Computers & Electrical Engineering最新文献_第3页

Detection and localization of dynamic load altering attacks in power systems

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-02-25 DOI: 10.1016/j.compeleceng.2025.110207

Fatemeh Najafi , Shaghayegh Nobakht , Marzieh Samimiat , Ali-Akbar Ahmadi , Abolfazl Nateghi

{"title":"Detection and localization of dynamic load altering attacks in power systems","authors":"Fatemeh Najafi , Shaghayegh Nobakht , Marzieh Samimiat , Ali-Akbar Ahmadi , Abolfazl Nateghi","doi":"10.1016/j.compeleceng.2025.110207","DOIUrl":"10.1016/j.compeleceng.2025.110207","url":null,"abstract":"<div><div>Cyber-attacks in power systems which are a type of cyber-physical systems (CPSs) can cause many problems, including system instability and blackouts. Meanwhile, dynamic load altering attacks (D-LAAs) could have a very destructive effect. In this paper, detection of the <span>d</span>-LAA in the power systems is discussed. The power system in presence of the <span>d</span>-LAA is modeled as a singular system and then an appropriate attack detection observer is designed. Then, using a bank of unknown input observers, the location of the attack is determined. Comparing to the existing results, no restrictive assumption such as presence of phasor measurement units (PMUs) in all buses or on the attack signal are considered. The design is done in the discrete-time domain and thus is suitable for practical implementation in the power systems where most of the relays and equipment are numerical. The design of the ADO and attack localization observers (ALOs) are performed using a centralized approach which facilitates taking the necessary actions to maintain the stability of the power system. Finally, simulation results on the IEEE 39-bus system with the help of MATLAB show the efficiency and capability of the proposed method.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110207"},"PeriodicalIF":4.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GAL-GAN: Global styles and local high-frequency learning based generative adversarial network for image cartoonization

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-02-25 DOI: 10.1016/j.compeleceng.2025.110164

Luoyi Li, Lintao Zheng, Chunlei Yang, Yongsheng Dong

{"title":"GAL-GAN: Global styles and local high-frequency learning based generative adversarial network for image cartoonization","authors":"Luoyi Li, Lintao Zheng, Chunlei Yang, Yongsheng Dong","doi":"10.1016/j.compeleceng.2025.110164","DOIUrl":"10.1016/j.compeleceng.2025.110164","url":null,"abstract":"<div><div>The style transfer of cartoon images has always been a challenging problem in computer vision. Currently, there are still two aspects that need to be improved in this field: (1) existing methods can only perform simple domain-to-domain cartoon style transfer, ignoring the global style information of the image, and (2) the neglect of local features in image style transfer, such as edge information and texture information, leads to lower quality of stylized images. To alleviate these two issues, we propose a novel global styles and local high-frequency learning based generative adversarial network (GAL-GAN) for image cartoonization. Specifically, the feature information of each channel is weighted by cartoon feature mapping to improve the quality of the global cartoon style of the generated image. In order to enrich the local feature information of generated images, we introduce a high-frequency learning strategy to reduce noise and enhance texture and detail extraction. Experiments reveal that GAL-GAN can generate high-quality stylized images with a specific style and have advantages over current state-of-the-art models.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110164"},"PeriodicalIF":4.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HybridCISN: Integrating 2D/3D convolutions and involutions with hyperspectral imaging and blood biomarkers for neonatal disease detection HybridCISN：将二维/三维卷积和渐开线与高光谱成像和血液生物标志物相结合，用于新生儿疾病检测

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-02-25 DOI: 10.1016/j.compeleceng.2025.110193

