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Aggregated DER_A Model Parameterization Via Online Moving Horizon Estimation

IEEE Transactions on Smart Grid 2025年第4期16卷 3030-3044页

作者： Vasquez-Plaza, Jesus D. Subedi, Sunil Bhujel, Niranjan Hansen, Timothy M. Tonkoski, Reinaldo Andrade, Fabio Department of Electrical and Computer Engineering University of Puerto Rico Mayaguez Mayagüez PR USA Electrification and Infrastructures Division Oak Ridge National Laboratory Knoxville TN USA Electrical Engineering and Computer Science Department The University of Maine Orono ME USA Electrical Engineering and Computer Science Department South Dakota State University Brookings SD USA Chair of Electric Power Transmission and Distribution School of Engineering and Design Technical University of Munich Munich Germany

This paper introduces a methodology for parameterizing the DER A model using a novel smooth mathematical representation, simplifying the process and preserving accuracy in modeling inverter-based generator (IBG). The methodology employs an online parameterization process that can operate in real-time. The model parameterization process is structured into five sequential steps, each targeting a specific aspect of the DER A model through moving horizon estimation. This approach adapts to systems with varying voltage and frequency support requirements by selectively applying each step. Simulation results on systems with both known and unknown parameters validate the methodologys effectiveness. The online moving horizon estimation technique accurately captures the dynamics of the overall system and ensures that the parameterized DER A model closely mirrors the real systems voltage, current, and power dynamics. The findings highlight the potential of this methodology to substantially improve and simplify the dynamic modeling of power systems, paving the way for more reliable and robust IBG and grid integration. © 2010-2012 IEEE.

关键词： Parameterization

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Transaction transmission model for blockchain channels based on non-cooperative games

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science China(Information sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

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computers, Materials & Continua 2023年第5期75卷 3713-3728页

作者： Qiqi Li Longfei Ma Zheng Jiang Mingyong Li Bo Jin College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Institute of Systems and Robotics(ISR) Department of Electrical and Computer Engineering(DEEC)University of CoimbraCoimbraPortugal

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low ***-modal retrieval technology can be applied to search engines,crossmodalmedical processing,*** existing main method is to use amulti-label matching paradigm to finish the retrieval ***,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal *** avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal ***,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for ***,this method uses the inference capabilities of the transformer encoder to generate global fine-grained ***,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching *** article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous *** experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

关键词： Deep learning cross-modal retrieval hash learning transformer

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A novel deep dual self-attention and Bi-LSTM fusion framework for Parkinson’s disease prediction using freezing of gait: a biometric application

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Multimedia Tools and Applications 2024年第33期83卷 80179-80200页

作者： Habib, Zeeshan Mughal, Muhammad Ali Khan, Muhammad Attique Hamza, Ameer Alturki, Nazik Jamel, Leila Department of Electrical Engineering HITEC University Taxila Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

Parkinson’s disease (PD) disorder is caused by the imbalance of inhibitory dopamine and excitatory acetylcholine neurotransmitters, which causes hindrance in locomotion. Freezing of gait (FOG), tremors, and bradykinesia are the most debilitating and disconcerting symptoms of Parkinson’s Disease (PD). FOG episodes occur due to neurological control disorder and motorized impairments, which sternly hinder voluntary movement and forward locomotion. The computational intelligence techniques can effectively predict PD from the sensor data. To detect the FOG episodes often experienced by PD patients, this paper utilizes a 5G spectrum that operates at 4.8 GHz. The main idea of the proposed system is to extract the wireless channel characteristics information (containing amplitude variances) that can be integrated into a 5G communication system. This system collects data through wireless devices such as dipole antennas, network interface cards, and RF signal generators. Five human activities were performed: fast walking, slow walking, sitting on a chair, FOG episodes, and voluntary stopping. We proposed a novel deep dual self-attention and Bi-LSTM fusion architecture for PD prediction to train the collected dataset. Two architectures have been proposed, namely Deep Dual Attention Neural Network (D2ANN) and BiLSTM Neural Network (BNN), for the classification of PD using Chanel State Information (CSI) data. After that, the most prominent information is extracted using self-attention activation. A newly informed weighted fusion approach is proposed, and tree growth optimization is employed for the best feature selection. Finally, the selected features are classified using a multilayer full SoftMax neural network. The experimental process was conducted on FOG wireless sensor network data and obtained the highest accuracy of 98.66%. Compared to the results with state-of-the-art techniques, the proposed framework shows improved accuracy, recall, and precision rates. © The Author(s

关键词： Network architecture

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Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

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computers, Materials & Continua 2023年第1期74卷 1819-1835页

作者： Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil Radzi Ambar Ahmed Abdu Alattab Anwar Ali Yahya Yousef Asiri Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn MalaysiaParit RajaBatu Pahat86400JohorMalaysia Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran61441Saudi Arabia Department of Computer Science Faculty of Computer Science and Info.SystemsThamar UniversityDhamar87246Yemen Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or ***,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different ***,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and *** was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable *** experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,*** model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification ***,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

关键词： Cardiac short-axis MRI images automatic left ventricle localization deep learning models faster R-CNN

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Area-Oriented Resubstitution For Networks of Look-Up Tables

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年第7期44卷 2571-2584页

作者： Costamagna, Andrea Calvino, Alessandro Tempia Mishchenko, Alan Micheli, Giovanni De Swiss Federal Institute of Technology Lausanne Integrated Systems Laboratory Lausanne1015 Switzerland University of California Department of Electrical Engineering and Computer Sciences Berkeley United States

