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Research on STLF Method Based on One-Dimensional Convolution and Slope Feature

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Research on STLF Method Based on One-Dimensional Convolution...

IEEE International Conference on Power Data Science (ICPDS)

作者： Qi Zeng Haihui Pan Bin Chen Zhifang Liao School of Computer Science and Engineering Central South University Changsha China State Grid Hunan Dispatching and Control Center State Grid Changsha China

ISBN: (数字)9781728137759

ISBN: (纸本)9781728137766

Short-term load forecasting is of great importance to how to efficiently utilize generating units, optimize resource allocation and ensure the normal transmission of power in power system. This paper mainly studies how to improve the accuracy of short-term load forecasting through feature engineering and deep learning technology. Specifically, we construct a feature based on the slope of load data according to the changing trend of load data. We applied the proposed method to two real datasets of Hunan power grid and obtained MAPE values of 0.5758 and 0.5745 respectively. The experimental results show the effectiveness of the proposed method.

关键词： Convolution Load modeling Load forecasting Predictive models Machine learning Analytical models Neural networks

Distributed generalized Nash equilibrium seeking in aggregative games on time-varying networks

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arXiv 2019年

作者： Belgioioso, Giuseppe Nedić, Angelia Grammatico, Sergio The Control Systems group TU Eindhoven Netherlands The School of Electrical Computer and Energy Engineering Arizona State University United States TU Delft Netherlands

We design the first fully-distributed algorithm for generalized Nash equilibrium seeking in aggregative games on a time-varying communication network, under partial-decision information, i.e., the agents have no direct access to the aggregate decision. The algorithm is derived by integrating dynamic tracking into a projected pseudo-gradient algorithm. The convergence analysis relies on the framework of monotone operator splitting and the Krasnosel’skii–Mann fixed-point iteration with errors. Copyright © 2019, The Authors. All rights reserved.

关键词： Time varying networks

Artificial intelligence enabled radio propagation for communications - Part I: Channel characterization and antenna-channel optimization

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arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Purple Mountain Laboratories Nanjing211111 China The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigate the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next generation networks of the topics covered in this part rounds off the paper. © 2021, CC BY-NC-ND.

关键词： Machine learning

Fault-tolerant architecture for Cache : Summaries, Assessments and Trends

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Journal of Physics: Conference Series 2021年第1期2113卷

作者： Xuru Wang Xin Gao Zongnan Liang Jiawei Nian Hongjin Liu Beijing Institute of Control Engineering Beijing 100090 China Beijing SunWise Space Technology Ltd Beijing 100090 China School of computer science and technology Xidian University Xi'an Shanxi 710071 China.

Fault-tolerant design of cache is a key aspect of highly reliable processor design. In this paper, based on the key metrics in Cache architecture design: reliability, power consumption, latency and area, we divided the related research into two categories: one is to maximize reliability with guaranteed latency, power consumption and area, the other is to minimize latency, power consumption and area loss while ensuring fault tolerance reliability. Based on the classification, by analyzing different studies of Data and Tag in Cache, this paper gives the characteristics of these methods and the future development trend.

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K-Means Clustering-based Kernel Canonical Correlation Analysis for Multimodal Emotion Recognition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10250-10254页

作者： Luefeng Chen Kuanlin Wang Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada Tokyo Institute of Technology Yokohama 226-8502 Japan

Emotion is an important part of human interaction. Emotional recognition can greatly promote human-centered interaction techniques. On this basis, multimodal feature fusion can effectively improve the emotion recognition rate. However, in the multimodal feature fusion at the feature level, most of the methods do not consider the intrinsic relationship between different modes. Only the fusion of analysis and transformation of the feature matrices of different modes does not make better use of modal differences to improve the recognition rate. This problem led us to propose feature fusion method based on K-Means clustering and kernel canonical correlation analysis (KCCA). Clustering makes the classification of features not classified by mode, but by the degree of influence on emotional labels, thus positively affecting the results of KCCA. The experimental results obtained on the Savee database show that the proposed K-Means based KCCA improves overall classification performance and produces higher recognition rate than that of the state of art methods, such as the Informed Segmentation and Labeling Approach.

关键词： Emotion Recognition K-Means Clustering Kernel Canonical Correlation Analysis Feature Fusion

Event-triggered optimal containment control for heterogeneous multi-agent systems

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Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica 2020年 41卷

作者： Wang, Dong Wang, Zehua Liu, Yang Gu, Dongbing Wang, Wei Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Eduction Dalian University of Technology Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China School of Computer Science and Electronic Engineering University of Essex Wivenhoe Park ColchesterCO4 3SQ United Kingdom

An edge-based event-triggered optimal control algorithm is proposed to solve the optimal output containment problem of general linear heterogeneous multi-agent systems. The proposed algorithm guarantees that the outputs of all followers enter into the convex hull formed by the outputs of leaders. At the same time, it can also render that the closed-loop system achieves the optimal performance and minimize the cost of control. Considering that the information of the leaders is unavailable to a part of followers, a distributed edge-based event-triggered observer is presented to estimate the trajectory for the interior point in the convex hull. In addition, a weighted performance function is designed to transform the output containment problem into the optimal state-feedback design problem. Based on the proposed function, the Bellman equation and algebraic Riccati equation are derived to solve the optimal output containment control problem of heterogeneous multi-agent systems. Multiple autonomous robots are employed as a group of multi-agent systems to verify the effectiveness of the proposed algorithm. © 2020, Beihang University Aerospace Knowledge Press. All right reserved.

关键词： Multi agent systems

Semi-autonomous greenhouse environment control by combining expert knowledge and machine learning

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Semi-autonomous greenhouse environment control by combining ...

