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Contraction Analysis of Almost Surely Monotone Discrete-Time systems with Unknown Time Delay

IEEE control systems Letters 2025年 9卷 426-431页

作者： Kawano, Yu Hosoe, Yohei Hiroshima University Graduate School of Advanced Science and Engineering Higashi-Hiroshima739-8527 Japan Kyoto University Department of Electrical Engineering Nishikyo-ku Kyoto615-8510 Japan

In this paper, we utilize monotonicity to simplify contraction analysis of discrete-time nonlinear time-delay systems with parameters following stochastic processes. First, we extend the concept of almost sure monotonicity to the time-delay systems, which can be verified via analysis of the corresponding prolonged systems. Next, we introduce a novel notion of uniform incremental asymptotic stability (UIAS) in the first moment to the time-delay systems and develop its sufficient condition. By virtue of almost sure monotonicity, if this condition holds, the time-delay systems are UIAS in the first moment for any finite length of delay. Moreover, the proposed result suggests that UIAS in the first moment has a strong connection with stability of the averaged deterministic systems. We formalize this observation for almost surely cooperative systems by establishing the equivalence between UIAS and uniform incremental asymptotic mean stability. © 2017 IEEE.

关键词： Asymptotic stability

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Cell-Based Microfluidic Device Utilizing Cell Sheet Technology

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Cyborg and Bionic systems 2022年第1期2022卷 35-42页

作者： Katsuhisa Sakaguchi Kei Akimoto Masanori Takaira Ryu-ichiro Tanaka Tatsuya Shimizu Shinjiro Umezu Department of Integrative Bioscience and Biomedical Engineering Graduate School of Advaneed Science and Enginring TWInsWaseda University2-2 Wakamatsu-ChoShinju-KuTokyo 162-8480Japan Department of Modern Mechanical Engineering Graduate School of Creative Science and Engineering Waseda University 1-104 Totsuka-ChoShinju-KuTokyo 169-8555Japan Insttute of Advanced BiomedicalEngineering and Seience TWIns Tokyo Women's Medical University8-1 Kawada-ChoShinju-KuTokyo 162-8666Japan

The developmentof microelectromechanical systems hasresulted in the rapid development of polydimethylpolysiloxane(PDMS)microfluidic devices for drug screening *** cell functions,such as the response of endothelial cells to fluids,have beenelucdated using microuidic ***,organon-achip systems that includeorgans that are importantfor biologicalcirculation,such as the heart,liver,pancreas,kidneys,and brain,have been *** organs realize the biologicalcirculation system in a manner that cannot be reproduced by artificial organs;however,the flow channels between the organsare often artifically created by *** this study,we developeda microfluidic device consisting only of cels,by combiningcell sheet technology with microtitanium *** were placed between stacked fibroblast cellsheets,and the celisheets adhered to each other,afer which the microwires were removed leaving a luminal structure with a size approximatelyequal to the arteriolar *** lumen structure was constructed using wires with diameters of 50,100,150,and 200μm,which were approximations of the arteriole *** using a perfusion device,we successfully perfused theluminal structure created inside the *** results revealed that a aulture solution can be supplied toa cellsheet witha very high cell *** biofabrication technology proposed in this study can contribute to the development of organ-on-a-chip systems.

关键词： organs fluid diameters

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Improved deeplab v3+ with metadata extraction for small object detection in intelligent visual surveillance systems

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IEIE Transactions on Smart Processing and Computing 2021年第3期10卷 209-218页

作者： Oh, Heungmin Lee, Minjung Kim, Hyungtae Paik, Joonki Department of Image Engineering Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University / Seoul06974 Korea Republic of

A surveillance system deploys multiple cameras to monitor a wide area in real time to detect abnormal situations such as a crime scene, traffic accident, and natural disaster. An Increased number of cameras results in the same number of monitors, which makes human decisions or automatic decisions difficult. To solve the problem, a smart surveillance scheme has recently been proposed. The smart surveillance system automatically detects an object and provides an alarm to a surveillant. In this paper, we present a metadata extraction method for object-based video summary. The proposed method adopts deep learning-based object detection and background elimination to correctly estimate an object region. Finally, metadata extraction is performed on the estimated object information. The proposed metadata consists of the representative color, size, aspect ratio, and patch of an object. The proposed method can extract reliable metadata without motion features in both static and dynamic cameras. The proposed method can be applied to various object detection areas using complex metadata. Copyrights © 2021 The Institute of Electronics and Information Engineers

关键词： Object detection

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EMHDRL: Adversarial Attacks on Graph Neural Networks via Hierarchical Reinforcement Learning 42

EMHDRL: Adversarial Attacks on Graph Neural Networks via Hie...

