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A model for the transportation and distribution of jellyfish Rhopilema esculentum for stock enhancement in the Liaodong Bay, China

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Acta Oceanologica Sinica 2019年第1期38卷 90-101页

作者： Liping Yin Xiujuan Shan Chang Zhao Xianshi Jin Guansuo Wang Fangli Qiao First Institute of Oceanography Ministry of Natural Resources Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and Technology (Qingdao) Key Laboratory of Marine Science and Numerical Modelling Ministry of Natural Resources Yellow Sea Fisheries Research Institute Chinese Academy of Fishery Sciences Laboratory for Marine Fisheries Science and Food Production Processes Pilot Qingdao National Laboratory for Marine Science and Technology (Qingdao) Key Laboratory of Data Analysis and Applications Ministry of Natural Resources

A numerical model for jellyfish Rhopilema esculentum stock enhancement is developed for the first time. The model is based on an operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas and uses a Lagrangian particle-tracking scheme to track the trajectories of released jellyfish. The Jellyfish are modeled as particles with diel vertical migration and are passively drifted by the current and dispersion due to the sub-grid processes. A comparison between the simulation and survey results demonstrate that the model can capture the primary distribution patterns of the released jellyfish. The model results show that the ocean current and indirect wind impact are the main drivers controlling the jellyfish transport. A connectivity matrix between the release sites and fishing grounds indicates the top of the bay is better than the eastern and western coasts for jellyfish fishing. The matrix also shows that only 45% and 27% of the jellyfish released from Wafangdian(WFD) can enter the fishing ground in 2008 and 2010; thus, the site near WFD is not an advisable location for jellyfish release. A Lagrangian probability density function based on a nine-year tracing experiment validates the results and further provides a "climatology" distribution of the released *** experiments are conducted to examine the sensitivity of the model to random walk schemes and to release conditions. The model requires a random walk but is insensitive to the random walk scheme. The experiments with different habitat depths show that if the jellyfish are fixed on the bottom of the water, most of them will be transported to the center, or even out of the bay, by the bottom circulation.

关键词： Liaodong Bay Jellyfish Rhopilema esculentum distribution and connectivity Lagrangian particle tracking current

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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Study of Advanced Low-Cost Smart Prepaid Electricity Meter Using Arduino and GSM 16th

Study of Advanced Low-Cost Smart Prepaid Electricity Meter U...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, applications and Tools, FITAT 2020

作者： Omer, Abdalaziz Altayeb Ibrahim Chang, Kuo-Chi Deng, Hui-Qiong Chu, Kai-Chun Amesimenu, Governor David Kwabena Zhou, Yu-Wen Chang, Fu-Hsiang School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial University Engineering Research Genter of Smart Grid Simulation Analysis and Integrated Control Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Business Management Fujian University of Technology Fuzhou China Department of Tourism Shih-Hsin University Taipei Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9789813364196

The use of prepaid electricity meters appeared in many countries a few years ago and has been widely recognized by consumers and managed to find solutions to too many problems faced by distribution companies. But for traditional systems, users must purchase electricity and manually charge the meter. For example, if he is in the office and needs to charge the house electricity meter, then he must go home to do this, which is considered a waste of time and energy. However, the advanced low-cost smart prepaid electricity meter is a solution to the problem mentioned earlier. By sending GSM text messages to the Arduino as a microcontroller through the GSM module, consumers can use their mobile phones to charge the electricity meter anywhere. This is the main reason for the microcontroller. The control process is also connected to an electricity meter and also to a relay module to provide power for consumers or consumers when the balance is equal to zero. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Global system for mobile communications

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C olumnSGD: A Column-oriented Framework for Distributed Stochastic Gradient Descent

C olumnSGD: A Column-oriented Framework for Distributed Stoc...

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International Conference on data Engineering

作者： Zhipeng Zhang Wentao Wu Jiawei Jiang Lele Yu Bin Cui Ce Zhang School of EECS & Key Laboratory of High Confidence Software Technologies (MOE) Peking University Microsoft Research Redmond USA ETH Zurich Switzerland Tencent Inc Center for Data Science Peking University & National Engineering Laboratory for Big Data Analysis and Applications

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Distributed machine learning (ML) has triggered tremendous research interest in recent years. Stochastic gradient descent (SGD) is one of the most popular algorithms for training ML models, and has been implemented in almost all distributed ML systems, such as Spark MLlib, Petuum, MXNet, and TensorFlow. However, current implementations often incur huge communication and memory overheads when it comes to large models. One important reason for this inefficiency is the row-oriented scheme (RowSGD) that existing systems use to partition the training data, which forces them to adopt a centralized model management strategy that leads to vast amount of data exchange over the network. We propose a novel, column-oriented scheme (ColumnSGD) that partitions training data by columns rather than by rows. As a result, ML model can be partitioned by columns as well, leading to a distributed configuration where individual data and model partitions can be collocated on the same machine. Following this locality property, we develop a simple yet powerful computation framework that significantly reduces communication overheads and memory footprints compared to RowSGD, for large-scale ML models such as generalized linear models (GLMs) and factorization machines (FMs). We implement ColumnSGD on top of Apache Spark, and study its performance both analytically and experimentally. Experimental results on both public and real-world datasets show that ColumnSGD is up to 930× faster than MLlib, 63× faster than Petuum, and 14× faster than MXNet.

