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Reconstruction Residuals Based Long-term Voltage Stability Assessment Using Autoencoders

Journal of Modern Power Systems and clean Energy 2020年第6期8卷 1092-1103页

作者： Haosen Yang robert c.qiu Houjie Tong the Department of Electrical Engineering Center for Big Data and Artificial IntelligenceShanghai Jiaotong UniversityShanghai 200240China School of Electronic Information and communication Huazhong University of Science and TechnologyWuhan 430000China IEEE

Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data.Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states,or even unstable state.However,in practice,the data of insecure or unstable state is very rare,as the power system should be guaranteed to operate far away from voltage collapse.Under this circumstance,this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system.The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data,while it fails to rebuild data of insecure states.Thus,the residual of reconstruction is effective in indicating VSA.Besides,to develop a more accurate and robust algorithm,long short-term memory(LSTM)networks combined with fully-connected(Fc)layers are used to build the autoencoder,and a moving strategy is introduced to bias the features of testing data toward the secure feature domain.Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.

关键词： Reconstruction loss autoencoders voltage stability long-short-term memory(LSTM) feature moving strategy

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Deep Reinforcement Learning for Power System Applications: An Overview

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cSEE Journal of Power and Energy Systems 2020年第1期6卷 213-225页

作者： Zidong Zhang Dongxia Zhang robert c.qiu Research center For Big Data Engineering And Technologies Shanghai Jiao Tong UniversityShanghai 200240China Department of Electrical and computer Engineering Tennessee Technological UniversityCookevilleTN 38505USA china Electric Power Research Institute Beijing 100192China

Due to increasing complexity, uncertainty and data dimensions in power systems, conventional methods often meet bottlenecks when attempting to solve decision and control prob- lems. Therefore, data-driven methods toward solving such prob- lems are being extensively studied. Deep reinforcement learning (DRL) is one of these data-driven methods and is regarded as real artificial intelligence (AI). DRL is a combination of deep learning (DL) and reinforcement learning (RL). This field of research has been applied to solve a wide range of complex sequential decision-making problems, including those in power systems. This paper firstly reviews the basic ideas, models, algorithms and techniques of DRL. Applications in power systems such as energy management, demand response, electricity market, operational control, and others are then considered. In addition, recent advances in DRL including the combination of RL with other classical methods, and the prospect and challenges of applications in power systems are also discussed.

关键词： Artificial intelligence deep reinforcement learning machine learning power system smart grids

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Detection and classification of Transmission Line Transient Faults Based on Graph convolutional Neural Network

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cSEE Journal of Power and Energy Systems 2021年第3期7卷 456-471页

作者： Houjie Tong robert c.qiu Dongxia Zhang Haosen Yang Qi Ding Xin Shi Department of Electrical Engineering Center for Big Data and Artificial IntelligenceShanghai Jiao Tong UniversityShanghai 200240China School of Electronic Information and communication Huazhong University of Science and TechnologyWuhan 430000China Electric Power Research Institute Haidian DistrictBeijing 100192China School of control and computer Engineering North China Electric Power UniversityBeijing 102206China

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network.compared with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction ability.On this basis,a framework for transient fault detection and classification is created.Graph structure is generated to provide topology information to the task.Our approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results rapidly.Furthermore,the proposed approach is tested in various situations and its generalization ability is verified by experimental results.The results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.

关键词： Graph convolutional network(GcN) power transmission line fault detection and classification spatio-temporal data topology information

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Bioactive compounds in Hericium erinaceus and their biological properties:a review

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Food Science and Human Wellness 2024年第4期13卷 1825-1844页

作者： Yue qiu Genglan Lin Weiming Liu Fuming Zhang robert J.Linhardt Xingli Wang Anqiang Zhang college of Food Science and Engineering Zhejiang University of TechnologyHangzhou 310014China Zhejiang Biosan Biotech co. Ltd.Hangzhou 310052China Department of chemical and Biological Engineering Center for Biotechnology and Interdisciplinary StudiesRensselaer Polytechnic InstituteTroyNY 12180USA Departments of chemistry and chemical Biology and Biomedical Engineering Biological ScienceCenter for Biotechnology and Interdisciplinary StudiesRensselaer Polytechnic InstituteTroyNY 12180USA

Hericium erinaceus is a nutritious edible and medicinal fungi,rich in a variety of functional active ingredients,with various physiological functions such as antioxidation,anticancer,and enhancing immunity.It is also effective in protecting the digestive system and preventing neurodegenerative diseases.In this review paper,we summarize the sources,structures and efficacies of the main active components in H.erinaceus fruiting body,mycelium,and culture media,and update the latest research progress on their biological activities and the related molecular mechanisms.Based on this information,we provide detailed challenges in current research,industrialization and information on the active ingredients of H.erinaceus.Perspectives for future studies and new applications of H.erinaceus are proposed.

