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Event Triggered and Self Triggered Formation control of Multi Agent systems 2

Event Triggered and Self Triggered Formation Control of Mult...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Sunil, Lakshmi Mathew, Sera Jacob, Jeevamma Mija, J.S. National Institute of Technology Department of Electrical Engineering Kerala Calicut India

ISBN: (纸本)9798350366570

This paper discusses formation acquisition and maneuvering of multi agent systems incorporating different triggering mechanisms. Practical scenario often involves limited communication bandwidth. Addressing this limitation is crucial for achieving effective formation control of multi agent systems. Implementing suitable triggering mechanism may overcome this limitations. Event Triggered and Self Triggered strategies employing Lyapunov stability are proposed in this work. In case of event triggered mechanism, an event condition is evaluated to determine the triggered instant, whereas the next triggered instant is anticipated in advance for self triggered mechanism. Simulation results using a five-agent model illustrate the feasibility of the proposed methodologies with self triggered approach having an upper hand in terms of reduction in sensor burden. © 2024 IEEE.

关键词： Multi agent systems

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Proceedings of 2024 2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems: Exploration and Blend of Emerging Technologies for Future Innovation, ICMACC 2024

Proceedings of 2024 2nd International Conference on Recent T...

引用

2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

ISBN: (纸本)9798350366570

The proceedings contain 142 papers. The topics discussed include: fault analysis of ship DC power system;event triggered and self-triggered formation control of multi agent systems;mitigating credit card fraud: a machine learning perspective;automatic license plate detection using information processing system vision and deep learning;face detection and recognition based intelligence surveillance robot for attendance system;Clusterboost: an Airbnb recommendation engine using metaclustering;key management in space integrated terrestrial network: a cybersecurity perspective;user profile based movie recommender system using machine learning;and utilization of non-orthogonal multiple access (NOMA) for spectral efficiency in mixed fading environments.

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Heuristic Integral Reinforcement for Unknown Continuous-time Multi-input systems 24

Heuristic Integral Reinforcement for Unknown Continuous-time...

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2nd international conference on Frontiers of intelligent Manufacturing and Automation, CFIMA 2024

作者： Lv, Yongfeng Zhao, Jun Huang, Yingbo Taiyuan University of Technology Shanxi Province Taiyuan China Shandong University of Science and Technology Shandong Qingdao China Kunming University of Science and Technology Yunnan Kunmin China

ISBN: (纸本)9798400710681

Adaptive dynamic programming (ADP) is widely used for the optimal control design of both the continuous-time and discrete-time nonlinear system, where the accurate mathematical dynamics are necessary and complicated. This paper proposes a data-based adaptive neural dynamic programming (AndP) to study the optimum policies of the unknown continuous-time nonlinear systems, where dynamic knowledge in the HJB equation is avoided. The data-based AndP is designed based on the heuristic dynamic programming (HDP) and integral reinforcement signal. First, the neural network is used to approximate the multi-input model, which can be used to design an integral reinforcement signal. Then, two critic and one action networks are constructed based on the integral reinforcement signal to update ith optimum policy of the multi-input model such that nonzero-sum Nash equilibrium can be reached. All the neural network weights are learned with the back-propagation algorithm. Moreover, the weight convergence and system stability are analyzed. Finally, simulation studies are presented to verify that the proposed AndP scheme can provide an effective optimal control design method for the multi-input continuous-time nonlinear system. © 2024 Copyright held by the owner/author(s).

关键词： Continuous time systems

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Neuron-Level control Based on Multi-Frequency Policy Dissection 2

Neuron-Level Control Based on Multi-Frequency Policy Dissect...

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2nd international conference on Cyber-Energy systems and intelligent Energy, ICCSIE 2024

作者： Ge, Yuhong Zong, Yu Institute of Data and Information Tsinghua University Shenzhen China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373318

Neuron-level control is an effective approach to enhancing the intrinsic decision-making capabilities of reinforcement learning models, obviating additional model training, as well as the need to modify reward functions. However, existing neuron-level control algorithms are difficult to be widely applied due to poor control performance, which is potentially attributed to the use of a single DC signal when overwriting the hidden states of neurons. To achieve high-performance neuron-level control, this study proposes the Multi-frequency Policy Dissection (MPD). MPD firstly identifies the neuron most relevant to each kinematic attribute by considering all frequency components within the temporal signals of both the neuronal hidden states and kinematic attributes comprehensively. Then MPD uses mixed signals containing multiple main frequency components to overwrite the hidden states of the identified neurons to achieve the desired agent behaviors. Compared with the state-of-the-art neuron-level control algorithm, MPD exhibits significant advantages across various evaluation metrics and unlocks complex behaviors in the bipedal robot. Furthermore, we visualize the correlation between the kinematic attributes and the hidden states of the matched neurons, and explore the gradual establishment of this correlation during the model training. © 2024 IEEE.

