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International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Xingyu Liu Esa Apriaskar Lyudmila Mihaylova Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Department of Electrical Engineering Faculty of Engineering Universitas Negeri Semarang Semarang Indonesia

ISBN: (数字)9798350368031

ISBN: (纸本)9798350368048

The introduction of autonomous vehicles (AVs) presents a novel approach to regulating and optimising traffic flow through the automated control of AVs. In this context, the AV is defined as the actuator and an optimal control policy is desired to make control decisions. Deep Reinforcement Learning (DRL) is a novel method which aims to maximize the cumulative rewards given by the predefined reward function by making sequential decisions in a stochastic environment. In light of the above, we propose a DRL-based vehicular control method to train an optimal policy for the control of AV in a model-free fashion, and consequently improve the traffic efficiency with the obtained control policy. A single-lane circular road environment with both AV and human-driven vehicles is selected to serve as the mixed autonomy traffic system in the Simulation of Urban MObility (SUMO) [1] traffic simulator, and the Proximal Policy Optimization (PPO) algorithm is applied for the policy improvement. Simulation results demonstrate that our strategy is effective in mitigating the unstable stop-and-go waves, increasing 67.7% of the average driving speed and reducing 19.3% of the average energy consumption in a closed-ring road environment.

关键词： Roads Simulation Heuristic algorithms Stochastic processes Optimal control Deep reinforcement learning Fuels Intelligent systems Traffic congestion Optimization

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Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

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computers, Materials & Continua 2022年第12期73卷 5771-5788页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz AAbdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance *** bandwidth restriction associated with small antennas can be solved using metamaterial *** learning is gaining popularity as a way to improve solutions in a range of *** learning approaches are currently a big part of current research,and they’re likely to be huge in the *** model utilized determines the accuracy of the prediction in large *** goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and *** basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)*** choose the best features from the dataset,the binary(bDTO)algorithm is *** proposed ensemble model is compared to the base models and results are recorded and analyzed *** addition,two other ensembles are incorporated in the conducted experiments for *** ensembles are average ensemble and K-nearest neighbors(KNN)-based *** comparison is performed in terms of eleven evaluation *** evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

关键词： Metamaterial antenna dipper throated optimization feature selection parameters prediction

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Tracking Trajectory by Using The Polynomial Method for ACC System Based on Smart Car Platform 17

Tracking Trajectory by Using The Polynomial Method for ACC S...

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17th International Conference on engineering of Modern Electric Systems, EMES 2023

作者： Luu, Duc Lich Hung, Nguyen Viet Lupu, Ciprian University of Science and Technology The University of Danang Faculty of Transportation Mechanical Engineering Da Nang550000 Viet Nam East Asia University of Technology Faculty of Information Technology Bac Ninh Viet Nam University Politehnica of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350310634

A well-known advanced driver assistance technology that can be employed for that is the Adaptive Cruise control System (ACC). Cars equipped with ACC system are to control the car speed to follow a driver's set speed closely when there is no leading car. When a slower leading car is present based on distance sensor, the ACC controlled car are able to keep desired distance with respect to the position and velocity of the car in front. This paper presents a design methodology of Adaptive Cruise control (ACC) system for two smart car platform consist of the leading smart car and a host smart car move on a single lane on a laboratory installation. RST algorithm in the multi algorithms structure is proposed to design in real-time architecture for ACC system. RST algorithm is one of the most effective solutions for the real-time control of nonlinear systems or working regimes. The results of the experiment have been conducted to illustrate the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Adaptive cruise control

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A Decentralized Paper Dissemination System Employing Blockchain Technology, Peer Review and Expert Badges

A Decentralized Paper Dissemination System Employing Blockch...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Delia-Elena Bărbuţă Adrian Alexandrescu Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iaşi Iaşi Romania

Peer review represents the status-quo when it comes to evaluating research articles that are submitted to conferences and journals. The significance of a computer science article is given by the prestige of the publication and is correlated with the inclusion in the ISI Web of Science *** paper discusses the issues of the current paper publication paradigm and proposes a decentralized approach to the paper dissemination and the peer review processes. On the one hand, decentralization and transparency are obtained by employing smart contracts, through blockchain technology. On the other hand, an optimization of the paper rating system is obtained by employing a system of expert badges, based on NFTs, which ensure that the peer review process is just and that only specialists in the fields associated to the contributed paper offer proficient feedback. Other proposed facets include the remuneration of reviewers, a method of allowing the proposed system to expand based on the community’s input, and a solution for allowing the organization of conferences.

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A Framework for Anything-as-a-Service on a Cloud platform

A Framework for Anything-as-a-Service on a Cloud platform

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Adrian Alexandrescu Cătălin Mironeanu Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iaşi Iaşi Romania

There is an increasing need for computational and storage capabilities for complex distributed applications. Existing solutions need to be deployed in an environment that allows for an increase in performance, scalability, and availability. This paper takes looks at the state-of-the-art regarding methods that take existing applications and make them more efficient by using Cloud services. The novelty of the paper consists of a proposed framework for deploying applications on three major Cloud providers (i.e., Amazon’s AWS, Google Cloud and Microsoft Azure) and on the OpenStack open-source Cloud. After the main services from the four Cloud providers are identified, different deployment methods are described depending on the Cloud services and on the requirements of the application. Also, some examples of migrations are discussed with reference to specific Cloud provider services. The proposed solution for Anythingas-a-Service (YaaS) is a straightforward framework for taking different types of applications and migrating them to the Cloud. Therefore, the deployed applications benefit from Cloud features such as resource pooling, availability or scalability, while also being wary of the incurring costs.

