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A review and discussion of decomposition-based hybrid models for wind energy forecasting applications

Applied Energy 2019年 235卷 939-953页

作者： Zheng Qian Yan Pei Hamidreza Zareipour Niya Chen School of Instrumentation Science and Opto-electronics Engineering Beihang University Beijing China Key Laboratory of Precision Opto-Mechatronics Technology Ministry of Education Beihang University Beijing China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing 100192 China Department of Electrical and Computer Engineering University of Calgary Calgary Alberta Canada ABB Corporate Research Center Beijing China

With the continuous growth of wind power integration into the electrical grid, accurate wind power forecasting is an important component in management and operation of power systems. Given the challenging nature of wind power forecasting, various methods are presented in the literature to improve wind power forecasting accuracy. Among them, combining different techniques to construct hybrid models has been frequently reported in the literature. Decomposition-based models are a family of hybrid models that firstly decompose the wind speed/power time series into relatively more stationary subseries, and then build forecasting models for each subseries. In this paper, we present a comprehensive review of decomposition-based wind forecasting methods in order to explore their effectiveness. Decomposition-based hybrid forecasting models are classified into different groups based on the decomposition methods, such as, wavelet, empirical mode decomposition, seasonal adjust methods, variational mode decomposition, intrinsic time-scale decomposition, and bernaola galvan algorithm. We discuss decomposition methods in the context of alternative forecasting algorithms, and explore the challenges of each method. Comparative analysis of various decomposition-based models is also provided. We also explore current research activities and challenges, and identify potential directions for future research on this subject.

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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

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Streaming data analysis framework for cyber-physical system of metal machining processes 1

Streaming data analysis framework for cyber-physical system ...

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1st IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2018

作者： Yang, Chao-Lung Sutrisno, Hendri Lo, Nai-Wei Chen, Zhi-Xuan Wei, Ching-Chih Zhang, Han-Wei Lin, Chin-Teng Wei, Chen-Lung Hsieh, Shang-Heng Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Department of Information Management National Taiwan University of Science and Technology Taipei Taiwan Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei Taiwan Institute of Electrical Control Engineering Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan Precision Machinery Research and Development Center Taichung City Taiwan CIMFORCE International Limited New Taipei City Taiwan

ISBN: (纸本)9781538665312

Online monitoring and offline batch analyzing the data stream obtained from computer numeric control (CNC) machines and sensors deployed on manufacturing site are the core technology of Cyber-Physical System (CPS). In order to analyze data stream collected continually, in this research, the data analysis framework consist of supervisory control and data acquisition (SCADA), edge computing and cloud computing is proposed. By integrating signal smoothing and anomaly pattern detection techniques, the significant unique patterns of data stream can be discovered and stored for further usage in CPS application. © 2018 IEEE.

关键词： Cyber Physical System

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Social Energy:Mining Energy From the Society

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 466-482页

作者： Jun Jason Zhang David Wenzhong Gao Yingchen Zhang Xiao Wang Xiangyang Zhao Dongliang Duan Xiaoxiao Dai Jun Hao Fei-Yue Wang Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of Denver IEEE National Renewable Energy Laboratory Qingdao Academy of Intelligent Industries(QAII) QingdaoShandongChinaand SKL-MCCSCASIA School of Automation Science and Electrical Engineering Beihang University Department of Electrical and Computer Engineering College of Engineering and Applied ScienceUniversity of Wyoming State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Research Center of Computational Experiments and Parallel Systems National University of Defense Technology

The inherent nature of energy,i.e.,physicality,sociality and informatization,implies the inevitable and intensive interaction between energy systems and social *** this perspective,we define "social energy" as a complex sociotechnical system of energy systems,social systems and the derived artificial virtual systems which characterize the intense intersystem and intra-system *** recent advancement in intelligent technology,including artificial intelligence and machine learning technologies,sensing and communication in Internet of Things technologies,and massive high performance computing and extreme-scale data analytics technologies,enables the possibility of substantial advancement in socio-technical system optimization,scheduling,control and *** this paper,we provide a discussion on the nature of energy,and then propose the concept and intention of social energy systems for electrical power.A general methodology of establishing and investigating social energy is proposed,which is based on the ACP approach,i.e., "artificial systems"(A), "computational experiments"(C) and "parallel execution"(P),and parallel system methodology.A case study on the University of Denver(DU) campus grid is provided and studied to demonstrate the social energy *** the concluding remarks,we discuss the technical pathway,in both social and nature sciences,to social energy,and our vision on its future.

