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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Raja, M. Karthick Shermatova, Makhbuba Al Ansari, Mohammed Saleh Abdufattokhov, Shokhjakhon Rao, Vuda Sreenivasa Raj, I. Infant Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Turin Polytechnic University in Tashkent Civil Engineering and Architecture Department Tashkent Uzbekistan University of Bahrain College of Engineering Department of Chemical Engineering Bahrain Turin Polytechnic University Automatic Control and Computer Engineering Dept Italy Tashkent Intl. University of Education Dept of Information Technologies Tashkent Uzbekistan Koneru Lakshmaiah Education Fou. Green Fields Department of Computer Science and Engineering A.P Vaddeswaram522302 India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy621 112 India

ISBN: (纸本)9798350372748

The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches. © 2024 IEEE.

关键词： Energy efficiency

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Pneumonia Detection from X-ray Images Using Convolutional Neural Networks 24

Pneumonia Detection from X-ray Images Using Convolutional Ne...

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24th International Conference on control Systems and computer Science, CSCS 2023

作者： Dediu, Viorel Udrea, Andreea Faculty of Automatic Control and Computer Science Univesity Politehnica of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

Pneumonia is one of the top causes of death in Romania and early detection of this disease improves the recovery chances and shortens the length of hospitalization. In this work, we develop a solution for automatic pneumonia detection based on convolutional neural networks. Four network models are investigated. They are trained on 4.163 images from a public dataset and tested on 530 images. The best results are obtained by one of the proposed models conducting to a sensitivity of 98.72%, an accuracy of 89.81%, and ROC 93.46%. Thus, this research proposes a lightweight screening tool that can help triaging the patients with pneumonia. © 2023 IEEE.

关键词： Convolution

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Fully integrated Windows framework for source localization with MNE Python and FreeSurfer

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Current Directions in Biomedical engineering 2023年第1期9卷 371-374页

作者： Oppermann, Hannes Wulf, Simon Komosar, Milana Haueisen, Jens Haueisen, Jens Department of Computer Science and Automation Institute of Biomedical Engineering and Informatics Technische Universität Ilmenau Ilmenau Germany Department of Neurology Biomagnetic Center University Hospital Jena Jena Germany

There is a variety of software packages, toolboxes, or libraries for the analysis and processing of neurophysiological data such as EEG and MEG. Many of these solutions provide algorithms for both, sensor-space analysis and sourcespace analysis. Especially with the solutions that run on Windows machines, it is noticeable that the step of the volume model generation is usually not included, since the state-ofthe- art software for this (FreeSurfer) is a Unix-based software and thus not available forWindows machines. Therefore, our goal was to develop a fully-integrated software solution for Windows machines, accessing all processing steps already implemented in an existing toolbox and using FreeSurfer in the same system. Due to its widespread use, we chose MNE Python as the basis for our fully integrated software solution. We used the Windows Subsystem for Linux to create a virtual Linux kernel for the FreeSurfer installation. To demonstrate the workflow, the libeep, and AutoReject libraries have been added. A 64-channel EEG recording during right-hand movement (ME) and imagination (MI) was used to test the implemented workflow. The developed framework consists of several modules within Python, mainly using existing scripts and functions. The library libeep was integrated to read the EEG data with the '.cnt', eeprope format. AutoReject was used to automatically interpolate detected bad channels or to reject complete epochs. FreeSurfer was successfully integrated and customized Python scripts enabled the communication between MNE Python on a Windows machine and FreeSurfer on a virtual Linux kernel. With the above-mentioned EEG dataset, we performed source reconstruction and were able to show ERD/S patterns for both, ME and MI. Our new, fullyintegrated software framework can be used on Windows machines to perform a complete process of source reconstruction. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Python

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Formal verification of observers supervising a cyber-physical system implemented using IEC 61499 32

Formal verification of observers supervising a cyber-physica...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Ovsiannikova, Polina Priol, Etienne Le Perret, Vincent Jhunjhunwala, Pranay Xavier, Midhun Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland École Normale Supérieure Paris-Saclay Department of Mechanical Engineering Gif-sur-Yvette France Luleå Tekniska Universitet Computer and Space Engineering Department of Computer Science Sweden

