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A study on wavelet selection in power signal denoising

IOP Conference Series: Earth and Environmental Science 2021年第1期675卷

作者： Jing Gong School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing 100044 China National Virtual Simulation Experimental Center for Smart City Education Beijing 100044 China

In the wavelet threshold denoising of power signal, the selection of wavelet has an important influence on the denoising effect, and the wavelet generating function has diversity, if not selected properly, it will directly lead to the failure of denoising. Firstly, an operator is introduced to modify the threshold of each scale to better reflect the variation of wavelet coefficients of signal and noise with scale. Then a controllable threshold function is proposed to adapt to different soft and hard characteristics, and it is used to denoise the wavelet coefficients. Based on the study of the characteristics of wavelet, such as orthogonality, vanishing moment, support length and symmetry, four principles of wavelet selection in power signal denoising are proposed. The voltage sag and harmonics model are established, and db5, coif1 and sym2 wavelets are selected to decompose the signal to the fourth scale for denoising. The signal-to-noise ratio, mean square error and the detailed features of the reconstructed signal after denoising are compared. The experimental results show that the orthogonal wavelet db5 with high vanishing moment order and long support length has better denoising effect than coif1 and sym2, which proves the correctness of the wavelet selection principles proposed in this paper.

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Unveiling optimal SDG pathways: an innovative automated recommendation approach integrating graph pruning, intent graph, and attention mechanism

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International Journal of Digital Earth 2025年第1期18卷

作者： Qiang Wang Zhihang Yu Shu Wang Yunqiang Zhu Xiaoliang Dai Zhiqiang Zou Weiming Huang Christophe Claramunt a State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing People’s Republic of Chinad University of Chinese Academy of Sciences Beijing People’s Republic of China b College of Computer Nanjing University of Posts and Telecommunications Nanjing People’s Republic of Chinac Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing People’s Republic of China a State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing People’s Republic of Chinad University of Chinese Academy of Sciences Beijing People’s Republic of Chinae Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing People’s Republic of China f School of Geography University of Leeds Leeds UK a State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing People’s Republic of Chinag Naval Academy Research Institute Lanvéoc France

ABSTRACTThe recommendation of Sustainable Development Pathways (SDPs) is crucial for achieving the United Nations Sustainable Development Goals (SDGs) at regional level. However, traditional recommendation algorithms struggle with two key challenges: spatial heterogeneity and sparse historical interaction records between regions and SDPs. To address these issues, we introduce the Regional Graph-Based Explainable Recommendation (RGB-ER) method. RGB-ER leverages a pruned Regional Graph (RG) to capture regional spatial heterogeneity, incorporating environmental, economic, and social factors into the recommendations. In addition, an Intent Graph models regional preferences across various attributes, bridging historical interactions with the RG and mitigating data sparsity. This dual approach significantly improves recommendation accuracy and interpretability. Extensive experiments show that RGB-ER outperforms state-of-the-art graph-based models, with a maximum improvement of 9.61% in Top-3 recommendation accuracy. A case study in Fujian Province – a region characterized by its mountainous terrain, complex socio-economic landscape, and significant sustainability challenges – illustrates RGB-ER’s practical applicability, aligning well with local government strategies for sustainable development. Furthermore, we assess SDPs at the county level across China, highlighting the method’s potential for guiding region-specific sustainable development planning. In conclusion, RGB-ER provides a robust, explainable framework for data-driven decision-making in sustainable development.

关键词： Sustainable development pathways ecological civilization pattern recommender system knowledge graph pruning intent graph

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Intrablock interleaving for Batched network coding with blockwise adaptive recoding

arXiv

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arXiv 2021年

作者： Yin, Hoover H.F. Ng, Ka Hei Zhong, Allen Z. Yeung, Raymond W. Yang, Shenghao Chan, Ian Y.Y. The N-hop Technologies Limited Hong Kong The Institute of Network Coding The Chinese University of Hong Kong Hong Kong The Department of Information Engineering The Chinese University of Hong Kong Hong Kong Limited The Department of Physics The Chinese University of Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China The Department of Economics The Chinese University of Hong Kong Hong Kong

Batched network coding (BNC) is a low-complexity solution to network transmission in multi-hop packet networks with packet loss. BNC encodes the source data into batches of packets. As a network coding scheme, the intermediate nodes perform recoding on the received packets belonging to the same batch instead of just forwarding them. A recoding scheme that may generate more recoded packets for batches of a higher rank is also called adaptive recoding. Meanwhile, in order to combat burst packet loss, the transmission of a block of batches can be interleaved. Stream interleaving studied in literature achieves the maximum separation among any two consecutive packets of a batch, but permutes packets across blocks and hence cannot bound the buffer size and the latency. To resolve the issue of stream interleaver, we design an intrablock interleaver for adaptive recoding that can preserve the advantages of using a block interleaver when the number of recoded packets is the same for all batches. We use potential energy in classical mechanics to measure the performance of an interleaver, and propose an algorithm to optimize the interleaver with this performance measure. Our problem formulation and algorithm for intrablock interleaving are also of independent interest. © 2021, CC BY-NC-ND.

