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China International Electrical and Energy Conference (CIEEC)

作者： Zhao Song Yiyan Wang Tianyi Xiong Computer and Electronics System and Engineering Department AVIC Xi'an Flight Automation Control Research Institute (FACRI) Xi'an Shanxi Province China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Vector limiter is a computationally complex component of vector control, but its hardware-friendly algorithm is rarely discussed. This paper presents an efficient solution with pure shift-and-add operations, namely the dual vector rotation algorithm (DVRA). Its programmable logic realization is also provided. The DVRA is then simulated in a motor driving system, and physical validation is made on a Xilinx FPGA. Furthermore, performance comparison is conducted between the DVRA and four control groups (conventional algorithms). All control groups are built by Xilinx IPs under diversified configurations, thus human bias can be minimized. Post-implementation results evince that the DVRA exhibits significantly less resource expenditure and better timing, with power consumption and accuracy comparable to the control groups.

关键词： Power demand Accuracy Motors Vectors Hardware Timing System-on-chip

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Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

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computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt 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

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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Visual Odometry with Depth-Wise Separable Convolution and Quaternion Neural Networks 9

Visual Odometry with Depth-Wise Separable Convolution and Qu...

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9th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2022

作者： De Magistris, Giorgio Comminiello, Danilo Napoli, Christian Starczewski, Janusz T. Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Via Eudossiana 18 Rome00184 Italy Department of Intelligent Computer Systems Czestochowa University of Technology al.Armii Krajowej 36 Czestochowa42-200C Poland

Monocular visual odometry is a fundamental problem in computer vision and it was extensively studied in literature. The vast majority of visual odometry algorithms are based on a standard pipeline consisting in feature detection, feature matching, motion estimation and local optimization. Only recently, deep learning approaches have shown cutting-edge performance, replacing the standard pipeline with an end-to-end solution. One of the main advantages of deep learning approaches over the standard methods is the reduced inference time, that is an important requirement for the application of visual odometry in real-time. Less emphasis, however, has been placed on memory requirements and training efficiency. The memory footprint, in particular, is important for real world applications such as robot navigation or autonomous driving, where the devices have limited memory resources. In this paper we tackle both aspects introducing novel architectures based on Depth-Wise Separable Convolutional Neural Network and deep Quaternion Recurrent Convolutional Neural Network. In particular, we obtain equal or better accuracy with respect to the other state-of-the-art methods on the KITTI VO dataset with a reduction of the number of parameters and a speed-up in the inference time. © 2022 Copyright for this paper by its authors.

关键词： Pipelines

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Personalized Semantic Trajectory Privacy Protection in Location-Based Services: A TD3-Based Approach

Personalized Semantic Trajectory Privacy Protection in Locat...

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IEEE Conference on Wireless Communications and Networking

作者： Jun Ye Minghui Dai Minghui Min Jinling Song Hongliang Zhang Zhu Han School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The swift advancement of Location-Based Services (LBSs) raises the danger of trajectory privacy being breached, since the location semantic tags can easily disclose users' sensitive information. Additionally, attackers can exploit temporal correlations to infer sensitive personal information. This paper formulates a personalized semantic trajectory privacy protection framework designed to protect locations with varying sensitivities on the trajectory from the attacker with temporal correlation information. We model the trajectory privacy protection problem as a Markov Decision Process (MDP) and introduce the Reinforcement Learning (RL) technique to dynamically adjust the privacy parameters. Specifically, we leverage the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to enhance the stability and accuracy of policy evaluation, enabling efficient learning of optimal policies in continuous action spaces. Simulation results indicate that the TD3-based personalized semantic trajectory privacy protection mechanism effectively balances the Quality of Service and semantic trajectory privacy while realizing personalized trajectory privacy protection.

关键词： Privacy Correlation Sensitivity Simulation Semantics Quality of service Reinforcement learning Stability analysis Trajectory Protection

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WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution

arXiv

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arXiv 2025年

作者： Bongini, Pietro Mandelli, Sara Montibeller, Andrea Casu, Mirko Pontorno, Orazio Ragaglia, Claudio Vittorio Zanchetta, Luca Aquilina, Mattia Wani, Taiba Majid Guarnera, Luca Tondi, Benedetta Boato, Giulia Bestagini, Paolo Amerini, Irene De Natale, Francesco Battiato, Sebastiano Barni, Mauro University of Siena Department of Information Engineering an Mathematics Italy Politecnico di Milano Department of Electronics Informatics and Bioengineering Italy University of Trento Department of Information Engineering and Computer Science Italy University of Catania Department of Mathematics and Computer Science Italy Sapienza University of Rome Departement of Computer Control and Management Engineering Italy

Synthetic image source attribution is an open challenge, with an increasing number of image generators being released yearly. The complexity and the sheer number of available generative techniques, as well as the scarcity of high-quality open source datasets of diverse nature for this task, make training and benchmarking synthetic image source attribution models very challenging. WILD1 is a new in-the-Wild Image Linkage Dataset designed to provide a powerful training and benchmarking tool for synthetic image attribution models. The dataset is built out of a closed set of 10 popular commercial generators, which constitutes the training base of attribution models, and an open set of 10 additional generators, simulating a real-world in-the-wild scenario. Each generator is represented by 1,000 images, for a total of 10,000 images in the closed set and 10,000 images in the open set. Half of the images are post-processed with a wide range of operators. WILD allows benchmarking attribution models in a wide range of tasks, including closed and open set identification and verification, and robust attribution with respect to post-processing and adversarial attacks. Models trained on WILD are expected to benefit from the challenging scenario represented by the dataset itself. Moreover, an assessment of seven baseline methodologies on closed and open set attribution is presented, including robustness tests with respect to post-processing. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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Joint Optimization of Power Allocation and Beamforming for UAV-RIS Communication System

