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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ma, Xinghua Fang, Xinyan Zou, Mingye Luo, Gongning Wang, Wei Wang, Kuanquan Qiu, Zhaowen Gao, Xin Li, Shuo Faculty of Computing Harbin Institute of Technology Harbin China Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Faculty of Computing Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China Department of Computer and Data Science Case Western Reserve University Cleveland United States Department of Biomedical Engineering Case Western Reserve University Cleveland United States

ISBN: (纸本)157735897X

Coronary Artery Disease (CAD) poses a significant threat to cardiovascular patients worldwide, underscoring the critical importance of automated CAD diagnostic technologies in clinical practice. Previous technologies for lesion assessment in Coronary CT Angiography (CCTA) images have been insufficient in terms of interpretability, resulting in solutions that lack clinical reliability in both network architecture and prediction outcomes, even when diagnoses are accurate. To address the limitation of interpretability, we introduce the Trusted Lesion-Assessment Network (TLA-Net), which provides a clinically reliable solution for multi-view CAD diagnosis: (1) The causality-informed evidence collection constructs a causal graph for the diagnostic process and implements causal interventions, preventing confounders’ interference and enhancing the transparency of the network architecture. (2) The clinically-aligned uncertainty integration hierarchically combines Dirichlet distributions from various views based on clinical priors, offering confidence coefficients for prediction outcomes that align with physicians’ image analysis procedures. Experimental results on a dataset of 2,618 lesions demonstrate that TLA-Net, supported by its interpretable methodological design, exhibits superior performance with outstanding generalization, domain adaptability, and robustness. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Finite-time parameter self-learning disturbance rejection control of MSV: Methodology and validation

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Peng, Bo Gu, Nan Wang, Dan Peng, Zhouhua School of Marine Electrical Engineering Dalian Maritime University Dalian116026 China Institute for Ocean Engineering Shenzhen lnternational Graduate School Tsinghua University Shenzhen518055 China School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia State Key Laboratory of Maritime Technology and Safety Dalian116026 China Dalian Key Laboratory of Swarm Control and Electrical Technology for Intelligent Ships Dalian116026 China

Dear Editor, This letter presents a finite-time online parameter self-learning disturbance rejection surge speed control method for a marine surface vehicle (MSV). Specifically, a finite-time parameter self-learning robust exact differentiator based (RED-based) observer is first employed for estimating the unknown hydrodynamic parameters and the unknown control input coefficient separately, ensuring the finitetime convergence for both. The environmental disturbances and the dynamic modeling errors are also estimated simultaneously. Subsequently, a self-learning disturbance rejection control law is designed based on the estimated information. Finally, Lyapunov method is applied to prove the stability of the system. Hardware-in-the-loop experiment verifies the effectiveness of the proposed control method. © 2014 Chinese Association of Automation.

关键词： Lyapunov methods

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Autonomous navigation of a tracked unmanned ground vehicle*

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IFAC-PapersOnLine 2022年第14期55卷 120-125页

作者： Marija Seder Anđela Jurić Ana Šelek Filip Marić Marija Lovrić Ivan Petrović University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics (LAMOR) Zagreb Croatia University of Zagreb Croatian Military Academy Dr. Franjo Tuđman Zagreb Croatia

This work proposes a complete autonomous navigation system for a tracked vehicle. The system enables a complete autonomous execution of waypoint and patrolling tasks selected by the user. It also enables user-vehicle shared autonomy, switching between the user teleoperation and the vehicle autonomous operation. Our navigation system uses the model predictive control scheme based on a navigation function. We propose the navigation function which takes into account changing environments, any-shape footprint, and non-holonomic motion of the tracked vehicle. Besides the waypoint and patrolling tasks, we implemented a fail-safe scenario in case of the user-vehicle communication loss, in which the vehicle returns autonomously to the previously visited goal where the communication was stable. The efficiency of the proposed system is validated by experimental results on the Komodo tracked vehicle.

关键词： Robot navigation path planning obstacle detection avoidance motion control map building

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Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback control

arXiv

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arXiv 2024年

作者： Song, Chunxiang Liu, Yanan Dong, Daoyi Yonezawa, Hidehiro School of Engineering and Technology University of New South Wales ACT Canberra2600 Australia School of Engineering University of Newcastle CallaghanNSW2308 Australia School of Engineering Australian National University ACT Canberra2601 Australia Optical Quantum Control Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan

