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2023 China automation Congress, CAC 2023

作者： Zou, Mengmeng Lu, Yiwen Liu, Hanxiao Mo, Yilin Ren, Xiaoqiang The School of Computer Engineering and Science Shanghai University Shanghai China The School of Institute of Artificial Intelligence Shanghai University Shanghai China The School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Automation BNRist Tsinghua University Beijing China

ISBN: (纸本)9798350303759

Deep reinforcement learning (DRL) has shown promise in solving the autonomous racing problem without requiring an accurate dynamics model, but random sampling of actions can lead to inefficient exploration. This paper proposes an efficient racing method, Constrained Residual Race (CRR), which combines a geometry-based reactive obstacle avoidance algorithm, Follow-The-Gap Method (FTG), with DRL. CRR generates safe guided actions with FTG and complementary actions with DRL, which are then fused into a final control action through a constraint equation. Using FTG as a prior controller enhances exploration efficiency and produces safe driving actions. Constraining the DRL policy output ensures that it does not overwrite the prior policy during early stages of exploration. Additionally, the DRL policy improves over the performance of the prior controller during training, reducing the lap time of the car. Validation on the F1TENTH simulator shows that CRR achieves lower lap times in most cases and exhibits excellent generalization capability. © 2023 IEEE.

关键词： Reinforcement learning

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An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

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computers, Materials & Continua 2022年第3期70卷 4393-4410页

作者： Abeer D.Algarni Walid El-Shafai Ghada M.El Banby Fathi E.Abd El-Samie Naglaa F.Soliman Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 84428Saudi Arabia Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia 44519Egypt

COVID-19 remains to proliferate precipitously in the *** has significantly influenced public health,the world economy,and the persons’***,there is a need to speed up the diagnosis and precautions to deal with COVID-19 *** this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical *** paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)*** proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT *** classification stage is firstly applied to detect and classify the input medical CT ***,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT *** classification stage is implemented based on a simple and efficient CNN deep learning *** model comprises four Rectified Linear Units(ReLUs),four batch normalization layers,and four convolutional(Conv)*** layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection *** the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid *** proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,*** summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis *** the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the *** region of interest can be assesses both automatically and through ***

关键词： COVID-19 segmentation classification CNN SPCNN CT images

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Phase chimera states on nonlocal hyperrings

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Physical Review E 2024年第2期109卷 L022201-L022201页

作者： Riccardo Muolo Thierry Njougouo Lucia Valentina Gambuzza Timoteo Carletti Mattia Frasca Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Department of Mathematics University of Namur B5000 Namur Belgium naXys Namur Institute for Complex Systems University of Namur B5000 Namur Belgium Faculty of Computer Science University of Namur B5000 Namur Belgium Department of Electrical and Electronic Engineering University of Buea P.O. Box 63 Buea Cameroon Department of Electrical Electronics and Computer Science Engineering University of Catania 95125 Catania Italy Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti” IASI-CNR 00185 Roma Italy

Chimera states are dynamical states where regions of synchronous trajectories coexist with incoherent ones. A significant amount of research has been devoted to studying chimera states in systems of identical oscillators, nonlocally coupled through pairwise interactions. Nevertheless, there is increasing evidence, also supported by available data, that complex systems are composed of multiple units experiencing many-body interactions that can be modeled by using higher-order structures beyond the paradigm of classic pairwise networks. In this work we investigate whether phase chimera states appear in this framework, by focusing on a topology solely involving many-body, nonlocal, and nonregular interactions, hereby named nonlocal d-hyperring, (d+1) being the order of the interactions. We present the theory by using the paradigmatic Stuart-Landau oscillators as node dynamics, and we show that phase chimera states emerge in a variety of structures and with different coupling functions. For comparison, we show that, when higher-order interactions are “flattened” to pairwise ones, the chimera behavior is weaker and more elusive.

关键词： Chimera states Collective behavior in networks Dynamics of networks Pattern formation Synchronization Synchronization transition

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First Order Features Extraction from Thermal Images for Human State Recognition 8

First Order Features Extraction from Thermal Images for Huma...

