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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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Optimization of Directional Landmark Deployment for Visual Observer on SE(3)

arXiv

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arXiv 2022年

作者： Lei, Zike Chen, Xi Tan, Ying Chen, Xiang Chai, Li The School of Information Science and Engineering Wuhan University of Science and Technology Hubei 430081 China The Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and Technology Hubei 430081 China The School of Electrical Mechanical and Infrastructure Engineering The University of Melbourne VIC3010 Australia The Department of Electrical and Computer Engineering University of Windsor ONN9B 3P4 Canada The College of Control Science and Engineering Zhejiang University Hangzhou310027 China

An optimization method is proposed in this paper for novel deployment of given number of directional landmarks (location and pose) within a given region in the 3-D task space. This new deployment technique is built on the geometric models of both landmarks and the monocular camera. In particular, a new concept of Multiple Coverage Probability (MCP) is defined to characterize the probability of at least n landmarks being covered simultaneously by a camera at a fixed position. The optimization is conducted with respect to the position and pose of the given number of landmarks to maximize MCP through globally exploration of the given 3-D space. By adopting the elimination genetic algorithm, the global optimal solutions can be obtained, which are then applied to improve the convergent performance of the visual observer on SE(3) as a demonstration example. Both simulation and experimental results are presented to validate the effectiveness of the proposed landmark deployment optimization method. © 2022, CC BY-NC-SA.

关键词： Probability

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Significant Wave Height Prediction Based on VMD-SA-MLP-BP

Significant Wave Height Prediction Based on VMD-SA-MLP-BP

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Chinese Automation Congress (CAC)

作者： Yining Wu Runfeng Zhang Fang Liu Zhenzhong Liu Jianguo Wu Haonan Dong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Yichang Research Institute of Testing Technology Yichang China Tianjin Hanhailanfan Marine Technology Co. Ltd. Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

Waves seriously impact port construction, worldwide route planning, military activities, and wave power generation. To improve the accuracy of significant wave height prediction, we proposed a novel prediction method, a multi-layer perceptron combined with a backpropagation adjustment (MLP-BP) prediction model that combines mutation mode decomposition (VMD) and a simulated annealing optimization algorithm (SA). Firstly, we use the variable modulus method to decompose the significant wave height sequence data and transform the wave height sequence into multiple different sub-modes (IMF) to reduce the complexity and non-stationarity of the data. Secondly, the simulated annealing algorithm (SA) is used to optimize the weight and bias of the MLP-BP neural network to find the optimal parameter configuration and improve the performance and generalization ability of the prediction model. Finally, the decomposed sub-modal components of each significant wave height sequence are inserted into the MLP-BP neural network and the predicted values of each element are summed to obtain the final significant wave height prediction. The prediction results of the SSA-MLP-BP, PSO-MLP-BP, SA-MLP-BP, and VMD-SA-MLP-BP were compared and demonstrated that the VMD-SA-MLP-BP model performed best. The MAE, MAPE, MSE, RMSE, and R2 of the prediction evaluation indexes were 0.036 m, 11.7%, 0.004 m 2 , 0.067 m, and 0.983, respectively, which performed well in predicting significant wave height.

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Designing, simulating, and performing the 100-AV field test for the CIRCLES consortium Methodology and Implementation of the Largest mobile traffic control experiment to date

arXiv

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arXiv 2024年

作者： Ameli, Mostafa Mcquade, Sean Lee, Jonathan W. Bunting, Matt Nice, Matt Wang, Han Barbour, Will Weightman, Ryan Denaro, Chris Delorenzo, Ryan Hornstein, Sharon Davis, Jon F. Timsit, Dan Wagner, Riley Xu, Rita Mahmood, Malaika Mahmood, Mikail Monache, Maria Laura Delle Seibold, Benjamin Work, Dan Sprinkle, Jonathan Piccoli, Benedetto Bayen, Alexandre M. Universite Gustave Eiffel COSYS-GRETTIA France University of California Berkeley Department of Electrical Engineering and Computer Sciences United States University of California Berkeley Department of Civil and Environmental Engineering United States Rutgers University-Camden Center for Computational and Integrated Biology United States Rutgers University-Camden Department of Mathematical Sciences United States Vanderbilt University Civil and Environmental Engineering United States General Motors Control and Learning Team United States Temple University Department of Mathematics United States Ecole Polytechnique France

