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检索条件"任意字段=25th International Conference on Methods and Models in Automation and Robotics"

共 1225 条记录，以下是361-370 订阅

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis

Personalized Federated Learning with Collaborative Aggregati...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Qian Si Yang Li School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Advanced Institute of Information Technology Peking University Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). this framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. the shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. this approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques.

关键词： Training Adaptation models Data centers Federated learning Service robots Collaboration Brain modeling Data models Servers Medical diagnostic imaging

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Enhancing Photovoltaic Array Performance under Partial Shading through Dynamic Reconfiguration and Layer Equalization Algorithm

Enhancing Photovoltaic Array Performance under Partial Shadi...

引用

international Carpathian Control conference (ICCC)

作者： Fatimah Ameen Attila Trohák Abdulrahman Siddiq Rabab Benotsmane Institute of Automation and Info-Communication University of Miskolc Miskolc Hungary Dept. of Electronic and Control Eng. Technical College Northern Technical University Kirkuk Iraq

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Partial shading (PS) significantly impacts the performance of photovoltaic (PV) systems, leading to reduced efficiency. Traditional mitigation methods involve bypass diodes or PV module reconfiguration. Accordingly, the highest and lowest layer-based exchange (HLLBE) algorithm was proposed. the HLLBE system employs a Switching Block Array (SBA) for flexible reconfiguration without rewiring. By sorting and swapping irradiance readings, the HLLBE algorithm achieves uniform current distribution across PV array layers. Unlike static configurations, HLLBE dynamically adapts to shade patterns, offering an efficient solution. Simulations on a 3 × 3 PV array demonstrated HLLBE's effectiveness in enhancing output power and reducing multiple peaks compared to conventional total-cross-tied (TCT) configurations. Additionally, HLLBE minimizes operating switches through a switching matrix during dynamic reconfiguration (DR).

关键词： Photovoltaic systems Adaptation models Heuristic algorithms Switches Current distribution Control systems Solar system

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Robust Object Recognition in Open Environments Based on Causal Inference

Robust Object Recognition in Open Environments Based on Caus...

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robotics, Control and automation Engineering (RCAE), international conference on

作者： Ruonan Liu Shuai Wu Ping Kong Wei Xie Weidong Zhang Department of Automation Shanghai Jiao Tong University Shanghai China College of Intelligence and Computing Tianjin University Tianjin China School of Information and Communication Engineering Hainan University Hainan China Department of Automation Shanghai Jiaotong University Shanghai China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

the rapid development of artificial intelligence technology has drawn widespread attention to the problem of target recognition in open environments. Traditional target recognition methods are easily affected by environmental changes and interference noise, leading to insufficient recognition accuracy and robustness. this paper proposes a robust target recognition method based on causal inference, aiming to improve the robust performance of image recognition models in open environments. the method first extracts features from images, analyzes the impact of noise features such as the environment in image features, and derives the essential feature properties of the target. then, the causal relationship is introduced into the target recognition, and by analyzing the relationship between the environment, the target, and the observation data, the features of the target are combined with the observation data to achieve accurate and stable recognition of the target. the proposed method has achieved good target recognition effects on multiple open datasets, and the experimental results show that the algorithm can effectively solve the problem of poor robustness of the target recognition model in open environments, providing strong support for practical applications. through the application of this method, the stability and robustness of the target recognition model can be significantly improved, which has important practical application value.

关键词： Accuracy Image recognition automation Target recognition Noise Interference Feature extraction Robustness Stability analysis Robots

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Linear Parameter Varying Model Predictive-Based Controller for Trajectory Tracking of High-Fidelity Autonomous Vehicle

Linear Parameter Varying Model Predictive-Based Controller f...

