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arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

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Distributed urban freeway traffic optimization considering congestion propagation

arXiv

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arXiv 2021年

作者： Gao, Fengkun Yang, Bo Chen, Cailian Guan, Xinping Zhang, Yang Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China Shanghai Municipal Urban-Rural Construction and Transportation Development Institute China

Traffic optimization strategies are imperative for improving the performance of transportation networks. Most traffic optimization strategies only depend on traffic states of congested road segments, where congestion propagation is neglected. Therefore, we propose a distributed traffic optimization strategy for urban freeways considering the potential congested road segments caused by congestion propagation, called potential-homogeneous-area (PHA). Utilizing the historical traffic density data, we firstly quantify the effect of congestion propagation and identify PHA by applying the proposed spatio-temporal lambda-connectedness method. Meanwhile, dynamic capacity constraints of PHA are determined and are integrated with cell transmission model (CTM) in a centralized traffic optimization problem. To reduce computational complexity and improve scalability, we then propose a doubleconsensus- based alternating direction method of multipliers algorithm (DC-ADMM) to handle the neighbor coupling constraints and global coupling constraints for solving the problem in a fully distributed way. We prove that the proposed DC-ADMM algorithm converges to the optimal solution in the condition of convex objective function. Finally, simulations based on real traffic density data, collected in Inner Ring Road, Shanghai, China, reveal the effectiveness of our proposed strategy. © 2021, CC BY.

关键词： Roads and streets

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Turbulent waves and oscillating amplitude of boundary-layers and radiative-heat transfer of nonlinear Williamson nanofluid over solar plate with periodic conditions

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Chaos, Solitons & Fractals 2025年 198卷

作者： Zia Ullah Md. Mahbub Alam Bagh Ali Ramzi Rzig Hanaa Abu-Zinadah Mohamed Boujelbene Nidhal Ben Khedher Center of Turbulence Control Harbin Institute of Technology (Shenzhen) Shenzhen 518055 China Department of Mathematics and Statistics The University of Lahore Sargodha-Campus 40100 Sargodha Pakistan School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen 518055 China Department of Software Engineering Faculty of Science & Technology ILMA University Karachi 75190 Pakistan Laboratory of Thermal and Energetic Systems Studies (LESTE) at the National School of Engineering of Monastir University of Monastir Tunisia University of Jeddah College of Science Department of Mathematics and Statistics Jeddah Saudi Arabia Industrial Engineering Department College of Engineering University of Ha'il Ha'il 81451 Saudi Arabia Department of Mechanical Engineering College of Engineering University of Ha'il Ha'il 81451 Saudi Arabia

Turbulence and oscillating amplitude of boundary layers and nonlinear radiative heat and concentration motion of transient Williamson nanomaterial model alongside inclined solar surface with entropy optimization, magnetic oscillation, dynamic solar energy, and thermo diffusion is the novelty of this work. The objective of this problem is to scrutinize the speed streamlines, isothermal lines, energy/temperature and mass/concentration fields, steady heating ratio and instability of fluctuating energy/heat transfer alongside inclined plate at angle π/4. Governing mathematical formulation is developed to find the numerical outcomes of unknown physical properties. Fluctuating Stokes transformations and dimensionless variables are developed to reduce governing equations into oscillatory and steady equations with defined boundary values. Various pertinent parameters are generated for physical analysis of heat rate such as Eckert number ( E c ), buoyancy number ( λ T ), oscillating magnetic parameter ( M f ), Schmidt number ( S c ), radiation number ( R d ), thermophoresis number ( N T ), Prandtl number ( Pr ), and Brownian motion number ( N b ). Primitive variable transformation is applied to generate symmetry in all governing equations in FORTRAN programming tool for accurate asymptotic results. Implicit formula of finite difference scheme with central and backward relation is used to convert system of equations into algebraic equations. The obtained algebraic equations are solved through Gaussian elimination technique to explore fluid flow, heat and mass rate, and periodical fluctuations in thermal and concentration gradients. It is perceived that high largeness in fluid speed and energy/temperature field is depicted as thermal radiating constraint upsurges. The magnitude of streamlines ad isothermal lines is decreased as oscillating magnetic number and Eckert number increases but isothermal lines increased with maximum strong magnetic field. High oscillations in turbule