关键词： Network coding

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Multiview feature selection combining latent space and similarity structure learning

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Pattern Recognition 2026年 169卷

作者： Zhuowen Li Hongmei Chen Tengyu Yin Zhong Yuan Chuan Luo Shi-Jinn Horng Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 PR China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 PR China College of Computer Science Sichuan University Chengdu 610065 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan

Unsupervised multiview feature selection dependent on similar or clustering structures has dramatically progressed, but both ignore the mutually reinforcing relationship between structure learning. Firstly, the two methods of appeal ignore the possibility of shared joint learning, although they can learn coherent information. Secondly, they are inadequate for data structure learning and lack awareness of co-learning global and local structures. This paper proposes an unsupervised feature selection method (SCSF_FS) that integrates similarity and clustering structures learning to capture intrinsic information in data. Specifically, we use latent space learning to partition multiple data matrices into view-specific base matrices and clustering metrics. Structural learning is used to transform specific clustering metrics into coherent clustering structures. In addition, adaptive weights are used on each view’s similarity matrix to learn a consistent similarity structure, while laplacian graph learning is introduced to unify the similarity and clustering structures. Consequently, a unified framework is designed to unify multiview consistency learning, global structure, and local structure preservation. Moreover, an optimization iteration algorithm is designed to solve it. Comparison with eight algorithms shows the effectiveness of the proposed method.

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Improved VGG Model for Road Traffic Sign Recognition

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Computers, Materials & Continua 2018年第10期57卷 11-24页

作者： Shuren Zhou Wenlong Liang Junguo Li Jeong-Uk Kim Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science&TechnologyChangsha410114China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Energy Grid Sangmyung UniversitySeoul110743Korea

Road traffic sign recognition is an important task in intelligent transportation *** neural networks(CNNs)have achieved a breakthrough in computer vision tasks and made great success in traffic sign *** this paper,it presents a road traffic sign recognition algorithm based on a convolutional neural *** natural scenes,traffic signs are disturbed by factors such as illumination,occlusion,missing and deformation,and the accuracy of recognition decreases,this paper proposes a model called Improved VGG(IVGG)inspired by VGG *** IVGG model includes 9 layers,compared with the original VGG model,it is added max-pooling operation and dropout operation after multiple convolutional layers,to catch the main features and save the training *** paper proposes the method which adds dropout and Batch Normalization(BN)operations after each fully-connected layer,to further accelerate the model convergence,and then it can get better classification *** uses the German Traffic Sign Recognition Benchmark(GTSRB)dataset in the *** IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning,and the spent time is also reduced greatly.

关键词： intelligent transportation traffic sign deep learning GTSRB data augmentation

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Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

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INFORMATION 2021年第11期12卷 471页

作者： Feng, You-Zhen Liu, Sidong Cheng, Zhong-Yuan Quiroz, Juan C. Rezazadegan, Dana Chen, Ping-Kang Lin, Qi-Ting Qian, Long Liu, Xiao-Fang Berkovsky, Shlomo Coiera, Enrico Song, Lei Qiu, Xiao-Ming Cai, Xiang-Ran Jinan Univ Affiliated Hosp 1 Med Imaging Ctr Guangzhou 510630 Peoples R China Macquarie Univ Fac Med Hlth & Human Sci Australian Inst Hlth Innovat Ctr Hlth Informat Sydney NSW 2113 Australia Univ New South Wales Ctr Big Data Res Hlth Sydney NSW 1466 Australia Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic 3000 Australia Peking Univ Dept Biomed Engn Beijing 100871 Peoples R China Nankai Univ Coll Artificial Intelligence Inst Robot & Automat Informat Syst Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China Hubei Univ Arts & Sci Affiliated Hosp Xiangyang Cent Hosp Dept Radiol Xiangyang 441003 Peoples R China Hubei Polytech Univ Affiliated Hosp Huangshi Cent Hosp Dept RadiolEdong Healthcare Grp Huangshi 435002 Peoples R China

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.

