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IEEE International Conference on Robotics and automation (ICRA)

作者： Jing Bi Jiahui Zhai Haitao Yuan Ziqi Wang Junfei Qiao Jia Zhang MengChu Zhou Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Computer Science Lyle School of Engineering at Southern Methodist University Dallas TX USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

High-dimensional expensive problems are often encountered in the design and optimization of complex robotic and automated systems and distributed computing systems, and they suffer from a time-consuming fitness evaluation process. It is extremely challenging and difficult to produce promising solutions in a high-dimensional search space. This work proposes an evolutionary optimization framework with embedded autoencoders that effectively solve optimization problems with high-dimensional search space. Autoencoders provide strong dimension reduction and feature extraction abilities that compress a high-dimensional space to an informative low-dimensional one. Search operations are performed in a low-dimensional space, thereby guiding whole population to converge to the optimal solution more efficiently. Multiple subpopulations coevolve iteratively in a distributed manner. One subpopulation is embedded by an autoencoder, and the other one is guided by a newly proposed Multi-swarm Gray-wolf-optimizer based on Genetic-learning (MGG). Thus, the proposed multi-swarm framework is named Autoencoder-based MGG (AMGG). AMGG consists of three proposed strategies that balance exploration and exploitation abilities, i.e., a dynamic subgroup number strategy for reducing the number of subpopulations, a subpopulation reorganization strategy for sharing useful information about each subpopulation, and a purposeful detection strategy for escaping from local optima and improving exploration ability. AMGG is compared with several widely used algorithms by solving benchmark problems and a real-life optimization one. The results well verify that AMGG outperforms its peers in terms of search accuracy and convergence efficiency.

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Time Delay Estimation with Fixed-time SMC Scheme for Nonlinear Dynamics

Time Delay Estimation with Fixed-time SMC Scheme for Nonline...

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2024 International Conference on Control, automation and Diagnosis, ICCAD 2024

作者： Ahmed, Saim Azar, Ahmad Taher Fekik, Arezki Wang, Haoping Njima, Chakib Ben Prince Sultan University College of Computer and Information Sciences Riyadh Saudi Arabia Riyadh11586 Saudi Arabia Benha University Faculty of Computers and Artificial Intelligence Benha Egypt University Akli Mohand Oulhadj-Bouria Department of Electrical Engineering Rue Drissi Yahia Bouira 10000 Algeria Nantes Université École Centrale Nantes Cnrs LS2N Umr 6004 NantesF-44000 France Nanjing University of Science and Technology School of Automation China University of Sousse Tunisia

ISBN: (纸本)9798350361025

Time delay estimation using fixed-time terminal sliding mode control (TDEFxSMC) for unknown nonlinear systems with uncertainty and external disturbances is presented in this study. Firstly, the notion of fixed-time terminal sliding mode control (FxSMC) is outlined and thoroughly examined. Using the FxSMC scheme, nonsingular fixed-time control, non-chatter control inputs, and high performance tracking positions have been achieved. To estimate the unknown dynamics, proposed method incorporates a TDE scheme. The Lyapunov theorem is applied to find whether the proposed system can achieve its stability in a fixed-time. The effectiveness of the suggested method is evaluated and demonstrated by presenting the results of the proposed method implemented to the nonlinear robot dynamics. © 2024 IEEE.

关键词： Dynamical systems

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Comprehensive electrical models for a wireless sensor network device

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Heliyon 2024年第23期10卷 e40415页

作者： León Ávila, Bernardo Yaser García Vázquez, Carlos Alberto Pérez Baluja, Osmel Cotfas, Daniel Tudor Cotfas, Petru Adrian Transilvania University of Brașov Department of Electronics and Computers Faculty of Electrical Engineering and Computer Science Brasov 500036 Romania University of Sancti Spiritus “José Martí Pérez” Department of Computer Engineering Faculty of Business and Technical Sciences Comandante Manuel Fajardo s/n Olivos 1 Sancti Spíritus 60100 Cuba Technological University of Havana Department of Automation and Computing Faculty of Automation and Biomedical Engineering Street 114 No. 11901 between Ciclovía and Rotonda Havana 19390 Cuba

This paper proposes a novel circuit model of a Wireless Sensor Network (WSN) device. The model is designed to accurately represent the behavior of a WSN device operating in a duty cycle, capturing the essential characteristics of its components, including the microcontroller, sensor, transceiver, and power supply. The proposed model incorporates static and dynamic power consumption aspects, reflecting the energy usage patterns during various operational states such as sensing, processing, and communication. The model's accuracy is validated through a comparative analysis with a LTspice® implementation and a real mote implemented for this study. Simulation results closely match the empirical data, demonstrating the model's effectiveness in predicting the mote's behavior. This approach provides a valuable tool for optimizing power consumption and extending the lifetime of WSN deployments within a simulation environment, offering significant insights for designers and researchers in this field. © 2024 The Authors

关键词： Electric model SPICE model Wireless sensor network node

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An asymptotic formula for Aldaz-Kounchev-Render operators on the hypercube

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arXiv 2023年

作者： Acu, Ana-Maria Raşa, Ioan Lucian Blaga University of Sibiu Department of Mathematics and Informatics Romania Technical University of Cluj-Napoca Faculty of Automation and Computer Science Department of Mathematics Str. Memorandumului nr. 28 Cluj-Napoca400114 Romania

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Searching for better spatio-temporal alignment in few-shot action recognition 22

Searching for better spatio-temporal alignment in few-shot a...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Yichao Cao Xiu Su Qingfei Tang Shan You Xiaobo Lu Chang Xu School of Automation Southeast University School of Computer Science Faculty of Engineering The University of Sydney Enbo Technology Co. Ltd. China SenseTime Research

ISBN: (纸本)9781713871088

Spatio-Temporal feature matching and alignment are essential for few-shot action recognition as they determine the coherence and effectiveness of the temporal patterns. Nevertheless, this process could be not reliable, especially when dealing with complex video scenarios. In this paper, we propose to improve the performance of matching and alignment from the end-to-end design of models. Our solution comes at two-folds. First, we encourage to enhance the extracted Spatio-Temporal representations from few-shot videos in the perspective of architectures. With this aim, we propose a specialized transformer search method for videos, thus the spatial and temporal attention can be well-organized and optimized for stronger feature representations. Second, we also design an efficient non-parametric spatio-temporal prototype alignment strategy to better handle the high variability of motion. In particular, a query-specific class prototype will be generated for each query sample and category, which can better match query sequences against all support sequences. By doing so, our method SST enjoys significant superiority over the benchmark UCF101 and HMDB51 datasets. For example, with no pretraining, our method achieves 17.1% Top-1 accuracy improvement than the baseline TRX on UCF101 5-way 1-shot setting but with only 3x fewer FLOPs.

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Modelling and automation of a process for obtaining coagulum in treatment of industrial 15

Modelling and automation of a process for obtaining coagulum...

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15th Global Congress on Manufacturing and Management, GCMM 2021

作者： Safonyk, A. Tarhonii, I. Burduk, A. Batako, A. National University of Water and Environmental Engineering Department of the Automation Electrotechnical and Computer-Integrated Technologies Soborna str. Rivne33028 Ukraine Wroclaw University of Science and Technology Faculty of Mechanical Engineering Wybrzeze Wyspianskiego 27 Wroclaw50-370 Poland Faculty of Engineering and Technology Liverpool John Moores University Byrom Street LiverpoolL3 3AF United Kingdom

This paper present the development a model for obtaining a coagulant for the process of cleaning and discoloration of industrial waste water and the development of a PID automated system for wastewater treatment. The description of the processes taking place in an electro-coagulator is given along with the solution obtained by model. The influence of current on the concentration of divalent iron water temperature is investigated. The algorithm of two-circuit regulation of concentration of pollution in sewage with the feedback current between the coagulator plates and the concentration pollutants in the water entering the automated purification system is developed. The automated control system for wastewater treatment process with the implementation of the coagulant forecasting algorithm in relation to the input pollution concentration is developed. The control of current magnitude in the electro-coagulator with minimal electricity consumption was realized. It is planned to manage the system with the ability to change the performance of the entire installation in real time using the SCADA - system. © Published under licence by IOP Publishing Ltd.

关键词： SCADA systems

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Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving

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Engineering Applications of Artificial Intelligence 2024年 135卷

作者： Azam, Shoaib Munir, Farzeen Kyrki, Ville Kucner, Tomasz Piotr Jeon, Moongu Pedrycz, Witold Department of Electrical Engineering and Automation Aalto University Espoo Finland Finnish Center for Artificial Intelligence Finland School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

Learning contextual and spatial environmental representations enhances autonomous vehicle's hazard anticipation and decision-making in complex scenarios. Recent perception systems enhance spatial understanding with sensor fusion but often lack global environmental context. Humans, when driving, naturally employ neural maps that integrate various factors such as historical data, situational subtleties, and behavioral predictions of other road users to form a rich contextual understanding of their surroundings. This neural map-based comprehension is integral to making informed decisions on the road. In contrast, even with their significant advancements, autonomous systems have yet to fully harness this depth of human-like contextual understanding. Motivated by this, our work draws inspiration from human driving patterns and seeks to formalize the sensor fusion approach within an end-to-end autonomous driving framework. We introduce a framework that integrates three cameras (left, right, and center) to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. The sensor data is fused and encoded using a self-attention mechanism, leading to an auto-regressive waypoint prediction module. We treat feature representation as a sequential problem, employing a vision transformer to distill the contextual interplay between sensor modalities. The efficacy of the proposed method is experimentally evaluated in both open and closed-loop settings. Our method achieves displacement error by 0.67m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset. In closed-loop evaluations on CARLA's Town05 Long and Longest6 benchmarks, the proposed method enhances driving performance, route completion, and reduces infractions. © 2024 The Authors

关键词： Autonomous vehicles

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Improving Hospital Preparedness for Future Pandemics with Machine Learning and Queue Management Solutions

Improving Hospital Preparedness for Future Pandemics with Ma...

