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International Conference on Wireless and Optical Communications ( WOCC)

作者： Qihui Xu Jinshu Chen Jiacheng Tang Qi Kang MengChu Zhou Department of Control Science and Engineering Tongji University Shanghai China The Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Generative adversarial networks (GANs) can be well used for image generation. Yet their training typically requires large amounts of data, which may not be available. This paper proposes a new algorithm for effective generative learning given a single image only. The proposed method involves building GAN models with a hierarchical pyramid structure and a parallel-branch design that enables independent learning of the foreground and background areas. This work conducts a set of well-designed experiments. The results well demonstrate that the proposed method produces the images of higher quality and better diversity than existing methods do. Thus, this work advances the field of generative learning for image generation.

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Navigation in Time-Varying Densities: An Operator Theoretic Approach

Navigation in Time-Varying Densities: An Operator Theoretic ...

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European control Conference (ECC)

作者： Shankar A. Deka Umesh Vaidya Dimos V. Dimarogonas Department of Electrical Engineering Computer Science Decision and Control Systems division KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical Engineering Clemson University Clemson South Carolina USA

This paper considers the problem of optimizing robot navigation with respect to a time-varying objective encoded into a navigation density function. We are interested in designing state feedback control laws that lead to an almost everywhere stabilization of the closed-loop system to an equilibrium point while navigating a region optimally and safely (that is, the transient leading to the final equilibrium point is optimal and satisfies safety constraints). Though this problem has been studied in literature within many different communities, it still remains a challenging non-convex control problem. In our approach, under certain assumptions on the time-varying navigation density, we use Koopman and Perron-Frobenius Operator theoretic tools to transform the problem into a convex one in infinite dimensional decision variables. In particular, the cost function and the safety constraints in the transformed formulation become linear in these functional variables. Finally, we present some numerical examples to illustrate our approach, as well as discuss the current limitations and future extensions of our framework to accommodate a wider range of robotics applications.

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An adaptive observer-based SOC estimation method for lithium-ion batteries against sensor measurement uncertainties

An adaptive observer-based SOC estimation method for lithium...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Xiaoyi Xu Hao Luo Mo-Yuen Chow Cong-Sheng Huang Yuchen Jiang Department of Control Science and Engineering Harbin Institute of Technology Harbin P. R. China Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Accurate estimation of battery state of charge (SOC) is critical for efficient and safe battery applications. The measurement uncertainties of sensors, including measurement noises and sensor bias will affect the estimation accuracy inevitably. Therefore, quantifying the relationship between the sensor measurement uncertainties and the SOC estimation errors and seeking better SOC estimation methods has been a hot topic of research. In this paper, model errors and SOC estimation errors under measurement noise and sensor bias are derived. The resulting analyses can be used to assess the robustness of the SOC estimates. In addition, an adaptive observer-based SOC estimation method for lithium-ion batteries is proposed to cope with the measurement uncertainty. Simulation experiments demonstrate the effectiveness of the proposed method.

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MaxCUCL: Max−Consensus with Deterministic Convergence in Networks with Unreliable Communication Links

arXiv

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arXiv 2024年

作者： Rikos, Apostolos I. Charalambous, Themistoklis Johansson, Karl H. Department of Electrical and Computer Engineering Division of Systems Engineering Boston University Boston United States Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Division of Decision and Control Systems KTH Royal Institute of Technology also affiliated with Digital Futures StockholmSE-100 44 Sweden

In this paper, we present a novel distributed algorithm (herein called MaxCUCL) designed to guarantee that max−consensus is reached in networks characterized by unreliable communication links (i.e., links suffering from packet drops). Our proposed algorithm is the first algorithm that achieves max−consensus in a deterministic manner (i.e., nodes always calculate the maximum of their states regardless of the nature of the probability distribution of the packet drops). Furthermore, it allows nodes to determine whether convergence has been achieved (enabling them to transition to subsequent tasks). The operation of MaxCUCL relies on the deployment of narrowband error-free feedback channels used for acknowledging whether a packet transmission between nodes was successful. We analyze the operation of our algorithm and show that it converges after a finite number of time steps. Finally, we demonstrate our algorithm’s effectiveness and practical applicability by applying it to a sensor network deployed for environmental monitoring. © 2024, CC BY.

关键词： Probability distributions

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EEG Signals Classification in Time-Frequency Images by Fusing Rotation-Invariant Local Binary Pattern and Gray Level Co-occurrence Matrix Features 1st

EEG Signals Classification in Time-Frequency Images by Fus...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Hu, Zhongyi Luo, Zhenzhen Jin, Shan Li, Zuoyong College of Computer and Artificial Intelligence Intelligent Information Systems Institute Wenzhou University Wenzhou China Zhejiang Topcheer Information Technology Co. Ltd Zhejiang China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9783030930455

Automatic epilepsy diagnosis system based on EEG signals is critical in the classification of epilepsy. This disease classification through doctors’ visual observation of transient EEG signals is more art than science. This paper proposes a novel EEG signals classification method based on time-frequency images by fusing rotation-invariant local binary pattern and gray level co-occurrence matrix features. Specifically, a continuous wavelet transform is applied to perform wavelet decomposition of mutated EEG signals and obtain their time-frequency images. Then, the binary particle swarm optimization algorithm is used to eliminate redundant features in feature selection and optimize the hyperparameters of SVM. The proposed method was verified by its comparison with other available cutting-edge classification methods. The proposed EEG signals classification framework achieved a better classification effect, which will help expert clinicians and neuroscientists to make more accurate and rapid epilepsy diagnosis. © 2021, Springer Nature Switzerland AG.