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42nd Chinese control Conference, CCC 2023

作者： Sun, Guowei Ding, Jie Li, Xiang School of Information Science and Engineering Fudan University Adaptive Networks and Control Lab Department of Electronic Engineering Shanghai200433 China Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China

ISBN: (纸本)9789887581543

Graph neural networks (GNNs), with its powerful ability to explore the topological structure of graph data, have achieved great success in various downstream tasks. However, recent studies have shown that due to the lack of robustness, GNNs are easily fooled by perturbations. For node classification tasks, the current attack methods mainly focus on manipulating edges, modifying features and adding fake nodes. Although these attacks are effective, the changes to the original graph data are easy to be detected. Therefore, to misclassify the target nodes, we propose an Edge Manipulation method via Hierarchical Deep Reinforcement Learning (EMHDRL) by taking accounting to the imperceptibility of perturbation on graph data. Through modeling the topology perturbation as Finite Horizon Markov Decision Process, EMHDRL adopts a hierarchical Q-function network to manipulate edges of graph, while trying to ensure that the degree of the attacked node and the total number of edges of the whole graph remain unchanged. Our experimental results show that the proposed method can effectively reduce the performance of GNNs in classifying the target nodes and has strong transferability. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Reinforcement learning

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Adaptive event-triggered state estimation for large-scale systems subject to deception attacks

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science China(Information sciences) 2022年第2期65卷 139-154页

作者： Hanchen XIAO Derui DING Hongli DONG Guoliang WEI College of Science University of Shanghai for Science and Technology Department of Control Science and Engineering University of Shanghai for Science and Technology School of Software and Electrical Engineering Swinburne University of Technology Institute of Complex Systems and Advanced Control Northeast Petroleum University

This paper addresses state estimation issues of large-scale systems with measurements subject to deception attacks, where the communication topology among sub-estimators is the same as the physical coupling structure of the subsystems. In consideration of the limited channel bandwidth, a novel adaptive event-triggered scheme is proposed for governing the data transmission among sub-estimators. With the help of Lyapunov analysis approaches, sufficient conditions are derived to ensure the input-to-state stability of the dynamics of estimation errors. Meanwhile, the bound of the estimation errors is obtained in the mean-square sense. The desired estimator parameters are presented in an analytical form dependent on the solution of a set of matrix inequalities. The developed scheme is related to the local information of the subsystems and thus satisfies the requirement of scalability. Finally, a simulation example of power systems is given to reveal the usefulness and effectiveness of the developed design scheme.

关键词： large-scale discrete-time systems adaptive event-triggering communication deception attacks input-to-state stability power systems

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Saddle Point Optimization with Approximate Minimization Oracle and Its Application to Robust Berthing control

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ACM Transactions on Evolutionary Learning and Optimization 2022年第1期2卷 1–32页

作者： Akimoto, Youhei Miyauchi, Yoshiki Maki, Atsuo Faculty of Engineering Information and Systems University of Tsukuba and RIKEN Center for Advanced Intelligence Project 1-1-1 Tennodai Tsukuba Ibaraki305-8573 Japan Department of Naval Architecture and Ocean Engineering Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka565-0971 Japan

We propose an approach to saddle point optimization relying only on oracles that solve minimization problems approximately. We analyze its convergence property on a strongly convex-concave problem and show its linear convergence toward the global min-max saddle point. Based on the convergence analysis, we develop a heuristic approach to adapt the learning rate. An implementation of the developed approach using the (1+1)-CMA-ES as the minimization oracle, namely, Adversarial-CMA-ES, is shown to outperform several existing approaches on test problems. Numerical evaluation confirms the tightness of the theoretical convergence rate bound as well as the efficiency of the learning rate adaptation mechanism. As an example of real-world problems, the suggested optimization method is applied to automatic berthing control problems under model uncertainties, showing its usefulness in obtaining solutions robust to uncertainty. © 2022 Association for Computing Machinery.