This paper addresses the challenge of reducing the number of nodes in Look-Up Table (LUT) networks with two significant applications. First, Field-Programmable Gate Arrays (FPGAs) can be modelled as networks of LUTs, and minimizing the node count is imperative to meet resource constraints. Second, in area-oriented design space exploration for standard-cell designs, collapsing a circuit into a LUT network, restructuring it, and later remapping to the original representation helps escape local minima. Thus, the development of algorithms for optimizing and restructuring LUT networks holds considerable promise for area-oriented optimization. Substitution (also called resubstitution) is a powerful logic minimization method that can identify non-local logic dependencies and exploit them for logic minimization. State-of-the-art substitution algorithms for LUT networks rely heavily on SAT solving, limiting the number of optimization attempts and the size of the substitution sub-networks to one node mishchenko2011scalable. Conversely, our method relies on circuit simulation to increase the number of substitution candidates and enables substitutions with more than one node. The experimental results show that the proposed method identifies optimization opportunities overlooked by other methods, improving 11 out of 23 best-known results in the EPFL synthesis competition and yielding a 3.46% area reduction compared to the state-of-the-art. © 1982-2012 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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Question Selection for Multi-Modal Code Search Synthesis using Probabilistic Version Spaces

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IEEE Transactions on Software engineering 2025年第06期51卷 1724-1744页

作者： Wu, Jiarong Jiang, Yanyan Wei, Lili Xu, Congying Cheung, Shing-Chi Xu, Chang The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong McGill University Department of Electrical and Computer Engineering Montreal Canada Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing China

Searching the occurrences of specific code patterns (code search) is a common task in software engineering, and programming by example (PBE) techniques have been applied to ease customizing code patterns. However, previous PBE tools only synthesize programs meeting the input-output examples, which may not always align with the user intent. To bridge this gap, this paper proposes Excalibur, a multi-modal (example and natural language description) and interactive synthesizer for code search. Excalibur ensures that the generated programs are correct for the provided examples (soundness) and include the user-intended program (bounded completeness). Furthermore, Excalibur helps the user identify the user-intended program through question-answer interaction. To minimize the required interaction efforts, question selection is crucial. To improve question selection for code search, we propose probabilistic version spaces (ProbVS), in which the user-intended program’s probability is high and others are low. ProbVS combines traditional version spaces for compactly representing extensive programs and large language models (on the user-provided natural language description) for adjusting programs’ probabilities to align with users’ intents. Extensive experiments on a benchmark of 44 tasks demonstrated the effectiveness of Excalibur and ProbVS and demystified how ProbVS affects probability distributions and how the configurable parameters affect ProbVS. © 1976-2012 IEEE.

关键词： Normal distribution

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Federated Learning With Meta-Layers Training for Privacy-Preserving in Vehicular Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 621-629页

作者： Shen, Xiaoyang Li, Haibin Li, Yaqian Zhang, Wenming Alenazi, Mohammed J.F. Agarwal, Kadambri Yanshan University College of Electrical Engineering Hebei Qinhuangdao066000 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Hebei Qinhuangdao066000 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia ABES Engineering College Department of Computer Science and Engineering Ghaziabad201009 India

Vehicular consumer electronics, such as autonomous vehicles (AVs), need collecting large amounts of private user information, which face the risk of privacy leakage. To protect the privacy of consumers, researchers have proposed to apply federated learning (FL) to privacy-preserving vehicular consumer electronics, that is, leveraging FL for collaborative training of a decision model without exchanging the sensitive information generated by AVs. However, FL generally needs to exchange the gradients of client models periodically with the central server, which can be attacked by adversaries to infer user information. Thereby, it may still face the risk of privacy leakage. To solve that challenge, we put forward a novel FL framework, called FL with Meta-Layers Training (FL-MLT). Instead of exchanging the gradients of the client models, it exchanges, meta-layers with the central servers. Since meta-layers are only a slice of the client models, exchanging them helps protect privacy. On the other hand, they contain meta-knowledge to help the FL training process. In the experiments, we conduct extensive visual classification simulation to evaluate FL-MLT, and the experimental results demonstrate the superior performance of FL-MLT. © 1975-2011 IEEE.

关键词： Federated learning

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MWRS: A MAB-based Worker Recruitment Scheme with Tripartite Stackelberg Game for Reliable Mobile Crowdsensing

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5665-5680页

作者： Ouyang, Yan Zeng, Feng Xiong, Neal N. Liu, Anfeng Pedrycz, Witold School of Computer Science and Engineering Central South University Changsha410083 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istinye University Istanbul Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Mobile Crowdsensing (MCS) has emerged as a compelling paradigm for data sensing and collection, leveraging the widespread adoption of mobile devices and the active participation of numerous users. Despite its potential, MCS faces critical challenges, particularly in recruiting reliable workers and acquiring high-quality sensing data. Most existing approaches assume prior information on worker quality and are vulnerable to collusion attacks, especially having not comprehensively considered workers' reliability and stability. To address these problems, we propose a Multi-Armed Bandit (MAB) based Worker Recruitment Scheme (MWRS) integrated with the Tripartite Stackelberg Game (TSG) for MCS. Specifically, a trust evaluation and truth inference mechanism is introduced to assess the trustworthiness of workers through active truth detection. To enhance recruitment quality, we employ a trust-aware worker selection mechanism that utilizes a modified Upper Confidence Bound (UCB) algorithm, achieving an optimal balance between exploration and exploitation. Furthermore, the interactions among participants are modeled using a TSG framework, which formulates their respective payoffs to determine optimal decision-making strategies, thus achieving mutually beneficial outcomes. Extensive evaluations on real-world datasets demonstrate that our proposed scheme improves total quality by up to 30.8% and reduces regret by up to 80.3% compared to existing methods. © 2025 IEEE.

关键词： Decision making

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