关键词： Conferences data engineering Radio frequency

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FeatureBand: A feature selection method by combining early stopping and genetic local search 3rd

FeatureBand: A feature selection method by combining early s...

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3rd APWeb and WAIM Joint Conference on Web and Big data, APWeb-WAIM 2019

作者： Xue, Huanran Jiang, Jiawei Shao, Yingxia Cui, Bin Center for Data Science National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China Tencent Inc. Shenzhen China Beijing Key Lab of Intelligent Telecommunications Software and Multimedia BUPT Beijing China

ISBN: (纸本)9783030260743

Feature selection is an important problem in machine learning and data mining. In reality, the wrapper methods are broadly used in feature selection. It treats feature selection as a search problem using a predictor as a black-box. However, most wrapper methods are time-consuming due to the large search space. In this paper, we propose a novel wrapper method, called FeatureBand, for feature selection. We use the early stopping strategy to terminate bad candidate feature subsets and avoid wasting of training time. Further, we use a genetic local search to generate new subsets based on previous ones. These two techniques are combined under an iterative framework in which we gradually allocate more resources for more promising candidate feature subsets. The experimental result shows that FeatureBand achieves a better trade-off between search time and search accuracy. It is 1.45 × to 17.6 × faster than the state-of-the-art wrapper-based methods without accuracy loss. © 2019, Springer Nature Switzerland AG.

关键词： Feature Selection

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An Isolable Radical Anion Featuring a 2-Center-3-Electron π-Bond without a Clearly Defined σ-Bond

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Angewandte Chemie 2023年第52期135卷

作者： Dr. Yanbo Mei Prof. Dr. Xiaodan Chen Dr. Rui Wei Dr. Xiao-Yong Chang Prof. Dr. Lizhi Tao Prof. Dr. Liu Leo Liu Department of Chemistry and Research Center for Chemical Biology and Omics Analysis College of Science Southern University of Science and Technology Shenzhen 518055 China Department of Chemistry and Dongguan Key Laboratory for Data Science and Intelligent Medicine Great Bay University Dongguan 523000 China Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Applications College of Chemistry and Materials Science Jinan University Guangzhou 510632 China State Key Laboratory of Elemento-Organic Chemistry Nankai University Tianjin 300071 China

Featuring an extra electron in the π* antibonding orbital, species with a 2-center-3-electron (2c3e) π bond without an underlying σ bond are scarcely known. Herein, we report the synthesis, isolation and characterization of a radical anion salt [K(18-C-6)] + {[(HCNDipp) 2 Si] 2 P 2 }⋅ − (i.e. [K(18-C-6)] + 3 ⋅ − ) (18-C-6=18-crown-6, Dipp=2,6-diisopropylphenyl), in which 3 ⋅ − features a perfectly planar Si 2 P 2 four-membered ring. This species represents the first example of a Si- and P-containing analog of a bicyclo[1.1.0]butane radical anion. The unusual bonding motif of 3 ⋅ − was thoroughly investigated via X-ray diffraction crystallography, electron paramagnetic resonance spectroscopy (EPR), and calculations by density functional theory (DFT), which collectively unveiled the existence of a 2c3e π bond between the bridgehead P atoms and no clearly defined supporting P−P σ bond.

关键词： 2-Center-3-Electron π-Bond Chemical Bonding DFT Calculations Main Group Element Radical Anion

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The long-term prediction of the oil-contaminated water from the Sanchi collision in the East China Sea

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Acta Oceanologica Sinica 2018年第3期37卷 69-72页

作者： YIN Liping ZHANG Min ZHANG Yuanling QIAO Fangli The First Institute of Oceanography State Oceanic Administration Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technology Key Laboratory of Marine Science and Numerical Modelling State Oceanic Administration Key Laboratory of Data Analysis and Applications State Oceanic Administration

The condensate and bunker oil leaked from the Sanchi collision would cause a persistent impact on marine ecosystems in the surrounding areas. The long-term prediction for the distribution of the oil-polluted water and the information for the most affected regions would provide valuable information for the oceanic environment protection and pollution assessment. Based on the operational forecast system developed by the First Institute of Oceanography, State Oceanic Administration, we precisely predicted the drifting path of the oil tanker Sanchi after its collision. Trajectories of virtual oil particles show that the oil leaked from the Sanchi after it sank is mainly transported to the northeastern part of the sink location, and quickly goes to the open ocean along with the Kuroshio. Risk probability analysis based on the outcomes from the operational forecast system for years 2009 to2017 shows that the most affected area is at the northeast of the sink location.

关键词： Sanchi collision long-term prediction oil spill

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Research on Pre-view Method of Safety Level of Cascading Trip for Power Grid 6th

Research on Pre-view Method of Safety Level of Cascading Tri...

