Pub Date : 2018-07-01DOI: 10.1109/ICWAPR.2018.8521240
Yanxia Zhong, Huayi Liu, Xiyan Sun, Rushi Lan, Xiaonan Luo
In this paper, a new image encryption algorithm is proposed by integrating Sine and piecewise linear chaotic maps. In order to realize the effect of encrypted image, the security key and 2D Sine-piecewise linear chaotic map (SPLCM) are used to encrypt the image by using random sequence and random matrix, and then using the replacement operation and the diffusion operation of the original image. The proposed image encryption algorithm is simple and practical, and the simulation results show that this algorithm is able to encrypt different types of digital images into unidentifiable random images. The security analysis also shows that this algorithm has a higher security leve.l
{"title":"Image Encryption Using 2D Sine-Piecewise Linear Chaotic Map","authors":"Yanxia Zhong, Huayi Liu, Xiyan Sun, Rushi Lan, Xiaonan Luo","doi":"10.1109/ICWAPR.2018.8521240","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521240","url":null,"abstract":"In this paper, a new image encryption algorithm is proposed by integrating Sine and piecewise linear chaotic maps. In order to realize the effect of encrypted image, the security key and 2D Sine-piecewise linear chaotic map (SPLCM) are used to encrypt the image by using random sequence and random matrix, and then using the replacement operation and the diffusion operation of the original image. The proposed image encryption algorithm is simple and practical, and the simulation results show that this algorithm is able to encrypt different types of digital images into unidentifiable random images. The security analysis also shows that this algorithm has a higher security leve.l","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"29 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120927180","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}
Pub Date : 2018-07-01DOI: 10.1109/icwapr.2018.8521269
{"title":"Greetings from the General Chairs","authors":"","doi":"10.1109/icwapr.2018.8521269","DOIUrl":"https://doi.org/10.1109/icwapr.2018.8521269","url":null,"abstract":"","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128628115","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}
Pub Date : 2018-07-01DOI: 10.1109/ICWAPR.2018.8521270
Xiaoning Sun, Chengtao Yu, A. Rinoshika, Li Li, Yan Zheng
Phase average techniques based on wavelet multiresolution analysis and continuous wavelet transform are developed to reveal the phase-averaged features of square cylinder wake measured by high-speed PIV. The multi-scale turbulent structures are phase-sorted to give phase-averaged representations of flow field. The phase-averaged measured flow fields suggest that the wake flow rolls up and down and is conveyed downstream together with the corresponding vortices, forming a vortex pair with opposite sense of rotation. The phase-averaged vorticity contours of large-scale flow structures show good correspondence to the topology of phase-averaged measured flow field, suggesting the alternative nature of the vortex street with strong periodicity. The phase averaged intermediate-scale structures tend to be conveyed downstream along streamwise direction, with the rotation sense vary from the first half period to the last half period, implying the nature of Kelvin-Helmholtz vortex.
{"title":"Phase Averaging on Square Cylinder Wake Based on Wavelet Analysis","authors":"Xiaoning Sun, Chengtao Yu, A. Rinoshika, Li Li, Yan Zheng","doi":"10.1109/ICWAPR.2018.8521270","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521270","url":null,"abstract":"Phase average techniques based on wavelet multiresolution analysis and continuous wavelet transform are developed to reveal the phase-averaged features of square cylinder wake measured by high-speed PIV. The multi-scale turbulent structures are phase-sorted to give phase-averaged representations of flow field. The phase-averaged measured flow fields suggest that the wake flow rolls up and down and is conveyed downstream together with the corresponding vortices, forming a vortex pair with opposite sense of rotation. The phase-averaged vorticity contours of large-scale flow structures show good correspondence to the topology of phase-averaged measured flow field, suggesting the alternative nature of the vortex street with strong periodicity. The phase averaged intermediate-scale structures tend to be conveyed downstream along streamwise direction, with the rotation sense vary from the first half period to the last half period, implying the nature of Kelvin-Helmholtz vortex.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116852697","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}
Pub Date : 2018-07-01DOI: 10.1109/ICWAPR.2018.8521358
Guanghui He, Guangfang Yang, Bin Fang, Wei Zhang
In the paper, an improved local or global active contour model driven by Legendre Polynomials(LGLP) is proposed. It implemented with a special method, which selectively penalizes the level set function and then uses a filter to regularize it. Firstly, utilizing Legendre Polynomials approximates region intensity. Secondly, an improved region-based signed pressure force (ISPF) function is proposed, which efficiently stop the contours at weak edges, especially for the segmented image with intensity inhomogeneity. Finally, an edge stopping function is added to robustly capture the boundaries of objects. Experimental results show that the improved method is faster and achieve higher accuracy than other models on real images with intensity inhomogeneity, noise and multiple objects.