Mücahit CİHAN, Murat CEYLAN

{"title":"HybridCISN: Integrating 2D/3D convolutions and involutions with hyperspectral imaging and blood biomarkers for neonatal disease detection","authors":"Mücahit CİHAN, Murat CEYLAN","doi":"10.1016/j.compeleceng.2025.110193","DOIUrl":"10.1016/j.compeleceng.2025.110193","url":null,"abstract":"<div><div>Early detection and accurate diagnosis of neonatal diseases are crucial for improving health outcomes and reducing infant mortality. This study introduces a novel Hybrid Convolutional and Involutional Spectral Network (HybridCISN) that integrates hyperspectral imaging (HSI) data with blood biomarker analysis to enhance neonatal health diagnostics. By combining 2D convolution, 3D convolution, and involution layers, the HybridCISN model extracts spatial, spectral, and channel-specific features, addressing limitations in traditional diagnostic methods. The model was evaluated through two distinct approaches: (1) using only HSI spectral data and (2) integrating HSI spectral data with blood biomarkers such as haemoglobin and bilirubin levels. These approaches were tested for both binary classification (healthy vs. unhealthy neonates) and multiclass classification (specific neonatal diseases such as intracranial hemorrhage, necrotizing enterocolitis, pneumothorax, and respiratory distress syndrome). Experimental results demonstrate the HybridCISN model's superior performance, achieving an overall accuracy of 93.64% for binary classification and 90.25% for multiclass classification. Compared to state-of-the-art methods such as the involution-based HarmonyNet and the 2D/3D convolution-based HybridSN, the HybridCISN model achieved accuracy improvements of 0.8% and 1.5%, respectively, in multiclass classification. The second approach, integrating blood biomarkers, improved diagnostic sensitivity and specificity, emphasizing the value of multimodal data fusion. Involution layers reduced channel redundancy and optimized feature extraction, as confirmed by ablation studies. The HybridCISN model offers a scalable and non-invasive diagnostic framework, addressing clinical applicability and biomarker accessibility, while combining precision, efficiency, and robustness to advance neonatal disease detection and set a benchmark for future research in medical imaging.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110193"},"PeriodicalIF":4.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating PQTCN-MPC with innovation: A new strategy for accurate PV power prediction

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-02-24 DOI: 10.1016/j.compeleceng.2025.110188

Zhongbao Lin, Desheng Rong

{"title":"Integrating PQTCN-MPC with innovation: A new strategy for accurate PV power prediction","authors":"Zhongbao Lin, Desheng Rong","doi":"10.1016/j.compeleceng.2025.110188","DOIUrl":"10.1016/j.compeleceng.2025.110188","url":null,"abstract":"<div><div>Accurately capturing long-term and short-term dependencies and cyclical relationships are core elements in determining photovoltaic prediction accuracy. In this paper, a novel method named PQTCN-MPC, which integrates a Parallel Quadratic Temporal Convolutional Network (PQTCN) with a Multi-Position Coding (MPC) Transformer, is proposed. First, PQTCN effectively extracts long and short-term depth dependencies of time series. Subsequently, the extracted features are encoded using MPC and Embedding, and the encoded features are concatenated. Finally, the output is obtained through an encoder and decoder structure. This study utilizes publicly available data from the Yulala solar system with three different resolutions. Ablation experiments validate that PQTCN-MPC enhances <em>R</em><sup>2</sup> by 3.69 %, <em>NRMSE</em> by 21.11 %, and <em>MAE</em> by 48.35 % at a minimum. Experimental results indicated that PQTCN-MPC enhanced <em>R</em><sup>2</sup> by 4.73 % under various seasonal conditions, while keeping <em>NRMSE</em> below 5 %, which underscores its high prediction accuracy and wide applicability.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110188"},"PeriodicalIF":4.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solar Irradiance Forecasting using Hybrid Long-Short-Term-Memory based Recurrent Ensemble Deep Random Vector Functional Link Network

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-02-24 DOI: 10.1016/j.compeleceng.2025.110174