ISBN: (纸本)9798350399714

A rigorous check is a significant phase in the design process of control programs of safety-critical cyber-physical systems. Here, we consider such programs to be implemented using IEC 61499 standard for industrial automation. After the check is performed (for example, using formal verification), the engineer needs to ensure that even in unexpected situations, the system will not fail during the runtime, and for this online verification methods can be utilized. In this work, we consider attaching monitors implemented as basic function blocks to the interface of the controller, thus having a property being monitored represented in the form of a state machine. Now, monitors make the system safer only if their quality is also ensured. Since their complexity is far lower than the complexity of the controller, they can be model checked, however, in the case of IEC 61499 function blocks, open-loop model checking will produce spurious counterexamples as it will allow combinations that are not possible according to the IEC 61499 function blocks semantics (e.g., data transferred without firing the event). The current work addresses this issue and proposes a method for close-loop model checking of monitors, using the non-deterministic twin of a controller under supervision. We present our approach using the system of two orthogonal pneumatic cylinders. © 2023 IEEE.

关键词： Model checking

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Parallel intelligent education with ChatGPT

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Frontiers of Information Technology & Electronic engineering 2024年第1期25卷 12-18页

作者： Jiacun WANG Ying TANG Ryan HARE Fei-Yue WANG Department of Computer Science and Software Engineering Monmouth UniversityNew Jersey 07764USA Department of Electrical and Computer Engineering Rowan UniversityNew Jersey 08028USA Institute of Automation Chinese Academy of SciencesBeijing 100190China

This paper presents a framework for parallel intelligent education that involves physical and virtual learning for a personalized learning *** especially focus on Chat Generative Pre-trained Transformer(ChatGPT)owing to its considerable potential to supplement regular class *** address the strengths and weaknesses of learning with ***,we discuss the challenges and solutions of the proposed parallel intelligent education with ChatGPT.

关键词： education GPT intelligent

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Dynamic Modeling of Strip Rolling Process Using Probabilistic Neural Network

Dynamic Modeling of Strip Rolling Process Using Probabilisti...

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18th IEEE International Conference on Advanced Motion control, AMC 2024

作者： Deng, Jifei Sierla, Seppo Sun, Jie Vyatkin, Valeriy Northeastern University State Key Laboratory of Rolling and Automation Shenyang China Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350342819

This paper proposes an intelligent dynamic modeling method for strip rolling process. Actuators of a cold rolling mill perform actions, including work roll bending, intermediate roll bending, and roll gap tilting, to regulate the product quality. Conventional first-principles modeling necessitates intricate mathematical equations derived from process analysis and action evaluation. However, the complexity of the model increases the difficulty of upgrading the model. To address this limitation, a novel approach involving a deep neural network with a Gaussian distribution layer is proposed to effectively capture the process dynamics. A negative log likelihood is established as the loss function for the model using real-world industrial data. Experiment outcomes demonstrate the efficacy of the proposed method in achieving an accurate dynamic model. © 2024 IEEE.

关键词： Deep neural networks

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Adaptive Consensus Quantized control for a Class of High-Order Nonlinear Multi-Agent Systems With Input Hysteresis and Full State Constraints

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1574-1589页

作者： Guoqiang Zhu Haoqi Li Xiuyu Zhang Chenliang Wang Chun-Yi Su Jiangping Hu the School of Automation Engineering Northeast Electric Power UniversityJilin 132012 the Jilin Province International Research Center of Precision Drive and Intelligent Control Jilin 132012China the School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China the Department of Mechanical and Industrial Engineering Concordia UniversityQCMontrealH3B 1R6Canada the School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China(

For a class of high-order nonlinear multi-agent systems with input hysteresis,an adaptive consensus output-feedback quantized control scheme with full state constraints is *** major properties of the proposed control scheme are:1)According to the different hysteresis input characteristics of each agent in the multi-agent system,a hysteresis quantization inverse compensator is designed to eliminate the influence of hysteresis characteristics on the system while ensuring that the quantized signal maintains the desired value.2)A barrier Lyapunov function is introduced for the first time in the hysteretic multi-agent *** constructing state constraint control strategy for the hysteretic multi-agent system,it ensures that all the states of the system are always maintained within a predetermined range.3)The designed adaptive consensus output-feedback quantization control scheme allows the hysteretic system to have unknown parameters and unknown disturbance,and ensures that the input signal transmitted between agents is the quantization value,and the introduced quantizer is implemented under the condition that only its sector bound property is *** stability analysis has proved that all signals of the closed-loop are semi-globally uniformly *** Star Sim hardware-in-the-loop simulation certificates the effectiveness of the proposed adaptive quantized control scheme.