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On PID control for synchronization of complex dynamical network with delayed nodes

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Science China(Technological Sciences) 2019年第8期62卷 1412-1422页

作者： GU HaiBo Lv JinHu LIN ZongLi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing 100083 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 USA

Over the past two decades, synchronization, as an interesting collective behavior of complex dynamical networks, has been attracting much attention. To reveal and analyze the inherent mechanism of synchronization in complex dynamical networks with time delays in nodes, this paper attempts to use PD and PI control protocols to achieve synchronization. Based on a classical network model, we investigate the PD and PI control for synchronization of complex dynamical networks with delayed nodes and obtain some sufficient conditions. By using Lyapunov functions and appropriate state transformations, we prove that global synchronization can be achieved via the above control protocols. Finally, some simulation examples are illustrated to validate the effectiveness of the proposed theoretical results.

关键词： synchronization PID control complex dynamical network networked control system delayed node

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Author Correction: ITO-free silicon-integrated perovskite electrochemical cell for light-emission and light-detection

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Opto-Electronic Advances 2024年第3期7卷 50-50页

作者： Maria Baeva Dmitry Gets Artem Polushkin Aleksandr Vorobyov Aleksandr Goltaev Vladimir Neplokh Alexey Mozharov Dmitry VKrasnikov Albert GNasibulin Ivan Mukhin Sergey Makarov Alferov University Khlopina 8/3St.Petersburg 194021Russia Department of Physics and Engineering ITMO UniversityLomonosova 9St.Petersburg 197101Russia Institute of Automation and Control Processes(IACP) Far Eastern Branch of Russian Academy of SciencesUlitsa Radio 5Vladivostok 690041Primorsky KraiRussia Peter the Great St.Petersburg Polytechnic University Polytechnicheskaya 29St.Petersburg 195251Russia Skolkovo Institute of Science and Technology Nobel 3Moscow 121205Russia Qingdao Innovation and Development Center Harbin Engineering UniversityQingdao 266000China.

Correction to:Opto-Electronic Advances https://***/10.29026/oea.2023.220154 published online 26 April 2023 After the publication of this article1,it was brought to our attention that calculations of the PeLEC device elec-troluminescent(EL)efficiency contained a mistake,leading to an inaccurate quantity *** device’s maxim-um EL efficiency constitutes not‘~120 klm/W’but‘4.3 lm/W’*** details are listed below.

关键词： value. perovskite listed

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MPC with Machine Learning Applied to Resource Allocation Problem using Lambda Architecture

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IFAC-PapersOnLine 2019年第1期52卷 550-555页

作者： Mauricio P. Dal Pont Raul S. Ferreira Wendell W. Teixeira Daniel M. Lima Julio E. Normey-Rico Automation & Systems department Federal University of Santa Catarina Florianópolis SC Brazil Research & Development department Radix Software & Engineering Rio de Janeiro RJ Brazil Research & Development department CPFL Energy Campinas SP Brazil

The resource allocation problem is the process of allocating limited resources for a vast amount of tasks. Within this problem there are several important variants such as the stochastic time-variant resource allocation problem. This problem is relevant within environments where the distribution of resources varies with time, bringing difficulties to forecasting. Related research generally address the problem by using model predictive control (MPC) techniques or machine learning (ML) algorithms. However, both can be applied together in order to improve the tasks prioritization and forecasting. Therefore, this paper proposes a solution using the concept of lambda architecture in order to tackle the time-variant and the distinct input information. First results show that the integration between MPC and ML prioritizes the resource allocation and a Markov chain model is capable of forecasting tasks, optimizing a strategic binary control. We analyze a case study of a real problem and show how the proposal was built and its advantages over the traditional method.

