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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： Image coding

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Adaptive control Scheme for Clustering of Nodes Based on the Signs of Connections in Dynamical Signed Networks 19th

Adaptive Control Scheme for Clustering of Nodes Based on ...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Wang, Qi Wang, Yinhe Gao, Zilin Gao, Peitao Xiong, Jianbin Cen, Jian Gao, Ying School of Automation Guangdong Polytechnic Normal University Guangzhou510665 China School of Automation Guangdong University of Technology Guangzhou510006 China College of Computer Science and Engineering Chongqing Three Gorges University Chongqing404020 China School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou510665 China Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory Guangzhou510665 China School of Computer Science and Engineering South China University of Technology Guagnzhou510641 China

ISBN: (纸本)9783031651229

The concept of clustering has garnered significant attention within the field of complex networks. Numerous clustering algorithms have been documented in the extant academic literature. However, most of them consider static signed networks, which are not suitable for dynamical networks. For dynamical signed networks, there is a class of algorithms called cluster dynamics. Nevertheless, they regard the connection to be time-invariant, which is a constraint. In this paper, for a kind of dynamical signed networks with time-varying connections, we discuss the adaptive control scheme for clustering of nodes, such that the network evolves asymptotically into a classifiable network that contains several friends’ groups with clear sign boundaries. In other words, through the careful design of the controller for the nodes and the establishment of coupling relationships between the nodes and their connections, we ensure that the connection of the dynamical signed network can approximate asymptotically the connection of a given classifiable network, as measured by the concept of uniformly ultimately bounded (UUB). Finally, the simulation is used to illustrate the validity of the method proposed in this paper. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Adaptive control Classifiable networks Multi-clustering

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Monitoring of Energy Processes in the Electrohydraulic Complex with a Variable-Frequency Electric Drive in Unsteady Operation Modes 4

Monitoring of Energy Processes in the Electrohydraulic Compl...

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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Qawaqzeh, Mohamed Zaidan Kovalchuk, Viktoriya Korenkova, Tetyana Serdiuk, Oleksandr Bilyk, Oleksandr Al-Balqa' Applied University Department of Electrical and Electronics Engineering Al-Salt66110 Jordan Kremenchuk Mykhailo Ostrohradskyi National University Department of Systems of Automatic Control and Electric Drive Kremenchuk39600 Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Computer Engineering and Electronics Kremenchuk39600 Ukraine

ISBN: (纸本)9798350346831

An energy model of an electrohydraulic complex has been developed. It allows analyzing the energy processes in the power channel in dynamic modes during the development of cavitation and turbulent oscillations in the pipeline network. It has been shown that the nature of energy processes in the considered cases is periodic constant power fluctuations in the analyzed time interval. The variable component in technological and energy signals of the electrohydraulic complex has been assessed. Their frequency analysis has been performed. The coefficient of power controllability of the electrohydraulic complex and its critical values, whereat the development of emergency modes is possible, have been determined. © 2022 IEEE.

关键词： Electric drives

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An Enhanced Analysisof Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

arXiv

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arXiv 2024年

作者： Ismaeel, Ayad Ghany Mary, S.J. Jereesha Anitha, C. Logeshwaran, Jaganathan Mahmood, Sarmad Nozad Alani, Sameer Shather, Akram H. Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk36001 Iraq Annai Velankanni College of Engineering Potalkulam Kanyakumari629401 India Saveetha School of Engineering Chennai600124 India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore641202 India Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk Iraq Computer Center University of Anbar Baghdad55431 Iraq Department of Computer Engineering Technology Al-Kitab University Altun Kopru Kirkuk Iraq

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating theserich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets. © 2024, CC BY-NC-ND.

关键词： Radial basis function networks

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Dual-Active-Bridge Converter with Triple Phase Shift control for a Wide Operating Voltage Range

Dual-Active-Bridge Converter with Triple Phase Shift Control...

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International Conference on Power electronics (ICPE)

作者： Yi-Hsuan Chen Ta-Wei Huang Shih-Hao Kuo Yu-Chen Chang Huang-Jen Chiu Serafin Bachman Marek Jasiński Department of Electronics and Computer Engineering National Taiwan University of Science and Technology Taiwan Institute of Control and Industrial Electronics Warsaw University of Technology Warszawa Poland

An optimal modulation scheme with Triple-Phase-Shift (TPS) control that increase efficiency in the whole load range is presented for a Dual Active Bridge (DAB) converter under wide output voltage range conditions. This paper provides a comprehensive analysis of the DAB with TPS modulation, identifying all the switching modes to operate the DAB where expressions for the transfer functions, the peak inductor current and RMS inductor current are provided. An analysis of minimum Peak Current and minimum RMS Current control also are investigated. On this basic all-ZVS modulation scheme is originally proposed to improve light-load efficiency and to reduce the switching losses. A 15 kW prototype circuit is applied, and experimental results are presented to validate that the modulation and efficiency improvement are realized by applying the optimized modulation scheme. The experimental results also verify the effectiveness of the closed-loop control strategy.

