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Progress in Electromagnetic Research Symposium (PIERS)

作者： Yinfei Zhou Gang Zheng Jingsong Yang Xiaofeng Li Bin Liu Jingliang Shao Han Jiang Ren Ren Peng Chen Lin Ren Bin Zhang Ocean College Zhejiang University Zhoushan China Ministry of Natural Resources State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography Hangzhou China Chinese Academy of Sciences CAS Key Laboratory of Ocean Circulation and Waves Big Data Center Institute of Oceanology Qingdao China Chinese Academy of Sciences Center for Ocean Mega-Science Institute of Oceanology Qingdao China College of Marine Sciences Shanghai Ocean University Shanghai China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665409889

The waterline is the transient boundary between land and sea, and the waterline extraction is of great significance for coastal research and monitoring. Spaceborne synthetic aperture radar (SAR) has the ability to penetrate clouds and thus can image large coastal areas with high-resolution under all-weather and all-day conditions. These advantages make SAR well complement the waterline observations by optical satellites. However, SAR images of coastal areas usually have complex patterns because of the diverse topography and geomorphology, and the SAR-imaging mechanism also results in large coherent speckle noise in the images. The above poses challenges to the waterline extraction from SAR imagery. Traditional manual annotation is highly accurate, but is laborious and time-consuming. Therefore, a more efficient method is urgent. The procedure of waterline extraction is sea-land segmentation followed by boundary-extraction where accurate segmentation is critical. From the perspective of the computer vision, sea-land segmentation can be viewed as the image semantic segmentation task. In recent years, deep neural network (DNN) models have achieved great success in the semantic segmentation task. The U-Net, named for its U-shape architecture, is one of the most popular DNN models for the task. Compared with others, the U-Net has a lighter structure that efficiently extract and fuse the multi-scale image features. In this study, we applied the U-Net and its variants to sea-land segmentation and systematically investigated the models’ performances. All models can segment the sea and land efficiently, and the results are overall consistent with the manual annotation. Our study demonstrates the application potential of DNN technology in waterline monitoring from SAR imagery.

关键词： Deep learning Image segmentation Neural networks Semantics Sea measurements Manuals Radar polarimetry

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A short-term output power prediction model of wind power based on deep learning of grouped time series

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European Journal of Electrical Engineering 2020年第1期22卷 29-38页

作者： Wang, Yongsheng Gao, Jing Xu, Zhiwei Li, Leixiao College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry Hohhot010018 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China

The output power prediction of wind power plants is an important guarantee to improve the utilization rate of wind energy and reduce wind curtailment. However, due to the strong randomness of wind energy, the ultra-short-term prediction accuracy of wind power output is poor. In view of the problem above, a prediction model based on deep learning of grouped time series (LW-CLSTM) was proposed in this paper. Based on this model, the authors attempted to explore a prediction method of wind power output. For this, first the multivariate data of wind power was fused, cleaned, dimension-reduced, and standardized, and the time period characteristics of the output power itself were extracted. Afterwards, it proposes a time sliding window (TSW) algorithm, and constructs a neural network input data set. Then a deep neural network prediction model combining the Convolutional Neutral Network (CNN) and Long-Short Term Memory (LSTM) was established, and the regression evaluation criteria for output power forecast accuracy in wind power production were designed, to compare the proposed model with four other prediction models. Finally, the experiments on the TensorFlow platform using real data show that this model has better prediction accuracy than the other four models, reaching a prediction accuracy rate of 92.5%, which verified the effectiveness of this prediction method. © 2020 International Information and Engineering technology Association. All rights reserved.

关键词： Wind power

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MC-Blur: A Comprehensive Benchmark for Image Deblurring

arXiv

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arXiv 2021年

作者： Zhang, Kaihao Wang, Tao Luo, Wenhan Ren, Wenqi Stenger, Björn Liu, Wei Li, Hongdong Yang, Ming-Hsuan College of Engineering and Computer Science Australian National University Canberra Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Sun Yat-sen University Guangzhou China Rakuten Institute of Technology Setagaya Tokyo Japan Tencent Data Platform Shenzhen China School of Engineering University of California at Merced MercedCA United States

