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Dynamic Resilient Network Games with Applications to Multi-Agent Consensus

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arXiv 2020年

作者： Nugraha, Yurid Cetinkaya, Ahmet Hayakawa, Tomohisa Ishii, Hideaki Zhu, Quanyan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo101-8430 Japan Department of Computer Science Tokyo Insitute of Technology Yokohama226-8502 Japan Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

A cyber security problem in a networked system formulated as a resilient graph problem based on a game-theoretic approach is considered. The connectivity of the underlying graph of the network system is reduced by an attacker who removes some of the edges whereas the defender attempts to recover them. Both players are subject to energy constraints so that their actions are restricted and cannot be performed continuously. For this two-stage game, which is played repeatedly over time, we characterize the optimal strategies for the attacker and the defender in terms of edge connectivity and the number of connected components of the graph. The resilient graph game is then applied to a multi-agent consensus problem. We study how the attacks and the recovery on the edges affect the consensus process. Finally, we also provide numerical simulation to illustrate the results. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

Reliability, fault tolerance and other critical components for survivability in information warfare 1

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14th International Joint Conference on e-Business and Telecommunications, ICETE 2017

作者： Stavroulakis, Peter Kolisnyk, Maryna Kharchenko, Vyacheslav Doukas, Nikolaos Markovskyi, Oleksandr P. Bardis, Nikolaos G. Chania Greece Department of Computer Systems and Networks National Aerospace University "KhAI" Kharkiv Ukraine Hellenic Army Academy Varis – Koropiou Avenue Vari 116 72 Greece Department of Computer Engineering National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kiev Ukraine Department of Automation and Control in Technical Systems National Technical University "KPI" Kharkiv Ukraine

ISBN: (数字)9783030110390

ISBN: (纸本)9783030110383

The information revolution has caused many aspects of human activity to critically depend on a wide variety of physically existing or virtual technological achievements such as electronic devices, computer systems, algorithms, cloud resources, artificial intelligence hardware and software entities etc. Many of these systems are used in highly sensitive contexts, such as military applications. This implies the existence of an increasing number of unintentional disturbances or malicious attacks. Successful operation requires qualities such as robustness, fault tolerance, reliability, availability and security. All these may be summarized by the title of survivability. Survivability of critical systems working for sensitive applications involves the ability to provide uninterrupted operation under severe disturbances, gracefully degrade when limiting conditions are reached and maintain the ability to resume normal service once the disturbances have been removed. Survivability is an important, even - though non – functional, lifecycle property of many engineering systems. Further desirable elements of survivability include the ability of systems to recognize and resist attacks or accidents, adapt in order to avoid them and modify their behavior in order to diminish the effects of similar future occurrences. This chapter presents a quantitative approach to assessing survivability and an account of survivability in military systems. A scheme for survivability via replica diversity in the implementation of the AES algorithm is then presented. Following that, an algorithm for adaptive attack aversion in user authentication systems is presented that is based on Boolean transformations. An approach for increased survivability in Internet of Things (IoT) systems is then presented. Finally, an algorithm for secure data storage in cloud resources is presented that allows attack detection and avoidance. © Springer Nature Switzerland AG 2019.

关键词： Fault tolerance

controlling a Networked SIS Model via a Single Input over Undirected Graphs ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10981-10986页

作者： Dan Wang Ji Liu Philip E. Paré Wei Chen Li Qiu Carolyn L. Beck Tamer Başar Department of Electronic & Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China Department of Electrical and Computer Engineering Stony Brook University NY 11794 USA Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mechanics and Engineering Science & Beijing Innovation Center for Engineering Science and Advanced Technology Peking University Beijing China Coordinated Science Laboratory University of Illinois at Urbana-Champaign IL 61801 USA

This paper formulates and studies the problem of controlling a networked SIS model using a single input in which the network structure is described by a connected undirected graph. A necessary and sufficient condition on the values of curing and infection rates for the healthy state to be exponentially stable is obtained via the analysis of signed Laplacians when the control input is the curing budget of a single agent. In the case when the healthy state is stabilizable, an explicit expression for the minimum curing budget is provided. The utility of the algorithm is demonstrated using a simulation over a network of cities in the northeastern United States.