Previous controlled experiments on single-lane ring roads [1] have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAVin- the-loop Lagrangian Energy Smoothing (CIRCLES) Consortium conducted MegaVanderTest (MVT), a live traffic control experiment involving 100 vehicles near Nashville, TN, USA. The purpose was to implement various controllers to smooth stop-and-go traffic waves. This article is a tutorial for developing analytical and simulation-based tools essential for designing and executing a live traffic control experiment like the MVT. It presents an overview of the proposed roadmap and various procedures used in designing, monitoring, and conducting the MVT, which is the largest mobile traffic control experiment at the time. The design process is aimed at evaluating the impact of the CIRCLES AVs on surrounding traffic. The article discusses the agent-based traffic simulation framework created for this evaluation. A novel methodological framework is introduced to calibrate this microsimulation, aiming to accurately capture traffic dynamics and assess the impact of adding 100 vehicles to existing traffic. The calibration model's effectiveness is verified using data from a six-mile section of Nashville's I-24 highway. The results indicate that the proposed model establishes an effective feedback loop between the optimizer and the simulator, thereby calibrating flow and speed with different spatiotemporal characteristics to minimize the error between simulated and real-world data. Finally, We simulate AVs in multiple scenarios to assess their effect on traffic congestion. This evaluation validates the AV routes, thereby contributing to the execution of a safe and successful live traffic control experiment via AVs. © 2024, CC BY-

关键词： Traffic congestion

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Fractional cyber-neural systems - a brief survey

arXiv

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arXiv 2021年

作者： Reed, Emily Chatterjee, Sarthak Ramos, Guilherme Bogdan, Paul Pequito, Sérgio Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Portugal Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Neurotechnology has made great strides in the last 20 years. However, we still have a long way to go to commercialize many of these technologies as we lack a unified framework to study cyber-neural systems (CNS) that bring the hardware, software, and the neural system together. Dynamical systems play a key role in developing these technologies as they capture different aspects of the brain and provide insight into their function. Converging evidence suggests that fractional-order dynamical systems are advantageous in modeling neural systems because of their compact representation and accuracy in capturing the long-range memory exhibited in neural behavior. In this brief survey, we provide an overview of fractional CNS that entails fractional-order systems in the context of CNS. In particular, we introduce basic definitions required for the analysis and synthesis of fractional CNS, encompassing system identification, state estimation, and closed-loop control. Additionally, we provide an illustration of some applications in the context of CNS and draw some possible future research directions. Ultimately, advancements in these three areas will be critical in developing the next generation of CNS, which will, ultimately, improve people’s quality of life. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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Deep Learning Models for Intelligent Healthcare: Implementation and Challenges 7th

Deep Learning Models for Intelligent Healthcare: Implementat...

引用

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Rehman, Sadaqat ur Tu, Shanshan Shah, Zubair Ahmad, Jawad Waqas, Muhammad Rehman, Obaid ur Kouba, Anis Abbasi, Qammer H. Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Namal Institute Mianwali42250 Pakistan Department of Computer Science and Engineering Hammad Bin Khalifa University Doha Qatar School of Computing Edinburgh Napier University Edinburgh United Kingdom Department of Electrical Engineering Sarhad University of Science and IT Peshawar25000 Pakistan Department of Computer Science Prince Sultan University Riyadh Saudi Arabia James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9783030786083

The rapid developments of artificial intelligent (AI) is being transformed for its extensive use-cases, people-centered intelligent systems focusing on care delivery, research encounter complex problems related to improve the overall infrastructure and management of intelligent delivery service;for instance, bringing transformation in healthcare sector for monitoring patients with chronic disease. Most of these systems are driven by state-of-the-art learning algorithms i.e., Convolution Neural Network. The CNN algorithm is considered to be one of the most prominent architectures of DL. Recently, due to enormous growth in the amount of annotated data and the development of CNN hardware accelerator, further, boost the research on CNN and accomplished benchmark enactment on different applications. This paper presents cutting-edge applications of CNN for an intelligent healthcare system. We provide useful findings of different CNN features such as optimization, fast computation, design, activation function, and loss function. To our knowledge, this is the first comprehensive work to address the recent trends in the architecture of CNN, which offers insight to the underlying problems and provides the potential solutions for any given intelligent healthcare applications. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Competitive Perimeter Defense of Conical Environments

arXiv

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arXiv 2021年

作者： Bajaj, Shivam Torng, Eric Bopardikar, Shaunak D. Moll, Alexander Von Weintraub, Isaac Garcia, Eloy Casbeer, David W. the Department of Electrical and Computer Engineering Michigan State University United States the Department of Computer Science and Engineering Michigan State University United States Control Science Center Air Force Research Laboratory

We consider a perimeter defense problem in a planar conical environment in which a single vehicle, having a finite capture radius, aims to defend a concentric perimeter from mobile intruders. The intruders are arbitrarily released at the circumference of the environment and they move radially toward the perimeter with fixed speed. We present a competitive analysis approach to this problem by measuring the performance of multiple online algorithms for the vehicle against arbitrary inputs, relative to an optimal offline algorithm that has information about entire input sequence in advance. In particular, we establish two necessary conditions on the parameter space to guarantee (i) finite competitiveness of any algorithm and (ii) a competitive ratio of at least 2 for any algorithm. We then design and analyze three online algorithms and characterize parameter regimes in which they have finite competitive ratios. Specifically, our first two algorithms are provably 1, and 2-competitive, respectively, whereas our third algorithm exhibits different competitive ratios in different regimes of problem parameters. Finally, we provide a numerical plot in the parameter space to reveal additional insights into the relative performance of our algorithms. © 2021, CC BY.