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automation, Control and robotics Engineering (CACRE), international conference on

作者： Kanani Kulwa-Juma Kanani B K Tiwari Tae-wan Kim Department of Naval Architecture and Ocean Engineering Seoul National University Seoul South Korea Department of Advance Computer Science and Engineering Vignan University Guntur India

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

the tracking control of an unmanned autonomous vehicle (UA V) is one of the key factors determining the performance and effectiveness of autonomous navigation. A controller based on Linear Parameter Varying - Model Predictive Control (LPV - MPC) is designed to track the desired trajectory of the UAV. the Linear Parameter Varying (LPV) modeling and control strategy allows the representation of the UAV nonlinear complex model in a linear-like manner which can be used in a linear Model Predictive Control (MPC). the main advantage of the LPV -MPC technique is its ability to improve tracking solutions very close to nonlinear MPC but with significantly low computational costs, enabling LPV-MPC to be used for real-time operations. the simulation results of this paper show that the designed LPV-MPC performs well in tracking the desired trajectory through each waypoint. Furthermore, experiments with different numerical integration methods have been investigated, especially Euler's method, and the Runge-Kutta method. the results showed that the Runge-Kutta method performed better with smaller horizon periods, with minimum tracking errors and shorter elapsed time.

关键词： Trajectory tracking Simulation Urban areas Predictive models Autonomous aerial vehicles Real-time systems Numerical models

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Human-Led Robotic Transportation of Elastic Objects with Adaptive Control

Human-Led Robotic Transportation of Elastic Objects with Ada...

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international conference on robotics and automation Engineering (ICRAE)

作者： Zhenxi Cui Jiewen Lai Bo Lu Yi Guo Henry K. Chu School of Computing and Engineering University of West London London United Kingdom Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China School of Mechanical and Electrical Engineering Soochow University Suzhou China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken New Jersey United States Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798331518301

ISBN: (纸本)9798331518318

the perception and transportation of soft objects are critical tasks in daily life, and recent sensor advancements enable robots to safely perform these tasks in human-shared environments. this study advances elastic linear object transportation by introducing a collaborative framework where a human leads, providing task-specific instructions to guide the robot. Unlike conventional methods focused on fixed object manipulation, our approach dynamically controls and converges feature points of the elastic object into desired configurations during transportation. We developed an online model estimation technique using a least-squares optimization algorithm, with an exponential forgetting mechanism to adaptively update the model under disturbances from sensors and human interaction. Validated in experiments with a 6-DOF robot and depth camera, our method demonstrated robustness across varied scenarios, achieving faster convergence and reduced positional fluctuation compared to conventional gradient descent approaches.

关键词： Deformable models Adaptation models Shape Robot kinematics Transportation Estimation Robot sensing systems Stability analysis Robots thermal stability

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Constrained Reachability Problems for a Planar Manipulator 1

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17th international conference on Informatics in Control, automation and robotics, ICINCO 2020

作者： Cacace, Simone Lai, Anna Chiara Loreti, Paola Dipartimento di Matematica e Fisica Università degli studi Roma Tre Rome Italy Dipartimento di Scienze di Base e Applicate per l’Ingegneria Sapienza Università di Roma Rome Italy

ISBN: (数字)9783030924423

ISBN: (纸本)9783030924416

We address an optimal reachability problem for a planar manipulator in a constrained environment. After introducing the optmization problem in full generality, we practically embed the geometry of the workspace in the problem, by considering some classes of obstacles. To this end, we present an analytical approximation of the distance function from the ellipse. We then apply our method to particular models of hyper-redundant and soft manipulators, by also presenting some numerical experiments. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Numerical methods

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Comparative Analysis of Listwise Reranking with Large Language models in Limited-Resource Language Contexts

Comparative Analysis of Listwise Reranking with Large Langua...

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Artificial Intelligence, robotics, and Communication (ICAIRC), IEEE international conference on

作者： Yanxin Shen Lun Wang Chuanqi Shi Shaoshuai Du Yiyi Tao Yixian Shen Hang Zhang Simon Fraser University Canada Duke University North Carolina USA University of California San Diego California USA University of Amsterdam Amsterdam Netherlands Johns Hopkins University Maryland USA

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Large Language models (LLMs) have demonstrated significant effectiveness across various NLP tasks, including text ranking. this study assesses the performance of large language models (LLMs) in listwise reranking for limited-resource African languages. We compare proprietary models RankGPT3.5, Rank4o-mini, RankGPTo1-mini and RankClaude-sonnet in cross-lingual contexts. Results indicate that these LLMs significantly outperform traditional baseline methods such as BM25-DT in most evaluation metrics, particularly in nDCG@10 and MRR@100. these findings highlight the potential of LLMs in enhancing reranking tasks for low-resource languages and offer insights into cost-effective solutions.