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Boundary Iterative Learning control for Repetitive Spatio-Temporal Processes

Boundary Iterative Learning Control for Repetitive Spatio-Te...

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IEEE Conference on Decision and control

作者： Maciej Patan Kamil Klimkowicz Krzysztof Patan Eric Rogers Institute of Control and Computation Engineering University of Zielona Góra Poland Institute of Control Electronic and Electric Engineering University of Zielona Góra Poland Department of Electronics and Computer Science University of Southampton UK

Iterative learning control for lumped processes is well established. Therefore, there is strong interest in developing designs that would produce similar flexibility for classes of distributed parameter systems. This paper develops a design for application to examples described by partial differential equations of convection-diffusion type in the multidimensional spatial domain, which have many applications, such as heat transfer problems. The system response is measured, and then the control is applied via specific boundary conditions using a sensor/actuator network, i.e., boundary control, as opposed to designs that require sensing and actuating application over the domain the dynamics are defined over. The convergence properties of the design are established in conjunction with rules for tuning its parameters for performance enhancement. Finally, the new design is applied to a laser heating problem in wafer staging, which requires boundary control.

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Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach 25

Real-Time Object Detection and Robotic Manipulation for Agri...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Zhao, Hongyu Tang, Zezhi Li, Zhenhong Dong, Yi Si, Yuancheng Lu, Mingyang Panoutsos, George Imperial College The Department Of Physics London United Kingdom University Of Sheffield The Department Of Automatic Control And Systems Engineering SheffieldS1 3JD United Kingdom University Of Manchester The Department Of Electrical And Electronic Engineering Manchester United Kingdom University Of Southampton The Department Of Electronics And Computer Science Southampton United Kingdom Fudan University The Department Of Economics Shanghai200433 China AmesIA50011 United States

ISBN: (纸本)9798350340266

The optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The You Only Look Once(YOLO) algorithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation. © 2024 IEEE.

关键词： Crops

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Multi-label transfer learning for identifying lung diseases using chest X-rays 2

Multi-label transfer learning for identifying lung diseases ...

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： El-Fiky, Azza Shouman, Marwa Ahmed Hamada, Salwa El-Sayed, Ayman Karar, Mohamed Esmail Electronics Research Institute Department of Informatics Cairo Egypt Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Minuf Egypt Menoufia University Faculty of Electronic Engineering Dept. Industrial Electronics and Control Engineering Minuf Egypt

ISBN: (纸本)9781665418423

Chest radiography presents one of the main medical imaging modalities for diagnosing lung diseases. To assist radiologists during interventional procedures, this paper aims at proposing a transfer learning-based classifier to automatically identify 14 different thoracic diseases in Chest X-ray (CXR) images. The proposed method is relied on deep residual neural networks with 50 layers (ResNet-50) to accomplish the diagnostic task of many chest diseases. In this study, a public dataset of 112, 120 frontal radiograph images for Chest X-ray has been used for validating the proposed deep learning classifier. It achieved the best performance of multi-label classification of normal and 14 different lung diseases with an average area under curve (AUC) of 0.911 and F1-score of 0.66. This study demonstrated that the proposed ResNet-50 classifier as a transfer learning model outperforms other relevant methods in the previous studies for automatic multi-label classification of chest X-rays. © 2021 IEEE.

关键词： computer aided diagnosis

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Recovering Missing Monitoring Data to Enhance Service Provisioning in the Edge-to-Cloud Continuum

Recovering Missing Monitoring Data to Enhance Service Provis...

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IEEE Conference on Network Softwarization (NetSoft)

作者： Gaetano Francesco Pittalà Cristian Zilli Nicola Di Cicco Gianluca Davoli Alessio Sacco Department of Electrical Electronic and Information Engineering (DEI) University of Bologna Italy Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Italy Department of Electronics Information and Bioengineering (DEIB) Politecnico di Milano Italy

ISBN: (数字)9798350369588

ISBN: (纸本)9798350369595

Efficient service provisioning in the Edge-to-Cloud Continuum is of utmost importance for modern applications. While sensible decisions can be taken if enough monitoring data is collected, maintaining continuous telemetry data streams amidst the continuum’s complexity is challenging. This paper introduces CRISP (reConstructing Resource Information for Service Placement), a solution combining data reconstruction and service placement strategies to optimize decisions despite incomplete monitoring data. CRISP utilizes Convolutional Neural Networks and Long Short-Term Memory models for data reconstruction, integrating them with a heuristic algorithm that selects nodes for service component placement. Numerical results demonstrate CRISP’s efficacy in optimizing service provisioning despite missing data, contributing to enhanced resource utilization and service performance in the considered context.

关键词： Heuristic algorithms Data models Numerical models Complexity theory Telemetry Resource management Convolutional neural networks

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