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Exponential Convergence for Distributed Optimization Under the Restricted Secant Inequality Condition ⁎

IFAC-PapersOnLine 2020年第2期53卷 2672-2677页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl H. Johansson School of Electrical Engineering and Computer Science KTH Royal Institute of Technology 100 44 Stockholm Sweden Department of Electrical Engineering University of North Texas Denton TX 76203 USA State Key Laboratory of Synthetical Automation for Process Industries Northeastern University 110819 Shenyang China

This paper considers the distributed optimization problem of minimizing a global cost function formed by a sum of local smooth cost functions by using local information exchange. A standard assumption for proving exponential/linear convergence of existing distributed first-order methods is strong convexity of the cost functions. This does not hold for many practical applications. In this paper, we propose a continuous-time distributed primal-dual gradient descent algorithm and show that it converges exponentially to a global minimizer under the assumption that the global cost function satisfies the restricted secant inequality condition. This condition is weaker than strong convexity and the global minimizer is not necessarily unique. Moreover, a discrete-time distributed primal-dual algorithm is developed from the continuous-time algorithm by Euler’s approximation method, which also linearly converges to a global minimizer under the same condition. The theoretical results are illustrated by numerical simulations.

关键词： Distributed optimization exponential convergence primal-dual algorithm restricted secant inequality

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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A Lexicographic Bi-objective Scheduling Problem From Steel Production Systems

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IFAC-PapersOnLine 2020年第5期53卷 158-163页

作者： Ziyan Zhao MengChu Zhou Shixin Liu Xiwang Guo Haoyue Liu State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Northeastern University Shenyang 110819 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 USA College of Computer and Communication Engineering Liaoning Shihua University Fushun 113001 China

Wire rod and bar rolling is an essential process in a steel production system (SPS). This work considers a lexicographic bi-objective scheduling problem originated from it. As a medium process in SPS, both the impacts of its upstream process, i.e., release time constraints, and the requirements of its downstream process, i.e., due time constraints, on SPS performance, must be considered. The concerned problem aims to minimize the number of tardy tasks as a main objective and total setup time as a secondary one. It is solved via a two-stage method. The first-stage problem is handled with an off-the-shelf optimization software, and the second one with iterated greedy algorithms (IGAs). We compare the performance of IGAs under different strategies and select the best one to solve it. By comparing the experimental results an exact single-objective method’s, this paper shows that the proposed method can solve the problem fast with excellent results in terms of the number of tardy tasks and setup time, i.e., the secondary objective, on average. Its usage can result in significant performance improvement in steel production over the existing practice.

关键词： Batch process batch scheduling iterated greedy algorithm lexicographic bi-objective optimization steel production systems

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A Distributed Primal-Dual Algorithm for Bandit Online Convex Optimization with Time-Varying Coupled Inequality Constraints

A Distributed Primal-Dual Algorithm for Bandit Online Convex...

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American Control Conference (ACC)

作者： Xinlei Yi Xiuxian Li Tao Yang Lihua Xie Tianyou Chai Karl H. Johansson School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden School of Electrical and Electronic Engineering Nanyang Technological University Singapore State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

This paper considers distributed bandit online optimization with time-varying coupled inequality constraints. The global cost and the coupled constraint functions are the summations of local convex cost and constraint functions, respectively. The local cost and constraint functions are held privately and only at the end of each period the constraint functions are fully revealed, while only the values of cost functions at queried points are revealed, i.e., in a so called bandit manner. A distributed bandit online primal-dual algorithm with two queries for the cost functions per period is proposed. The performance of the algorithm is evaluated using its expected regret, i.e., the expected difference between the outcome of the algorithm and the optimal choice in hindsight, as well as its constraint violation. We show that O(T c ) expected regret and O(T 1-c/2 ) constraint violation are achieved by the proposed algorithm, where T is the total number of iterations and c ∈ [0.5, 1) is a user-defined trade-off parameter. Assuming √ Slater's condition, we show that O(√T) expected regret and O(√T) constraint violation are achieved. The theoretical results are illustrated by numerical simulations.

关键词： Cost function Convex functions Numerical simulation Routing Graph theory Numerical models

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Hyperchaos on the dynamics of memristive Tabu learning neuron model under influence of electromagnetic radiation: Application in biomedical data privacy

Franklin Open

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Franklin Open 2025年 10卷

作者： Boya, Bertrand Frederick Boui A Kengne, Jacques Nanfak, Arnaud Muni, Sishu Shankar Nkapkop, Jean de Dieu Kenmoe, Germaine Djuidje Babenko, Lyudmila Klimentyevna Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA) IUT-FV Bandjoun University of Dschang Bandjoun P.O. Box 134 Cameroon Laboratory of Energy-Electric and Electronic Systems Department of Physics Faculty of Science University of Yaoundé I Yaoundé P.O. Box 812 Cameroon Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Institute of Computer Technology and Information Security Southern Federal University P.O. Box 347922 Taganrog Russian Federation Laboratory for electronics electrical engineering automation and telecommunications National Higher Polytechnic School of Douala University of Douala Douala PO Box 2701 Cameroon School of Digital Sciences Digital University Kerala Technocity campus Kerala Mangalapuram India Indian Institute of Information Technology and Management Kerala Technopark road Kerala India Department of Electrical Engineering and Industrial Computing University Institute of Technology Douala P.O. Box 8698 Cameroon