{"title":"An Improved Local or Global Active Contour Driven by Legendre Polynomials","authors":"Guanghui He, Guangfang Yang, Bin Fang, Wei Zhang","doi":"10.1109/ICWAPR.2018.8521358","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521358","url":null,"abstract":"In the paper, an improved local or global active contour model driven by Legendre Polynomials(LGLP) is proposed. It implemented with a special method, which selectively penalizes the level set function and then uses a filter to regularize it. Firstly, utilizing Legendre Polynomials approximates region intensity. Secondly, an improved region-based signed pressure force (ISPF) function is proposed, which efficiently stop the contours at weak edges, especially for the segmented image with intensity inhomogeneity. Finally, an edge stopping function is added to robustly capture the boundaries of objects. Experimental results show that the improved method is faster and achieve higher accuracy than other models on real images with intensity inhomogeneity, noise and multiple objects.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116929465","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}
Pub Date : 2018-07-01DOI: 10.1109/ICWAPR.2018.8521362
A. Su, Zhimin He, Junjian Su, Yan Zhou, Yun Fan, Yuan Kong
Machine learning technique has been widely applied in many applications, e.g., stock prediction and image classification. In this paper, we construct an ensemble model to detect whether there are tax arrears in enterprises. Tax department can use this model to detect tax arrears in advance, avoiding tax arrears. The ensemble learning model consists of six base classifiers, i.e., Multi-Layer Perceptron(MLP), k-Nearest Neighbor (KNN), Random Forest(RF), Extremely randomized Trees (ET), Gradient Tree Boosting (GTB) and XGBoost. Soft voting with weight is used to combine the base classifiers. Experimental results show satisfying performance of the proposed method on the tax dataset of N anhai, Foshan, China in 2015 and 2016.
{"title":"Detection of Tax Arrears Based on Ensemble Leaering Model","authors":"A. Su, Zhimin He, Junjian Su, Yan Zhou, Yun Fan, Yuan Kong","doi":"10.1109/ICWAPR.2018.8521362","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521362","url":null,"abstract":"Machine learning technique has been widely applied in many applications, e.g., stock prediction and image classification. In this paper, we construct an ensemble model to detect whether there are tax arrears in enterprises. Tax department can use this model to detect tax arrears in advance, avoiding tax arrears. The ensemble learning model consists of six base classifiers, i.e., Multi-Layer Perceptron(MLP), k-Nearest Neighbor (KNN), Random Forest(RF), Extremely randomized Trees (ET), Gradient Tree Boosting (GTB) and XGBoost. Soft voting with weight is used to combine the base classifiers. Experimental results show satisfying performance of the proposed method on the tax dataset of N anhai, Foshan, China in 2015 and 2016.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129212316","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}
Pub Date : 2018-07-01DOI: 10.1109/ICWAPR.2018.8521311
Cui-Mei Li, Zengxiang Li, Nan Jia, Zhi-Liang Qi, Jianhua Wu
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.
{"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":"12 1","pages":"0"},"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}
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