关键词： Hericium erinaceus Bioactive compounds Biological activities Antioxidation Neuroprotection Immunoregulation

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Oligodendrocytes in central nervous system diseases:the effect of cytokine regulation

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Neural Regeneration Research 2024年第10期19卷 2132-2143页

作者： chengfu Zhang Mengsheng qiu Hui Fu center for cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiang ProvinceChina Department of Psychiatry National Clinical Research Center for Mental Disordersand National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunan ProvinceChina Institute of Life Sciences Key Laboratory of Organ Development and Regeneration of Zhejiang ProvinceCollege of Life and Environment SciencesHangzhou Normal UniversityHangzhouZhejiang ProvinceChina Key Laboratory of Aging and cancer Biology of Zhejiang Province School of Basic Medical SciencesHangzhou Normal UniversityHangzhouZhejiang ProvinceChina

cytokines including tumor necrosis factor, interleukins, interferons, and chemokines are abundantly produced in various diseases. As pleiotropic factors, cytokines are involved in nearly every aspect of cellular functions such as migration, survival, proliferation, and differentiation. Oligodendrocytes are the myelin-forming cells in the central nervous system and play critical roles in the conduction of action potentials, supply of metabolic components for axons, and other functions. Emerging evidence suggests that both oligodendrocytes and oligodendrocyte precursor cells are vulnerable to cytokines released under pathological conditions. This review mainly summarizes the effects of cytokines on oligodendrocyte lineage cells in central nervous system diseases. A comprehensive understanding of the effects of cytokines on oligodendrocyte lineage cells contributes to our understanding of central nervous system diseases and offers insights into treatment strategies.

关键词： astrocyte central nervous system disease cXc chemokine cytokine interferonγ interleukin microglia oligodendrocyte oligodendrocyte precursor cell tumor necrosis factorα

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An Accurate and Real-time Method of Self-blast Glass Insulator Location Based on Faster R-cNN and U-net with Aerial Images

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cSEE Journal of Power and Energy Systems 2019年第4期5卷 474-482页

作者： Zenan Ling Dongxia Zhang robert c.qiu Zhijian Jin Yuhang Zhang Xing He Haichun Liu Department of Electrical Engineering Center for Big Data and Artificial IntelligenceState Energy Smart Grid Research and Development CenterShanghai Jiao Tong UniversityShanghai 200240China china Electric Power Research Institute Beijing 100192China Department of Electrical and computer Engineering Tennessee Technological UniversityCookevilleTN 38505USA

This paper proposes a new deep learning framework for the location of broken insulators(in particular the self-blast glass insulator)in aerial images.We address the broken insulators location problem in a low signal-noise-ratio(SNR)setting.We deal with two modules:1)object detection based on Faster R-cNN,and 2)classification of pixels based on U-net.For the first time,our paper combines the above two modules.This combination is motivated as follows:Faster R-cNN is used to improve SNR,while the U-net is used for classification of pixels.A diverse aerial image set measured by a power grid in china is tested to validate the proposed approach.Furthermore,a comparison is made among different methods and the result shows that our approach is accurate in real time.

关键词： Aerial images deep learning faster R-cNN insulators location real-time U-net

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Targeted drug approvals in 2023:breakthroughs by the FDA and NMPA

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Signal Transduction and Targeted Therapy 2024年第3期9卷 813-821页

作者： Lang Zheng Wenjing Wang qiu Sun Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu 610041China West china Medical Publishers West China HospitalSichuan UniversityChengdu 610041China

According to the official website of china’s National Medicines and Pharmaceutical Administration(NMPA),a total of 87 novel drugs were approved in china in 2023,with targeted drugs accounting for 67.8%of the total,amounting to 59 drugs(Fig.1a;Table 1)[https://***/yaopin/ypjgdt/index.html].Notably,domestic innovation is flourishing,with five first-in-class drugs,including Glumetinib,a c-Met inhibitor from Haihe Biopharma;Leritrelvir,a 3cL protease inhibitor from Raynovent;Anaprazole,a proton pump inhibitor from Xuanzhu Biopharm;Pegol-Sihematide,an EPO drug from Hansoh Pharma;and Zuberitamab from BioRay Biopharmaceutical co.,Ltd.Additionally,the world’s first allosteric inhibitor targeting TYK2,Sotyktu(deucravacitinib),has been approved for the treatment of psoriasis and Selumetinib,a MEK inhibitor co-developed by AstraZeneca and Merck Sharp&Dohme(MSD),became the first approved drug in china for the treatment of neurofibromatosis type I(NF1).Beyond these drugs,the approval of novel drug types such as cAR-T cell products,siRNA,monoclonal antibodies,dual antibodies,and ADcs in china is a significant development in the country’s pharmaceutical industry(Fig.1a).