关键词： Optimization

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Contraction Analysis of Differentially Positive Discrete-Time Stochastic systems

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IEEE Transactions on Automatic control 2025年

作者： Kawano, Yu Hosoe, Yohei Hiroshima University Graduate School of Advanced Science and Engineering Higashi-Hiroshima Japan Kyoto University Department of Electrical Engineering Nishikyo-ku Kyoto615-8510 Japan

In this paper, we examine the role of differential positivity in the contraction analysis of discrete-time nonlinear systems with parameters that follow stochastic processes. We investigate the concept of almost sure differential positivity, which parallels the positivity of linear systems, and utilize it to derive vector-type sufficient conditions for uniform incremental exponential stability in the first moment. Employing these conditions, we demonstrate that network stability analysis can be made scalable by decomposing it into analysis of sub-components and interconnection structures. Our results are applicable to a range of systems, including nonlinear Markov jump systems, nonlinear deterministic systems, and linear stochastic systems, by specifying the system structures. Additionally, we perform converse analysis for almost surely cooperative systems, i.e., almost surely differentially positive systems with respect to the positive orthant cones. © 1963-2012 IEEE.

关键词： Stochastic systems

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Predicting Protein Interactions with BteA in Bordetella pertussis Pathogenesis Using Machine Learning 24

Predicting Protein Interactions with BteA in Bordetella pert...

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Proceedings of the 2024 8th International Conference on Computational Biology and Bioinformatics

作者： Toshiki Koshiba Chisato Sudo Toshinobu Ogawa Toshihiko Iuchi Niken Prasasti Martono Asaomi Kuwae Hayato Ohwada Department of Industrial and Systems Engineering Tokyo University of Science Noda Chiba Japan Graduate School of Infection Control Science Kitasato University Minato-ku Tokyo Japan

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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Computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Net-Zero Energy House-oriented Linear Programming for the Sizing Problem of Photovoltaic Panels and Batteries

arXiv

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arXiv 2024年

作者： Carnerero, A. Daniel Tanaka, Taichi Li, Mengmou Hatanaka, Takeshi Wasa, Yasuaki Hirata, Kenji Ushifusa, Yoshiaki Ida, Takanori Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima739-0046 Japan Department of Electrical Engineering and Bioscience School of Advanced Science and Engineering Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan Department of Electrical and Electronic Engineering School of Engineering University of Toyama Toyama930-8555 Japan Faculty of Economics and Business Administration The University of Kitakyushu Fukuoka802-8577 Japan Graduate School of Economics Kyoto University Kyoto606-8501 Japan

The global drive towards carbon neutrality has led to a significant increase in the number of power plants based on renewable energy sources (RES). Concurrently, numerous households are adopting RES to generate their own energy, aiming to decrease both electricity costs and carbon footprints. To support these users, many papers have been devoted to developing optimal investment strategies for residential energy systems. However, there is still a significant gap as these studies often neglect important aspects like carbon neutrality. For this reason, in this paper, we explore the concept of net-zero energy houses (ZEHs)—houses designed to have an annual net energy consumption around zero—by presenting a constrained optimization problem to find the optimal number of photovoltaic panels and the optimal size of the battery system for home integration. Solving this constrained optimization problem is difficult due to its nonconvex constraints. Nevertheless, by applying a series of transformations, we reveal that it is possible to find an equivalent linear programming (LP) problem which is computationally tractable. The attainment of ZEH can be tackled by introducing a single constraint in the optimization problem. Additionally, we propose a sharing economy approach to the investment problem, offering a strategy that could potentially reduce investment costs and facilitate the attainment of ZEH more efficiently. Finally, we apply the proposed frameworks to a neighborhood in Japan as a case study, demonstrating the potential for long-term ZEH attainment. The results show that, under the right incentive, users can achieve ZEH, reduce their electricity costs and have a minimal impact on the main grid. Copyright © 2024, The Authors. All rights reserved.

关键词： Linear programming

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