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arXiv 2021年

作者： Li, Yixin Wu, Xinran Li, Chen Sun, Changhao Rahaman, Md Chen, Haoyuan Yao, Yudong Li, Xiaoyan Zhang, Yong Jiang, Tao Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States China Medical University Liaoning Cancer Hospital and Institute Shenyang China Control Engineering College Chengdu University of Information Technology Chengdu China

In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module. In the AM module, an HCRF model is built to extract attention regions. In the IC module, a Convolutional Neural Network (CNN) model is trained with the attention regions selected, and then an algorithm called Classification Probability-based Ensemble Learning is applied to obtain the image-level results from the patch-level output of the CNN. In the experiment, a classification specificity of 96:67% is achieved on a gastric histopathology dataset with 700 images. Our HCRF-AM model demonstrates high classification performance and shows its effectiveness and future potential in the GHIC field. Copyright © 2021, The Authors. All rights reserved.

关键词： Image classification

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Gradient Eigen-decomposition Invariance Biogeography-based Optimization for Mobile Robot Path Planning

Research Square

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Research Square 2021年

作者： Wang, Jiaqian Na, Xiaodong Han, Min Li, Deicai Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Professional Technology Innovation Center of Distributed Control for Industrial Equipment of Liaoning Province Dalian University of Technology Dalian116024 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

The path planning for mobile robots has attracted extensive attention, and evolutionary algorithms have been applied to this problem increasingly. In this paper, we propose a novel gradient eigen-decomposition invariance biogeography-based optimization (GEI-BBO) for mobile robot path planning, which has the merits of high rotation invariance and excellent search performance. In GEI-BBO, we design an eigen-decomposition mechanism for migration operation, which can reduce the dependence of biogeography-based optimization (BBO) on the coordinate system, improve the rotation invariance and share the information between eigen solutions more effectively. Meanwhile, to find the local optimal solution better, gradient descent is added, and the system search strategy can reduce the occurrence of local trapping phenomenon. In addition, combining the GEI-BBO with cubic spline interpolation will solve the problem of mobile robot path planning through a defined coding method and fitness function. A series of experiments are implemented on benchmark functions, whose results indicated that the optimization performance of GEI-BBO is superior to other algorithms. And the successful application of GEI-BBO for path planning in different environments confirms its effectiveness and practicability. © 2021, CC BY.

关键词： Mobile robots

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Disturbance Attenuation via Nonlinear Feedback for Chemical Reaction Networks

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IFAC-PapersOnLine 2020年第2期53卷 11497-11502页

作者： Lőrinc Márton Katalin M. Hangos Gábor Szederkényi Department of Electrical Engineering Sapientia Hungarian University of Transylvania Tirgu Mures Romania Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary Faculty of Information Technology and Bionics Pázmány Péter Catholic University Budapest Hungary Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary

This paper deals with the setpoint control of Chemical Reaction Networks (CRNs) in the presence of disturbances. The addressed control problem is traced back to the output feedback-based stabilization of passive systems. First, the passivity properties of complex balanced CRNs are discussed. Second, it is shown that, by combining the kinetic feedback based control with the passivity theory-based output feedback approach, arbitrary disturbance attenuation in controlled CRNs can be achieved. A case study is provided to demonstrate the applicability of the proposed control design approach.

关键词： Chemical Reaction Networks Passivity Setpoint control Disturbance Attenuation

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Estimating Red Noise Spectrum of Time Series Using Bayesian Inference

Estimating Red Noise Spectrum of Time Series Using Bayesian ...