关键词： Measurement Translation Costs Large language models Robots Context modeling

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the Comprehensive Evaluation Method for the Visibility Ability of ATCT Based on BP Network

The Comprehensive Evaluation Method for The Visibility Abili...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Bo Wang Xiaofei Peng Jiaquan Ye Gang Su Chenyu Liu The Second Research Institute of Civil Aviation Administration of China Chengdu China Civil Aviation Flight University of China Guanghan China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Airport Traffic Control Tower (ATCT) is one of the most important facilities at civil aviation airports. the location and height of ATCT will directly affect the controller's observation and judgment. In order to evaluate and compare the visibility capability of different tower construction schemes, a comprehensive evaluation method is set up. this method includes building an index system of obligatory and preference indicators, forming calculation methods for each indicator, and establishing a comprehensive evaluation model. In the evaluation model, two networks have been constructed based on Coefficient Strategy (CS) and Level Strategy (LS) respectively. Selecting a certain airport in East China as a case study, CS-BP and LS-BP network models of 5-10-1 in topology have been set up after 12000 typical samples for training respectively. the research results show that LS-BP network has the smaller Mean Squared Error (MSE) and greater regression coefficient compared to CS-BP network. And the results of CS-BP were similar to Traditional Strategy (TS). therefore, selecting LS-BP network to evaluate calculated indicators of given schemes and considering that scheme 3 was best. the entire method can serve as a reference for decision-making by air traffic control unit.

关键词： Training Service robots Network topology Poles and towers Evaluation models Lighting Traffic control Airports Topology Indexes

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Atr loitmBaeo Deluy Trangulation and Its Improvement

Atr loitmBaeo Deluy Trangulation and Its Improvement

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IEEE international conference on Communication and Information Systems (ICCIS)

作者： Wenyu Li Senchun Chai School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9798331531171

ISBN: (纸本)9798331531188

Path planning is an important part of robotics research. A * algorithm and RRT algorithm are very common algorithms in path planning. Each of these algorithms has its own advantages, but there is still room for improvement in planning time, path cost, and adaptability to different environment. In this paper, we propose a map processing method based on Delaunay triangulation, while applying the strategy of obstacle expansion and compensated sampling, and the A * algorithm is used on this basis. Finally, we have improved the path given by the A * algorithm with straight-first strategy. Simulations show that the A * algorithm based on Delaunay triangulation can plan paths efficiently in different environments.

关键词： Adaptation models Costs Simulation Optimization methods Path planning Planning Robots Information systems

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

ISBN: (纸本)9783031829598

the proceedings contain 104 papers. the special focus in this conference is on Computer Aided Systems theory. the topics include: Influence of Spike Encoding, Neuron models and Quantization on SNN Performance;adaptive Combination in Frequency Domain: An Approach for Robust Nonlinear Acoustic Echo Cancellation;using of a Robotic Platform to Detect Acoustic Events for Indoor Environments;modeling Wildlife Accident Risk with Gaussian Mixture models;towards a Unified Incident Detection and Response System for Autonomous Transportation;edge-Processing of Myoelectric Signals for the Control of Hand and Arm-Prostheses;AI-Driven Gesture and Action Recognition for Learning Medicine through Virtual Reality;Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices;a Survey of Machine Learning methods for Analyzing Synovitis Arthritis in Human Joints;motion Tracking in Augmented and Mixed Realities for Healthcare and Medicine Applications;advancements and Applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, therapy, and Rehabilitation;Transformation of IEC 61131-3 onto an Embedded Platform Using LLVM;machine Learning Based Parameter Estimation of Energy models in Digital Production Environments;efficient Classification of Live Sensor Data on Low-Energy IoT Devices with Simple Machine Learning methods;machine Learning Using a Hybrid Quantum Classical Algorithm with Amplitude Data Encoding;quantitative Trend Analysis of Reinforcement Learning Algorithms in Production Systems;Using automationML for Advanced Simulation in Industrial automation;AR Digital Twin Demonstrator for Industrial robotics Education;Accelerating Manual Pick-and-Place Operations with AR-Projected CAD Plans and AI-Assisted Object Recognition;variety Engineering – A Cybernetic Concept with Practical Implications;using a System Archetype to Explore a Business Model for Digital Textile Microfactories;interacting with the Water Cycle -

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