关键词： chest CT COVID-19 severity assessment progression prediction U-Net RNN

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Residual Networks for Light Field Image Super-Resolution

Residual Networks for Light Field Image Super-Resolution

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Shuo Zhang Youfang Lin Hao Sheng Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.

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Sledge: Towards Efficient Live Migration of Docker Containers

Sledge: Towards Efficient Live Migration of Docker Container...

引用

IEEE International Conference on Cloud Computing, CLOUD

作者： Bo Xu Song Wu Jiang Xiao Hai Jin Yingxi Zhang Guoqiang Shi Tingyu Lin Jia Rao Li Yi Jizhong Jiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China The University of Texas at Arlington Arlington TX USA Alibaba Cloud Computing Co. Ltd. Hangzhou China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Modern large-scale cloud platforms require live migration technique on Docker containers with stateful workload to support load balancing, host maintenance, and Quality of Service (QoS) improvement. Efficient and scalable Docker live migration is expected to guarantee the component-integrity (image, runtime, and management context) with negligible downtime. In this paper, we present a highly efficient live migration system called Sledge, which ensures the component-integrity by integrating both images and management context during runtime migration. The key insight is that the layered image can be leveraged to reduce the migration overhead, and appropriately selective migration of management context will effectively improve QoS with negligible downtime. To achieve good scalability, a lightweight container registry mechanism for end-to-end image migration is designed to avoid the redundant layers transmission. In addition, a dynamic context loading scheme is proposed to precisely load the management context into the running daemon, which can significantly reduce downtime. Experiments show that, compared with the state-of-the-art, Sledge reduces 57% of total migration time, 55% of image migration time, and 70% downtime.

关键词： Cloud computing Runtime Scalability Loading Quality of service Containers Maintenance engineering

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A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction

arXiv

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arXiv 2020年

作者： Yang, Jun Li, Qing Sun, Yixuan Wang, Wei Li, Linchao Wang, Xuwei Jia, Shengyao Tong, Renyuan National and Local Joint Engineering research center for Disaster Monitoring Technologies and Instruments China Jiliang University Hangzhou310051 China Zhejiang Provincial Key Laboratory of Intelligent Manufacturing Quality Big Data Traceability Analysis and Application China Jiliang University Hangzhou310051 China Zhejiang PeckerAI Technology. Ltd Hangzhou310018 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Tailings ponds are places for storing industrial waste. The saturation line is the key factor of quantifying the safety of tailings pond. Existing saturation line time-series prediction methods are mainly based on statistical models or shallow machine learning models. Although these models aim to capture the time dependence of the sequence data, the channel and temporal are even unavailable in principle. In this paper, we presents a two-stage forecasting method, which embeds the channel and temporal attention into a hybrid CNN-LSTM model to predict the saturation line. In the first stage, the discrete wavelet transform (DWT) is applied to capture the refined sequence information. In the second stage, the CNN-LSTM model is utilized to learn the basic spatial and temporal features in the time series. Furthermore, the channel and temporal attention model are embedded into CNN-LSTM model to enhance the feature extracting ability in channel and temporal dimension. Consequently, our proposed model is shown to outperform classic models on five real-world datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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ProFPred: a two-step protein function prediction model based on sequence and evolutionary information

ProFPred: a two-step protein function prediction model based...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ruiquan Ge Guanwen Feng Pu Wang Qiguang Miao School of Computer Science and Technology Hangzhou Dianzi University Zhejiang University Hangzhou Yiyitaidi Information Technology Co. Ltd. Hangzhou China Xi’an Key Laboratory of Big Data and Intelligent Vision School of Computer Science and Technology Xidian University Xi’an China Computer School Hubei University of Arts and Science Xiangyang China School of Computer Science and Technology Xidian University Xi’an China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

In post-genomic era, the understanding of protein function has been seriously behind the development of sequencing technology. Experimental verification for protein function is difficult, time consuming and expensive. Meanwhile, the new proteins vary in function. It is difficult for traditional methods to fully and accurately understand its functions. In this work, we present a novel method ProFPred to predict protein function based on sequence and evolutionary information to deal with small samples data corresponding to new diseases or discoveries. Experimental results demonstrate that our method can achieve better or comparable performances compared with current state-of-the-art methods.

关键词： Proteins Amino acids Peptides Feature extraction Correlation Tools Protein engineering

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