This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical investigation without the influence of electromagnetic radiation reveal that when increasing the memristor strength induces complex phenomena, including the coexistence of infinite bubbles of bifurcations, offset plane coexistence of various attractors. Furthermore, electromagnetic radiation can significantly impact the system's behavior, giving rise to hyperchaotic dynamics. A decent understanding is seen between the mathematical outcomes, approved through trial execution on an Arduino microcontroller-based. The insights gained from neural dynamics could pave the way for applications in neuromorphic computing, engineering, and brain-machine interfaces. Based on the complex dynamics of the model, a data privacy of biomedical data has been build and reveals good results, and enable to resist to robust attacks. © 2024 The Author(s)

关键词： Data privacy Electromagnetic radiation Hyperchaos Microcontroller design MTLN model Offset plane coexisting behaviors

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Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization

arXiv

引用

arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Digital Futures Stockholm10044 Sweden The Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

This paper considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve O(T max{κ,1-κ}) static network regret and O(T 1-κ/2) network cumulative constraint violation, where T is the time horizon and κ ∈ (0, 1) is a user-defined trade-off parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to O(T κ). Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

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Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search

arXiv

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arXiv 2021年

作者： Ma, Lianbo Li, Nan Yu, Guo Geng, Xiaoyu Cheng, Shi Wang, Xingwei Huang, Min Jin, Yaochu College of Software Northeastern University Shenyang110819 China The Institute of Intelligent Manufacturing Nanjing Tech University Nanjing211816 China The School of Computer Science Shaanxi Normal University Xi’an710062 China The College of Computer Science Northeastern University Shenyang110819 China The College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Faculty of Technology Bielefeld University Bielefeld33619 Germany The Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

In multi-objective evolutionary neural architecture search (NAS), existing predictor-based methods commonly suffer from the rank disorder issue that a candidate high-performance architecture may have a poor ranking compared with the worse architecture in terms of the trained *** alleviate the above issue, we aim to train a Pareto-wise end-to-end ranking classifier to simplify the architecture search process by transforming the complex multi-objective NAS task into a simple classification task. To this end, a classifier-based Pareto evolution approach is proposed, where an online classifier is trained to directly predict the dominance relationship between the candidate and reference architectures. Besides, an adaptive clustering method is designed to select reference architectures for the classifier, and an α-domination assisted approach is developed to address the imbalance issue of positive and negative samples. The proposed approach is compared with a number of state-of-the-art NAS methods on widely-used test datasets, and computation results show that the proposed approach is able to alleviate the rank disorder issue and outperforms other methods. Especially, the proposed method is able to find a set of promising network architectures with different model sizes ranging from 2M to 5M under diverse objectives and constraints. © 2021, CC BY.

关键词： Network architecture

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Corrigendum to “Realistic acceleration of neural networks with fine-grained tensor decomposition” [Neurocomputing 512 (2022) 52–68]

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Neurocomputing 2023年 527卷 47-47页

作者： Rui Lv Dingheng Wang Jiangbin Zheng Yefan Xie Zhao-Xu Yang School of Computer Science Northwestern Polytechnical University Xi’an Shaanxi China Northwest Institute of Mechanical & Electrical Engineering Xianyang Shaanxi China School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an Shaanxi China State Key Laboratory for Strength and Vibration of Mechanical Structures Shaanxi Key Laboratory of Environment and Control for Flight Vehicle and School of Aerospace Engineering Xi’an Jiaotong University Xi’an Shaanxi China

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Optimal Unbiased Linear Sensor Fusion over Multiple Lossy Channels with Collective Observability

arXiv

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arXiv 2020年

作者： Wu, Yuchi Ding, Kemi Li, Yuzhe Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong School of Electrical and Electronic Engineering Nanyang Technological University Singapore State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110004 China

In this paper, we consider optimal linear sensor fusion for obtaining a remote state estimate of a linear process based on the sensor data transmitted over lossy channels. There is no local observability guarantee for any of the sensors. It is assumed that the state of the linear process is collectively observable. We transform the problem of finding the optimal linear sensor fusion coefficients as a convex optimization problem which can be efficiently solved. Moreover, the closed-form expression is also derived for the optimal coefficients. Simulation results are presented to illustrate the performance of the developed algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： State estimation

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Data-driven Surplus Material Prediction in Steel Coil Production

Data-driven Surplus Material Prediction in Steel Coil Produc...

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International Conference on Wireless and Optical Communications ( WOCC)

作者： Ziyan Zhao Xiaoyue Yong Shixin Liu Mengchu Zhou State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Northeastern University Shenyang China Shanghai Baosight Software Co. Ltd Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark USA

ISBN: (数字)9781728161242

ISBN: (纸本)9781728161259

A steel enterprise is currently trying to avoid the presence of surplus materials since they can greatly increase its operational cost. The complicated production process of steel products makes it difficult to find the causes of surplus materials. In this work, we propose a surplus material prediction problem and solve it based on statistical analysis and machine learning methods. In the concerned problem, we predict whether there are surplus materials under a given group of production parameters. The dataset used in this work is from a real-world three-month steel coil production process. First, data cleaning is conducted to standardize the industrial dataset. Then, the production parameters highly correlated with surplus material prediction results are selected by a series of feature selection methods. Finally, two prediction models based on extreme gradient boosting and logistic regression are presented according to the selected features. The experimental results reveal that the proposed prediction models have similar effectiveness. A visible regression function makes the logistic regression method more suitable for practical application.

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