Chinese Automation Congress (CAC)

作者： Mengzhen Kang Yuchen Weng Haitong Pang Liangde Li Xing-Rong Fan Jing Hua Fangle Chang Xiujuan Wang Longhua Ma The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Ningbo Institute of Technology Zhejiang University Ningbo China The School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao AgriTech Co. Ltd Qingdao Qingdao China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The concept of "autonomous greenhouse" hints the automatic setting of control target, and giving orders on facilities to reach these targets (set point). Information and communication technologies are widely used, but how to integrate common sense or knowledge in decision-making is still challenging. Semi-autonomous greenhouse control is to set rules with joint advantages of experienced grower and powerful machinery, with the support of knowledge graph and semantic analysis. The contribution of each side is dependent on the intelligence level of computers, the availability of data, etc. In this work, the hybrid system between human and machinery is shown with the temperature control in greenhouse. The target is set mainly by expert knowledge, while the temperature controlled by vent opening is decided by an algorithm. Result shows that the target is better reached compared to that of the experience-based approach. This example shows the feasibility of human-machine hybrid intelligence system for greenhouse environment control. Although this approach is tested for temperature control, it can be extended to control multiple factors simultaneously (temperature, light intensity, humidity, etc.) by driving multiple facilities (heating, lightening, venting, etc.).

关键词： Green products Temperature measurement Monitoring Temperature sensors Vents Agriculture Temperature distribution

Outage behaviors of NOMA-based satellite network over shadowed-rician fading channels

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arXiv 2020年

作者： Yue, Xinwei Liu, Yuanwei Yao, Yuanyuan Li, Tian Li, Xuehua Liu, Rongke Nallanathan, Arumugam Key Laboratory of Modern Measurement & Control Technology Ministry of Education School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang Hebei050081 China Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic and Information Engineering Beihang University Beijing100191 China

This paper investigates the application of non-orthogonal multiple access (NOMA) to satellite communication network over Shadowed-Rician fading channels. The impact of imperfect successive interference cancellation (ipSIC) on NOMA-based satellite network is taken into consideration from the perspective of practical scenarios. We first derive new exact expressions of outage probability for the p-th terrestrial user and provide the corresponding asymptotic analysis results. The diversity order of zero and p are achieved by the p-th terrestrial user with ipSIC and perfect successive interference cancellation (pSIC), respectively. Finally, the presented simulation results show that: 1) On the condition of pSIC, the outage behaviors of NOMA-based satellite network are superior to that of orthogonal multiple access;2) With the value of residual interference increasing, the outage performance of terrestrial users with ipSIC is becoming worse seriously;and 3) Infrequent light shadowing of Shadowed-Rician fading brings the better outage probability compared to frequent heavy and average shadowing. Copyright © 2020, The Authors. All rights reserved.

关键词： Asymptotic analysis

Contrast detection is enhanced by deterministic, high-frequency transcranial alternating current stimulation with triangle and sine waveform.

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arXiv 2023年

作者： Potok, Weronika van der Groen, Onno Sivachelvam, Sahana Bächinger, Marc Fröhlich, Flavio Kish, Laszlo B. Wenderoth, Nicole Neural Control of Movement Lab Department of Health Sciences and Technology Zurich8093 Switzerland University of Zurich Federal Institute of Technology Zurich University and Balgrist Hospital Zurich Zurich8057 Switzerland 138602 Singapore Neurorehabilitation and Robotics Laboratory School of Medical and Health Sciences Edith Cowan University Joondalup6027 Australia Department of Psychiatry University of North Carolina at Chapel Hill Chapel HillNC United States Carolina Center for Neurostimulation University of North Carolina at Chapel Hill Chapel HillNC United States Department of Neurology University of North Carolina at Chapel Hill Chapel HillNC United States Department of Cell Biology and Physiology University of North Carolina at Chapel Hill Chapel HillNC United States Department of Biomedical Engineering University of North Carolina at Chapel Hill Chapel HillNC United States Neuroscience Center University of North Carolina at Chapel Hill Chapel HillNC United States Department of Electrical & Computer Engineering Texas A&M University TAMUS 3128 College StationTX77843-3128 United States

Stochastic Resonance (SR) describes a phenomenon where an additive noise (stochastic carrier-wave) enhances the signal transmission in a nonlinear system. In the nervous system, nonlinear properties are present from the level of single ion channels all the way to perception and appear to support the emergence of SR. For example, SR has been repeatedly demonstrated for visual detection tasks, also by adding noise directly to cortical areas via transcranial random noise stimulation (tRNS). When dealing with nonlinear physical systems, it has been suggested that resonance can be induced not only by adding stochastic signals (i.e., noise) but also by adding a large class of signals that are not stochastic in nature which cause "deterministic amplitude resonance" (DAR). Here we mathematically show that high-frequency, deterministic, periodic signals can yield resonance-like effects with linear transfer and infinite signal-to-noise ratio at the output. We tested this prediction empirically and investigated whether non-random, high-frequency, transcranial alternating current stimulation applied to visual cortex could induce resonance-like effects and enhance performance of a visual detection task. We demonstrated in 28 participants that applying 80 Hz triangular-waves or sine-waves with tACS reduced visual contrast detection threshold for optimal brain stimulation intensities. The influence of tACS on contrast sensitivity was equally effective to tRNS-induced modulation, demonstrating that both tACS and tRNS can reduce contrast detection thresholds. Our findings suggest that a resonance-like mechanism can also emerge when deterministic electrical waveforms are applied via tACS. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems