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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Shuhua Ma, Lifeng Charalambous, Themistoklis Nanjing University of Science and Technology School of Automation Nanjing210094 China University of Cyprus School of Electrical Engineering Department of Electrical and Computer Engineering Nicosia1678 Cyprus Aalto University School of Electrical Engineering Department of Electrical Engineering and Automation Espoo Finland FinEst Centre for Smart Cities Tallinn Estonia

ISBN: (纸本)9798350316339

This paper proposes a data-driven control (DDC) strategy for nonlinear automated vehicles, employing a multidescription coding (MDC) mechanism based on scalar quantization to address the challenges of data dropouts and limited bandwidth in networked communication environments. The developed MDC-based communication protocol enhances system robustness by reducing the probability of data dropout. It achieves this by transmitting multiple descriptions of source data through diverse channels, incorporating quantization and index reassignment to efficiently alleviate bandwidth constraints. Based on the reconstructed real-time data, a novel data-driven controller and a parameter estimation algorithm are designed, offering adaptability to varying driving conditions and rapid response in dynamic environments. A numerical simulation showcases the potential of the proposed approach as a reliable and efficient solution for the speed problem in nonlinear automated vehicles with challenging communication environments. © 2024 IEEE.

关键词： Bandwidth

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Lighting the Smart Way: A Technological Odyssey in Urban Street Illumination 10

Lighting the Smart Way: A Technological Odyssey in Urban Str...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vinu, M.S. Priya, P Kanaga Thilakaveni, P. Hermina, J. M. Ashforn Prashanthini, K. Misba, M. Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Psg Polytechnic College Department of Computer Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Ramakrishna Engineering College Department of Robotics and Automation Coimbatore India Bethlahem Institute of Engineering Department of Computer Science and Engineering Nagercoil Karungal India

ISBN: (纸本)9798350384369

The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things (IoT) technologies to increase the functionality of street lights. The major components include Light Dependent Resistors (LDR) for automatic light management, an MQ135 sensor for air quality monitoring, and a fog detection system employing a laser module and LDR. These sensors are integrated with the ESP8266, providing cloud integration for real-time data visualization and monitoring. Additionally, solar panels are employed to enhance energy efficiency. The proposed methodology digs into the precise implementation, data analysis, and the potential of this system to alter urban lighting infrastructure © 2024 IEEE.

关键词： Air quality

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GAN-Based Deblurring Algorithm for Drug Package Detection 42

GAN-Based Deblurring Algorithm for Drug Package Detection

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42nd Chinese Control Conference, CCC 2023

作者： Yang, Hao Fei, Zixiang Zhou, Wenju Fei, Minrui School of Mechatronic Engineering and Automation Shanghai University Shanghai Key Laboratory of Power Station Automation Technology Shanghai200444 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

ISBN: (纸本)9789887581543

With the development of Industry 4.0, many manufacturers are moving in the direction of automation and intelligence. Drug package inspection quality testing methods have also changed from traditional manual inspection to machine vision inspection. However, due to the long exposure time or vibration of the camera, the images are blurred, which will seriously affect the accuracy of the defect detection. We propose a deblur model with higher level of image restoration and more lightweight. Using reserving and merging blocks to replace the original reserving and merging blocks, which can be used in the inference phase to turn ResBlocks into equivalent straight structures using reserving and merging operations, making the network lighter. The pyramid squeeze attention module is also incorporated to enable the model to efficiently extract finer-grained multi-specimen scale spatial information, thus making more efficient use of feature space information. Finally, a drug package dataset was produced and the model was trained to obtain good deblurring results. The comparative experiments confirmed that the model has better image reduction and excellent reduction speed, and can be further cited in production inspection pipelines to improve detection accuracy. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Merging

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Uncertainty Estimation for Gradient Boosting Models based on Gaussian Graph Model and Natural Gradient 3

Uncertainty Estimation for Gradient Boosting Models based on...

