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2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)最新文献

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[ICWAPR 2020 Front cover] [ICWAPR 2020年封面]
2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2020-12-02 DOI: 10.1109/icwapr51924.2020.9494376
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引用次数: 0
A Machine Learning Model On Bids Submission For Power Demand Response (DR) Resources In Gas Market 天然气市场电力需求响应(DR)资源投标的机器学习模型
2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2020-12-02 DOI: 10.1109/ICWAPR51924.2020.9494620
Ting Jin, Zhengxin Fu, Jiejun Chen, Yinglong Lv, Qiang Sun, Fangyuan Xu
{"title":"A Machine Learning Model On Bids Submission For Power Demand Response (DR) Resources In Gas Market","authors":"Ting Jin, Zhengxin Fu, Jiejun Chen, Yinglong Lv, Qiang Sun, Fangyuan Xu","doi":"10.1109/ICWAPR51924.2020.9494620","DOIUrl":"https://doi.org/10.1109/ICWAPR51924.2020.9494620","url":null,"abstract":"With the further development of the competitive gas market, market participants such as gas selling companies and large gas users have become one of the important trading entities. In this paper, gas turbine is used as demand response (DR) resources of the power system to support the energy transfer point of the gas network, and a machine learning model for bidding for power demand response resources in the gas market is proposed. The model uses General Regression Neural Network (GRNN) to fit discrete data points to 'Cost-Capacity' bidding curve to participate in the competition of the gas market, and to achieve the optimal distribution of power demand response resources. Finally, the validity of the model is verified by relevant case.","PeriodicalId":111814,"journal":{"name":"2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121968501","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
Selection Of Variables And Indicators In Financial Distress Prediction Model-Svm Method Based On Sparse Principal Component Analysis 财务困境预测模型中变量和指标的选择——基于稀疏主成分分析的svm方法
2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2020-12-02 DOI: 10.1109/ICWAPR51924.2020.9494625
S. Zeng, Wanjun Yang
{"title":"Selection Of Variables And Indicators In Financial Distress Prediction Model-Svm Method Based On Sparse Principal Component Analysis","authors":"S. Zeng, Wanjun Yang","doi":"10.1109/ICWAPR51924.2020.9494625","DOIUrl":"https://doi.org/10.1109/ICWAPR51924.2020.9494625","url":null,"abstract":"How to screen out the early warning indicators from a large number of alternative financial indicators is an important step in the prediction of financial distress. In order to design the financial distress prediction model more effectively, this paper proposes a novel method that combines the sparse algorithm and support vector machine. Firstly, according to the financial management theory, financial indicators are divided into several groups, and then variable screening was conducted for each group of financial indicators by sparse principal component analysis. Finally, after variable screening, the data set is input to the SVM for classification and prediction (classifier prediction). The empirical results show that this method can more effectively identify companies in financial distress, improve the prediction results of the model, and reduce the risk of investors facing financial distress.","PeriodicalId":111814,"journal":{"name":"2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129454137","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
Overview And Comparison Of Common Load Identification Models For Non-Intrusive Load Detection 非侵入式负荷检测常用负荷识别模型综述与比较
2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2020-12-02 DOI: 10.1109/ICWAPR51924.2020.9494615
Qintao Du, Peijie Li, Yijie Huang, Weixian Chen, Zelun Lin
{"title":"Overview And Comparison Of Common Load Identification Models For Non-Intrusive Load Detection","authors":"Qintao Du, Peijie Li, Yijie Huang, Weixian Chen, Zelun Lin","doi":"10.1109/ICWAPR51924.2020.9494615","DOIUrl":"https://doi.org/10.1109/ICWAPR51924.2020.9494615","url":null,"abstract":"Due to the increasing shortage of energy, people pay more attention to the energy conservation and environmental protection. By providing consumers with monitoring of individual device consumption, consumers can adjust their consumption habits to achieve energy conservation and emission reduction. One way to provide this capability is non-intrusive load monitoring model (NILM). The main challenge of NILM is to select a suitable identification model for load identification and to solve the low accuracy problem of some equipment identification. This paper implements a variety of common load identification models. By comparing the accuracy of various identification models, we obtain the optimal load identification model for multiple equipment combinations prediction. At the same time, we discuss two situations separately due to the different effect of load recognition mode between single load operation scenario and multi-load operation scenario. By comparing the recognition effect between different load recognition models and the recognition effect of various equipment, we provide suggestions for the training method of load recognition model to make the model training effect better.","PeriodicalId":111814,"journal":{"name":"2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128378152","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