The stabilization of quantum states is a fundamental problem for realizing various quantum technologies. Measurement-based-feedback strategies have demonstrated powerful performance, and the construction of quantum control signals using measurement information has attracted great interest. However, the interaction between quantum systems and the environment is inevitable, especially when measurements are introduced, which leads to decoherence. To mitigate decoherence, it is desirable to stabilize quantum systems faster, thereby reducing the time of interaction with the environment. In this paper, we utilize information obtained from measurement and apply deep reinforcement learning (DRL) algorithms, without explicitly constructing specific complex measurement-control mappings, to rapidly drive random initial quantum state to the target state. The proposed DRL algorithm has the ability to speed up the convergence to a target state, which shortens the interaction between quantum systems and their environments to protect coherence. Simulations are performed on two-qubit and three-qubit systems, and the results show that our algorithm can successfully stabilize random initial quantum system to the target entangled state, with a convergence time faster than traditional methods such as Lyapunov feedback control and several DRL algorithms with different reward functions. Moreover, it exhibits robustness against imperfect measurements and delays in system evolution. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Local Neighbor Propagation on Graphs for Robust Feature Matching

SSRN

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SSRN 2023年

作者： Guo, Hanlin Xiao, Guobao Su, Lumei Zhou, Jiaxing Wang, Dahan Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control School of Electrical Engineering and Automation Xiamen University of Technology China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Information Science and Engineering Xiamen University China College of Computer and Control Engineering Minjiang University China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology China

Establishing reliable correspondences between two sets of feature points is a critical preprocessing step in many computer vision and pattern recognition tasks. In this paper, we propose a novel robust Local Neighbor Propagation on Graphs based feature matching (LNPG) method, to obtain good correspondences for feature matching. LNPG starts from a novel neighborhood graph construction strategy. The strategy leverages the spatial consistency constraint to generate a series of neighborhood sets, and employs the residual information to preserve the local neighborhood relationships of potential inliers (i.e., true matches). Subsequently, LNPG incorporates local neighbor propagation into the graph to enhance connections between data in different neighborhoods, by using the path-based similarity measurement and the adaptive graph partition. In addition, LNPG includes a novel consistency-filtering-based clustering algorithm for robust feature matching. This clustering algorithm introduces a reliable neighborhood consistency measure function and an effective cluster merging criterion for cluster detection and cluster merging during the clustering process, respectively. Overall, LNPG not only effectively distinguishes inliers from outliers, but also reliably classifies inliers into different transformation models between pairs of images. Experiments on publicly available datasets with different types of image transformations show the superiority of our LNPG in comparison with other state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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Simulation-oriented model reuse in cyber-physical systems: A method based on constrained directed graph

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International Journal of Modeling, Simulation, and Scientific computing 2022年第2期13卷 91-110页

作者： Wenzheng Liu Heming Zhang Chao Tang Shuangfei Wu Hongguang Zhu National CIMS Engineering Research Center Tsinghua University Beijing 100084P.R.China Gridsum Holding Inc.Beijing 100086P.R.China Center of Intelligent Control and Telescience Tsinghua Shenzhen International Graduate School Shenzhen 518055 P.R.China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing 100084 P.R.China

Modeling and Simulation of Cyber-Physical Systems(MSCPS)is demanding in terms of immediate response to dynamic and complex changes of ***-oriented model reuse can be used to build a whole CPS model by reusing developed models in a new sim-ulation application,which avoid repeated modeling and thus reduce the redevelopment of *** composition,one of the important methods,enables model reuse by selecting and adopting diversified integration solutions of simulation components to meet the requirements of simulation application *** this paper,a real-time model integration approach for global CPS modeling is proposed,which reuses devel-oped submodels by compositing submodel ***,a constrained directed graph of submodels for the whole system which can meet the simulation requirements is constructed by reverse *** properties,including co-simulation distance between submodel nodes,reuse benefit and simulation performance of model nodes,are *** on the properties,the model-integrated solution for the whole CPS simulation is retrieved throughout the model constrained digraph by the Genetic Algo-rithm(GA).In the experiment,the proposed method is applied to a typical model integrated computing scenario containing multiple model-integration solutions,among which the Pareto optimal solutions are *** show that the effectiveness of the model integration method proposed in this paper is verified.

关键词： CPS model reuse model composition co-simulation distance multi-objective genetic algorithm

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Complex Permeability Determination for EE Core Based on Flexible PCB Fixture and Magnetic Flux Path Length Analysis

Complex Permeability Determination for EE Core Based on Flex...

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China Power Supply Society Electromagnetic Compatibility Conference (CPEMC)

作者： Huamin Jie Zhenyu Zhao Yongqi Chang Changdong Wang Shiqi Jiang Richard Xian-Ke Gao Kye Yak See School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering National Univeristy of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China A*STAR Institute of High Performance Computing Singapore Singapore

ISBN: (数字)9798331509552

ISBN: (纸本)9798331509569

The complex permeability of magnetic cores acts as a vital factor in electromagnetic interference (EMI) filtering choke design and optimization. Many efforts have been reported for toroid cores, but fewer target those with other shapes, such as EE, EI, UU, and UI cores. Owing to their commonly used in planar EMI filters, determining the complex permeability of them is of significance. Targeting EE cores, this paper presents a flexible printed circuit board (PCB)-based fixture adapter tailored designed for performing the measurement, while the corresponding core permeability can be calculated through magnetic flux path length analysis. The experiments validate the effectiveness and accuracy of the proposed methods, spanning a wide frequency range up to 120 MHz.