关键词： Adaptive quantized control barrier Lyapunov function input hysteresis multi-agent systems

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Event-Triggered Extended Dissipative FTB for T-S Fuzzy Switched Systems With Mismatched Phenomena and Deception Attacks: A Multidomain Framework

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IEEE Transactions on Cybernetics 2024年第9期54卷 4928-4938页

作者： Chen, Haiyang Zong, Guangdeng Su, Shun-Feng Gao, Fangzheng Tiangong University School of Control Science and Engineering Tianjin300387 China Nanjing Institute of Technology School of Automation Jiangsu Collaborative Innovation Center for Smart Distribution Network Nanjing211167 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Nanjing Institute of Technology School of Automation Nanjing211167 China

This article investigates the extended dissipative finite-time boundedness (ED-FTB) problem for fuzzy switched systems under deception attacks. To improve the network resource efficiency, a multidomain probabilistic event-triggered mechanism (MDPETM) is proposed. The mode mismatched phenomenon is modeled based on the switching delay information between the controller mode and the system mode. To extract the true signal generated by the MDPETM, a virtual delay concept is developed. The constraint that the controller and the system must have the same premise variables is removed. Based on the MDPETM, mismatched fuzzy state feedback controllers are first devised which may not share the same modes with the system. Then, by establishing fuzzy basis and controller mode-dependent Lyapunov functionals, sufficient criteria free of nonlinear terms existing in the literature are derived, which ensure the ED-FTB of the closed-loop system under admissible delays and deception attacks. Finally, an application-oriented one-link robotic arm system is utilized to validate the theoretical results. © 2013 IEEE.

关键词： controllers

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Fractional Order Sliding Mode control with Fuzzy Parameter Tuning Applied to a SCARA Robot

Fractional Order Sliding Mode Control with Fuzzy Parameter T...

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2024 IEEE International Multi-Conference on Smart Systems and Green Process, IMC-SSGP 2024

作者： Znidi, Aicha Abdelhedi, Fatma Nouri, Ahmed Said Derbel, Nabil National Engineering School of Gabes University of Gabes Lab: Numerical Control of Industrial Processes Tunisia College of Engineering King Abdulaziz University Department of Electrical and Computer Engineering Jeddah22254 Saudi Arabia University of Sfax National Engineering School of Sfax Control & Energy Management Laboratory Tunisia

ISBN: (纸本)9798350378382

This paper presents the application of Fractional Order Sliding Mode control (FO-SMC) in order to achieve a robust motion trajectory regulation in dynamic robot systems. The proposed control strategy benefits of both advantages of fractional calculus and sliding mode control to upgrade the robustness and the performances of the controller. Moreover, the paper proposes to use a fuzzy tuning of sliding mode controller in order to satisfy the capabilities of the actuators. The stability of the closed loop system has been demonstrated using the Lyapunov theory. Simulation results demonstrate the efficiency of the proposed method achieving precise trajectory tracking and robust performances, it has been shown that fuzzy tuning of the sliding function parameters significantly reduces control variable peaks, making the control more suitable for actuator application. © 2024 IEEE.

关键词： Sliding mode control

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Domain adaptation in reinforcement learning:a comprehensive and systematic study

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Frontiers of Information Technology & Electronic engineering 2024年第11期25卷 1446-1465页

作者： Amirfarhad FARHADI Mitra MIRZAREZAEE Arash SHARIFI Mohammad TESHNEHLAB Department of Computer Engineering Science and Research BranchIslamic Azad UniversityTehran ***Iran Department of Control Engineering K.N.Toosi University of TechnologyTehran ***Iran

Reinforcement learning(RL)has shown significant potential for dealing with complex decision-making ***,its performance relies heavily on the availability of a large amount of high-quality *** many real-world situations,data distribution in the target domain may differ significantly from that in the source domain,leading to a significant drop in the performance of RL *** adaptation(DA)strategies have been proposed to address this issue by transferring knowledge from a source domain to a target ***,there have been no comprehensive and in-depth studies to evaluate these *** this paper we present a comprehensive and systematic study of DA in *** first introduce the basic concepts and formulations of DA in RL and then review the existing DA methods used in *** main objective is to fill the existing literature gap regarding DA in *** achieve this,we conduct a rigorous evaluation of state-of-the-art DA *** aim to provide comprehensive insights into DA in RL and contribute to advancing knowledge in this *** existing DA approaches are divided into seven categories based on application *** approaches in each category are discussed based on the important data adaptation metrics,and then their key characteristics are ***,challenging issues and future research trends are highlighted to assist researchers in developing innovative improvements.

关键词： Reinforcement learning Domain adaptation Machine learning

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