Construction Plant Security Detection And Risk Assessment System 建筑工厂安全检测与风险评估系统
2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2020-12-02 DOI: 10.1109/ICWAPR51924.2020.9494621
Runbin Chen, Hao Yu, R. Liu, Ruixin Tang, Fangyuan Xu
{"title":"Construction Plant Security Detection And Risk Assessment System","authors":"Runbin Chen, Hao Yu, R. Liu, Ruixin Tang, Fangyuan Xu","doi":"10.1109/ICWAPR51924.2020.9494621","DOIUrl":"https://doi.org/10.1109/ICWAPR51924.2020.9494621","url":null,"abstract":"Construction plant needs to mobilize personnel for safety inspection and supervision. However, due to the large number of Construction plant, conducting one-by-one supervision investigations consumes a lot of human resources. Therefore, this article collects construction site inform action through safety helmet recognition, face recognition, and external sensors. While monitoring construction site irregularities, the risk index of each construction site can be obtained through the risk assessment system to provide judgment information for local managers. It can supervise and manage the construction site with higher risk index.","PeriodicalId":111814,"journal":{"name":"2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122205690","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
Application Of Improved VMD And Wavelet Threshold Denoising Method In Bolt Detection 改进VMD和小波阈值去噪方法在螺栓检测中的应用
2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2020-12-02 DOI: 10.1109/ICWAPR51924.2020.9494619
Yi-ming Liu
{"title":"Application Of Improved VMD And Wavelet Threshold Denoising Method In Bolt Detection","authors":"Yi-ming Liu","doi":"10.1109/ICWAPR51924.2020.9494619","DOIUrl":"https://doi.org/10.1109/ICWAPR51924.2020.9494619","url":null,"abstract":"Rock bolts are often used for reinforcement of slopes, tunnels, and mines in transportation, water conservancy, coal mining and other industries. Thus, identifying the quality of rock bolts is important. To address the difficult of obtaining useful information on rock bolts in complex environment, this study proposes a noise reduction method based on the combination of improved variational mode decomposition (VMD) and wavelet threshold denoising (WTD) method. In this method, the number of decomposition modes in improved VMD algorithm is determined by weighted kurtosis values. First, the electromagnetic ultrasonic signal is decomposed into a finite number of intrinsic mode functions (IMFs) through improved VMD. Second, noisy and invalid IMF components are removed according to weighted kurtosis values. Finally, the residual IMF components are used to reconstruct signal and wavelet threshold processing to improve denoising effect. This VMDWTD method is applied in both simulation signal and magnetostrictive guided wave signal. The results showed that the method retained more valid information and had better noise reduction effect than ensemble empirical mode decomposition with WTD and traditional WTD method.","PeriodicalId":111814,"journal":{"name":"2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133883217","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
Face Angle Identification with Ensemble Learning 基于集成学习的人脸角度识别
2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2020-12-02 DOI: 10.1109/ICWAPR51924.2020.9494613
Ziling Zhou, Keke Chen
{"title":"Face Angle Identification with Ensemble Learning","authors":"Ziling Zhou, Keke Chen","doi":"10.1109/ICWAPR51924.2020.9494613","DOIUrl":"https://doi.org/10.1109/ICWAPR51924.2020.9494613","url":null,"abstract":"The current face recognition methods may not work well on non-upright faces due to the heterogeneity of the training set. This study proposes an ensemble model to identify the angle of a face. The face is then rotated according to the predicted angle before feeding to face recognition. Our proposed model takes advantages of Divide-and-Conquer methods which breaks down a complicated problem to several simple tasks. A number of based classifier, i.e. Convolutional Neural Network, with a simple structure is used to classify whether a face is in a given range or not. The final angle prediction is determined by the majority voting. The experimental results suggest that our method achieve excellent performance in terms of accuracy and speed in face angle identification.","PeriodicalId":111814,"journal":{"name":"2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115733634","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
Digital Image Watermarking Method Using The Gyrator And Dyadicwavelet Transforms 基于旋转体和二向小波变换的数字图像水印方法