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Chinese control Conference (CCC)

作者： Lan YU Yao MENG Song FENG Department of mechanical and electrical engineering Yunnan Land and Resources Vocational College Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Spectral analysis of a time series is to reveal the frequency components in the series and estimate the noise level for characterizing the spectral densities. The frequency components and spectral densities contain abundant and complex physical information. However, the background spectrum of the time series in many research fields, such as climatology, geography, and astronomy, is usually dominated by red noises, rather than random white noise. A common practice estimating red noise is to calculate the periodogram and then to fit the red noise spectrum. Markov Chain Monte Carlo (MCMC) sampling is usually employed to infer the significance of the peaks on top of an underlying red noise continuum in a power spectral density (PSD). In the study, we proposed a novel method based on Hamilton Monte Carlo (HMC) to estimate and assess the significance for discriminating them whether physical processes or noise. We first used synthetic signals dominated by red noise to illustrate the process estimating the red noise spectrum. The root-mean-square error of parameter estimation is only 1.08, indicating that the HMC method has a more high fitting accuracy to parameter estimation. We further checked the reliability of the method using climatology and astronomy data. The results prove that the proposed method estimating red noise with MCMC is effective and credible.

关键词： Bayes methods Time series analysis Ocean temperature Parameter estimation Market research White noise Time-frequency analysis

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Experimental Prototype of High-Efficiency Wind Turbine Based on Magnus Effect

Experimental Prototype of High-Efficiency Wind Turbine Based...

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International Workshop on Electric Drives: Optimization in control of Electric Drives (IWED)

作者： Aleksandr Lukin Galina L. Demidova Dmitry V. Lukichev Anton Rassõlkin Ants Kallaste Toomas Vaimann Anouar Belahcen Faculty of Control System and Robotics ITMO University Saint Petersburg Russia Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallin Estonia Department of Electrical Engineering and Automation Aalto University Helsinki Finland

ISBN: (数字)9781728141589

ISBN: (纸本)9781728141596

The main feature of Magnus wind turbine is the ability to produce energy at low wind speed. This benefit lets to use such types of turbines in smart homes, which are an important part of the development of distributed energy systems, growth of blockchain electricity technology and needs for duplication of the solar energy system in the private buildings. This paper presents topology of two cylinders Magnus wind turbine in order to ensure the highest values of power coefficient and the Magnus force. The optimal ratio of wind speed and cylinder rotation speed were detected to eliminate the oscillations caused by the Von Karman vortex street phenomenon.

关键词： Wind turbines Force Drag Rotors Mathematical model Wind speed Generators

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Magnus Wind Turbine: Finite Element Analysis and control System

Magnus Wind Turbine: Finite Element Analysis and Control Sys...

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International Symposium on Power Electronics, electrical Drives, automation and Motion, SPEEDAM

作者： Galina L. Demidova Alecksey Anuchin Aleksandr Lukin Dmitry Lukichev Anton Rassõlkin Anouar Belahcen Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Department of Electrical Engineering and Automation Aalto University Helsinki Finland

ISBN: (数字)9781728170190

ISBN: (纸本)9781728170206

Due to its special features, the Magnus effect wind turbine allows you to produce energy at a very low speed of the wind. This fact, as well as the growing interest in the of blockchain technology, makes it possible to use this type of wind turbine in private household as part of a distributed energy system in addition to solar panels. The article describes a two blades Magnus wind turbine whose cylinders rotate by means of embedded motors. To avoid a von Karman vortex street effect that occur when the cylinders rotate in the air flow, as well as to ensure maximum power, the optimal ratio between the wind speed and the cylinder rotation speed is determined using finite element analysis. Further, the obtained relations are used in the algorithms of the wind turbine control system.

关键词： Wind turbines Force Wind speed Generators Mathematical model Torque control systems

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H2 model reduction for diffusively coupled second-order networks by convex-optimization

arXiv

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arXiv 2021年

作者： Yu, Lanlin Cheng, Xiaodong Scherpen, Jacquelien M.A. Xiong, Junlin School of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China Westlake Institute for Advanced Study Westlake University Hangzhou310024 China Department of Engineering University of Cambridge Trumpington Street CambridgeCB2 1PZ United Kingdom Jan C. Willems Center for Systems and Control Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands Department of Automation University of Science and Technology of China Hefei230026 China

This paper provides an H2 optimal scheme for reducing diffusively coupled second-order systems evolving over undirected networks. The aim is to find a reduced-order model that not only approximates the input-output mapping of the original system but also preserves crucial structures, such as the second-order form, asymptotically stability, and diffusive couplings. To this end, an H2 optimal approach based on a convex relaxation is used to reduce the dimension, yielding a lower order asymptotically stable approximation of the original second-order network system. Then, a novel graph reconstruction approach is employed to convert the obtained model to a reduced system that is interpretable as an undirected diffusively coupled network. Finally, the effectiveness of the proposed method is illustrated via a large-scale networked mass-spring-damper system. Copyright © 2021, The Authors. All rights reserved.

