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Study on sensitivity and selectivity of three-stage current protection in distribution network with distributed generation

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IOP Conference Series: Earth and Environmental Science 2020年第1期467卷

作者： Jing Gong Beijing University of Civil Engineering and Architecture Beijing 100044 China Scholarship Council (***) the Scientific Research Fund of BUCEA Beijing Key Laboratory Researching on Intelligent Processing of Building Big Data

The connection of DG (Distributed Generation) changes the topology of distribution network, which will lead to the change of current detected by relay protection, and the sensitivity and scope of protection will be affected. On the basis of introducing the setting calculation principle of three-stage current protection in distribution network, taking a 10kV distribution network with DG as a model, the influence of DG capacity change on the selectivity and sensitivity of three-stage current protection is discussed in detail when short circuit occurs in different locations such as downstream, adjacent and upstream lines. The corresponding restrictive factors are analyzed and the influence degree of DG access on the original protection is given after comprehensive comparison. It is concluded that comparing different short circuit locations, DG has the most serious impact on the stage-I selectivity of downstream protection when the short circuit occurs at DG downstream line. The conclusions in this paper are of great value to ensure the reliability of power supply of distribution network when a large number of DGs are connected to the grid.

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Deep short text classification with knowledge powered attention

arXiv

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arXiv 2019年

作者： Chen, Jindong Hu, Yizhou Liu, Jingping Xiao, Yanghua Jiang, Haiyun Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China CETC Big Data Research Institute Co. Ltd. Guizhou China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Shuyan Technology Shanghai China Alibaba Group Zhejiang China

Short text classification is one of important tasks in Natural Language Processing (NLP). Unlike paragraphs or documents, short texts are more ambiguous since they have not enough contextual information, which poses a great challenge for classification. In this paper, we retrieve knowledge from external knowledge source to enhance the semantic representation of short texts. We take conceptual information as a kind of knowledge and incorporate it into deep neural networks. For the purpose of measuring the importance of knowledge, we introduce attention mechanisms and propose deep Short Text Classification with Knowledge powered Attention (STCKA). We utilize Concept towards Short Text (CST) attention and Concept towards Concept Set (C-CS) attention to acquire the weight of concepts from two aspects. And we classify a short text with the help of conceptual information. Unlike traditional approaches, our model acts like a human being who has intrinsic ability to make decisions based on observation (i.e., training data for machines) and pays more attention to important knowledge. We also conduct extensive experiments on four public datasets for different tasks. The experimental results and case studies show that our model outperforms the state-of-the-art methods, justifying the effectiveness of knowledge powered attention. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Air Quality Inference with Deep Convolutional Conditional Random Field

Air Quality Inference with Deep Convolutional Conditional Ra...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Zhe Luo You Yu Daijun Zhang Shijie Feng Huapeng Yu Yongxin Chang Wei Shen School of Electrical Engineering and Information Southwest Petroleum University Chengdu China College of Environment and Ecology Chongqing University Big Data Application Center of Ecology and Environment of Chongqing Chongqing China College of Environment and Ecology Chongqing University State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing University Chongqing China College of Information Science and Engineering Chengdu University Chengdu China Chongqing Zhixing Shulian Intelligent Technology Co. Ltd Chongqing China

ISBN: (数字)9781728166612

ISBN: (纸本)9781728112350

Conditional Random Field is a discriminative model for time series data. In this paper, we propose an improved CRF and apply it to the task of air quality inference. Different from the classical CRF, our linear chain CRF is a supervised learning based on the deep convolution neural network, which has a strong learning ability and fast processing speed for the engineering big data. Specifically, we model the state feature function and the state transition feature function with deep convolutional neural network. The parameter space can store more feature expressions learned from a large number of data. For the state transition feature function of linear conditional random field, we add the influence of input sequence on this function. Through the modelling and learning both vertex features and edge features from data, we obtain a more powerful and more efficient CRF. Experiments on natural language and air quality data show our CRF can achieve higher accuracy.

关键词： Air quality Convolution Neural networks Indexes Electronic mail data models Biological system modeling

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LAP-Net: Level-Aware Progressive Network for Image Dehazing

LAP-Net: Level-Aware Progressive Network for Image Dehazing

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International Conference on Computer Vision (ICCV)

作者： Yunan Li Qiguang Miao Wanli Ouyang Zhenxin Ma Huijuan Fang Chao Dong Yining Quan School of Computer Science and Technology Xidian Univeristy China Xi’an Key Laboratory of Big Data and Intelligent Vision China SenseTime Computer Vision Research Group The University of Sydney Australia SChinese Academy of Science Shenzhen Institutes of Advanced Technology China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

In this paper, we propose a level-aware progressive network (LAP-Net) for single image dehazing. Unlike previous multi-stage algorithms that generally learn in a coarse-to-fine fashion, each stage of LAP-Net learns different levels of haze with different supervision. Then the network can progressively learn the gradually aggravating haze. With this design, each stage can focus on a region with specific haze level and restore clear details. To effectively fuse the results of varying haze levels at different stages, we develop an adaptive integration strategy to yield the final dehazed image. This strategy is achieved by a hierarchical integration scheme, which is in cooperation with the memory network and the domain knowledge of dehazing to highlight the best-restored regions of each stage. Extensive experiments on both real-world images and two dehazing benchmarks validate the effectiveness of our proposed method.