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Learning the Dynamic Environment of an Original Game Using Hierarchical Reinforcement Learning Methods

Learning the Dynamic Environment of an Original Game Using H...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Vlad Batalan Florin Leon Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iași Iași Romania

This paper compares the performance of two reinforcement learning algorithms, Q-Learning and MAXQ-0, in learning to play an original game. An extension of MAXQ-0 algorithm, MAXQ-P is introduced, which enhances the variety of the tree nodes with simple, ordered and repetitive nodes. The hierarchical approach provided by MAXQ-P finds the optimal solution faster than the flat Q-Learning approach but converges more slowly. Furthermore, the performance of the MAXQ-P algorithm decreases after a certain number of episodes due to representation error in the weights of the model. To address this issue, the model is periodically tested with an exploration value of 0, and if the model successfully finds the solution, it is stored for future use. This study provides insights into the benefits and drawbacks of using hierarchical reinforcement learning algorithms for complex tasks and highlights the importance of carefully designing and training such algorithms for optimal performance.

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Cloud-based Architecture for Deploying a Distributed Ambient Assisted Living Environment

Cloud-based Architecture for Deploying a Distributed Ambient...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Nicolae Botezatu Adrian Alexandrescu Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iaşi Iaşi Romania

Ambient Assisted Living (AAL) is starting to become the norm as more and more smart devices and sensors are installed in people’s homes. This is an important aspect in improving the quality of life, especially for the elderly and for people with *** solution presented in this paper approaches this paradigm in a large scale deployment context, as it proposes an AAL architecture on three layers (Edge, Dew, and Cloud), which is capable of handling high amount of sensor data in near realtime. Using a distributed system of rules engines, the system is able to take actions based on the acquired data. An important novel concept is the “new home deployment”, which facilitates the integration of the devices from a new installation site - the software setup of the new edge infrastructure is done by using a dedicated Cloud service. In order to showcase the practicality and efficiency of the architecture, this paper presents the intricacies of the employed services and service-interaction when deploying the solution on the OpenStack Cloud platform.

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Modeling, Identification and Compensation of Friction in Solo12 Quadruped Robot

Modeling, Identification and Compensation of Friction in Sol...

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Electrical, Electronic and Computing engineering (IcETRAN), International Conference on

作者： Vladimir Mitić Vladimir Sibinović Milutin Nikolić Mirko Raković Department of Automatic Control Faculty of Electronic Engineering University of Niš Niš Serbia Department of Industrial Engineering and Engineering Management Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

The accuracy of the dynamic model of a robot plays an important role for robots performing highly dynamic tasks. Quadruped robots are often performing highly dynamic tasks and in order to be able to do that, fine and precise control of all joints is necessary. Friction compensation can play an important role in accomplishing this. A friction identification procedure for quadruped legged robots is presented in this paper. The procedure consists of a predefined joint motion that minimizes the joint coupling and collects the necessary data for friction estimation. Using the identified friction terms, a PD torque controller with friction compensation was implemented. The identification procedure and the torque controller are tested on a Solo12 quadruped robot using MuJoCo simulation software and on the real robot.

关键词： Legged locomotion Couplings Torque Friction Computational modeling Dynamics Estimation

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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning 13

Quality Assessment of Electrocardiogram Signals Using Contra...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Zhong, Meiping Li, Zuoyong Wang, ChuanSheng Huang, Jiayan Zhao, WanQi Fan, Haoyi College of Computer and Big Data Fuzhou University Fuzhou350108 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China Polytechnic University of Catalonia Department of Automatic Control Technical Barcelona08034 Spain New Engineering Industry College Putian University Putian351100 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9798350319156

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods. © 2023 IEEE.

关键词： Electrocardiograms

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A Review of Evaluation Metrics Algorithmic Used in Scholarly Research Recommender System

A Review of Evaluation Metrics Algorithmic Used in Scholarly...

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International Multidisciplinary Information Technology and engineering Conference (IMITEC)

作者： Gerald Ovono Travos Zuva Temidayo Otunniyi Department of Computer Faculty of Applied and Computer Sciences South Africa Department of Process Control and Computer Systems Faculty of Engineering and Technology VaalUniversity of Technology South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

The exponential growth of academic publications has made scholarly research recommender systems indispensable tools for researchers. These systems rely on diverse evaluation metrics to assess their effectiveness and relevance. This paper provides a comprehensive review and analysis of these metrics, categorizing them into accuracy, ranking, diversity, user-centric, coverage, temporal, and computational metrics. By employing a mixed-methods approach, combining systematic literature review and empirical analysis, we identify the prevalence and application of these metrics across various scholarly domains such as literature, conferences, and reviewers. Our findings highlight the widespread use of traditional metrics like precision, recall, and Fl-score, while emphasizing the growing importance of novel metrics such as unexpectedness and serendipity, especially in literature and conference recommendations. We also underscore the domain-specific nature of metric selection, with user-centric evaluations more prominent in literature recommendations, and ranking-focused metrics dominant in conference and reviewer recommendations. Furthermore, the study reveals a significant gap in the use of temporal and computational metrics, suggesting key areas for future research. This review offers valuable insights for researchers and practitioners, guiding them in metric selection and highlighting potential directions for enhancing the effectiveness, adaptability, and user satisfaction of scholarly recommender systems.

关键词： Measurement Accuracy Diversity reception Solids Information technology Recommender systems Systematic literature review

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