关键词： Network security

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Simple Multiuser Twin-Field Quantum Key Distribution Network

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Physical Review Applied 2022年第1期17卷 014025-014025页

作者： Xiaoqing Zhong Wenyuan Wang Reem Mandil Hoi-Kwong Lo Li Qian Center for Quantum Information and Quantum Control Department of Physics University of Toronto Toronto Ontario M5S 1A7 Canada Center for Quantum Information and Quantum Control Dept. of Electrical & Computer Engineering University of Toronto Toronto Ontario M5S 3G4 Canada Department of Physics University of Hong Kong Pokfulam Road Hong Kong

Twin-field quantum key distribution (TFQKD) systems have shown great promise for implementing practical long-distance secure quantum communication due to its measurement-device-independent nature and its ability to offer fundamentally superior rate-loss scaling than point-to-point QKD systems. A surge of research and development effort in the last two years has produced many variants of protocols and experimental demonstrations. In terms of hardware topology, TFQKD systems interfering quantum signals from two remotely phase-locked laser sources are in essence giant Mach-Zehnder interferometers (MZIs) requiring active phase stabilization. Such configurations are inherently unsuitable for a TFQKD network, where more than one user pair share the common quantum measurement station, because it is practically extremely difficult, if not impossible, to stabilize MZIs of largely disparate path lengths, a situation that is inevitable in a multi-user-pair TFQKD network. On the other hand, Sagnac interferometer-based TFQKD systems exploiting the inherent phase stability of the Sagnac ring can implement asymmetric TFQKD, and are therefore eminently suitable for implementing a TFQKD network. In this work, we experimentally demonstrate a proof-of-principle multi-user-pair Sagnac TFQKD network where three user pairs sharing the same measurement station can perform pairwise TFQKD through time multiplexing, with channel losses up to 58.00 dB, and channel loss asymmetry up to 15.00 dB. In some cases, the secure key rates still beat the rate-loss bound for point-to-point repeaterless QKD systems, even in this network configuration. Our demonstration of this multi-user-pair TFQKD network is a step in advancing quantum-communication network technologies.

关键词： Optical quantum information processing Quantum communication Quantum cryptography Quantum networks Quantum optics Communication networks Optical fibers Optical interferometry

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Improving the speed of variational quantum algorithms for quantum error correction

arXiv

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arXiv 2023年

作者： Zoratti, Fabio De Palma, Giacomo Kiani, Bobak Nguyen, Quynh T. Marvian, Milad Lloyd, Seth Giovannetti, Vittorio Scuola Normale Superiore PisaI-56126 Italy Department of Mathematics University of Bologna Bologna40126 Italy Department of Electrical Engineering and Computer Science MIT 77 Massachusetts Avenue CambridgeMA02139 United States Department of Electrical and Computer Engineering Center for Quantum Information and Control University of New Mexico United States Department of Mechanical Engineering MIT 77 Massachusetts Avenue CambridgeMA02139 United States Turing Inc. CambridgeMA02139 United States NEST Scuola Normale Superiore Istituto Nanoscienze-CNR PisaI-56126 Italy

We consider the problem of devising a suitable Quantum Error Correction (QEC) procedures for a generic quantum noise acting on a quantum circuit. In general, there is no analytic universal procedure to obtain the encoding and correction unitary gates, and the problem is even harder if the noise is unknown and has to be reconstructed. The existing procedures rely on Variational Quantum Algorithms (VQAs) and are very difficult to train since the size of the gradient of the cost function decays exponentially with the number of qubits. We address this problem using a cost function based on the Quantum Wasserstein distance of order 1 (QW1). At variance with other quantum distances typically adopted in quantum information processing, QW1 lacks the unitary invariance property which makes it a suitable tool to avoid to get trapped in local minima. Focusing on a simple noise model for which an exact QEC solution is known and can be used as a theoretical benchmark, we run a series of numerical tests that show how, guiding the VQA search through the QW1, can indeed significantly increase both the probability of a successful training and the fidelity of the recovered state, with respect to the results one obtains when using conventional approaches. © 2023, CC BY-SA.

关键词： Cost functions

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