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3rd International Conference on Machine Learning and computer Application, ICMLCA 2022

作者： Chen, Muyi Wang, Daling College of Computer Science and Engineering Northeastern University Shenyang China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (纸本)9781510664814

We study the multi-output probability regression prediction and uncertainty quantification problem in multi-output regression prediction with gradient boosting models, and propose an uncertainty estimation method based on Gaussian Graph Model (GMM) constraint and natural gradient. Under the assumption of Gaussian distribution, we employ GMM to model the conditional independence relationship of the outputs, and propose a mixed parameterization to represent the subfamily of Gaussian distributions defined by the graph model. We set this mixed parameters as the learning target of the gradient boosting model, and use natural gradient in Riemann space that based on Fisher information matrix (FIM) instead of the vanilla gradient to be the steepest descent direction. The formula to compute FIM and natural gradient for the subfamily defined by GMM is deduced. Experiments show that the proposed method is able to incorporate the priors on the outputs to optimize the search space, therefore, lead to better uncertainty estimation results on multi-output regression for gradient boosting models. © 2023 SPIE.

关键词： Fisher information matrix

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Isolated Bridgeless PFC Converter Based on Active-Clamped SEPIC

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CPSS Transactions on Power Electronics and Applications 2022年第3期7卷 239-250页

作者： Wu, Deliang Sondharangalla, Madhura Ayyanar, Rajapandian School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Electrical Computer and Energy Engineering Arizona State University Arizona United States

A new isolated bridgeless PFC converter based on active-clamped SEPIC is proposed. Compared to conventional active-clamped SEPIC PFC converter, the proposed converter reduces conduction loss by removing two diodes in the line current path, while preserving the advantages of the active-clamped SEPIC such as zero-voltage switching (ZVS) and active voltage clamp for all the high frequency switches, soft turn-off of the out-put diodes and well damped control-to-input-current response. In addition, the peak voltage stress on the output diode is inherently clamped by the center-tapped transformer configuration. Experimental results from a 3.3 kW prototype verified the operation of the converter and demonstrated peak efficiency of 97.26% with 140 kHz switching frequency. The power density of the single-phase PFC module is 101 W/in3. © 2017 CPSS.

关键词： Diodes

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Distributed Fault-Tolerant Consensus Tracking of Multi-Agent Systems Under Cyber-Attacks

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1037-1048页

作者： Chun Liu Bin Jiang Xiaofan Wang Huiliao Yang Shaorong Xie School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444 School of Artificial Intelligence Shanghai UniversityShanghai 200444China IEEE College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China College of Energy and Electrical Engineering Hohai UniversityNanjing 210016China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China

This paper investigates the distributed fault-tolerant consensus tracking problem of nonlinear multi-agent systems with general incipient and abrupt time-varying actuator faults under ***,a decentralized unknown input observer is established to estimate relative states and actuator ***,the estimated and output neighboring information is combined with distributed fault-tolerant consensus tracking *** of reaching leader-following exponential consensus tracking of multi-agent systems under both connectivity-maintained and connectivity-mixed attacks are derived with average dwelling time,attack frequency,and attack activation rate technique,*** example verifies the effectiveness of the fault-tolerant consensus tracking algorithm.

关键词： Cyberattacks fault-tolerant consensus tracking incipient and abrupt actuator faults nonlinear multi-agent systems unknown input observer

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Flare Forecast Model Based on DS-SMOTE and SVM with Optimized Regular Term

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Research in Astronomy and Astrophysics 2023年第6期23卷 38-46页

作者： Jie Wan Jun-Feng Fu Ren-Qing Wen Ke Han Meng-Yao Yu Peng E School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Laboratory for Space Environment and Physical Sciences Harbin Institute of TechnologyHarbin 150001China School of Computer and Information Engineering Harbin University of CommerceHarbin 150028China

The research of flare forecast based on the machine learning algorithm is an important content of space *** order to improve the reliability of the data-driven model and weaken the impact of imbalanced data set on its forecast performance,we proposes a resampling method suitable for flare forecasting and a Particle Swarm Optimization(PSO)-based Support Vector Machine(SVM)regular term optimization *** the problem of intra-class imbalance and inter-class imbalance in flare samples,we adopt the density clustering method combined with the Synthetic Minority Over-sampling Technique(SMOTE)oversampling method,and performs the interpolation operation based on Euclidean distance on the basis of analyzing the clustering space in the minority *** the same time,for the problem that the objective function used for strong classification in SVM cannot adapt to the sample noise,In this research,on the basis of adding regularization parameters,the PSO algorithm is used to optimize the hyperparameters,which can maximize the performance of the ***,through a comprehensive comparison test,it is proved that the method designed can be well applied to the flare forecast problem,and the effectiveness of the method is proved.