关键词： Image restoration Atmospheric modeling Scattering Entropy Degradation Cameras Fuses

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Genome Sequences Provide Insights into the Reticulate Origin and Unique Traits of Woody Bamboos

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Molecular Plant 2019年第10期12卷 1353-1365页

作者： Zhen-Hua Guo Peng-Fei Ma Guo-Qian Yang Jin-Yong Hu Yun-Long Liu En-Hua Xia Mi-Cai Zhong Lei Zhao Gui-Ling Sun Yu-Xing Xu You-Jie Zhao Yi-Chi Zhang Yu-Xiao Zhang Xue-Mei Zhang Meng-Yuan Zhou Ying Guo Cen Guo Jing-Xia Liu Xia-Ying Ye Yun-Mei Chen Yang Yang Bin Han Choun-Sea Lin Ying Lu De-Zhu Li Germplasm Bank of Wild Species Kunming Institute of BotanyChinese Academy of SciencesKunmingYunnan 650201China Key Laboratory for Plant Diversity and Biogeography in East Asia Kunming Institute of BotanyChinese Academy of SciencesKunmingYunnan 650201China State Key Laboratory of Tea Plant Biology and Utilization Anhui Agricultural UniversityHefeiAnhui 230036China Kunming College of Life Science University of Chinese Academy of SciencesKunmingYunnan 650201China lnstitute of Plant Stress Biology State Key Laboratory of Cotton BiologyDepartment of BiologyHenan UniversityKaifengHenan 475001China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunmingYunnan 650224China Yunnan Academy of Biodiversity Southwest Forestry UniversityKunmingYunnan 650224China National Center for Gene Research Shanghai Institute of Plant Physiology and EcologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai 200032China Agricultural Biotechnology Research Center Academia SinicaTaipei College of Fisheries and Life Science Shanghai Ocean UniversityShanghai 201306China

Polyploidization is a major driver of speciation and its importance to plant evolution has been well *** comprise one diploid herbaceous and three polyploid woody lineages,and are members of the only major subfamily in grasses that diversified in forests,with the woody members having a tree-like lignified *** this study,we generated four draft genome assemblies of major bamboo lineages with three different ploidy levels(diploid,tetraploid,and hexaploid).We also constructed a high-density genetic linkage map for a hexaploid species of bamboo,and used a linkage-map-based strategy for genome assembly and identification of subgenomes in *** phylogenomic analyses using a large dataset of syntenic genes with expected copies based on ploidy levels revealed that woody bamboos originated subsequent to the divergence of the herbaceous bamboo lineage,and experienced complex reticulate evolution through three independent allopolyploid events involving four extinct diploid ancestors.A shared but distinct subgenome was identified in all polyploid forms,and the progenitor of this subgenome could have been critical in ancient polyploidizations and the origin of woody *** genetic clues to the unique flowering behavior and woody trait in bamboos were also *** together,our study provides significant insights into ancient reticulate evolution at the subgenome level in the absence of extant donor species,and offers a potential model scenario for broad-scale study of angiosperm origination by allopolyploidization.

关键词： Bambusoideae polyploidization comparative genomics subgenome evolution flowering

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A Method of Searching for Optimal Coalition Structure for Solving Resource Scheduling Problem of Overall Load Balancing in Edge Computing Environments

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Journal of Physics: Conference Series 2020年第3期1550卷

作者： Hu Yanan Li Chunsheng Zhang Kejia School of Computer & Information Technology Northeast Petroleum University Daqing 163318 (PR China) Heilongjiang Provincial Key Laboratory of Oil Big data & Intelligent Analysis Daqing 163318 (PR China)

The cooperative computing resource scheduling problem in the distributed computing environment is usually transformed into the problem about how to find the optimal coalition structure. However, affected by factors such as high node density and large number of concurrent tasks in edge computing environments, it is difficult to formulate resource scheduling schemes for overall load balancing in a large strategic space by traditional methods. In order to solve this problem, we proposed a method of searching for optimal coalition structure based on discrete particle swarm optimization (DPSO). Firstly, we converted the resource scheduling problem of edge computing into an optimization problem model, constructed a new structure of coalition structure to express the resource scheduling scheme, and gave key elements such as optimization objectives, decision variables, and constraint conditions. Secondly, we improved DPSO and designed M-ary discrete particle swarm optimization (MDPSO): improved particle state determination method for adapting to the new expression of the coalition structure, and proposed a power series-based inertia weight computing method to improve the optimization speed and the optimal solution quality. Finally, we simulated real application scenarios and designed comparative experiments on the optimization speed, the optimal solution quality, stability, etc., which verifies that the method proposed in this paper has obvious advantages in solving the resource scheduling problem of overall load balancing in edge computing.