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8th International Symposium on Electrical and Electronics engineering, ISEEE 2023

作者： Moldovanu, Simona Pavel, Sorin Aiordachioaie, Dorel Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Computer Science and Information Technology Galati Romania Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Electronics and Telecommunications Galati Romania

ISBN: (纸本)9798350301670

Due to the common limitation of the human visual system, internal features of thermal images cannot be fully discovered. To overcome these drawbacks, a lot of studies analyzed the facial expressions corroborating the features extracted from thermal images with machine learning tools. This paper proposed the first-order features extracted from the image histogram and a binary classification performed by the k-nearest neighbor algorithm (K-NN). The proposed method tests separately the performance of different input data, firstly, the features were extracted from raw images and the robustness of the K-NN algorithm led to an accuracy of 92.6%. Secondly, the features were extracted sequentially from corrupted images with noise. The novelty of the proposed method consists of verifying the quality classification when the images have bad quality, in this case, the accuracy decreased to 70.7%, as a result, the classification of facial expressions is sensitive to the quality of images. © 2023 IEEE.

关键词： Emotion Recognition

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Computing Performance Analysis of IEC 61499 Distributed automation Systems

Computing Performance Analysis of IEC 61499 Distributed Auto...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Tuojian Lyu Valeriy Vyatkin Department of Electrical Engineering and Automation Aalto University Espoo Finland Department of Computer Science Computer and Space Engineering Luleå Tekniska Universitet Sweden

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This work analyzes the computing performance of distributed control systems in IEC 61499 on two hardware devices, Dell XPS workstation and Raspi 4B. Based on the test IEC 61499 application, three different system configurations are tested to simulate the distribution options of singleprocess and single-thread, multi-threading, and multi-process on both hardware platforms. Experimental results show that multiprocess configurations yield more consistent execution time due to dedicated resource allocation, while multi-threading, though resource-efficient, can lead to latency variability. These results aim at help in choosing optimal configurations and selecting hardware for performance in distributed automation systems.

关键词： Performance evaluation automation Decentralized control Hardware Performance analysis Workstations Resource management IEC Standards

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Research on Distributed Cooperative control Strategy of Microgrid Hybrid Energy Storage Based on Adaptive Event Triggering

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computer Modeling in engineering & Sciences 2022年第8期132卷 585-604页

作者： Wenqian Zhang Jingwen Chen Saleem Riaz Naiwen Zheng Li Li School of Electrical and Control Engineering Shaanxi University of Science and TechnologyXi’an710021China School of Mathematics and Computer Science Yan’an UniversityXi’an716000China School of Automation Northwestern Polytechnical UniversityXi’an170072China

Distributed collaborative control strategies for microgrids often use periodic time to trigger communication,which is likely to enhance the burden of communication and increase the frequency of controller updates,leading to greater waste of communication *** response to this problem,a distributed cooperative control strategy triggered by an adaptive event is *** introducing an adaptive event triggering mechanism in the distributed controller,the triggering parameters are dynamically adjusted so that the distributed controller can communicate only at a certain time,the communication pressure is reduced,and the DC bus voltage deviation is effectively reduced,at the same time,the accuracy of power distribution is *** MATLAB/Simulink modeling and simulation results prove the correctness and effectiveness of the proposed control strategy.

关键词： DC microgrid hybrid energy storage adaptive event trigger distributed collaborative control

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Diabetic Retinopathy Image Classification Using Machine Learning and Local Binary Patterns Features 8

Diabetic Retinopathy Image Classification Using Machine Lear...

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8th International Symposium on Electrical and Electronics engineering, ISEEE 2023

作者： Miron, Mihaela Culea-Florescu, Anisia Moldovanu, Simona Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Computer Science and Information Technology Galati Romania Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Electronics and Telecommunications Galati Romania

ISBN: (纸本)9798350301670

Diabetic Retinopathy (DR) is a condition caused by diabetes that affects the blood vessels in the retina. Detecting the disease early and providing appropriate treatment are crucial in slowing its progression. Therefore, there is great potential in utilizing Machine Learning (ML) to improve the identification and monitoring of DR development in patients. Our study aims to explore the performance of six ML algorithms, namely Random Forest (RF), Adaptive Boosting (AB), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), and Quadratic Discriminant Analysis (QDA), in two binary classifications involving three classes: non-diabetic retinopathy (NoDR), moderate retinopathy (MR), and severe retinopathy (SV). These ML algorithms were applied to ten features extracted using local binary patterns (LBP). The first classification task involved distinguishing between NoDR and MR, while the second task involved differentiating between NoDR and SV. The RF technique achieved the highest classification accuracy, with 0.912 for the first task and 0.94 for the second task. © 2023 IEEE.