Smruti Rekha Pattnaik , Ranjeeta Bisoi , P.K. Dash

{"title":"Solar Irradiance Forecasting using Hybrid Long-Short-Term-Memory based Recurrent Ensemble Deep Random Vector Functional Link Network","authors":"Smruti Rekha Pattnaik , Ranjeeta Bisoi , P.K. Dash","doi":"10.1016/j.compeleceng.2025.110174","DOIUrl":"10.1016/j.compeleceng.2025.110174","url":null,"abstract":"<div><div>Accurate and reliable forecasting of solar irradiance is necessary for an efficient grid performance with large scale penetration of photovoltaic (PV) generation. Thus, with an aim to improve solar irradiance forecasting accuracy, a new decomposition based hybrid model known as Stacked Long-Short-Term-Memory (LSTM) recurrent neural network is proposed in this paper. Further the dense layer of the stacked LSTM architecture is replaced by a novel Recurrent Ensemble Deep Random Vector Functional Link Network (REDRVFLN) to improve generalisation, speed up computation, and prediction accuracy. The raw irradiance data is pre-processed using Isolation Forest (IF) algorithm to remove the presence of outliers from the data and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm decomposes the pre-processed data into Intrinsic Mode Functions (IMFs) with zero reconstruction error and better separation of spectral components. The recurrent stacked LSTM neural network effectively captures the temporal features and long term dependencies of decomposed solar irradiance time series data. On the other hand REDRVFLN model comprising several stacked layers of locally recurrent neurons with fixed random weights and biases effectively handles processed temporal features from the LSTM module with optimal generalisation and improved stability. Further the ensemble of the outputs from each layer produces the final forecast with better accuracy in comparison to many widely used deep neural network and other benchmark models. The performance of the proposed stacked LSTM integrated REDRVFLN model has been validated using solar irradiance data samples both hourly and with seasonal variations producing superior accuracy.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110174"},"PeriodicalIF":4.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An improved blockchain-based multi-region Federated Learning framework for crop disease diagnosis

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-02-22 DOI: 10.1016/j.compeleceng.2025.110181

Yuanze Qin , Chang Xu , Qin Zhou , Lingxian Zhang , Yiding Zhang

{"title":"An improved blockchain-based multi-region Federated Learning framework for crop disease diagnosis","authors":"Yuanze Qin , Chang Xu , Qin Zhou , Lingxian Zhang , Yiding Zhang","doi":"10.1016/j.compeleceng.2025.110181","DOIUrl":"10.1016/j.compeleceng.2025.110181","url":null,"abstract":"<div><div>Plant Electronic Medical Records (PEMRs), containing crop disease and environmental data, provide a novel approach for disease diagnosis. However, training a federated learning (FL) model with PEMRs distributed across multiple devices poses challenges, such as opaque aggregation processes, risks of Byzantine faults, and high communication overhead. In this paper, we develop a blockchain-based multi-region federated learning (BMRFL) framework for crop disease diagnosis, incorporating the consortium blockchain technology to ensure that the process is both verifiable and resistant to attacks. We introduce the Musig2 Signature-based Practical Byzantine Fault Tolerance (M2SPBFT) protocol, which leverages the Musig2 algorithm to improve efficiency by reducing communication overhead and streamlining the verification process. Furthermore, we develop a aggregation strategy that boosts the global model's accuracy in diagnosing crop diseases. We constructed PEMR datasets with 23,702 samples from Beijing Plant Clinics to validate the BMRFL. Extensive experiments revealed that the BMRFL framework improved Byzantine fault resistance, lowered consensus communication overhead, and enhanced diagnostic accuracy across districts, achieving a 10.44 % accuracy increase in Haidian over previous methods. These results demonstrate the effectiveness and security of BMRFL in crop disease diagnosis, suggesting its potential for related diagnostic applications.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110181"},"PeriodicalIF":4.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancement design of eleven-level cascaded h-bridge motor driver application

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-02-22 DOI: 10.1016/j.compeleceng.2025.110179