2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2020-12-02 DOI: 10.1109/ICWAPR51924.2020.9494610
Arata Ide, Ryuji Ohura, Teruya Minamoto
{"title":"Digital Image Watermarking Method Using The Gyrator And Dyadicwavelet Transforms","authors":"Arata Ide, Ryuji Ohura, Teruya Minamoto","doi":"10.1109/ICWAPR51924.2020.9494610","DOIUrl":"https://doi.org/10.1109/ICWAPR51924.2020.9494610","url":null,"abstract":"We propose a new digital watermarking method combining the gyrator transform and the dyadic wavelet transform (DYWT). The gyrator transform produces a rotation dependent on the angle in the twisted phase plane, while DYWT has the properties of redundancy and shift invariance. Combining these two transforms, we develop a new digital watermarking method that enables multi-resolution analyses of images twisted in the spatial frequency plane. Using experiments, we confirm the image quality of the watermarked images and their resistance to various attacks, including clipping, JPEG compression, and geometric manipulation. The results are shown, and the effectiveness of this new method is examined.","PeriodicalId":111814,"journal":{"name":"2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115912316","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 DNA-Binding Proteins Prediction Model Using Different Property Distance Transformation 基于不同性质距离变换的dna结合蛋白预测模型
2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2020-12-02 DOI: 10.1109/ICWAPR51924.2020.9494609
Xiangyu Li, Lina Yang, Y. Tang, P. Wang
{"title":"A DNA-Binding Proteins Prediction Model Using Different Property Distance Transformation","authors":"Xiangyu Li, Lina Yang, Y. Tang, P. Wang","doi":"10.1109/ICWAPR51924.2020.9494609","DOIUrl":"https://doi.org/10.1109/ICWAPR51924.2020.9494609","url":null,"abstract":"DNA-binding proteins refers to a class of proteins that can combine with DNA to produce complexes. It is an indispensable part of cell life activities, such as DNA recombination, modification, replication, virus integration and transcription. With the rapid development of gene sequencing technology and the increasing demand for sequencing technology, more and more unknown DNA-binding proteins are waiting for researchers to predict. However, develop a high quality and short time prediction method still face more challenges. In this article the author puts forward a new method named PSFM-DDT, which combines the Position Specific Frequency Matrix(PSFM) and Different Property Distance Transformation(DDT). Firstly, the evolutionary information of protein sequence was expressed by frequency matrix, and then using distance transformation of different amino acids is transformed into a series of new feature vector. The extracted vectors features are trained by using Support Vector Machine(SVM) linear kernel method to choice the last model. The accuracy of this method reached 83.16% using jackknife test on the benchmark dataset and 79.57% on the independent dataset. Through the experimental results indicated that performance of this method obtain significantly improved compared with other prediction method.","PeriodicalId":111814,"journal":{"name":"2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115449919","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
Human Motion Capture Technology Based On Multi-Binocular Vision And Image Recognition 基于多双目视觉和图像识别的人体动作捕捉技术
2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2020-12-02 DOI: 10.1109/ICWAPR51924.2020.9494612
Weile Zhang, Kaizhu Yang, Y. Tang, Junpeng Yang, Xin Liang, Yiliang Fan, Qintao Du
{"title":"Human Motion Capture Technology Based On Multi-Binocular Vision And Image Recognition","authors":"Weile Zhang, Kaizhu Yang, Y. Tang, Junpeng Yang, Xin Liang, Yiliang Fan, Qintao Du","doi":"10.1109/ICWAPR51924.2020.9494612","DOIUrl":"https://doi.org/10.1109/ICWAPR51924.2020.9494612","url":null,"abstract":"This paper proposes a human motion capture system based on multi-binocular vision and image recognition. The system which relies on the principle of binocular imaging and stereo matching of the same name points of the human joints in the left and right views to obtain the 3D coordinates of the human joint points in real time, uses multiple binocular cameras placed at different angles for simultaneous shooting, expanding the integrity of the original binocular vision system. At the same time, the detection model of 3D abnormal estimation points of human joints under multi-binocular vision is established, so that the system can more accurately identify the 3D abnormal estimation points of human joints due to occlusion. Finally, the average coordinates of the non-abnormal human 3D joint points obtained by other binocular cameras are dynamically selected as system’s final estimated coordinates, thereby further improving the accuracy of human motion capture.","PeriodicalId":111814,"journal":{"name":"2020 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129355624","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
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