关键词： drugs Merck approval

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Nitrogen availability mediates the effects of roots and mycorrhizal fungi on soil organic carbon decomposition:A meta-analysis

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Pedosphere 2024年第2期34卷 289-296页

作者： Tongshuo BAI Yunpeng qiu Shuijin HU Ecosystem Ecology Laboratory College of Resources and Environmental SciencesNanjing Agricultural UniversityNanjing 210095China Department of Entomology&Plant Pathology North Carolina State UniversityRaleigh NC 27695USA

Plant roots and their associated mycorrhizal fungi critically mediate the decomposition of soil organic carbon(c),but the general patterns of their impacts over a broad geographical range and the primary mediating factors remain unclear.Based on a synthesis of 596 paired observations from both field and greenhouse experiments,we found that living roots and/or mycorrhizal fungi increased organic c decomposition by 30.9%,but low soil nitrogen(N)availability(i.e.,high soil c:N ratio)critically mitigated this promotion effect.In addition,the positive effects of living roots and/or mycorrhizal fungi on organic c decomposition were higher under herbaceous and leguminous plants than under woody and non-leguminous plants,respectively.Surprisingly,there was no significant difference between arbuscular mycorrhizal fungi and ectomycorrhizal fungi in their effects on organic c decomposition.Furthermore,roots and/or mycorrhizal fungi significantly enhanced the decomposition of leaf litter but not root litter.These findings advance our understanding of how roots and their symbiotic fungi modulate soil c dynamics in the rhizosphere or mycorrhizosphere and may help improve predictions of soil global c balance under a changing climate.

关键词： arbuscular mycorrhizal fungi ectomycorrhizal fungi leaf litter leguminous plant mycorhizosphere rhizosphere root litter soil c:N ratio

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coaxial Wet Spinning of Boron Nitride Nanosheet‑Based composite Fibers with Enhanced Thermal conductivity and Mechanical Strength

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Nano-Micro Letters 2024年第2期16卷 126-138页

作者： Wenjiang Lu Qixuan Deng Minsu Liu Baofu Ding Zhiyuan Xiong Ling qiu Tsinghua Shenzhen International Graduate School(TSIGS) Tsinghua UniversityShenzhen 518055People’s Republic of China Monash Suzhou Research Institute(MSRI) Monash UniversitySuzhou 215000People’s Republic of China Faculty of Materials Science and Engineering/Institute of Technology for carbon Neutrality Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055People’s Republic of China Shenzhen Key Laboratory of Energy Materials for carbon Neutrality Shenzhen 518055People’s Republic of China School of Light Industry and Engineering South China University of TechnologyGuangzhou 510614People’s Republic of China

Hexagonal boron nitride nanosheets(BNNSs)exhibit remarkable thermal and dielectric properties.However,their self-assembly and alignment in macroscopic forms remain challenging due to the chemical inertness of boron nitride,thereby limiting their performance in applications such as thermal management.In this study,we present a coaxial wet spinning approach for the fabrication of BNNSs/polymer composite fibers with high nanosheet orientation.The composite fibers were prepared using a superacid-based solvent system and showed a layered structure comprising an aramid core and an aramid/BNNSs sheath.Notably,the coaxial fibers exhibited significantly higher BNNSs alignment compared to uniaxial aramid/BNNSs fibers,primarily due to the additional compressive forces exerted at the core-sheath interface during the hot drawing process.With a BNNSs loading of 60 wt%,the resulting coaxial fibers showed exceptional properties,including an ultrahigh Herman orientation parameter of 0.81,thermal conductivity of 17.2 W m^(-1)K^(-1),and tensile strength of 192.5 MPa.These results surpassed those of uniaxial fibers and previously reported BNNSs composite fibers,making them highly suitable for applications such as wearable thermal management textiles.Our findings present a promising strategy for fabricating high-performance composite fibers based on BNNSs.

关键词： Boron nitride nanosheets coaxial fiber Interfacial compression Nanosheet aligning Wearable thermal management

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An Efficient Local Radial Basis Function Method for Image Segmentation Based on the chan-Vese Model

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computer Modeling in Engineering & Sciences 2024年第4期139卷 1119-1134页

作者： Shupeng qiu chujin Lin Wei Zhao School of Mathematics and Statistics Guangdong University of TechnologyGuangzhou510520China

In this paper,we consider the chan–Vese(c-V)model for image segmentation and obtain its numerical solution accurately and efficiently.For this purpose,we present a local radial basis function method based on a Gaussian kernel(GA-LRBF)for spatial discretization.compared to the standard radial basis functionmethod,this approach consumes less cPU time and maintains good stability because it uses only a small subset of points in the whole computational domain.Additionally,since the Gaussian function has the property of dimensional separation,the GA-LRBF method is suitable for dealing with isotropic images.Finally,a numerical scheme that couples GA-LRBF with the fourth-order Runge–Kutta method is applied to the c-V model,and a comparison of some numerical results demonstrates that this scheme achieves much more reliable image segmentation.

关键词： Image segmentation chan–Vese model local radial basis functionmethod Gaussian kernel Runge–Kuttamethod

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