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An Energy-Efficient Context Aware Solution for Environmental Assessment ⁎

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IFAC-PapersOnLine 2020年第5期53卷 756-761页

作者： Isabela Birs Cristina Muresan Ioan Nascu Clara Ionescu Ghent University Faculty of Engineering and Architecture Research Group of Dynamical Systems and Control Tech Lane Science Park 125 B-9052 Ghent Belgium EEDT Decision & Control Flanders Make Consortium Tech Lane Science Park 131 B-9052 Ghent Belgium Department of Automation Technical University of Cluj-Napoca Memorandumului 28 Cluj-Napoca Romania

The paper focuses on presenting the advantages of context aware cyber-physical systems through an experimental platform capable of assessing its surroundings and self-performing decisions. The context aware paradigm is present in the control law implementation with various advantages such as energy efficiency as well as in the environmental measurements that trigger the robot to perform context-relevant decisions. The platform provides high versatility and the results presented throughout the study can be adapted to a manifold of multidisciplinary fields.

关键词： Submerged Vehicle Context Awareness Environmental Assessment Event-based control

Spin-phonon interaction in yttrium iron garnet

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arXiv 2021年

作者： Olsson, Kevin S. Choe, Jeongheon Rodriguez-Vega, Martin Khalsa, Guru Benedek, Nicole A. Fang, Bin Zhou, Jianshi Fiete, Gregory A. Li, Xiaoqin Department of Physics Center of Complex Quantum Systems The University of Texas at Austin AustinTX78712 United States Center for Dynamics and Control of Materials The University of Texas at Austin AustinTX78712 United States Department of Physics Northeastern University BostonMA02115 United States Department of Materials Science and Engineering Cornell University IthacaNY14853 United States Department of Mechanical Engineering The University of Texas at Austin AustinTX78712 United States Texas Materials Institute The University of Texas at Austin AustinTX78712 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Computer and Electrical Engineering University of Maryland College ParkMD20740 United States

Spin-phonon interaction is an important channel for spin and energy relaxation in magnetic insulators. Understanding this interaction is critical for developing magnetic insulator-based spintronic devices. Quantifying this interaction in yttrium iron garnet (YIG), one of the most extensively investigated magnetic insulators, remains challenging because of the large number of atoms in a unit cell. Here, we report temperature-dependent and polarization-resolved Raman measurements in a YIG bulk crystal. We first classify the phonon modes based on their symmetry. We then develop a modified mean-field theory and define a symmetry-adapted parameter to quantify spin-phonon interaction in a phononmode specific way for the first time in YIG. Based on this improved mean-field theory, we discover a positive correlation between the spin-phonon interaction strength and the phonon frequency. Copyright © 2021, The Authors. All rights reserved.

关键词： Phonons

Boost AI power: Data augmentation strategies with unlabelled data and conformal prediction, a case in alternative herbal medicine discrimination with electronic nose

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arXiv 2021年

作者： Liu, Li Zhan, Xianghao Wu, Rumeng Guan, Xiaoqing Wang, Zhan Zhang, Wei Pilanci, Mert Wang, You Luo, Zhiyuan Li, Guang The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Department of Bioengineering Stanford University StanfordCA94305 United States The Computer Learning Research Center Royal Holloway University of London Egham Hill Surrey EghamTW20 0EX United Kingdom Department of Electrical Engineering Stanford University StanfordCA94305 United States

Electronic nose has been proven effective in alternative herbal medicine classification, but due to the nature of supervised learning, previous research heavily relies on the labelled training data, which are time-costly and labor-intensive to collect. To alleviate the critical dependency on the training data in real-world applications, this study aims to improve classification accuracy via data augmentation strategies. The effectiveness of five data augmentation strategies under different training data inadequacy are investigated in two scenarios: the noise-free scenario where different availabilities of unlabelled data were considered, and the noisy scenario where different levels of Gaussian noises and translational shift were added to represent sensor drifts. The five augmentation strategies, namely noise-adding data augmentation, semi-supervised learning, classifier-based online learning, Inductive Conformal Prediction (ICP) online learning and our novel ensemble ICP online learning (EICP) proposed in this study, are compared against supervised learning baselines, with Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) as the classifiers. This study provides a systematic analysis of different augmentation strategies. Our novel strategy, EICP, outperforms the others by showing non-decreasing classification accuracy on all tasks and a significant improvement on most simulated tasks (25 out of 36 tasks, p ≤ 0.05), which demonstrated both effectiveness and robustness in boosting the classification model generalizability. This strategy can be employed in other machine learning applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Electronic nose