Blur artifacts can seriously degrade the visual quality of images, and numerous deblurring methods have been proposed for specific scenarios. However, in most real-world images, blur is caused by different factors, e.g., motion, and defocus. In this paper, we address how other deblurring methods perform in the case of multiple types of blur. For in-depth performance evaluation, we construct a new large-scale multi-cause image deblurring dataset (MC-Blur), including real-world and synthesized blurry images with different blur factors. The images in the proposed MC-Blur dataset are collected using other techniques: averaging sharp images captured by a 1000-fps high-speed camera, convolving Ultra-High-Definition (UHD) sharp images with large-size kernels, adding defocus to images, and real-world blurry images captured by various camera models. Based on the MC-Blur dataset, we conduct extensive benchmarking studies to compare SOTA methods in different scenarios, analyze their efficiency, and investigate the buildataset’s capacity. These benchmarking results provide a comprehensive overview of the advantages and limitations of current deblurring methods, revealing our dataset’s advances. The dataset is available to the public at https://***/HDCVLab/MC-Blur-dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Benchmarking

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MBLSTM is a contextual interaction refined method for time series prediction

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Scientific Reports 2025年第1期15卷 1-15页

作者： Qiu, Weihan Zhu, Fangjie Hao, Tianyong Wang, Maojie Huang, Runyue School of Computer Science South China Normal University Guangzhou China Second Clinical Medical College Guangzhou University of Chinese Medicine Guangzhou China Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome Guangzhou China State Key Laboratory of Dampness Syndrome of Chinese Medicine The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Guangzhou China Clinical Research and Big Data Laboratory South China Research Center for Acupuncture and Moxibustion Guangzhou University of Chinese Medicine Guangzhou China

Time series prediction has been widely used in the medical field to predict patient recurrence or physiological fluctuations. However, the adequacy of the existing methods for contextual information interaction is still insufficient when dealing with a longer memory need in clinical data modelling. In order to enhance the utilization of memory interaction, this paper introduces a new contextual interaction refinement method MB-LSTM by incorporating a Hidden Layer Information Interaction Intensifier. The MB-LSTM method allows for simultaneous interaction of input and hidden layer states at each time step to enhance capability of capturing complex temporal relationships. Besides, more features of time series data are learned utilizing contrastive learning and a data augmentation scheme based on Kernel Density Estimation is designed to identify more accurate features from time series data. The method is evaluated on a real clinical dataset including 1053 records of patient with Gouty arthritis from the Guangdong Provincial Traditional Chinese Medicine Hospital by predicting the subsequent status of patients. The results show the proposed method achieves state-of-the-art performance by 0.5–7.2% using four different evaluation metrics compared with baseline methods. © The Author(s) 2025.

关键词： Contrastive learning data augmentation Gouty arthritis Time series prediction

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Deep Learning Based Valid Bug Reports Determination and Explanation

Deep Learning Based Valid Bug Reports Determination and Expl...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Jianjun He Ling Xu Yuanrui Fan Zhou Xu Meng Yan Yan Lei Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9781728198705

ISBN: (纸本)9781728198712

Bug reports are widely used by developers to fix bugs. Due to the lack of experience, reporters may submit numerous invalid bug reports. Manually determining valid bug reports is a laborious task. Automatically identifying valid bug reports can save time and effort for bug analysis. In this paper, we propose a deep learning-based approach to determine and explain valid bug reports using only textual information i.e., summaries and descriptions of bug reports. Convolutional neural network (CNN) is applied to capture their contextual and semantic features. Moreover, by analyzing the spatial structure of CNN, we backtrack the trained CNN model to get phrases that can explain valid bug reports determination. After inspecting the phrases manually, we summarize some valid bug report patterns. We evaluate our approach on five large-scale open-source projects containing a total of 540491 bug reports. On average, across the five projects, our approach achieves 0.85, 0.80, 0.69 and improves the state-of-the-art approach by 8.97%, 9.59%, 9.52% in terms of AUC, F1-score for valid bug reports, and F1-score for invalid bug reports, respectively. From the summarized patterns, we can find that determining valid bug reports is mainly due to three categories of patterns: Attachment, Environment, and Reproduce.