关键词： ACP distribution locational marginal pricing intelligent operation in power systems parallel control parallel intelligence parallel management social energy socio-technical energy systems

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Perturbed Tumor Immunotherapy Domain of Attraction Estimation via the Arc-Length Function

Perturbed Tumor Immunotherapy Domain of Attraction Estimatio...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Mojtaba Zarei Kimia Javadi Ahmad Kalhor Cyber-Physical Systems Lab (CPSL) Departments of Electrical and Computer Engineering Duke University NC USA Faculty of Computer and Electrical Engineering K.N.Toosi University of Technology Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781538679531;9781538679524

This paper aims at the estimation of the Domain of Attraction (DoA) of the free tumor equilibrium point of perturbed tumor immunotherapy model via the Arc-Length Function (ALF). The ALFs are categorized among the maximal Lyapunov functions which are able to provide a more accurate estimation of the DoA in comparison to their other counterparts such as Rational Lyapunov Functions (RLFs), Sum Of Square (SOS) polynomial Lyapunov functions, and Optimal Quadratic Lyapunov Functions (OQLFs). There is no analytical method to construct the ALFs, however, some numerical methods have been proposed in the literature. Based on the existing method, one can approximate the ALF with a certain degree of a polynomial function. That the system under study has a polynomial structure was the main basis of the previously proposed method to estimate the DoA via the ALFs. However, the intended model in this paper describing the tumor-immune system competition dynamics contains non-polynomial terms. To cope with the aforementioned problem, the Taylor expansion of the non-polynomial terms are considered and by solving an optimization problem, one can calculate the corresponding lower boundary of the level set with the approximated ALF as an estimation of the DoA. In order to represent the performance of the employed method, the obtained result is compared with the reported result in the literature.

关键词： Tumors Direction-of-arrival estimation Lyapunov methods Estimation Immune system Mathematical model Cancer

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Neuroprotective effects of Ginkgo biloba extract and GinkgoUde B against oxygen-glucose deprivation/reoxygenation and glucose injury in a new in vitro multicellular network model

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Frontiers of Medicine 2018年第3期12卷 307-318页

作者： Xiaohan Yang Tiezheng Zheng Hao Hong Nan Cai Xiaofeng Zhou Changkai Sun Liying Wu Shuhong Liu Yongqi Zhao Lingling Zhu Ming Fan Xuezhong Zhou Fengxie Jin Department of Biomedical Engineering Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian 116024 China Research Center for the Control Engineering of Translational Precision Medicine Dalian University of Technology Dalian 116024 China State Key Laboratory of Fine Chemicals Dalian R&D Center for Stem Cell and Tissue Engineering Dalian University of Technology Dalian 116024 China Liaoning Provincial Key Laboratory of Cerebral Diseases Institute for Brain Disorders Dalian Medical University Dalian 116044 China Institute of Basic Medical Sciences Academy of Military Medical Sciences Beijing 100850 China School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing 100044 China College of Biotecknology Dalian Polytechnic University Dalian 116034 China

Acute ischemic stroke （AIS）, as the third leading cause of death worldwide, is characterized by its high incidence, mortality rate, high incurred disability rate, and frequent reoccurrence. The neuroprotective effects of Ginkgo biloba extract （GBE） against several cerebral diseases have been reported in previous studies, but the underlying mechanisms of action are still unclear. Using a novel in vitro rat cortical capillary endothelial cell- astrocyte-neuron network model, we investigated the neuroprotective effects of GBE and one of its important constituents, Ginkgolide B （GB）, against oxygenglucose deprivation/reoxygenation and glucose （OGD/R） injury. In this model, rat cortical capillary endothelial cells, astrocytes, and neurons were cocultured so that they could be synchronously observed in the same system. Pretreatment with GBE or GB increased the neuron cell viability, ameliorated cell injury, and inhibited the cell apoptotic rate through Bax and Bcl-2 expression regulation after OGD/R injury. Furthermore, GBE or GB pretreatment enhanced the transendothelial electrical resistance of capillary endothelial monolayers, reduced the endothelial permeability coefficients for sodium fluorescein （Na-F）, and increased the expression levels of tight junction proteins, namely, ZO-1 and occludin, in endothelial cells. Results demonstrated the preventive effects of GBE on neuronal cell death and enhancement of the function of brain capillary endothelial monolayers after OGD/R injury in vitro; thus, GBE could be used as an effective neuroprotective agent for AIS/reperfusion, with GB as one of its significant constituents.