An arbitrary-order predefined-time exact differentiator for signals with exponential growth bound

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arXiv 2021年

作者： Gómez-Gutiérrez, David Aldana-López, Rodrigo Seeber, Richard Angulo, Marco Tulio Fridman, Leonid Intel Tecnología de México Intel Labs Intelligent Systems Research Lab Jalisco Mexico Tecnológico Nacional de México Instituto Tecnológico José Mario Molina Pasquel y Henríquez Unidad Académica Zapopan Jalisco Mexico Zaragoza Spain Graz University of Technology Institute of Automation and Control Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Graz Austria CONACyT - Institute of Mathematics Universidad Nacional Autonoma de México Juriquilla Mexico Universidad Nacional Autonoma de México Engineering Faculty Ciudad Universitaria Mexico

There is a growing interest in differentiation algorithms that converge in fixed time with a predefined Upper Bound on the Settling Time (UBST). However, existing differentiation algorithms are limited to signals having an n-th order Lipschitz derivative. Here, we introduce a general methodology based on time-varying gains to circumvent this limitation, allowing us to design n-th order differentiators with a predefined UBST for the broader class of signals whose (n+1)-th derivative is bounded by a function with bounded logarithmic derivative. Unlike existing methods whose time-varying gain tends to infinity, our approach yields a time-varying gain that remains bounded at convergence time. We show how this last property maintains exact convergence using bounded gains when considering a compact set of initial conditions and improves the algorithm’s performance to measurement noise. © 2021, CC BY-NC-ND.

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One-step Predictive Encoder - Gaussian Segment Model for Time Series Anomaly Detection

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One-step Predictive Encoder - Gaussian Segment Model for Tim...

International Joint Conference on Neural Networks (IJCNN)

作者： Jiachen Zhao Yongling Li Haibo He Fang Deng Department of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Unsupervised anomaly detection for time series is of great importance for various applications, such as Web monitoring, medical monitoring, and device fault diagnosis. Time series anomaly detection (TSAD) aims to find the observations that most different from others in a sequence of observations. With the development of deep learning, deep-autoencoder-based methods achieve state-of-the-art performance. These methods are usually able to find single anomaly points but fail to detect the anomaly segment and the change point. To tackle this problem, this paper proposes a novel TSAD method, which consists of a bidirectional LSTM (BiLSTM) autoencoder and a subsequent Gaussian segmentation model. BiLSTM encodes a time series in a predictive format from both positive and negative time directions, then outputs the latent feature vectors and restructured errors. After that, the latent features are used to find anomaly segments by the Gaussian segment model; the restructured errors are used to find change points and extreme single anomaly by a scoring function. In this way, our method can find all three kinds of anomaly points. Experiments on two real-world datasets demonstrate the effectiveness of the proposed method.

关键词： Time series analysis Anomaly detection Feature extraction Xenon Predictive models Monitoring Neural networks

Fire Detection based on Convolutional Neural Networks with Channel Attention

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Fire Detection based on Convolutional Neural Networks with C...

Chinese automation Congress (CAC)

作者： Xiaobo Zhang Kun Qian Kaihe Jing Jianwei Yang Hai Yu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education of China Future Science & Technology Park Beijing China Global Energy Interconnection Research Institute State Grid Nanjing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The existing research on fire detection is basically based on a two-stage method, resulting in slower detection speed, and the positioning accuracy is limited by the first-stage candidate region extraction algorithm. In order to solve the problem of high-precision real-time fire detection, this paper proposes a Yolo detection network combined with attention mechanism. The attention module is serially added to the final three convolutional networks of different scales of Yolo v3. The channel attention module updates the feature map by weighting and summing all channels, which captures the semantic dependencies between channels in the deep layers of the network, and improves the generalization ability of the model. Experiments show that the method proposed in this paper improves the accuracy of fire detection without reducing the detection speed.

关键词： Fires Semantics Feature extraction Annotations Image color analysis Deep learning Cameras