关键词： Reinforcement learning

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Deep Learning-Based Prediction of Driver Intent and Optimization of Vehicle Longitudinal Adaptive Cruise control Performance 2

Deep Learning-Based Prediction of Driver Intent and Optimiza...

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2nd IEEE international conference on Image processing and Computer Applications, ICIPCA 2024

作者： Feng, Junkai Zhao, Haiyi Cao, Han Yuan, Tianqi Yang, Zhuohan Wang, Zhiding Chang'an University Xi'an China

ISBN: (纸本)9798350360240

This study focuses on driver intention prediction models and their application to adaptive cruise control (ACC) system optimization. Through a deep learning approach, we built a highly accurate and stable prediction model and implemented it in the upgrading of the ACC system. The experimental data show that the optimized ACC system has significantly improved in terms of response time, control strategy accuracy, and driving safety and comfort. At the same time, the study also points out the limitations of the model in dealing with unconventional driving behaviors and extreme conditions, and proposes directions for future improvement, including expanding the diversity and scale of driving data, enhancing the model's ability to deal with prediction uncertainty, and fusing multimodal information into the model. The research results not only highlight the advancement in the field of intelligent driving, but also provide strong support for the future development of automated driving technology and vehicle-road cooperative systems. © 2024 IEEE.

关键词： Adaptive cruise control

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Research on intelligent Technology for Economic Responsibility Audit Based on Text Classification and Semantic Recognition 2

Research on Intelligent Technology for Economic Responsibili...

引用

2nd international conference on Artificial Intelligence and Automation control, AIAC 2024

作者： Zhang, Jingyao Cai, Guoming Zhang, Dihua Tang, Quanquan Yan, Peng Information Engineering University Henan450000 China Guangzhou Audit Center Audit Office of the Central Military Commission Guangdong510000 China

ISBN: (纸本)9798331529147

In recent years, the rise of big data technology has provided a new approach for audit systems to utilize audit data to accelerate audit work, promote full audit coverage, and improve audit supervision levels. Text classification and semantic recognition technology are mainly applied in audit systems in areas such as text intelligent recognition, state perception, and reliability evaluation. They play a role in improving the comprehensiveness of audit coverage and the accuracy of audit data processing in conducting economic responsibility audits. In response to this, this article conducts research on the application of text classification and semantic recognition technology in the field of economic responsibility auditing, and proposes an intelligent audit data processing algorithm based on the BERT BiGRU algorithm. Firstly, the BERT model is used to vector represent the audit text of leaders fulfilling their economic responsibilities, and a vector containing semantic information is output;Then use the BiGRU algorithm to mine the dependency relationships between text vectors;Finally, the hidden state of the final calculation step of the BiGRU algorithm is input into the linear layer for classification, and the classification result is obtained through the layers. This article provides auxiliary decision-making for distinguishing responsibilities in economic responsibility auditing, thereby improving the accuracy and comprehensiveness of economic responsibility auditing work. © 2024 IEEE.

关键词： Data accuracy

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Overview of Machine Learning for Bioengineering EEG signal processing 2

Overview of Machine Learning for Bioengineering EEG Signal P...

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2nd international conference on intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Dhande, Siddhi Kamble, Aahash Gundewar, Swapnil Babu, Naathi Naresh Kumar, Praveen Datta Meghe Inst Higher Educ & Res Dept Comp Sci & Med Engn Wardha India Datta Meghe Inst Higher Educ & Res DU Dept Artificial Intelligence & Data Sci Wardha Maharashtra India Datta Meghe Inst Higher Educ & Res Dept Artificial Intelligence & Machine Learning Wardha India

ISBN: (纸本)9798331540661;9798331540678

Electroencephalography (EEG) is a well-established method in neuroscience and bioengineering that offers valuable information about brain function. Recent technological advancements have led to more complex EEG recordings, which necessitate more advanced analysis techniques. Machine learning has emerged as an asset in EEG signal processing, enabling novel approaches to comprehension and utilization of brain activity. This article provides an in-depth analysis of the intersection of machine learning and EEG signal processing in bioengineering applications. This text presents a comprehensive overview of EEG data analysis techniques, focusing on the key steps of acquisition, preprocessing, and feature extraction. It highlights the challenges and strategies used to extract valuable information from raw EEG recordings. Additionally, it surveys various machine learning algorithms, including classic and modern deep learning methods, demonstrating their effectiveness in analyzing EEG signals and opening new frontiers in the field of bioengineering. This paper explores the growing connection between machine learning and EEG signal processing, examining how they work together to enhance healthcare, neurotechnology, and our knowledge of the brain. By studying existing research and cutting-edge developments, this study intends to focus on this synergistic relationship and its importance in these fields.

关键词： EEG signal processing Machine learning algorithms Bioengineering applications Neurotechnology

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Exploration on intelligent Virtual Reality Technology and Algorithms for VR Platforms under intelligent control and Computer Vision 2

Exploration on Intelligent Virtual Reality Technology and Al...