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International Conference on Intelligent Computing in Data sciences (ICDS)

作者： Khalil Bouramtane Said Kharraja Jamal Riffi Omar EL Beqqali Saïd Boujraf University of Jean Monnet Saint-Etienne IUT of Roanne LASPI RU ROANNE FRANCE Faculty of science Dhar El Mahraz Laboratory of Computer Science Signals Automation and Cognitivism (LISAC) University Sidi Mohamed Ben Abdellah Fez Morocco Dept. Biophysics & Clinical MRI Methods Faculty of Medicine Clinical Neuroscience Laboratory University of Sidi Mohamed Ben Abdellah Fez Morocco

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

The COVID-19 pandemic has underscored the critical need for resilient and efficient hospital workflows capable of managing sudden increases in patient volume. This study presents a comprehensive approach that integrates advanced machine learning algorithms with queue management techniques to enhance hospital preparedness and response during pandemics. By Employing predictive analytics and classification systems, our methodology improves patient triage, optimizes resource allocation, and reduces wait times, thereby enhancing overall patient care. The study includes a detailed analysis of machine learning models used for patient prioritization and an exploration of queue management strategies to streamline patient flow. Our findings indicate substantial improvements in hospital efficiency and the ability to handle peak demands during health crises. This research provides actionable insights and a robust framework for healthcare administrators aiming to fortify their facilities against future pandemic scenarios, ultimately contributing to better healthcare outcomes.

关键词： Machine learning algorithms Pandemics Hospitals Heuristic algorithms Machine learning Data models Resource management Servers Predictive analytics Queueing analysis

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Hybrid Water Quality Prediction with Graph Attention and Spatio-Temporal Fusion

Hybrid Water Quality Prediction with Graph Attention and Spa...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Lin, Yongze Qiao, Junfei Bi, Jing Yuan, Haitao Gao, Han Zhou, MengChu Beijing University of Technology Faculty of Information Technology Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Chinese Academy of Environmental Planning Beijing100012 China New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Spatio-temporal prediction has a wide range of applications in many fields, e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is also one of spatio-temporal prediction tasks. However, it faces the following challenges: 1) Water quality in river networks has complex spatial dependencies;2) There are complex nonlinear relations in water quality time series;and 3) It is difficult to realize long-term forecasting. To address these challenges, this work proposes a spatio-temporal prediction model called a Graph Attention-based Spatio-Temporal (GAST) neural network. GAST investigates spatial and temporal dependencies of water quality time series. First, we introduce a temporal attention mechanism to capture time series dependencies, which can effectively handle nonlinear relationships in time series. Second, we adopt a spatial attention mechanism to extract spatial dependencies of river networks and fuse temporal features of spatial nodes. Third, we adopt a temporal convolution residual mechanism based on the spatio-temporal fusion, which improves the accuracy of long-term series prediction. This work adopts two real-world datasets to evaluate the proposed GAST and experiments demonstrate that GAST outperforms several state-of-the-art methods in terms of prediction accuracy. © 2022 IEEE.

关键词： Time series

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Data-Driven DLT3 Federated Deep Reinforcement Learning for Secure and Efficient Autonomous Driving

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Rong Li, Qianxi Wang, Kan Khan, Fazlullah Alturki, Ryan Lan, Dapeng Xi’an University of Technology School of Computer and Engineering Xi’an710000 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Ningbo315104 China Umm Al-Qura University College of Computing Department of Software Engineering Makkah21955 Saudi Arabia Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110003 China

As autonomous vehicles (AVs) become increasingly widespread, the intelligent driving control and safety concerns have emerged. Recent advents in Internet of Things (IoTs) and 6G technologies have vastly boosted AVs’ sensing and communication capabilities, thereby enabling the high-speed and real-time data exchange. However, efficiently utilizing data while safeguarding the privacy remains challenges. Thus, we propose a federated learning (FL) framework with the privacy protection to facilitate the deep reinforcement learning (DRL) for longitudinal vehicle safety tracking control. First, the proposed framework enables the efficient information sharing and policy updates via encrypted policy gradient transmission. Then, the twin delayed deep deterministic policy gradient (TD3) method makes the velocity decision in the local model, while the dual long short-term memory (LSTM) network captures the temporal trajectory information. Finally, stacked denoising autoencoders (SDAE) is used to extract high-level environmental features, and the dual-LSTM-TD3 (DLT3)-SDAE model is introduced to enhance the policy decision efficiency and accuracy. Experimental results demonstrate that within the FL framework supported by homomorphic encryption, the DLT3-SDAE-based AV agent surpasses other DRL methods in terms of collision avoidance and average rewards, in the LAF environment. The safety, efficiency and comfort of the DLT3-SDAE AV agent are also evaluated and demonstrated. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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