Adil Adam , Firat Kacar , Nikos Mastorakis

{"title":"Enhancement design of eleven-level cascaded h-bridge motor driver application","authors":"Adil Adam , Firat Kacar , Nikos Mastorakis","doi":"10.1016/j.compeleceng.2025.110179","DOIUrl":"10.1016/j.compeleceng.2025.110179","url":null,"abstract":"<div><div>This study focuses on the design and implementation of an innovative eleven-level cascaded H-Bridge motor drive (11L-CHBMD) controlled by a three-phase step-sine pulse width modulation (SSPWM) technique. A novel mathematical model was developed by converting control equations into matrix format, facilitating precise simulation and practical realization of the system. MATLAB Simulink was employed for the simulation, while the STM32F429ZGT6 microcontroller and power MOSFETs were used for hardware implementation. The proposed system ensures a regulated high-voltage, variable-current output and achieves harmonic distortion levels below 5 %, in compliance with IEEE-519 standards. Experimental results showed the motor driver 11L-CHBMD's high capability to drive three-phase induction motors efficiently, offering superior performance compared to conventional topologies. The SSPWM method reduced total harmonic distortion (THD) while maintaining system stability under ohmic, inductive, and unbalanced load conditions. Fuzzy and PID controllers enabled precise torque, speed, and current regulation while stabilising faster. The 11L-CHBMD proposed circuit, developed using commonly available components, achieves a cost reduction of approximately 90 % compared to market-available designs, making it suitable for industrial, renewable energy and different applications. Its modular design supports scalability and offers potential for driving motors in hazardous environments or remote areas using solar energy. With its adaptability and efficiency, the proposed 11L-CHBMD stands as a compelling alternative to traditional power inverters.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110179"},"PeriodicalIF":4.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EEG-CNN-Souping: Interpretable emotion recognition from EEG signals using EEG-CNN-souping model and explainable AI

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-02-22 DOI: 10.1016/j.compeleceng.2025.110189

Eamin Chaudary, Sheeraz Ahmad Khan, Wajid Mumtaz

{"title":"EEG-CNN-Souping: Interpretable emotion recognition from EEG signals using EEG-CNN-souping model and explainable AI","authors":"Eamin Chaudary, Sheeraz Ahmad Khan, Wajid Mumtaz","doi":"10.1016/j.compeleceng.2025.110189","DOIUrl":"10.1016/j.compeleceng.2025.110189","url":null,"abstract":"<div><div>Emotion recognition is a key aspect of human–robot interaction (HRI), which requires social intelligence to perceive and react to human affective states. This paper introduces EEG-CNN-Souping, a novel approach that applies the “Model Soups” technique to a self-designed EEG-CNN model for classifying electroencephalogram (EEG) signals into emotions. EEG-CNN-Souping improves the model performance and efficiency by averaging the weights of multiple EEG-CNN models trained on different sizes of scalograms, which are acquired by applying continuous wavelet transform (CWT) and normalization to the EEG signals. The scalograms capture the time-varying patterns of the EEG signals effectively. The approach also uses data augmentation and gradient class activation map (Grad-Cam) visualization for robustness and interpretability respectively. The model is evaluated on a common dataset that is the SEED dataset and achieves a 99.31% accuracy, surpassing other state-of-the-art deep learning (DL) models in terms of accuracy, computational cost, and time efficiency. The prediction time for EEG-CNN-Souping is only 6 ms. The explainable artificial intelligence (XAI) method Grad-CAM is utilized for interpretation of predictions. EEG-CNN-Souping is computationally inexpensive and time-efficient.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110189"},"PeriodicalIF":4.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward the development of learning methods with distributed processing using securely divided data

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-02-22 DOI: 10.1016/j.compeleceng.2025.110160

