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Probabilistic supply and demand balance control method based on electricity market mechanism considering N–1 criterion

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IEEJ Transactions on Power and Energy 2021年第1期141卷 1-12页

作者： Koide, Akira Tsuji, Takao Tanaka, Kazuyuki Sugimoto, Hitoshi Advanced Power System Engineering Laboratory University of Toyama 3190 Gofuku Toyama930-8555 Japan Graduate School of Sience and Engineering Yokohama National University 72-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan Engineering Research and Development Center Hokuriku Electric Power Company 2-54 Hisakata-cho Toyama930-0848 Japan

To solve energy and environmental issue, Variable Renewable Energy (VRE) has been greatly promoted and power flow in transmission system becomes more uncertain due to VRE output fluctuation. N–1 criterion has been applied to determine secure generation dispatch or clearing point in the market so far, however, more strict risk management will be needed in the near future with more VRE integration. In this paper, a new market mechanism is proposed to determine clearing point considering overloading probability in addition to N–1 criterion. Supposing the clearing point can be given as a solution of optimal power flow calculation, congestion management is available with adjusting upper and lower limits of thermal capacity of transmission lines in the formulation. The effectiveness of the proposed method was verified through numerical simulation using modified IEEE New England 39 bus system model with taking into account bidding curves of both suppliers and customers. © 2021 The Institute of Electrical Engineers of Japan.

关键词： Power markets

Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification

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arXiv 2024年

作者： Peng, Ruimin Du, Zhenbang Zhao, Changming Luo, Jingwei Liu, Wenzhong Chen, Xinxing Wu, Dongrui Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China AI Platform Software Engineering Research Center Dongfeng Corporation Research & Development Institute Wuhan430072 China China Electronic System Technology Co. Ltd. Beijing100089 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China

Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However, it usually requires a large amount of training data, which may not always be available in clinical practice. This paper proposes Multi-Branch Mutual-Distillation (MBMD) Transformer for cross-subject EEG-based seizure subtype classification, which can be effectively trained from small labeled data. MBMD Transformer replaces all even-numbered encoder blocks of the vanilla Vision Transformer by our designed multi-branch encoder blocks. A mutual-distillation strategy is proposed to transfer knowledge between the raw EEG data and its wavelets of different frequency bands. Experiments on two public EEG datasets demonstrated that our proposed MBMD Transformer outperformed several traditional machine learning and state-of-the-art deep learning approaches. To our knowledge, this is the first work on knowledge distillation for EEG-based seizure subtype classification. © 2024, CC BY.

关键词： Electroencephalography

Influence of insulation layer on the thermal behavior of subgrade soils

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Influence of insulation layer on the thermal behavior of sub...

2021 International Foundations Congress and Equipment Expo: Geoenvironmental Engineering, Geomaterial Modeling, Transporation Geotechnics, and Case Histories, IFCEE 2021

作者： Zhuo, Zhuang Xie, Zhaoxing Ali, Ayman Mehta, Yusuf Zhu, Cheng Lein, Wade Decarlo, Christopher Center for Research and Education in Advanced Transportation Engineering System Rowan Univ. GlassboroNJ United States Louisiana Transportation Research Center Baton RougeLA United States U.S. Army Corps of Engineers Engineering Research and Development Center Cold Regions Research and Engineering Laboratory HanoverNH United States

ISBN: (数字)9780784483435

An insulation layer on the top of the frost-susceptible soil has been proved as an efficient strategy to mitigate frost heave and thaw weakening for pavement in cold regions. To investigate the heat-transfer process in insulated pavements and compare the performance of different insulation materials under similar environmental conditions, we build five large-scale (4 ft by 4 ft by 3 ft deep;or 1.21 m by 1.21 m by 0.91 m deep) insulated pavement samples using different insulation materials. One of the samples is a control sample with no insulating layer, while the other four samples are insulated by extruded polystyrene (XPS) boards, tire chips, foamed concrete, and foamed glass aggregates (FGA), respectively. We place a frost panel on the top of each sample to control the freezing process from the surface. We adopt two types of environmental conditions, including constantly freezing and natural freeze-thaw environment with internal temperature distribution monitored by embedded thermocouples at different depths. Experimental results indicate that the insulation layer has a significant impact on the temperature distribution in both layers above and below the insulation layer. By comparing the thermal performance of the control sample and insulated sample under two environmental conditions, the XPS board is more suitable to utilize as an insulating layer compared to other insulating materials, followed by tire chips, foam glass aggregates, and foamed concrete. © ASCE.

关键词： Temperature distribution

3D Anatomical Structure-Guided Deep Learning for Accurate Diffusion Microstructure Imaging

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3D Anatomical Structure-Guided Deep Learning for Accurate Di...

IEEE International Symposium on Biomedical Imaging

作者： Xinrui Ma Jian Cheng Wenxin Fan Ruoyou Wu Yongquan Ye Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Hangzhou Zhejiang China Pengcheng Laboratory Shenzhen Guangdong China United Imaging Healthcare Houston TX USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue microstructure information. Recent advances in deep learning have shown promise in microstructure estimation; however, accurately estimating tissue microstructure from clinically feasible dMRI scans remains challenging without appropriate constraints. This paper introduces a novel framework that achieves high-fidelity and rapid diffusion microstructure imaging by simultaneously leveraging anatomical information from macro-level priors and mutual information across parameters. This approach enhances time efficiency while maintaining accuracy in microstructure estimation. Experimental results demonstrate that our method outperforms four state-of-the-art techniques, achieving a peak signal-to-noise ratio (PSNR) of 30.51±0.58 and a structural similarity index measure (SSIM) of 0.97±0.004 in estimating parametric maps of multiple diffusion models. Notably, our method achieves a 15× acceleration compared to the dense sampling approach, which typically utilizes 270 diffusion gradients.

