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Predicting extreme events from data using deep machine learning: when and where

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arXiv 2022年

作者： Jiang, Junjie Huang, Zi-Gang Grebogi, Celso Lai, Ying-Cheng The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi’an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Shaanxi Xi’an China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King’s College University of Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University TempeAZ85287 United States

We develop a deep convolutional neural network (DCNN) based framework for model-free prediction of the occurrence of extreme events both in time ("when") and in space ("where") in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the 2D complex Ginzburg-Landau equation and empirical wind speed data of the North Atlantic ocean to demonstrate and validate our machine-learning based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme event on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world. © 2022, CC BY.

关键词： Forecasting

A Learner Model Integrating Cognitive and Metacognitive And Its Application on Scratch Programming Projects

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IFAC-PapersOnLine 2020年第5期53卷 644-649页

作者： Sifeng Jing Ying Tang Xiwei Liu Xiaoyan Gong The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao 266044 China Department of Electrical and Computer Engineering Rowan University Glassboro NJ 08028 USA

learner’s cognitive and metacognitive are key personal profile for individualized teaching. To evaluate learner’s comprehensive characteristics, existing learner model were reviewed. Two challenges of constructing an accurate and comprehensive learner model integrating cognitive and metacognitive were summarized. A plan of constructing a comprehensive learner model was made based on analysis of existing massive online learning environment, sensor information technology and educational data-mining. As a case study, a method of how to map learning data onto learners’ cognitive and metacognitive was proposed based on an analysis of a number of pupils’ Scratch projects. Three mapping table were established. Pupil’s cognitive skill could be evaluated from technology shown from Scratch project, namely, data structure, algorithm, computational practices and overall evaluation. Content shown from Scratch project were used to infer pupil’s cognitive style. Meta-cognitive ability can be measured from computational practices and behavior in programming process.

关键词： learner model cognitive state metacognitive ability individualized teaching

Visualtracking based on moving monocular camera

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International Journal of Performability engineering 2020年第8期16卷 1215-1224页

作者： Kan, Xiu He, Jia Xi, Zhenghao School of Mathematics Southeast University Nanjing210096 China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China State Key Laboratory of Intelligent Technology and Systems Department of Computer Science Tsinghua University Shanghai201620 China

This paper investigates the problem of moving target tracking in the case of a monocular mobile camera. Firstly, a scene space model was established under a moving camera based on the constraint conditions of ground plane. Thus, a three-dimensional (3D) motion model of target could be established. The scene space model and the target 3D motion model could both be compensated by the posture of the camera, which can be calculated according to the vanishing point in the image or the motion parameters of the camera acquired by sensors. Secondly, an interception function was established to extract the target motion range according to the target 3D motion model. Both the influence of background factors and the computational complexity of the algorithm were reduced. Then, a dual-kernels tracking method was used to track the target under a situation of non-obvious color characteristics and partial shielding. Finally, a public data set was used to conduct comparison experiments with three other algorithms to verify the effectiveness and robustness of the proposed algorithm. © 2020 Totem Publisher, Inc.

关键词： Motion compensation

On the mechanism and control for the ultra-low frequency oscillation in NY Power Grid with large-scale hydropower

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Control Theory and Technology 2019年第3期17卷 241-251页

作者： Ancheng XUE Jiawei WANG Chao ZHENG Joe H.CHOW Tianshu BI State Key Laboratory of Alternate Electrical Power System with Renewable Energy Source (North China Electric Power University) Changping District Beijing 102206 China China Electric Power Research Institute Haidian District Beijing 100192 China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy 12180 New York State U.S.A.