关键词： Local binary pattern

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Integrating Mamba Sequence Model and Hierarchical Upsampling Network for Accurate Semantic Segmentation of Multiple Sclerosis Lesion 8th

Integrating Mamba Sequence Model and Hierarchical Upsamplin...

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Shahriar Sanjid, Kazi Hossain, Md. Tanzim Shahariar Junayed, Md. Shakib Monir Uddin, M. Wang, Yu-Long Uddin, Nasir M. Department of Electrical and Computer Engineering North South University Dhaka1229 Bangladesh Department of Mathematics and Physics North South University Dhaka1229 Bangladesh School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China PrecisionDxLabs TampaFL United States

ISBN: (纸本)9789819601875

Integrating components from convolutional neural networks and state space models in medical image segmentation presents a compelling approach to enhance accuracy and efficiency. We introduce Mamba-HUNet, a novel architecture tailored for robust and efficient segmentation tasks. Leveraging strengths from Mamba-UNet and the lighter version of Hierarchical Upsampling Network (HUNet), Mamba-HUNet combines convolutional neural networks’ local feature extraction power with state space models’ long-range dependency modeling capabilities. The lighter HUNet was integrated into Mamba-HUNet, maintaining performance parity while improving computational efficiency. Mamba-HUNet improves existing semantic segmentation methods by efficiently capturing localized fine-grained features and long-range dependencies in medical images through Visual State Space blocks and patch merging layers. This results in enhanced segmentation accuracy and robustness. Experimental results on publicly available Magnetic Resonance Imaging scans, notably in Multiple Sclerosis lesion segmentation, demonstrate Mamba-HUNet’s superior performance across diverse segmentation tasks, highlighting its potential in improving clinical decision-making processes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantic Segmentation

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Multi-Agent Coordination for a Partially Observable and Dynamic Robot Soccer Environment with Limited Communication 10

Multi-Agent Coordination for a Partially Observable and Dyna...

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10th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2023

作者： Affinita, Daniele Volpi, Flavio Spagnoli, Valerio Suriani, Vincenzo Nardi, Daniele Bloisi, Domenico D. Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Italy Faculty of Political Science and Sociopsychological Dynamics UNINT University Rome Italy

RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soccer champion team by the year 2050. To achieve this goal, autonomous humanoid robots' coordination is crucial. This paper explores novel solutions within the RoboCup Standard Platform League (SPL), where a reduction in WiFi communication is imperative, leading to the development of new coordination paradigms. The SPL has experienced a substantial decrease in network packet rate, compelling the need for advanced coordination architectures to maintain optimal team functionality in dynamic environments. Inspired by market-based task assignment, we introduce a novel distributed coordination system to orchestrate autonomous robots' actions efficiently in low communication scenarios. This approach has been tested with NAO robots during official RoboCup competitions and in the SimRobot simulator, demonstrating a notable reduction in task overlaps in limited communication settings. © 2024 Copyright for this paper by its authors.

关键词： Anthropomorphic robots

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Fast sparse optimization via adaptive shrinkage

arXiv

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arXiv 2025年

作者： Cerone, Vito Fosson, Sophie M. Regruto, Diego Department of Control and Computer Engineering Politecnico di Torino Italy

The need for fast sparse optimization is emerging, e.g., to deal with large-dimensional data-driven problems and to track time-varying systems. In the framework of linear sparse optimization, the iterative shrinkage-thresholding algorithm is a valuable method to solve Lasso, which is particularly appreciated for its ease of implementation. Nevertheless, it converges slowly. In this paper, we develop a proximal method, based on logarithmic regularization, which turns out to be an iterative shrinkage-thresholding algorithm with adaptive shrinkage hyperparameter. This adaptivity substantially enhances the trajectory of the algorithm, in a way that yields faster convergence, while keeping the simplicity of the original method. Our contribution is twofold: on the one hand, we derive and analyze the proposed algorithm;on the other hand, we validate its fast convergence via numerical experiments and we discuss the performance with respect to state-of-the-art algorithms. © 2025, CC BY-NC-ND.

关键词： Optimization algorithms

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