2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)最新文献_第5页

Greetings from the General Chairs 各位主席的问候

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/icwapr.2018.8521269

引用次数: 0

Phase Averaging on Square Cylinder Wake Based on Wavelet Analysis 基于小波分析的方形圆柱尾迹相位平均

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521270

Xiaoning Sun, Chengtao Yu, A. Rinoshika, Li Li, Yan Zheng

引用次数: 0

An Improved Local or Global Active Contour Driven by Legendre Polynomials 一种由Legendre多项式驱动的改进局部或全局活动轮廓

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521358

Guanghui He, Guangfang Yang, Bin Fang, Wei Zhang

引用次数: 2

Detection of Tax Arrears Based on Ensemble Leaering Model 基于集合领导模型的欠税检测

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521362

A. Su, Zhimin He, Junjian Su, Yan Zhou, Yun Fan, Yuan Kong

引用次数: 3

Classification of Power-Quality Disturbances Using Deep Belief Network 基于深度信念网络的电能质量扰动分类

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521311

Cui-Mei Li, Zengxiang Li, Nan Jia, Zhi-Liang Qi, Jianhua Wu

{"title":"Classification of Power-Quality Disturbances Using Deep Belief Network","authors":"Cui-Mei Li, Zengxiang Li, Nan Jia, Zhi-Liang Qi, Jianhua Wu","doi":"10.1109/ICWAPR.2018.8521311","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521311","url":null,"abstract":"This paper proposes to utilize an approach of deep belief network (DBN) for the classification of power-quality disturbances (PQDs). DBN is a deep learning algorithm which has been widely used in computer vision, voice recognition, natural language processing and etc., but barely been used in recognizing PQDs. The structure of the DBN consists of several stacked restricted Boltzmann machines (RBMs) for unsupervised learning. The frame of DBN is organized as follows: firstly, the first RBM is fully trained with the original signal by using contrastive divergence (CD) algorithm to obtain desirable features. Secondly, by fixing the weights and bias of the first RBM, the features turn into the next RBM, which is trained similarly as in the first step. Finally, after enough RBM pre-training, the network is fine-tuned with supervised training by back propagation (BP). The PQDs in this paper includes five single disturbance signal such as interruption, sag, swell, harmonic, oscillatory, and two mixed disturbance signals such as sag-harmonic and swell-harmonic. Experimental results demonstrate that the proposed approach achieves a higher classification rate than traditional algorithms.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126140648","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: 11

Design and Application of Image Enhancement Operator Based on Fractional Differential Interpolation Operation 基于分数阶微分插值运算的图像增强算子的设计与应用

2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521398

Wen Yang, Chaobang Gao, Yudie Zhong, Qiang-feng Zhou

{"title":"Design and Application of Image Enhancement Operator Based on Fractional Differential Interpolation Operation","authors":"Wen Yang, Chaobang Gao, Yudie Zhong, Qiang-feng Zhou","doi":"10.1109/ICWAPR.2018.8521398","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521398","url":null,"abstract":"Image enhancement is an important part of image processing, affected by the process of image acquisition, transformation, transmission, etc., the image quality will be reduced, the subsequent processing will be limited by the problems. Based on the characteristic of texture enhancement and noise suppression, fractional differentiation operation has been used in image processing, since it can not only strengthen the high and middle frequency components of the signal, but also can preserve the low frequency components. The paper designs an improved image enhanced operator based on the combination of fractional differentiation and interpolating operation called the maximum of interpolating operation fractional differentiation (MIOFD). Experiments show that the texture and edges of the images processed by MIOFD are enhanced. The enhancement is better than the integer differentiation, compared with traditional fractional differentiation, our method can preserve non-node pixels information and enlarge the difference of details, also makes the enhancement better.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130107620","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