关键词： acute ischemic stroke Ginkgo biloba extract Ginkgolide B network model neuroprotection

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Almost Sure Attitude Consensus in Multispacecraft Systems With Stochastic Communication Links

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IFAC-PapersOnLine 2017年第1期50卷 9392-9397页

作者： Rezaee H. Abdollahi F. The Center of Excellence on Control and Robotics Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical and Computer Engineering Concordia University Montreal Quebec Canada

Leaderless attitude consensus problem over multispacecraft systems is studied. The base of convergence analysis of existing results devoted to attitude consensus control of multispacecraft systems is deterministic knowledge on networks communication links. The main contribution of the present study is to design a control strategy guaranteeing almost sure attitude consensus in a multispacecraft system when communication links stochastically fail over time. Moreover, it is assumed that there exist constraints on the spacecraft input torques. To achieve the mentioned objectives, we propose a magnitude-constrained consensus control law for the spacecraft; then, by invoking the generalized invariance principle and the super-martingales convergence theorem, almost sure convergence of quaternion-based consensus errors to zero is concluded. The results are verified via a simulation example. © 2017

关键词： Attitude consensus coordination generalized invariance principle spacecraft stochastic communication

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Proof-of-principle experimental demonstration of twin-field type quantum key distribution

arXiv

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arXiv 2019年

作者： Zhong, Xiaoqing Hu, Jianyong Curty, Marcos Qian, Li Lo, Hoi-Kwong Center for Quantum Information and Quantum Control Dept. of Physics University of Toronto TorontoONM5S 1A7 Canada Center for Quantum Information and Quantum Control Dept. of Electrical & Computer Engineering University of Toronto TorontoONM5S 3G4 Canada EI Telecomunicación Dept. of Signal Theory and Communications University of Vigo VigoE-36310 Spain

The twin-field (TF) quantum key distribution (QKD) protocol and its variants are highly attractive because they can beat the well-known rate-loss limit (i.e., the PLOB bound) for QKD protocols without quantum repeaters. In this paper, we perform a proof-of-principle experimental demonstration of TF-QKD based on the protocol proposed by Curty et al. [38] which removes from the original TF-QKD scheme the need for post-selection on the matching of a global phase, and can deliver nearly an order of magnitude higher secret key rate. Furthermore, we overcome the major difficulty in the practical implementation of TF-QKD, namely, the need to stabilize the phase of the quantum state over kilometers of fiber. A Sagnac loop structure is utilized to ensure excellent phase stability between the different parties. Using decoy states, we demonstrate secret-key generation rates that beat the PLOB bound when the channel loss is above 40 dB. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum cryptography

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SAM: A Haskell Parallel Programming Model for Many-Core Systems

SAM: A Haskell Parallel Programming Model for Many-Core Syst...

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International Conference on Applied System Innovation

作者： Yeoneo Kim Junseok Cheon Xiao Liu Sugwoo Byun Gyun Woo Department of Electrical and Computer Engineering Pusan National University Busan Korea Department of Computer Engineering Kyungsung University Busan Korea Smart Control Center of LG Electronics Pusan National University Busan Korea

Since the multicore CPU has been released, the parallel programming method has become a significant issue to programmers. For parallel programming, functional languages such as Haskell are especially attractive since they reduce the data dependency hindering parallelism. One of the popular parallel programming models of Haskell is Cloud Haskell. Though it is an effective model for the manycore environments, it is quite difficult to use compared with other models. This paper proposes an efficient Haskell programming model for the manycore system named SAM. SAM takes advantages of the outstanding performance of Cloud Haskell and overcomes its disadvantages. To present the performance of SAM, we developed four different applications for the experiment. According to the experimental result, the compilation time of using SAM increased 5%, and execution time reduced 0.2%, respectively, compared with Cloud Haskell. In addition, the code size of using SAM reduced to 86% on the parallel part.

关键词： Actor Model Manycore Haskell Cloud Haskell

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Permutation matrix representation quantum Monte Carlo

arXiv

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arXiv 2019年

作者： Gupta, Lalit Albash, Tameem Hen, Itay Department of Physics and Astronomy and Center for Quantum Information Science and Technology University of Southern California Los AngelesCA90089 United States Department of Electrical and Computer Engineering Center for Quantum Information and Control CQuIC University of New Mexico AlbuquerqueNM87131 United States Information Sciences Institute University of Southern California Marina del ReyCA90292 United States

We present a quantum Monte Carlo algorithm for the simulation of general quantum and classical many-body models within a single unifying framework. The algorithm builds on a power series expansion of the quantum partition function in its off-diagonal terms and is both parameter-free and Trotter error-free. In our approach, the quantum dimension consists of products of elements of a permutation group. As such, it allows for the study of a very wide variety of models on an equal footing. To demonstrate the utility of our technique, we use it to clarify the emergence of the sign problem in the simulations of non-stoquastic physical models. We also study the thermal properties of the transverse-field Ising model augmented with randomly chosen two-body transverse-field interactions. Copyright © 2019, The Authors. All rights reserved.

关键词： Monte Carlo methods

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