The power grid with large proportion of hydropower may engage the ultra-low frequency oscillation (ULFO),which threatening the operation of *** the NY Power Grid system,whose dimension is over thousands,this paper reveals the cause of ULFO with the phase portrait method,i.e.,lies in the saturation of the different limits of the governor system of the generators,in other words,the occurrence of limit cycle is due to the non-smooth *** furthermore,it proposes two methods to suppress ULFO with the philosophy of destroying the condition of non-smooth *** detail,first,the phenomenon of ULFO in NY Power Grid,the damping characteristics and the limits in the governor system of the hydropower units are ***,based on the non-smooth bifurcation theory,the mathematical basis of the oscillation in the non-smooth system caused by the saturation of limits is *** then,the non-oscillation and oscillation dynamic of the system corresponding to without/with limits in the governor system are ***,it discusses the influence of the saturation of single limit on the characteristics of ULFO with the phase ***,the phase portraits of the ULFO under different faults is analyzed,and it shows that the limiting saturation occurs with a large enough ***,the ULFO is resulting from the negative damping of generation and the saturation of multiple limits under a large enough *** addition,according to the above mechanism,two strategies to suppress the ULFO,i.e.,the governor parameters tuning and the fault strength relief are *** simulation shows the effectiveness of proposed strategies.

关键词： Ultra -low frequency oscillatio n gover nor system limit no rnsmooth bifurcation limit cycle

基于深度学习技术的集群电动汽车及家庭热水系统灵活性预测

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engineering 2021年第8期7卷 141-169页

作者：胡俊杰周华嫣然周羿宏张海婧 Lars Nordstr?md 杨光亚 State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) Division of Electric Power and Energy Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Center for Electric Power and Energy Department of Electrical Engineering Technical University of Denmark

随着电网中间歇性可再生能源发电规模的增大，为了保证电能质量和频率的稳定性，电网对可控资源的需求也随之增加。需求响应（demand response, DR）资源的灵活性已成为解决这一问题的一个有价值的方法。然而，目前关于DR资源的灵活性... 详细信息

随着电网中间歇性可再生能源发电规模的增大，为了保证电能质量和频率的稳定性，电网对可控资源的需求也随之增加。需求响应（demand response, DR）资源的灵活性已成为解决这一问题的一个有价值的方法。然而，目前关于DR资源的灵活性预测问题尚未得到充分的研究。本研究应用一种深度学习技术，即结合时间卷积神经网络的TRANSFORMER模型（temporal convolution network-combined transformer）来预测电动汽车与家庭热水系统两种DR资源的聚合灵活性。所提出的灵活性预测方法使用了基于DR资源的历史用电数据以及为了辅助预测所提出的DR信号数据。所提方法不仅可以预测聚合灵活性的大小，还可以预测其维持时间。最后，本文通过算例仿真验证了灵活性预测结果的准确性。仿真结果表明，在不同的灵活性维持时间下，DR资源灵活性的大小会发生变化。文中所提出的DR资源灵活性预测方法展现了其在释放需求侧资源的灵活性以向电网提供备用容量方面的应用。

关键词：负荷灵活性电动汽车家庭热水系统结合时间卷积神经网络的Transformer模型深度学习

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

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arXiv 2022年

作者： Placed, Julio A. Strader, Jared Carrillo, Henry Atanasov, Nikolay Indelman, Vadim Carlone, Luca Castellanos, José A. Universidad de Zaragoza C/María de Luna 1 Zaragoza50018 Spain The Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Genius Sports One Plaza Avenida El Poblado #5A - 113 North Tower 2nd & 10th Flr Medellín AN Colombia The Department of Electrical and Computer Engineering UC San Diego 9500 Gilman Drive La Jolla CA92093-0411 United States The Department of Aerospace Engineering Technion - Israel Institute of Technology Haifa32000 Israel

Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this work, we survey the state-of-the-art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multi-robot coordination. The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics. © 2022, CC BY.

关键词： Reinforcement learning

LocalViT: Analyzing Locality in Vision Transformers

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arXiv 2021年

作者： Li, Yawei Zhang, Kai Cao, Jiezhang Timofte, Radu Magno, Michele Benini, Luca Van Gool, Luc Computer Vision Lab D-ITET ETH Zürich Switzerland the University of Würzburg Germany Center for Project-Based Learning D-ITET ETH Zürich Switzerland Integrated Systems Laboratory D-ITET ETH Zürich Switzerland Department of Electrical Electronic and Information Engineering University of Bologna Italy KU Leuven Belgium