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IEEE Wireless Communications Letters 2025年第6期14卷 1733-1737页

作者： Guo, Xinying Liu, Longfei Zhang, Jiankang Chen, Sheng Ministry of Education Key Laboratory of Grain Information Processing and Control Henan University of Technology China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing and Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

This paper considers the communication system assisted by a fixed-trajectory unmanned aerial vehicle (UAV), which is equipped with a reconfigurable intelligent surface (RIS). For this UAV-RIS assisted system, we jointly optimize the power allocation and active beamforming at the base station (BS) and the passive beamforming at the RIS, by a novel phase block coordinate descent algorithm framework aimed at maximizing the system sum-rate. Specifically, the joint optimization problem is decomposed into two phases, and we propose two optimization algorithms: one for BS power allocation using fractional programming (FP) and the other for jointly optimizing active and passive beamforming using FP-manifold, which alternately optimize two phases. Simulation results not only highlight the rapid convergence and evident superiority of our proposed framework but also reveal that the optimal UAV-RIS placement is related to the flight height. © 2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Multiphase LLC Resonant Converter with Natural Current Sharing and Phase-Shedding

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IEEE Transactions on Industry Applications 2025年

作者： Ahmad, Ubaid Giral, Roberto Olalla, Carlos Sangwongwanich, Ariya Davari, Pooya Blaabjerg, Frede Universitat Rovira i Virgili Països Catalans Department of Electrical Electronics and Automatic Control Engineering School of Electrical and Computer Engineering Catalonia Tarragona26 43007 Spain Aalborg University Department of Energy Aalborg Ost9220 Denmark

The multiphase LLC converter is commonly used for high-power and high step-down applications. However, the tolerances in the tank circuit parameters cause uneven current sharing (CS), reducing system reliability. Additionally, phase-shedding (PS) can improve converter efficiency at partial load by turning-OFF phases. This paper proposes a multiphase input-parallel output-parallel LLC resonant converter with a common inductor-capacitor branch, with the objective to achieve excellent CS and phase-shedding at the same time. Thanks to the shared branch, current-sharing is natural, and phase-shedding is realized without affecting the operating point. With fewer passive comp- onents and a simple phase connection, the design offers low-cost, high-power density, and improved light-load efficiency compared to existing solutions. Detailed analysis shows improved CS on both the primary and secondary sides, when compared to previous approaches. Simulation and experimental results for the proposed two-phase LLC resonant converter validate the analysis and illustrate the effectiveness of the CS and PS capabilities. At the rated load of 800 W, CS error is 2.48% on the primary side and 2.0% on the secondary side. Phase-shedding improves efficiency by 3.60% at 50% load and 6.82% at 25% load. © 1972-2012 IEEE.

关键词： Resonant converters

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Metric localization for lunar rovers via cross-view image matching

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Visual Intelligence 2024年第1期2卷 132-145页

作者： Zihao Chen Kunhong Li Haoran Li Zhiheng Fu Hanmo Zhang Yulan Guo School of Electronics and Comunication Engineering the Shenzhen Campus of Sun Yat-sen UniversitySun Yat-sen UniversityShenzhen510275China Peng Cheng Laboratory ShenzhenGuangdong518055China Department of Computer Science and Software Engineering The University of Western AustraliaPerth6009Australia Department of Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai Aerospace Control Technology InstituteShanghai201109China College of Electronic Science and Technology National University of Defense TechnologyChangshaHunan410073China.

Accurate localization is critical for lunar rovers exploring lunar terrain ***,lunar rover localization relies on sensor data from odometers,inertial measurement units and stereo ***,localization errors accumulate over long traverses,limiting the rover’s localization *** paper presents a metric localization framework based on cross-view images(ground view from a rover and air view from an orbiter)to eliminate accumulated localization ***,we employ perspective projection to reduce the geometric differences in cross-view ***,we propose an image-based metric localization network to extract image features and generate a location *** heatmap serves as the basis for accurate estimation of query *** also create the first large-area lunar cross-view image(Lunar-CV)dataset to evaluate the localization *** dataset consists of 30 digital orthophoto maps(DOMs)with a resolution of 7 m/pix,collected by the Chang’e-2 lunar orbiter,along with 8100 simulated rover *** results on the Lunar-CV dataset demonstrate the superior performance of our proposed *** to the second best method,our method significantly reduces the average localization error by 26% and the median localization error by 22%.

关键词： Cross-view metric localization(CVML) Lunar rover Aerial patch Panorama Perspective projection

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Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

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Synthesis of Decentralized Variable Gain Robust controllers with Guaranteed L2Gain Performance via Piecewise Lyapunov Functions for a Class of Uncertain Large-Scale Interconnected Systems 48

Synthesis of Decentralized Variable Gain Robust Controllers ...

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48th Annual Conference of the IEEE Industrial electronics Society, IECON 2022

作者： Nagai, Shunya Oya, Hidetoshi Kubo, Tomohiro Matsuki, Tsuyoshi Niihama College Department of Electronics and Control Engineering Ehime Japan Tokyo City University Faculty of Knowledge Engineering Department of Computer Science Tokyo Japan Tokushima University Graduate School of Technology Industrial and Social Sciences Tokushima Japan

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

For a class of uncertain large-scale interconnected systems, a design method of decentralized variable gain robust controllers with guaranteed { mathcal{L}-{{2}}} gain performance based on piecewise Lyapunov functions is shown in this paper. Furthermore, LMI-based sufficient conditions for the existence of the proposed decentralized variable gain robust controller are shown. Finally, the effectiveness of the proposed robust controller is presented through a simple numerical example. © 2022 IEEE.

关键词： Lyapunov functions

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