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2nd IEEE international conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Wei, Yuhua Chen, Yunxing Guangzhou Huali Science and Technology Vocation College Guangzhou China

ISBN: (纸本)9798350317558

intelligent control and computer vision algorithms can achieve clearer, more delicate, and realistic image effects through extremely high image quality and target recognition technology, as well as complete image processing tasks faster, thereby improving user experience and interaction effects. This study is based on intelligent control and computer vision technology and algorithms, aiming to design a highly integrated VR (virtual reality) platform. This article collected and established evaluation data and related parameters for the intelligent VR platform. intelligent control and computer vision algorithms can evaluate and analyze intelligent VR platforms, and improve their user experience and interaction effects by optimizing structural parameters and performance indicators. After experimental testing, the subjective image quality rating of the algorithm platform in this article was between 8.41 and 9.32, and the target recognition rate was between 6.72 and 9.87. The design based on intelligent control and computer vision algorithms can improve the image quality and target recognition rate of the VR platform, achieving precise detection and recognition of target objects. This study provided an effective method and technology for the design of intelligent VR platforms, and provided strong support for improving the image quality and target recognition rate of VR platforms. © 2024 IEEE.

关键词： computer vision image quality intelligent control intelligent VR platform target recognition rate

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Open Circuit Fault Diagnosis Method for DC Microgrids Based on Improved Pearson Correlation Algorithm 2

Open Circuit Fault Diagnosis Method for DC Microgrids Based ...

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2nd international conference on Cyber-Energy systems and intelligent Energy, ICCSIE 2024

作者： Zhuo, Hangyi Yang, Yang He, Siming Li, Ka Pu, Xuebin Yang, Houhua School of Electronic Information Engineering Changchun Science University China School of Electrical and Information Engineering Changchun Institute of Technology China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China

ISBN: (纸本)9798350373318

A fault diagnosis method based on improved Pearson correlation algorithm and Convolutional Neural Network (CNN) is proposed to address the issue of high diagnostic speed and high proportion of semiconductor power transistor faults in DC-DC converters in DC microgrids. This method uses the triangular wave algorithm to convert the PWM fault signal output by the power electronic controller into a triangular wave signal, and performs a rolling window correlation operation with the DC bus voltage ripple characteristic signal. Subsequently, the calculated correlation coefficients are combined with convolutional neural networks (CNN) to achieve fast and accurate diagnosis of open circuit faults in semiconductor power transistors. The simulation test results based on MATLAB/Simulink show that the proposed fault diagnosis method can quickly and accurately identify the open circuit fault of semiconductor power transistors in DC microgrids. © 2024 IEEE.

关键词： MATLAB

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An Innovative control Approach for Cyber-Physical Transportation systems: The Case of Monte-Carlo Workflow Computations 32

An Innovative Control Approach for Cyber-Physical Transporta...

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32nd Euromicro international conference on Parallel, Distributed and Network-Based processing (PDP)

作者： Kashansky, Vladislav Kashani, Sara Agha Hossein Garcia-Blas, Javier Marozzo, Fabrizio Hai Zhuge Sun, Xiaoping Eteronix GmbH Res Lab Vienna Austria Johannes Kepler Univ Linz Inst Prod & Logist Management Sch Business Linz Austria Univ Carlos III Madrid ARCOS Grp Leganes Spain Univ Calabria DIMES Dept Arcavacata Di Rende Italy Inst Comp Technol ICTCAS Key Lab Intelligent Informat Proc Beijing Peoples R China

ISBN: (纸本)9798350363074;9798350363081

Contemporarily, in light of the intelligent transportation systems (ITS) sector, the tendency can be observed that the solution of the multi-objective cyber-physical optimization problems with imperfect information takes an increasingly weighted role. In the present scientific work, the authors want to take these developments into account by introducing an innovative cyber-physical architectural design and corresponding the two-stage heuristic computing approach. It is utilized in synergy with the MCSA1 and DCEx architectural principles for the workflow scheduling of Monte-Carlo simulation, which is based on the intelligent and sustainable route-order dispatching process model. Factors such as emissions, transport costs, risks, and the individual weighting of orders are reflected in the model. In particular, the authors define a stochastic ILP-based2 monte-carlo workflow model. They further propose two-stage scheduling heuristic with d-HEFT DAG relaxation as first stage and apply state-of-the-art techniques as a part of SCIP framework to solve 2nd 1-0 ILP-based stage;evaluate the performance of the scheduling approach. The authors obtain preliminary results of the second stage behavior using a realistic heterogeneous computing scenario and corresponding constraint structures within MACS simulator engine3. The results from the experiments illustrate moderate complexity of the approach. Scalability of the model looks promising for the applicability in various industry-related scenarios and corresponding computing environments.

关键词： Cyber-Physical Transportation systems Monte-Carlo Workflows Scheduling Discrete Optimization Sustainable Transportation Dispatching systems

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