The aim of this paper is to study the influence of locality mechanisms in vision transformers. Transformers originated from machine translation and are particularly good at modelling long-range dependencies within a long sequence. Although the global interaction between the token embeddings could be well modelled by the self-attention mechanism of transformers, what is lacking is a locality mechanism for information exchange within a local region. In this paper, locality mechanism is systematically investigated by carefully designed controlled experiments. We add locality to vision transformers into the feed-forward network. This seemingly simple solution is inspired by the comparison between feed-forward networks and inverted residual blocks. The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to vision transformers with different architecture designs, which shows the generalization of the locality concept. For ImageNet2012 classification, the locality-enhanced transformers outperform the baselines Swin-T [1], DeiT-T [2] and PVT-T [3] by 1.0%, 2.6% and 3.1% with a negligible increase in the number of parameters and computational effort. Code is available at https://***/ofsoundof/LocalViT. © 2021, CC BY-NC-SA.

关键词： Distribution transformers

Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface Assisted NOMA Networks

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arXiv 2021年

作者： Yue, Xinwei Xie, Jin Liu, Yuanwei Han, Zhihao Liu, Rongke Ding, Zhiguo The Key Laboratory of Information and Communication Systems Ministry of Information Industry China The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The School of Electronic and Information Engineering Beihang University Beijing100191 China The Department of Electrical Engineering Princeton University Princeton United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

Simultaneously transmitting/refracting and reflecting reconfigurable intelligent surface (STAR-RIS) has been introduced to achieve full coverage area. This paper investigate the performance of STAR-RIS assisted non-orthogonal multiple access (NOMA) networks over Rician fading channels, where the incidence signals sent by base station are reflected and transmitted to the nearby user and distant user, respectively. To evaluate the performance of STAR-RIS-NOMA networks, we derive new approximate expressions of outage probability and ergodic rate for a pair of users, in which the imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) schemes are taken into consideration. Based on the asymptotic expressions, the diversity orders of the nearby user with ipSIC/pSIC and distant user are achieved carefully. The high signal-to-noise ratio slopes of ergodic rates for nearby user with pSIC and distant user are equal to one and zero, respectively. In addition, the system throughput of STAR-RIS-NOMA is discussed in delay-limited and delay-tolerant modes. Simulation results are provided to verify the accuracy of the theoretical analyses and demonstrate that: 1) The outage probability of STAR-RIS-NOMA outperforms that of STAR-RIS assisted orthogonal multiple access (OMA) and conventional cooperative communication systems;2) With the increasing of reflecting elements K and Rician factor κ, the STAR-RIS-NOMA networks are capable of attaining the enhanced performance;and 3) The ergodic rates of STAR-RIS-NOMA are superior to that of STAR-RIS-OMA. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal to noise ratio

A 2.0-2.9 GHz Digital Ring-Based Injection-Locked Clock Multiplier Using a Self-Alignment Frequency Tracking Loop for Reference Spur Reduction

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A 2.0-2.9 GHz Digital Ring-Based Injection-Locked Clock Mult...

IEEE Symposium on Radio Frequency Integrated Circuits (RFIC)

作者： Rongjin Xu Dawei Ye Liangjian Lyu C.-J. Richard Shi State Key Laboratory of ASIC and System Fudan University China Institute of Brain-inspired Circuits and Systems (iBiCAS) Fudan University China Department of Electrical and Computer Engineering University of Washington Seattle USA

ISBN: (数字)9781728168098

ISBN: (纸本)9781728168104

This paper presents a 2.0-2.9 GHz digital ring-based injection-locked clock multiplier (ILCM) in a 65 nm CMOS process. A self-alignment frequency tracking loop (SA-FTL) with background delay calibration is proposed to improve the accuracy of injection timing. Since the frequency mismatch of the free-running oscillator and the target frequency is minimized without delay error, the reference spur is reduced to -55.6 dBc, exhibiting 15.9 dB improvement comparing to the case that the SA-FTL is off. The proposed ILCM achieves 545 fs RMS jitter at 2.5 GHz output frequency with a power consumption of 3.1 mW, achieving the FoM of 240.3 dB.

关键词： Delays Clocks Oscillators Jitter Calibration Tracking loops