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6th International Conference on Advanced Machine Learning Technologies and applications, AMLTA 2021

作者： Deng, Huiqiong Li, Qinbin Zheng, Rongjin Li, Peiqiang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China Fujian Provincial University Engineering Research Center of Smart Grid Simulation Analysis and Integrated Control Fuzhou Fujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China College of Mechanical & Electrical Engineering National Taipei University of Technology No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan Department of Business Administration North Borneo University College Lot 47 Block-F Alamesra Permai Plaza 2 Jln Sulaman Kota Kinabalu Sabah88400 Malaysia

ISBN: (纸本)9783030697167

This article introduces a method by which the power grid's security level can be observed in advance based on the expected initial failure. Firstly, based on the general form of cascading trip, this paper gives a mathematical expression of grid tripping based on cascading trip relay protection action behavior. According to the mathematical expression, the relationship between grid tripping and grid node injection power is discussed. According to this relationship, the distance between the node injection power under the current operation state and the nearest critical state node injection power is taken as the index to measure the power grid's security for cascading trip. According to this index, combined with various power grid constraints before and after the initial fault, a mathematical model for obtaining this safety index is given. This model is an optimized mathematical model. For the model, this paper gives a solution idea based on particle swarm. Finally, a few cases are performed in the IEEE-14 node system, and the calculations of the cases proved the accuracy of the proposed model and method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electric load flow

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Redesigning electrification of China’s ammonia and methanol industry to balance decarbonization with power system security

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Nature Energy 2025年 1-12页

作者： Li, Jiarong Lin, Jin Wang, Jianxiao Lu, Xi Nielsen, Chris P. McElroy, Michael B. Song, Yonghua Song, Jie Lyu, Xuefeng Yu, Mingkai Wu, Sirui Yu, Zhipeng Department of Electrical Engineering Tsinghua University Beijing China Harvard-China Project on Energy Economy and Environment Harvard University Cambridge MA United States State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China State Key Laboratory of Regional Environment and Sustainability School of Environment Tsinghua University Beijing China Institute for Carbon Neutrality Tsinghua University Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macao Department of Industrial Engineering and Management College of Engineering Peking University Beijing China School of Nuclear Science and Engineering North China Electric Power University Beijing China China Electric Power Research Institute Beijing China

Electrification represents a critical pathway to decarbonize the ammonia and methanol industries by reducing fossil fuel reliance. However, a greater understanding is needed of how the spatio-temporal mismatch between renewable generation and chemical load patterns may impact on power system emissions and security. Here we assess different electrification pathways across 22 Chinese provinces from 2020 to 2050. Using 2020 data, we show that reliance on grid electricity offsets CO2 reductions by increasing power system emissions, raising national emissions by 1%. Integrating co-located renewables without self-balancing flexibility exacerbates power system security risks, potentially increasing balancing requirements by 9%. To address this challenge, we propose the ‘Green Flexible Chemical Electrification’ pathway, transitioning from requirements for stringent co-located renewables to rigid temporal self flexibility. By 2030, Green Flexible Chemical Electrification demonstrates cost competitiveness nationwide, with green ammonia potentially generating 2 billion RMB in profit. Additionally, redesigning electricity pricing to incentivize chemical-side demand management further enhances power–chemical synergies. © The Author(s), under exclusive licence to Springer Nature Limited 2025.

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Abnormal Traffic Detection Method Based on DCNN-GRU Architecture in SDN

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International Journal of Intelligent Systems 2025年第1期2025卷

作者： Xueyuan Duan Kun Wang Yu Fu Taotao Liu Yihan Yu Jianqiao Xu Lu Wang College of Computer and Information Technology Xinyang Normal University Xinyang 464000 Henan China *** Department of Information Security Naval University of Engineering Wuhan 430033 Hubei China *** Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang 464000 China *** School of Information and Communication Engineering Xinyang Vocational and Technical College Xinyang 464000 Henan China *** Department of Operational Research and Programming Naval University of Engineering Wuhan 430033 China *** Department of Economic Crime Investigation Henan Police College Zhengzhou 450046 Henan China

In response to the centralized single-architecture abnormal traffic detection method in Software Defined Network (SDN), which consumes massive computational and network resources, and may lead to the decline of service quality of SDN network, this paper proposes a large-scale abnormal traffic detection method of SDN network based on Distributed Convolutional Neural Networks and Gate Recurrent Unit (DCNN-GRU) architecture. This method utilizes lightweight detection agents based on CNN deployed on each controller to extract traffic features preliminarily. Then it inputs the feature data into the GRU-based deep detection model hosted in the cloud for collaborative training and completes the final abnormal detection task. Since the feature extraction tasks are distributed across multiple controllers, the cloud server only needs to relearn and classify the extracted feature data, which is less costly than directly extracting feature information from the original traffic data and occupies less bandwidth resources than transmitting complete data packets. The experiment shows that the method achieves an abnormal detection accuracy of 0.9939, a recall rate of 0.9831, and a false alarm rate of only 0.0244, obtaining a higher precision and lower false alarm rate than traditional detection methods.

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