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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

Stealthy False Data Injection Attacks against Extended Kalman Filter Detection in Power Grids

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Stealthy False Data Injection Attacks against Extended Kalma...

International Conference on Informative and Cybernetics for Computational Social systems (ICCSS)

作者： Yifa Liu Wenchao Xue Shuping He Long Cheng State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Key Laboratory of System and Control Academy of Mathematics and System Science Chinese Academy of Sciences Beijing China School of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9781665402460

The power grid is a kind of national critical infrastructure directly affiliated to human daily life. Because of the vital functions and potentially significant losses, the power grid becomes an excellent target for many malicious attacks. Due to the special nonlinear measurements, many detection methods do not match the grid very well. The extended Kalman filter based detection is one of the few methods suitable for nonlinear system detection, and therefore can be used in power system to spot malicious attacks. However, the reliability and effectiveness of the extended Kalman filter based detection have not been fully studied and adequately guaranteed. By proposing a two-step false data injection attack strategy, this paper gives a stealthy way to inject false data of increasing magnitude into the power grid, which can eventually cause a certain degree of deviation of the grid state without being detected. In the simulation, the method proposed in this paper caused a voltage deviation of more than 25% before being discovered in the power system.

关键词： Power measurement Cyber-physical systems Information filters Power grids Safety Power system reliability Kalman filters

Abstraction-Based Verification of Approximate Pre-Opacity for control systems

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arXiv 2022年

作者： Hou, Junyao Liu, Siyuan Yin, Xiang Zamani, Majid Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University China Department of Electrical and Computer Engineering Technical University of Munich Germany Department of Computer Science LMU Munich Germany Department of Computer Science University of Colorado Boulder United States

In this paper, we consider the problem of verifying pre-opacity for discrete-time control systems. Preopacity is an important information-flow security property that secures the intention of a system to execute some secret behaviors in the future. Existing works on pre-opacity only consider non-metric discrete systems, where it is assumed that intruders can distinguish different output behaviors precisely. However, for continuous-space control systems whose output sets are equipped with metrics (which is the case for most real-world applications), it is too restrictive to assume precise measurements from outside observers. In this paper, we first introduce a concept of approximate pre-opacity by capturing the security level of control systems with respect to the measurement precision of the intruder. Based on this new notion of pre-opacity, we propose a verification approach for continuous-space control systems by leveraging abstraction-based techniques. In particular, a new concept of approximate pre-opacity preserving simulation relation is introduced to characterize the distance between two systems in terms of preserving pre-opacity. This new system relation allows us to verify pre-opacity of complex continuous-space control systems using their finite abstractions. We also present a method to construct pre-opacity preserving finite abstractions for a class of discrete-time control systems under certain stability assumptions. Copyright © 2022, The Authors. All rights reserved.

关键词： Opacity

Dominant Mode Identification and Influence Mechanism Investigation of Frequency Oscillations as Affected by Automatic Generation control

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SSRN

SSRN 2022年

作者： Zhou, Tao Chen, Liufu Bu, Siqi Ye, Hua Sun, Jie School of Automation Nanjing University of Science and Technology Nanjing China Department of Electrical Engineering The Hong Kong Polytechnic University Hong Kong Special Administrative Region Hong Kong Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

Several frequency oscillation events have recently been observed in practical power grids, which are strongly related with dynamic process of AGC (automatic generation control). To investigate the dynamic performance and oscillation mechanism of frequency oscillations, this paper establishes the state-space model of multi-area AGC systems containing different kinds of generator units and time-scale dynamics. Without distinguishing AGC and PFR (primary frequency regulation) modes, Homotopy method is applied to identify the dominant mode and thus analyze the key influencing factors of frequency oscillations affected by AGC dynamics. The method can clearly establish the congruent relationship between eigenvalues and state variables and is more rapid and intuitive than participation factors in this scene. Finally, simulations are performed in single-area as well as multi-area system based on real power grid data to demonstrate the proposed model and method. And relevant parameter influence mechanism and oscillation performances are investigated for AGC system oscillations. © 2022, The Authors. All rights reserved.