关键词： Sun:flares Sun:magnetic fields Sun:X-rays gamma-rays (Sun:)sunspots

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Adaptive dynamic surface control for quadrotor-slung load transportation system with uncertainties

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Science China(Technological Sciences) 2023年第10期66卷 2917-2929页

作者： DING Feng HUANG Jian SUN Chong AI Yong XU WenXia YANG ChenGuang School of Computer Science South-Central Minzu UniversityWuhan 430074China Key Laboratory of Ministryof EducationforImage Processing and Intelligent ControlHubei Key Laboratoryof Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Engineering Wuhan Institute of TechnologyWuhan 430205China Key Laboratory of Ministry of Education for Autonomous Systems and Networked Control Guangdong Engineering Technology Research Center of Control of Intelligent SystemsCollege of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Quadrotors play a significant role in our lives and are transforming our *** cable-suspended loads is an unavoidable quadrotor application trend and a hot research topic in the control ***,the load swing and unpredictability pose significant challenges to the quadrotor's *** this paper,an anti-swing controller with an inner-outer control strategy for the quadrotor-slung load transportation system is *** facilitate the controller design,the outer position dynamics are restructured in the form of ***,a virtual controller is created to force the underactuated states to the dynamic surface to ensure the position subsystem's *** improve robustness,an adaptive law is used to eliminate the effects of uncertain cable ***,a dynamic surface controller for the inner attitude subsystem is presented to drive the actual force to the virtual *** is demonstrated that the control strategy can stabilize the quadrotor despite mass and cable length *** results are provided to demonstrate the efficacy and durability of the proposed method.

关键词： quadrotor-slung load transportation system dynamic surface control adaptive law cascade system model uncertainties

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Optimizing Route Efficiency in Formula One (F1) Vehicles Using Reinforcement Learning Algorithms 4

Optimizing Route Efficiency in Formula One (F1) Vehicles Usi...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Narejo, Sanam Khan, Muhammad Taimoor Shaikh, Muhammad Zakir Dhirani, Lubna Luxmi Chowdhery, Bhawani Shankar Mehran Univerity of Engineering and Technology Department of Computer Systems Jamshoro Pakistan Mehran Univerity of Engineering and Technology Jamshoro National Centre for Robotics Automation and Artificial Intelligence Pakistan University of Malaga Mechanical Engineering and Energy Efficiency School of Industrial Engineering Malaga Spain University of Limerick Department of Electronic and Computer Engineering Limerick Ireland

ISBN: (纸本)9798331518882

This research explores the application of reinforcement learning (RL) to enhance route efficiency and performance of a Formula One (F1) car within a simulation environment. The simulation is implemented using Python, NEAT (NeuroEvolution of Augmenting Topologies), and PyGame to create a dynamic system where neural networks control the car's navigation. RL enables the F1 car, acting as an agent, to learn optimal decisions through a fitness-based reward mechanism by interacting with its environment. Equipped with radar sensors to detect obstacles and measure distances, the virtual car adjusts its speed and steering to avoid collisions and optimize movement. Over successive generations, the RL algorithm refines the car's driving ability, improving speed and directional control to maximize distance covered and minimize lap times. A fitness-based evaluation system tracks progress, providing metrics such as best and average fitness scores, which highlight the car's evolving performance. Results demonstrate the effectiveness of RL in enhancing autonomous driving capabilities, enabling the car to navigate complex environments and improve decision-making across generations. © 2025 IEEE.

关键词： Reinforcement learning

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Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1530-1543页

作者： Guijun Ma Zidong Wang Weibo Liu Jingzhong Fang Yong Zhang Han Ding Ye Yuan The School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China the Department of Computer Science Brunel University LondonUxbridgeMiddlesexUB83PHUnited Kingdom the School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan 430081China the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized *** this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution *** standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based *** proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge *** results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based *** Py Torch implementation of the proposed PSO-DDA method is available at https://***/mxt0607/PSO-DDA.

关键词： Deep transfer learning domain adaptation hyperparameter selection lithium-ion batteries(LIBs) particle swarm optimization state of health estimation(SOH)

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