关键词： Fans computer bugs Semantics Feature extraction Software reliability Task analysis Open source software

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A spatial correlation based partial coverage scheduling scheme in wireless sensor networks

Journal of Network Intelligence

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Journal of Network Intelligence 2020年第2期5卷 34-43页

作者： Zhang, Jing Nian, Hongrui Ye, Xiucai Ji, Xiaorong He, Yao College of Information Science and Engineering Institute of Artificial Intelligence Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology 33th Xueyuan Road University Town Minhou FuzhouFujian350118 China College of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Department of Computer Science University of Tsukuba Tsukuba3058573 Japan

Wireless Sensor Networks are formed by a mass of various sensors, they are adopted in lots of applications. Because of the existence of spatial correlation in WSNs, the information from some geographic independence sensors are more significant than that from high correlation ones. Thus, it is unnecessary to transmit information from every sensor to the sink node. This paper focus on the coverage area for data monitoring in WSNs. To construct the virtual backbone network, we propose an efficient connected dominating set construction method based on clique, by taking advantage of the high spatial correlation characteristics of clique construction. Furthermore, by minimizing the number of sensors to activate, we propose a Spatial Correlation based partial Coverage Scheduling Scheme (SCCS) to prolong the network lifetime. The corresponding experiment indicate the efficiency and the feasibility of the proposed SCCS scheme. Moreover, this algorithm is able to guarantee better performance by comparing to the existing schemes. © 2020, Taiwan Ubiquitous Information. All rights reserved.

关键词： Scheduling

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Throughput Maximization for UAV-Enabled data Collection

Throughput Maximization for UAV-Enabled Data Collection

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Junchao Gong Xiaojun Zhu Lijie Xu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

In this paper, we consider a scenario where an unmanned aerial vehicle (UAV) collects data from a set of nodes placed on a two-dimensional (2-D) plane. The UAV flies along a given line to collect data. To prevent transmission collisions, only one node is allowed to transmit data to the UAV at any time, and the number of nodes that can transmit data to the UAV cannot exceed a specified number to avoid frequent switches. The problem is to select a subset of nodes and schedule their transmission time to maximize the UAV's throughput. After formulating the problem, we find that it is difficult to solve due to mixture of real variables and integer variables. We then propose to decompose the problem into subproblems, and give a polynomial time algorithm to solve each subproblem to optimality. We then get an exact algorithm by solving all subproblems. Unfortunately, due to an exponential number of subproblems, the overall running time is exponential with respect to the input size. We then propose two polynomial-time suboptimal algorithms to solve the problem, both of which explore a polynomial number of subproblems. Simulations show that the suboptimal algorithms perform comparably with the exponential-time exact algorithm, while the running time is much smaller.

关键词： Schedules Heuristic algorithms Two dimensional displays data collection Throughput Unmanned aerial vehicles Sensors

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Pipeline Network Simulation Calculation Based on Improved Newton Jacobian Iterative Method

Pipeline Network Simulation Calculation Based on Improved Ne...

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作者： Zhiqiang Geng Chunyang Wei Yongming Han Qin Wei Zhi Ouyang Guizhou Provincial Key Laboratory of Public Big Data College of Information Science& Technology Beijing University of Chemical Technology

The accurate calculation of the pipe network module contributes to the effective simulation of chemical processes and can improve the production of chemical *** present,the pipe network calculation of chemical processes is generally solved by the secant *** this method is convenient,it cannot meet the accuracy of the pipe network calculation,and even cannot ***,this paper proposes an improved Newton Jacobian iterative method to improve the accuracy of the pipe network calculation,which realizes the efficient,stable and accurate solution of the flow and pressure of the pipe network *** improving the selection of the initial value,the convergence of the pipe network calculation and the operation efficiency of the Jacobian iterative method are *** effectiveness of the proposed method is illustrated by comparison with the secant ***,the minimum branch and the merge-branch pipeline are actually calculated for the simulation *** results show that the flow rate calculated by the pipe network and the pressure accuracy are significantly improved.

关键词： Newton Jacobi iteration Pipe Network Modeling

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A novel lattice-based ciphertext-policy attribute-based proxy re-encryption for cloud sharing 1

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5th International Symposium on Security and Privacy in Social Networks and big data, SocialSec 2019

作者： Li, Juyan Ma, Chunguang Zhang, Kejia College of Data Science and Technology Heilongjiang University Harbin150080 China College of Computer Science and Technology Harbin Engineering University Harbin150001 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

ISBN: (数字)9789811507588

ISBN: (纸本)9789811507571

Proxy re-encryption plays an important role in cloud sharing. Ciphertext-policy attribute-based proxy re-encryption (CP-ABPRE) scheme supports access control and can convert the ciphertext under an access policy to a ciphertext under another access policy, which is flexible and efficient for cloud sharing. The existing CP-ABPRE schemes are constructed by bilinear pairing or multi-linear maps which are fragile when the post-quantum comes. In this paper, a unidirectional single-hop CP-ABPRE scheme with small size of public parameters was presented by using trapdoor sampling, and proved secure under learning with errors assumption which is widely believed secure in quantum computer attacks. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Access control

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