关键词： Predictive control machine learning Markov models stochastic control lambda architecture resource allocation binary decision systems energy distribution

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Further Results on Dominant Pole Placement via Stability Mapping Approach

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IFAC-PapersOnLine 2018年第4期51卷 918-923页

作者： Dincel, Emre Mutlu, lhan Schrödel, Frank Söylemez, Mehmet Turan Control and Automation Engineering Department Istanbul Technical University Istanbul34469 Turkey Development Center Chemnitz/Stollberg IAV GmbH Stollberg09366 Germany

Dominant roots of the closed loop characteristic equation play a crucial role in terms of the performance of Linear Time Invariant (LTI) systems. Within the scope of this study, a dominant pole placement approach which has two main phases is proposed for PI/PID type controllers. In the first phase, characteristic equation is partitioned into its dominant and non-dominant polynomial pairs and dominant poles are placed to predetermined locations. In the second phase, it is required to determine how far the non-dominant poles can be placed. In the current study, this requirement is transformed into a stability problem and Lyapunov Equation-based stability mapping approach is used. This combined approach creates a more flexible design environment compared to the currently existing approaches in literature. In order to demonstrate this flexibility, two benchmark case studies are included with different definitions of dominant pole placement problem. © 2018

关键词： Three term control systems Lyapunov functions Mapping Poles Poles and zeros Benchmark case studies Characteristic equation Dominant pole placements Flexible designs Linear time invariant system Lyapunov equation Performance limitations PID controllers

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Human Operator Modelling with Interval-valued Takagi-Sugeno Fuzzy Models

Human Operator Modelling with Interval-valued Takagi-Sugeno ...

引用

International Conference on Electrical and Electronics engineering, ELECO

作者： Anıl Erdinç Tüfekçi Tufan Kumbasar Research and Development Department TORA A.S. Istanbul Turkey Control and Automation Engineering Department Istanbul Technical University Istanbul Turkey

The dynamic model of a Human Operator (HO) inherits not only nonlinear dynamics but also a large amount of uncertainty due to the reaction time and expertize level of humans. In this study, the Interval valued Takagi-Sugeno (TS) fuzzy modelling approach is employed to efficiently capture the dynamics of HOs. For this purpose, we have conducted experimental studies in which a simple target tracking computer game is used as a platform. We have collected input-output data from individual players that have different talent levels. Then, by using symbolic data analysis, we have obtained interval valued TS fuzzy models of the HOs and evaluated their performances. It is concluded that, via the interval-valued TS fuzzy modelling approach, HOs can be modelled successfully as the predicted upper and lower bounds can successfully envelope the uncertainties and nonlinearities of HOs.

关键词： Games Uncertainty Computational modeling Prototypes Predictive models Target tracking Data models

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A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

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NATURE NEUROSCIENCE 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes Desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, Diego Ayad, Fadi Barriere, David A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, Luis Cramer, Samuel Daniele, Tolomeo Dempsey, Elaine Diao, Yujian Doelemeyer, Arno Dopfel, David Dvorakova, Lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, Lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene Lambers, Henriette Lazari, Alberto Lee, Sung-Ho Lillywhite, Amanda Liu, Yikang Liu, Yanyan Y. Lopez-Castro, Alejandra Lopez-Gil, Xavier Ma, Zilu MacNicol, Eilidh Madularu, Dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, Xianzong Meyer-Baese, Lisa Missault, Stephan Moro, Federico Naessens, Daphne M. P. Nava-Gomez, Laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, Lore M. Pereira, Mickael Perez, Pablo D. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan Del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, Daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora X. Wank, Isabel Watson, David J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, Dorothee P. Barbier, Emmanuel L. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico Biological and Biomedical Engineering McGill University Montreal QC Canada McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Department of Neurology and Neurosurgery McGill University Montreal QC Canada Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tuebingen Germany Graduate Training Centre of Neuroscience International Max

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

关键词： Databases Neural circuits

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A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

arXiv

引用

arXiv 2021年

作者： Li, Zekun Zhao, Wei Shi, Feng Qi, Lei Xie, Xingzhi Wei, Ying Ding, Zhongxiang Gao, Yang Wu, Shangjie Liu, Jun Shi, Yinghuan Shen, Dinggang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University Nanjing210046 China School of Computer Science and Artificial Intelligence Southeast University Nanjing210018 China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai201807 China School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of Department of Radiology Second Xiangya Hospital Central South University Hunan410011 China Department of Radiology Quality Control Center Changsha410011 China Department of Radiology Hangzhou First People's Hospital Zhejiang University School of Medicine Hangzhou Zhejiang China Department of Pulmonary and Critical Care Medicine Second Xiangya Hospital Central South University Changsha410011 China

How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues - weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works. Copyright © 2021, The Authors. All rights reserved.

关键词： Computerized tomography

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