on histogram and spatial restraint","authors":"Shaoyi Li, Jun Ma","doi":"10.1109/CISP-BMEI.2016.7852728","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852728","url":null,"abstract":"Because the contrast of the image for guiding the high-speed infrared air-to-air missile is low, its signal to noise ratio is poor and the target and its background gray-scale coupling is strong, the paper analyzes the reasons why the threshold value segmentation method and the fuzzy C-means clustering method have the over-segmentation and under-segmentation in segmenting the above type of image. Hence we propose the kernel fuzzy clustering segmentation algorithm based on histogram and spatial constraint, which utilizes the global first-moment histogram of the infrared image to restrict the number of clusters and the clustering center, improves the spatial correlation function that fully manifests the correlations among pixels inside a neighbor domain and reconstructs the membership degree matrix and the clustering central function, thus segmenting the infrared image with the kernel fuzzy clustering algorithm. The results on the experiments on a sequential infrared image show preliminarily that, compared with the traditional threshold value segmentation algorithm, the fuzzy C-means segmentation algorithm and the kernel fuzzy clustering algorithm, the improved algorithm proposed in the paper can reduce entropy segmentation by about 60% on average and increase the correlation degrees among clusters by around 10%, thus enhancing to a certain extent the efficiency and precision for segmenting the fuzzy image whose target gray-scale and background gray-scale are strongly coupled.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128657711","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

Analysis of multiscale sign series entropy of the young and middle-aged electroencephalogram signals 中青年脑电图信号的多尺度符号序列熵分析

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2016-10-01 DOI: 10.1109/CISP-BMEI.2016.7852958

Fei Du, Shitong Wang, Jun Wang, Jiafei Dai, F. Hou, Jin Li

{"title":"Analysis of multiscale sign series entropy of the young and middle-aged electroencephalogram signals","authors":"Fei Du, Shitong Wang, Jun Wang, Jiafei Dai, F. Hou, Jin Li","doi":"10.1109/CISP-BMEI.2016.7852958","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852958","url":null,"abstract":"The physiological analysis of electroencephalogram (EEG) signals is of great significance in assessing the activity of the brain function and the physiological state. EEG is a means of clinical examination of brain diseases. Age is one of the important factors that affect the results of the EEG. EEG signal analysis is mainly to analyze the time series of the signal, multiscale entropy (MSE) analysis [1-3] is the method that used to analyze the finite length of the time series. Multiscale sign series entropy (MSSE) method is proposed for the analysis of EEG signals in the young and middle-aged. We use the proposed method to analyze the signals from several aspects of data length, word length, noise, multi scale etc. By analyzing the influence of these factors, we can still distinguish the EEG signals of different ages. Multiscale sign series entropy (MSSE) analysis algorithm can effectively separate the brain electrical signals from the young and middle aged, which is expected to have a certain reference value for the traditional pathological analysis of the EEG signals.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128969453","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

A multi-sensor based pre-impact fall detection system with a hierarchical classifier 基于多传感器的分层分类器预碰撞坠落检测系统

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2016-10-01 DOI: 10.1109/CISP-BMEI.2016.7852995

Yiwen Su, D. Liu, Yingfeng Wu

引用次数: 15