关键词： Deep learning PSNR Accuracy Three-dimensional displays Magnetic resonance imaging Estimation Reconstruction algorithms Diffusion models Feature extraction Microstructure

DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense

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arXiv 2023年

作者： Wang, Jingyuan Wu, Yufan Li, Mingxuan Lin, Xin Wu, Junjie Li, Chao MOE Engineering Research Center of ACAT School of Computer Science Engineering Beihang University China Beijing Key Laboratory of ESSTCO School of Economics and Management Beihang University Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Beijing Advanced Innovation Center for BDBC Beihang University Beijing China

While having achieved great success in rich real-life applications, deep neural network (DNN) models have long been criticized for their vulnerability to adversarial attacks. Tremendous research efforts have been dedicated to mitigating the threats of adversarial attacks, but the essential trait of adversarial examples is not yet clear, and most existing methods are yet vulnerable to hybrid attacks and suffer from counterattacks. In light of this, in this paper, we first reveal a gradient-based correlation between sensitivity analysis-based DNN interpreters and the generation process of adversarial examples, which indicates the Achilles's heel of adversarial attacks and sheds light on linking together the two long-standing challenges of DNN: fragility and unexplainability. We then propose an interpreter-based ensemble framework called X-Ensemble for robust adversary defense. X-Ensemble adopts a novel detection-rectification process and features in building multiple sub-detectors and a rectifier upon various types of interpretation information toward target classifiers. Moreover, X-Ensemble employs the Random Forests (RF) model to combine sub-detectors into an ensemble detector for adversarial hybrid attacks defense. The non-differentiable property of RF further makes it a precious choice against the counterattack of adversaries. Extensive experiments under various types of state-of-the-art attacks and diverse attack scenarios demonstrate the advantages of X-Ensemble to competitive baseline methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection

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arXiv 2022年

作者： Wu, Jiaxi Chen, Jiaxin He, Mengzhe Wang, Yiru Li, Bo Ma, Bingqi Gan, Weihao Wu, Wei Wang, Yali Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Shenzhen Institutes of Advanced Technology Chinese Academy of Science China SenseTime Research Shanghai AI Laboratory China

Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source domain adaptation, a more generalized task with multiple source domains remains not being well explored, due to knowledge degradation during their combination. To address this issue, we propose a novel approach, namely target-relevant knowledge preservation (TRKP), to unsupervised multi-source DAOD. Specifically, TRKP adopts the teacher-student framework, where the multi-head teacher network is built to extract knowledge from labeled source domains and guide the student network to learn detectors in unlabeled target domain. The teacher network is further equipped with an adversarial multi-source disentanglement (AMSD) module to preserve source domain-specific knowledge and simultaneously perform cross-domain alignment. Besides, a holistic target-relevant mining (HTRM) scheme is developed to re-weight the source images according to the source-target relevance. By this means, the teacher network is enforced to capture target-relevant knowledge, thus benefiting decreasing domain shift when mentoring object detection in the target domain. Extensive experiments are conducted on various widely used benchmarks with new state-of-the-art scores reported, highlighting the effectiveness. Copyright © 2022, The Authors. All rights reserved.

关键词： Object recognition

RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under Continuous Representation

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arXiv 2024年

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical & Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we verify for the first time that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. Copyright © 2024, The Authors. All rights reserved.

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Fast Heuristic Algorithms for the Multiple Strip Packing Problem 1

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20th International Conference on Mathematical Optimization Theory and Operations research , MOTOR 2021

作者： Vasilyev, Igor Ushakov, Anton V. Barkova, Maria V. Zhang, Dong Ren, Jie Chen, Juan Matrosov Institute for System Dynamics and Control Theory of SB RAS 134 Lermontov Street Irkutsk664033 Russia Algorithm and Technology Development Department Global Technical Service Department Huawei Technologies Co. Ltd. Dongguan China Moscow Advanced Software Technology Lab Huawei Russian Research Institute Moscow Russia

ISBN: (数字)9783030864330

ISBN: (纸本)9783030864323

In this paper we address the so-called two-dimensional Multiple Strip Packing Problem (MSPP). There is a set of rectangles and a set of strips, where each strip has a pre-defined width. The problem is to find a feasible packing of all the rectangles into the strips so as the maximal height of the packing over all the strips is minimized. A packing is feasible if all the rectangles are placed into the strips and not overlapped. In case of only one strip, the problem is widely known as the Strip Packing Problem (SPP). Many effective constructive heuristics for SPP are based on the so-called skyline representation of a packing pattern. We generalized this approach for the case of multiple strips and developed a randomized local search, which embeds the proposed skyline heuristic to compute the objective value for a packing. We also introduced the two-dimensional level multiple strip packing problem and developed some level-based constructive heuristics. We report computational results on real-life problem instances arisen in an application of MSPP in scheduling computing jobs on multiple Spark computer clusters. © 2021, Springer Nature Switzerland AG.

关键词： Heuristic algorithms