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Prediction Research on the Energy Consumption of Public Building Based on MLR-BP Neural Network

Prediction Research on the Energy Consumption of Public Buil...

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Chinese Automation Congress (CAC)

作者： Jingjian Yang Yuning Amy Xie Hongyan Ma College of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China The Madeira School McLean VA United States Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

Accompanying the constant development of data mining technology and neural network, the application of computer technology in energy consumption prediction of large public buildings is the trend of green buildings and intelligent life in the field of engineering. In this paper, a new energy consumption prediction algorithm based on MLR-BP is proposed, which solves the issue of current energy consumption algorithm: the massive amount of calculation and the low prediction accuracy. Firstly, factors that affect building energy consumption are categorized as external factors (meteorological factors) and internal factors. In addition, the multiple linear regression is used to model the external factors, and the influencing factors with high correlation are screened out. Then, the selected external factors and internal factors are combined into the training of BP neural network to obtain a new energy consumption prediction model. The simulation results show that in comparison with the BP neural network, the proposed prediction algorithm in this paper can predict the building energy consumption more accurately.

关键词： Energy consumption Buildings Neural networks Predictive models Temperature Linear regression Mathematical model

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Nonlinear variational inequality problems involving uncertain variable

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2019年第4期81卷 65-74页

作者： Li, Cunlin Jia, Zhifu Ningxia Key Laboratory of intelligent information and Big Data Processing Governance and social management research center of Northwest Ethnic regions North Minzu University Yinchuan China State Key Laboratory of Mechanics Control of Mechanical Structures Institute of Nano Science and Department of Mathematics Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Mathematics and Information Science North Minzu University Yinchuan China

Variational inequality problems have been extensively studied and applied in the area of optimization theory, but much of it has been developed in a deterministic domain. In this paper, the uncertain variational inequality is studied. Under the compact uncertain event space, we establish an uncertain nonlinear expected residual minimization (UNERM) model. Considering that uncertain variable usually has no density function, we propose integration by parts method to solve UNERM model. Under reasonable assumptions and Slater’s constraint qualification, two kinds of convergence, such as convergence of global optimal solutions and convergence of stationary points, are investigated. © 2019, Politechnica University of Bucharest. All rights reserved.

关键词： Uncertainty analysis

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DUE Distribution and Pairing in D2D Communication

DUE Distribution and Pairing in D2D Communication

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International Conference on Computer Communications and Networks (ICCCN)

作者： Weifeng Lu Xiaoqiang Ren Jia Xu Siguang Chen Lijun Yang Jian Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China College of IoT Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

The D2D (Device-to-Device) communication has been very popular as it is a promising and low-cost solution to reduce the burden on the cellular network. However, there are rare concerns about the distribution and pairing of DUEs(D2D user equipments), which have a significant impact on QoS (Quality of Service) of D2D communication. In this paper, we propose a novel algorithm based on the coalitional game to optimally adjust the distribution of DUEs. The proposed algorithm aims to form the optimal coalition structure, which achieves a balance between the throughput and power consumption of each coalition, obtaining the enhanced QoS of D2D. We show that our algorithm is superior to the benchmark models in terms of the throughput and energy efficiency of the DUE coalition. To further improve the QoS, we also propose a method to predict and maximize the pairing probability of DUEs. The proposed prediction method adopts the Logistic Regression to model the global pairing probability according to the communication parameters of DUEs. Experimental results show that the proposed prediction method is significantly superior to the benchmark methods in terms of prediction accuracy. In addition, the pairing probability maximization algorithm proposed also significantly improves the pairing probability.

关键词： Device-to-device communication Throughput Power demand Fading channels Games Quality of service Time division multiple access

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Adaptive Teacher-and-Student Model for Heterogeneous Domain Adaptation

Adaptive Teacher-and-Student Model for Heterogeneous Domain ...

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IEEE International Conference on data Mining (ICDM)

作者： Yunyun Wang Xuzhang Chen Songcan Chen Hui Xue College of Computer Science Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts & Telecommunications Nanjing China College of Computer Science & Technology/AI MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics & Astronautics Nanjing China College of Computer Science and Engineering Southeast University Nanjing China

In heterogeneous domain adaptation (HDA), since the feature spaces of the source and target domains are different, knowledge transfer from the source to the target domain is really challenging. How to align the different feature spaces and then adaptively transfer the related knowledge is critical for HDA. In this paper, we develop an adaptive teacher-and-student model for heterogeneous domain adaptation (AtsHDA). In AtsHDA, the source domain as a teacher and the target domain as a student are aligned or co-adapted to each other first, so that their correlation can be maximized. Then the target domain adaptively learns from the source domain. Specifically, there is a balance between the learning by the target domain itself and the instruction from the source domain. That is, when the guidance from the source domain is helpful for learning, the learning of target classifier emphasizes the instruction of source knowledge, and considers its own knowledge more, otherwise. Further, an ensemble method is designed to decide such a balance. Finally, empirical results show that AtsHDA can achieve competitive results compared with the state-of-arts.

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