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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Abood, Layla H. Ali, Inaam I. Abood, May H. Control and System Engineering Department University of Technology Baghdad Iraq Electrical Engineering Department University of Technology Baghdad Iraq Computer Engineering Department University of Al Iraqia Baghdad Iraq

ISBN: (纸本)9783031648465

The prominence growth of the Electric Vehicle (EV) industry requests from researcher to adopts effective controlling techniques for marinating different issues. In this paper an augmented PID controller is suggested for controlling speed in EV based using sigmoid function relation and the smart and unique Gray wolf Optimization (GWO) algorithm to achieve a satisfied and robust system desired value, a comparison is utilized with PID controllers (Optimal PID, Normal PID) to evaluate its behavior then a robustness analysis is applied by varying system desired speed in multiple periods, the controller track the desired value perfectly and force the system to an efficient response with robust system behavior. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electric vehicles

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Exploring Deep Learning Methods for computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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computer Modeling in engineering & Sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Practical Realization of Only Active Floating Lossless Inductance Simulator 10

Practical Realization of Only Active Floating Lossless Induc...

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10th International Conference on engineering, Applied Sciences, and Technology, ICEAST 2024

作者： Roongmuanpha, Natchanai Unhavanich, Sumalee Pukkalanun, Tattaya Tangsrirat, Worapong School Of Engineering Department Of Computer Engineering Bangkok10520 Thailand Faculty Of Digital Technology Bangkok10300 Thailand School Of Engineering Department Of Instrumentation And Control Engineering Bangkok10520 Thailand

ISBN: (纸本)9798350385946

This paper describes an electronically controllable floating lossless inductance simulator circuit implemented with only off-the-shelf available integrated circuits, namely LT1228 and operational amplifier (OA). The proposed design consists solely of two LT1228s and a single OA. By using an active element named LT1228, the equivalent inductance can be tuned electronically with depended on the externally supplied current applied to LT1228. The proposed simulator imposed just one component matching restriction that required the equality of two transconductances. It is easily attainable with a simple current mirror. The worthiness of the proposed floating inductance simulator has been proven and applied to the construction of the 2nd-order RLC highpass/bandpass filter as an application. PSPICE simulations are used to investigate the characteristics of the proposed simulator and its filter realization. © 2024 IEEE.

关键词： Operational amplifiers

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DeepLeaf: an optimized deep learning approach for automated recognition of grapevine leaf diseases

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Neural Computing and Applications 2025年第15期37卷 8799-8823页

作者： Talaat, Fatma M. Shams, Mahmoud Y. Gamel, Samah A. ZainEldin, Hanaa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Faculty of Engineering Horus University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Plant diseases can cause severe losses in agricultural production, impacting food security and safety. Early detection of plant diseases is crucial to minimize crop damage and ensure agricultural sustainability. Manual monitoring is often impractical due to the complexity and time involved, making automated disease recognition essential. This study presents a new Plant Disease Detection Algorithm (PDDA) called DeepLeaf focused on identifying four common grapevine diseases: leaf blight, black rot, stable, and black measles. The PDDA integrates three key modules: an Image Preprocessing Module, a Feature Extraction Module, and an Optimized Convolutional Neural Network (OCNN)-based Classification Module. The OCNN forms the core of the classification system, with its hyperparameters fine-tuned using fuzzy optimization to enhance performance. Preprocessing techniques are applied to analyze diseased leaves, and a logistic regression algorithm is used to downsample the features for better analysis. The CNN is trained on images from the Plant Village dataset, allowing it to detect and classify grapevine leaf diseases accurately. The proposed model's efficiency in the automated diagnosis of grapevine diseases is demonstrated by its remarkable 99.7% accuracy rate. This high accuracy indicates that the PDDA may help with more effective and scalable plant disease monitoring, which will ultimately allow better agricultural practices. © The Author(s) 2025.

关键词： CNN Detection of leaf disease Extracting features Plant disease

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Satellite Integration into 5G:Deep Reinforcement Learning for Network Selection

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Machine Intelligence Research 2022年第2期19卷 127-137页

作者： Emanuele De Santis Alessandro Giuseppi Antonio Pietrabissa Michael Capponi Francesco Delli Priscoli Department of Computer Control and Management Engineering“Antonio Ruberti”University of Rome La SapienzaRome 00185Italy

This paper proposes a deep-Q-network(DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process(MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G. For this reason, the present work considers a realistic network model that takes into account path-loss models and intra-RAT(radio access technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved in terms of connection acceptance, resource allocation, and load *** particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches.