关键词： Magnetic flux Accuracy Flexible printed circuits Magnetic cores Fixtures Electromagnetic interference Permeability measurement Length measurement Permeability Inductors

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Autonomous On-ramp Merge Strategy Using Deep Reinforcement Learning in Uncertain Highway Environment

Autonomous On-ramp Merge Strategy Using Deep Reinforcement L...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Wu, Sifan Tian, Daxin Zhou, Jianshan Duan, Xuting Sheng, Zhengguo Zhao, Dezong Beijing Key Laboratory For Cooperative Vehicle Infrastructure Systems&Safety Cooperative Control Beijing China Beijing Advanced Innovation Center For Big Data and Brain Computing Beijing China Department of Engineering and Design Richmond United Kingdom University of Sussex Richmond United Kingdom James Watt School of Engineering Glasgow United Kingdom University of Glasgow Glasgow United Kingdom

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

On-ramp merge is a complex traffic scenario in autonomous driving. Because of the uncertainty of the driving environment, most rule-based models cannot solve such a problem. In this study, we design a Deep Reinforcement Learning (DRL) method to solve the issue of ramp merges in uncertain scenarios and modify the structure of the Twin Delayed Deep Deterministic policy gradient algorithm (TD3), using Long Short-Term Memory (LSTM) to select an action based on temporal information. The proposed method is applied in the on-ramp merge and verified in the Simulation of Urban Mobility (SUMO). Results show that the proposed method performs significantly better generalization in uncertain traffic scenarios. © 2022 IEEE.

关键词： Long short-term memory

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A Comparative Study of Three Meta-Heuristic MPPT Techniques for PV Systems Under Complex Partial Shading Conditions

A Comparative Study of Three Meta-Heuristic MPPT Techniques ...

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Australasian Universities Power engineering Conference, AUPEC

作者： Abdulbari Talib Naser Karam Khairullah Mohammed Nur Fadilah Ab Aziz Karmila Binti Kamil Saad Mekhilef Dept. of Electrical and Electronic Engineering University Tenaga Nasional Selangor Malaysia Dept. Control and Mechatronics Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Institute of Power Engineering (IPE) University Tenaga Nasional Selangor Malaysia School of Science Computing and Engineering Technologies University of Technology Melbourne VIC Australia

The energy generation performance of solar panels can be greatly influenced by partial shading conditions (PSCs). The phenomenon is ascribed to the presence of several maximum power points (MPPs) evident on the characteristic curves of power-voltage (P-V). These MPPs encompass diverse local peaks (LPs) along with a solitary global peak (GP). Traditional (MPPT) techniques fall short in precisely tracing the GP owing to the complex and nonlinear features of PV characteristic curves during instances of PSC. Hence, the adoption of MPPT techniques based on artificial intelligence (AI) becomes essential to effectively track the GP. In this research endeavor, three metaheuristic approaches for MPPT are investigated, namely the Team Game Algorithm (TGA), Gray Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). These algorithms are practically implemented, and their efficacy is experimentally validated while considering different solar irradiation levels while keeping the temperature constant. The experimental evaluation aims to compare the dynamic behavior of the photovoltaic (PV) array under various weather conditions. In contrast to other approaches, the TGA technique offers outstanding convergence capabilities, as shown by the experimental tests carried out in this study.

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Classification of Attention Performance Post-Longitudinal tDCS via Functional Connectivity and Machine Learning Methods

Classification of Attention Performance Post-Longitudinal tD...

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International Conference for Convergence of Technology (I2CT)

作者： Akash K Rao Vishnu K Menon Arnav Bhavsar Shubhajit Roy Chowdhury Ramsingh Negi Varun Dutt Applied Cognitive Science Laboratory Indian Institute of Technology Mandi Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India Cognitive Control and Machine Learning Group Institute of Nuclear Medicine and Allied Sciences Delhi India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following long-term interventions (such as transcranial direct current stimulation (tDCS)) has seldom been emphasized in the literature. To classify attention performance post-tDCS, this study uses functional connectivity and machine learning algorithms. Fifty individuals were split into experimental and control conditions. On Day 1, EEG data was obtained as subjects executed an attention task. From Day 2 through Day 8, the experimental group was administered 1mA tDCS, while the control group received sham tDCS. On Day 10, subjects repeated the task mentioned on Day 1. Functional connectivity metrics were used to classify attention performance using various machine learning methods. Results revealed that combining the Adaboost model and recursive feature elimination yielded a classification accuracy of 91.84%. We discuss the implications of our results in developing neurofeedback frameworks to assess attention.

关键词： Measurement Machine learning algorithms Filtering Machine learning Brain modeling Electroencephalography Task analysis

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