关键词： Neurology

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Backflipping with Miniature Quadcopters by Gaussian Process Based control and Planning

arXiv

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arXiv 2022年

作者： Antal, Péter Péni, Tamás Tóth, Roland The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary The Vehicle Industry Research Center Széchenyi István University Hungary The Control Systems Group The Eindhoven University of Technology Netherlands

The paper proposes two control methods for performing a backflip maneuver with miniature quadcopters. First, an existing feedforward control approach is improved by finding the optimal sequence of motion primitives via Bayesian optimization, using a surrogate Gaussian Process model. To evaluate the cost function, the flip maneuver is performed repeatedly in a simulation environment. The second method is based on closed-loop control and it consists of two main steps: first a novel robust, adaptive controller is designed to provide reliable reference tracking even in case of model uncertainties. The controller is constructed by augmenting the nominal model of the drone with a Gaussian Process that is trained by using measurement data. Second, an efficient trajectory planning algorithm is proposed, which designs feasible trajectories for the flip maneuver by using only quadratic programming. The two approaches are analyzed in simulations and in real experiments using Bitcraze Crazyflie 2.1 quadcopters. © 2022, CC BY-NC-SA.

关键词： Quadratic programming

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A survey on secure decentralized optimization and learning

arXiv

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arXiv 2024年

作者： Liu, Changxin Bastianello, Nicola Huo, Wei Shi, Yang Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Mechanical Engineering University of Victoria VictoriaBC Canada

Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data. However, this paradigm introduces new privacy and security risks, with malicious agents potentially able to infer private data or impair the model accuracy. Over the past decade, significant advancements have been made in developing secure decentralized optimization and learning frameworks and algorithms. This survey provides a comprehensive tutorial on these advancements. We begin with the fundamentals of decentralized optimization and learning, highlighting centralized aggregation and distributed consensus as key modules exposed to security risks in federated and distributed optimization, respectively. Next, we focus on privacy-preserving algorithms, detailing three cryptographic tools and their integration into decentralized optimization and learning systems. Additionally, we examine resilient algorithms, exploring the design and analysis of resilient aggregation and consensus protocols that support these systems. We conclude the survey by discussing current trends and potential future directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Analyses of cell-to-cell communication combining a heterogeneous deep ensemble framework and scoring approaches from single-cell RNA sequencing data

Analyses of cell-to-cell communication combining a heterogen...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lihong Peng Ruya Yuan Chendi Han Jingwei Tan Zhao Wang Min Chen Xing Chen School of Computer Science Hunan University of Technology Zhuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Computer Science Hunan Institute of Technology Hengyang China Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Cell-to-cell communication (CCC) plays essential roles in multicellular organisms. the identification of CCC between cancer cells themselves and one between cancer cells and normal cells in tumor microenvironment contributes to the understanding of carcinogenesis, cancer development and metastasis. CCC is usually mediated by Ligand-Receptor Interactions (LRIs). In this manuscript, we developed an LRI-mediated CCC estimation framework (LRI-EnABCLG) by incorporating LRI collection, prediction and filtering, CCC inference and visualization. First, four LRI datasets were collected. Second, LRIs were predicted by a heterogeneous deep ensemble model. Third, LRIs were filtered by combining single-cell sequencing (scRNA-seq) data. Fourth, CCC was inferred by combining the filtered LRIs and scRNA-seq data. Finally, the proposed CCC prediction framework was applied to CCC analysis in colorectal tumor tissues. Our proposed LRI-EnABCLG model obtained better LRI prediction performance. Case study demonstrated that fibroblasts was more likely to communicate with colorectal cancer cells, which was in accord with the results from iTALK (a classical CCC analysis pipeline). We anticipate that this work can contribute to diagnosis and treatment of cancers.

关键词： Sequential analysis Filtering RNA Pipelines Predictive models Fibroblasts Organisms

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Fault Diagnostic Opportunities for Robot Servo Motors Using the Physics of Failure Analysis

Fault Diagnostic Opportunities for Robot Servo Motors Using ...

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International Conference on Reliability, Maintainability and Safety,ICRMS

作者： Bo Zhang Shan Liang Kai Wang Key Laboratory of Networked Control Systems Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China College of Computer Science and Technology University of Chemical Technology Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350329988

ISBN: (纸本)9798350329995

Joint robots are widely used in various industries, such as aviation, aerospace, and automotive manufacturing. The performance degradation of servo motors can significantly affect the overall performance of the robots because of the core component of joint robots, and therefore, it is critical to detect and address servo motor failures promptly. In this study, we analyze some common causes and mechanisms of performance degradation in robot servo motors using the physics of failure analysis. We also discuss strategies for detecting and diagnosing faults using these mechanisms before the servo motors fail completely.

关键词： Degradation Industries Service robots Failure analysis Safety Manufacturing Reliability Servomotors Physics Automotive engineering

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A Recurrent Neural Network Based Bicriteria Repetitive Motion Collision Avoidance Scheme for Motion Planning of Dual Redundant Manipulators 13

A Recurrent Neural Network Based Bicriteria Repetitive Motio...

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13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. The linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 IEEE.

关键词： Recurrent neural networks

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