Hirofumi Miyajima , Noritaka Shigei , Hiromi Miyajima , Norio Shiratori

{"title":"Toward the development of learning methods with distributed processing using securely divided data","authors":"Hirofumi Miyajima , Noritaka Shigei , Hiromi Miyajima , Norio Shiratori","doi":"10.1016/j.compeleceng.2025.110160","DOIUrl":"10.1016/j.compeleceng.2025.110160","url":null,"abstract":"<div><div>To pave the way to a super-smart society, artificial intelligence (AI) methods are being developed to discover and analyze necessary information instantly from cyberspace and utilize it in physical space. However, privacy protection is necessary for AI to process big data in cyberspace. From the viewpoint of developing safe and secure machine learning methods, research on (1) homomorphic cryptography, (2) differential privacy, (3) secure multiparty computation, and (4) federated learning is underway. The goal of these studies is to develop useful learning methods while maintaining data privacy.</div><div>We propose a method to address the trade-off between security and usability in machine learning. This method balances usability and data confidentiality by using decomposed data to achieve secure distributed processing. However, such methods using distributed processing increase computational and communication overhead as the number of servers increases. To address this problem, we propose a method to control the computational complexity as the number of servers increases. On the basis of these studies, this study first systematically addresses the construction of secure distributed processing methods with decomposed data. A comprehensive approach is essential to advance the field and allow these methods to be effectively applied to different domains. On the basis of these methods, we propose back-propagation and neural gas learning methods with reduced computational and communication requirements. We then apply the proposed methods to numerical simulations of class classification and clustering problems and show that accuracy comparable to that of conventional models can be achieved with <span><math><mrow><mn>1</mn><mo>/</mo><mi>Q</mi></mrow></math></span> computational and communication complexity for distributed models with <span><math><mi>Q</mi></math></span> servers.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110160"},"PeriodicalIF":4.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent traffic management via personalized group consensus based on chimp optimization-guided random vector functional link and quantum theory: A perspective of randomization

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-02-21 DOI: 10.1016/j.compeleceng.2025.110178

Yuge Niu , Chao Zhang , Arun Kumar Sangaiah , Kexin Liu , Fanghui Lu , Mohammed J.F. Alenazi , Salman A. AlQahtani

{"title":"Intelligent traffic management via personalized group consensus based on chimp optimization-guided random vector functional link and quantum theory: A perspective of randomization","authors":"Yuge Niu , Chao Zhang , Arun Kumar Sangaiah , Kexin Liu , Fanghui Lu , Mohammed J.F. Alenazi , Salman A. AlQahtani","doi":"10.1016/j.compeleceng.2025.110178","DOIUrl":"10.1016/j.compeleceng.2025.110178","url":null,"abstract":"<div><div>In urban traffic management, bike-sharing systems are crucial for green transportation. However, due to the uneven distribution of shared bikes and randomization of user behavior, the urban dockless bicycle sharing system (UDBSS) faces issues of randomization. Since rebalancing in UDBSS involves the opinion and preference of multiple stakeholders, it can be modeled as a group consensus problem. Nevertheless, mutual influence among users, changing preferences, and psychological inconsistencies, along with the absence of personalized strategies in traditional methods, adversely affect demand decisions for UDBSS. To address this issue, this paper innovatively combines random vector functional link (RVFL) networks, quantum theory (QT), and prospect–regret theory (P–RT), to construct a personalized two-stage group consensus framework. First, with the support of three-way decisions, an improved K-means++ algorithm based on Euclidean distances and Hausdorff distances is used for clustering, which reduces the uncertainty in the UDBSS problem. Additionally, to address the randomization issue, RVFL is used to calculate intragroup user weights, and the chimp optimization algorithm (CHOA) is applied for the hyperparameter optimization. Furthermore, considering users’ psychological behavior, a two-stage consensus reaching process (CRP) is designed, and a personalized adjustment mechanism based on QT, P–RT, and hesitation degrees is proposed. Finally, the proposed model is applied to a shared bicycle deployment scenario, with experimental analysis using data from the Citi Bike system and survey data to verify its effectiveness and feasibility.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110178"},"PeriodicalIF":4.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0