关键词： Convex optimization

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Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning

TechRxiv

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TechRxiv 2022年

作者： Chen, Hongtian Liu, Zhigang Alippi, Cesare Huang, Biao Liu, Derong The Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 1H9 Canada Institute of Rail Transit Tongji University Shanghai201804 China School of Electrical Engineering Southwest Jiaotong University Chengdu611756 China The Faculty of Informatics Università della Svizzera italiana Lugano69000 Switzerland The Department of Electronics Information and Bioengineering Politecnico di Milano Milan20133 Italy The School of Automation Guangdong University of Technology Guangzhou510006 China

The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More in detail, we parameterize nonlinear systems through a generalized kernel representation used for system modeling and the associated fault diagnosis. An important result obtained is a unified form of kernel representations, applicable to both unsupervised and supervised learning. More importantly, through a rigorous theoretical analysis we discover the existence of a bridge (i.e., a bijective mapping) between some supervised and unsupervised learning-based entities. Notably, the designed IFD approaches achieve the same performance by the use of this bridge. In order to have a better understanding of the results obtained, unsupervised and supervised neural networks are chosen as the learning tools to identify generalized kernel representations and design the IFD schemes;an invertible neural network is then employed to build the bridge between them. This study is a perspective article, whose contribution lies in proposing and detailing the fundamental concepts for explainable intelligent learning methods, contributing to system modeling and data-driven IFD designs for nonlinear dynamic systems. © 2022, CC BY.

关键词： Failure analysis

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A Survey for Large Language Models in Biomedicine

arXiv

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arXiv 2024年

作者： Wang, Chong Li, Mengyao He, Junjun Wang, Zhongruo Darzi, Erfan Chen, Zan Ye, Jin Li, Tianbin Su, Yanzhou Ke, Jing Qu, Kaili Li, Shuxin Yu, Yi Liò, Pietro Wang, Tianyun Wang, Yu Guang Shen, Yiqing School of Medical Engineering Xinxiang Medical University Xinxiang China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Shanghai AI Laboratory Shanghai China Amazon Palo AltoCA United States Boston Children's Hospital MA United States Harvard Medical School Harvard University MA United States Toursun Synbio Shanghai China Department of Data Science & AI Faculty of IT Monash University Melbourne Australia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom School of Basic Medical Sciences Xinxiang Medical University Xinxiang China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Department of Computer Science Johns Hopkins University MD United States

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of Science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their respon

关键词： Federated learning

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Arbitrary state transition of open qubit system based on switching control

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Automatica 2025年 179卷

作者： Guangpu Wu Shibei Xue Shan Ma Sen Kuang Daoyi Dong Ian R. Petersen Department of Automation Shanghai Jiao Tong University Shanghai 200240 PR China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 PR China School of Automation Central South University Changsha 410083 PR China Department of Automation University of Science and Technology of China Hefei 230027 PR China Australian Artificial Intelligence Institute Faculty of Engineering and IT University of Technology Sydney Ultimo 2007 NSW Australia School of Engineering Australian National University Canberra ACT 2601 Australia

We present a switching control strategy based on Lyapunov control for arbitrary state transitions in open qubit systems. This strategy can prevent the state from entering invariant or singularity sets, effectively driving the system ultimately to a sufficiently small neighborhood of target states, which relaxes strict constraints on system models imposed by special target states compared to existing works. For the arbitrary transition, practical finite-time stability (PFTS) and its contractive version (PFTCS) conditions are given, respectively, where asymptotic stability is unattainable in open quantum systems in general. The efficacy of our method is demonstrated in examples of a qubit affected by amplitude damping, dephasing, and depolarizing decoherence.

关键词： Asymptotic stability

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