关键词： Network selection HetNet deep reinforcement learning deep-Q-network(DQN) 5G communications

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Annotated survey and perspectives on rail transport energy system RAMS evaluation technology

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Green Energy and Intelligent Transportation 2024年第3期3卷 75-90页

作者： Ruifeng Shi Lingzhi Zhang Feng Lin Jin Ning Limin Jia Kwang Y.Lee School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China China Institute of Energy and Transportation Integrated Development Beijing 102206China State Key Lab of Rail Traffic Control&Safety Beijing Jiaotong UniversityBeijing 100044China Department of Electrical and Computer Engineering Baylor UniversityWacoTX 76798USA

The rail transit system plays a crucial role in modern *** the increasing demand for clean and green energy in the transport sector,its energy system is expected to achieve low-carbon and highly efficient energy utilization in rail ***,the gradual development of the rail transport energy system has led to an increase in its complexity,and the rising difficulty of system assessment has faced the limitations of traditional assessment ***,it is essential to develop effective assessment *** paper begins by providing a systematic review of the development status of Reliability,Availability,Maintainability and Safety(RAMS)assessment and analyzing the shortcomings of traditional RAMS assessment technology in the context of rail transit energy ***,based on the four fundamental properties of RAMS,it summarizes the current state of key assessment technologies in the field of rail ***,the paper delves into the challenges and potential solutions concerning the implementation of RAMS assessment technology for rail transit energy ***,the paper offers an outlook on the future development of RAMS assessment for rail transport energy *** comprehensively analyzing these aspects,the paper aims to contribute valuable insights into optimizing the rail transit energy system,promoting its sustainable and efficient operation in the context of clean and green energy utilization.

关键词： Integration of energy and transportation RAMS evaluation Rail transit energy system

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Comparative analysis of permissioned blockchain frameworks for industrial applications

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Blockchain(Research and Applications) 2023年第1期4卷 58-70页

作者： Vittorio Capocasale Danilo Gotta Guido Perboli Department of Control and Computer Engineering Politecnico di Torino10129 TurinItaly Service Innovation TIMTurinItaly Department of Management and Production Engineering Politecnico di Torino10129 TurinItaly

Blockchain is a technology that creates trust among non-trusting parties without relying on any ***,it has attracted the interest of companies operating in a multitude of ***,due to the number of different blockchain solutions that have emerged in the last few years and their rapid changes,it is challenging for such companies to orient their technological *** paper presents a comparative analysis of the key dimensions—namely,governance,maturity,support,latency,privacy,interoperability,flexibility,efficiency,resiliency,and scalability—of some of the most-used permissioned blockchain ***,we present the results of a performance evaluation considering the following frameworks:Hyperledger Fabric 2.2,Hyperledger Sawtooth 1.2,and ConsenSys Quorum 21.1(with both the GoQuorum client and the Hyperledger Besu client).The platforms were tested under similar conditions,and official releases were used,such that our findings provide a reference for companies establishing their technological orientation.

关键词： Blockchain performance evaluation Hyperledger Besu Hyperledger Fabric Hyperledger Sawtooth Quorum

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Accelerated Quantum Selection Optimization of a Microwave Filter 1

Accelerated Quantum Selection Optimization of a Microwave Fi...

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1st IEEE Latin American Conference on Antennas and Propagation, LACAP 2024

作者： Martinez, Gabriel F.E. Niccolai, Alessandro Zich, Eleonora L. Zich, Riccardo E. Politecnico di Milano Department of Energy Milan Italy Politecnico di Torino Department of Control and Computer Engineering Turin Italy

ISBN: (纸本)9798331527587

Optimization techniques have long been employed in designing electromagnetic structures. With the emergence of quantum computing, hybrid quantum-classical optimization algorithms are appearing as promising solutions. This paper presents a new approach that integrates quantum algorithms with classical optimization to profit from the advantages of both quantum and classical computing. © 2024 IEEE.

关键词： Quantum computers

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New Approaches Based on PRNU-CNN for Image Camera Source Attribution in Forensic Investigations 10

New Approaches Based on PRNU-CNN for Image Camera Source Att...

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10th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2024

作者： De Magistris, Giorgio Grycuk, Rafal Mandelli, Lorenzo Scherer, Rafal Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Computational Intelligence Czestochowa University of Technology Poland

Digital image forensics currently mainly uses PRNU noise as a fingerprint to attribute an image to a particular camera. However PRNU is usually extracted manually using Maximum Likelihood estimation from multiple images from the same source device. In this paper we show that the PRNU can be learned in a data driven fashion using a ResNet based neural network. We also show that it is possible to train a neural network for camera attribution directly on the residual noise, that contains both the PRNU and a random component. We show that both approaches are valid as we obtained results comparable with the state of the art. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Digital forensics

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