关键词： Time delay

Model-Based and Data-Driven control of Event- and Self-Triggered Discrete-Time LTI systems

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arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

Decentralized coordination between economic dispatch and demand response in multi-energy systems

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arXiv 2021年

作者： Liu, Zishun Zhu, Shanying Xu, Jinming Chen, Cailian The Department of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China College of Control Science and Engineering Zhejiang University HangzhouZhejiang310027 China

In this paper, we investigate the problem of coordination between economic dispatch (ED) and demand response (DR) in multi-energy systems (MESs), aiming to improve the economic utility and reduce the waste of energy in MESs. Since multiple energy sources are coupled through energy hubs (EHs), the supply-demand constraints are nonconvex. To deal with this issue, we propose a linearization method to transform the coordination problem to a convex social welfare optimization one. Then a decentralized algorithm based on parallel Alternating Direction Method of Multipliers (ADMM) and dynamic average tracking protocol is developed, where each agent could only make decisions based on information from their neighbors. Moreover, by using variational inequality and Lyapunov-based techniques, we show that our algorithm could always converge to the global optimal solution. Finally, a case study on the modified IEEE 14-bus network verifies the feasibility and effectiveness of our algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Electric load dispatching

An Integrated Market-Operation Optimization Framework for EV-Renewable Energy Coordination in Distribution systems

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An Integrated Market-Operation Optimization Framework for EV...

Power and Energy Conference at Illinois (PECI)

作者： M. Ghofrani C. Hsu M. Majidi School of STEM University of Washington Bothell Bothell USA System Protection Automation and Control Engineering department San Diego Gas and Electric (SDG&E) San Diego USA

This paper proposes a charging/discharging optimization framework for electric vehicles (EVs) in coordination with renewable distributed generation (DG). The proposed framework integrates market and operational objectives to provide economic benefits for both entities while ensuring reliable operation of the distribution system. The market objective is to minimize the renewable under/over production and the operational objective is to minimize the annual cost of the distribution system. A market-based optimization is first developed and evaluated for its potential adverse grid effects. A capacitor siting/sizing optimization is then proposed to reduce such effects. The integration of these optimization methods provides the proposed EV-renewable DG coordination. The coordination framework is tested on a real 13.8 kV, 134-bus distribution network and its efficiency is assessed by several case studies.

关键词： Simulation Capacitors Optimization methods Production Distribution networks Electric vehicles Distributed power generation

A Constructive Semi-global Differential Game Design Method for Multi-agent systems under Input Constraint and Collision Avoidance

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A Constructive Semi-global Differential Game Design Method f...

2021 China automation Congress, CAC 2021

作者： Wu, Xiongjun Zhou, Jialing Li, Yuanlong Chen, Chunfang Li, Kai The 802 Institute of Shanghai Academy of Space Flight Technology Eighth Academy of China Aerospace Science and Technology Corporation National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering Shanghai201109 China School of Automation Nanjing University Science and Technology Nanjing210094 China Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The 802 Institute of Shanghai Academy of Space Flight Shanghai210094 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781665426473

The constructive semi-global differential game concept is introduced into the multi-agent's cooperative controller designing of typical autonomous unmanned systems subject to controller input constraint as well as the collision avoidance requirements simultaneously. The problem of driving each agent which is assume to subject to input constraint nonlinearity from the initial position to a desired one and at the same time avoiding collisions (referring to both static obstacles and other agents) is modeled and transformed into multi-agent's collision avoidance problem. In view of the requirement that different agents working together might possibly have individual but conflicting purpose (such as in the multi-missile cooperative detection requiring slowly and continuing actions vs fast formation control scenario, and multi-agents' fast arriving vs avoiding different kinds of obstacles). The differential game theory provided a mechanism or framework for investigating different kinds of problems in which several players seek to achieve individual purposes or goals, which might be competing rather than cooperation. In view of the input constraints, the problem is reduced as a semi-global differential game. Conditions are established to guarantee input constraints are satisfied, and mainly by using the Hamilton-Jacobi-Isaacs (HJI for short) PDIs and as well as Ε-Nash equilibria. The semi-global property of the states are also guaranteed. It provides a description in terms of the invariant set for the overall differential game system under constraint controller input. Illustrative examples are carried out to verify the effectiveness of the method, and also the feasibility of the suggested control protocols. © 2021 IEEE

关键词： Multi agent systems

Efficient Depth Selection for the Implementation of Noisy Quantum Approximate Optimization Algorithm

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arXiv 2022年

作者： Pan, Yu Tong, Yifan Xue, Shibei Zhang, Guofeng State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA). One significant consequence is that the performance of QAOA may fail to monotonically improve with depth. In particular, optimal depth can be found at a certain point where the noise effects just outweigh the benefits brought by increasing the depth. In this work, we propose to use the model selection algorithm to identify the optimal depth with a few iterations of regularization parameters. Numerical experiments show that the algorithm can efficiently locate the optimal depth under relaxation and dephasing noises. © 2022, CC BY.

关键词： Optimization