Computers & Electrical Engineering最新文献

{"title":"Cross spatial and Cross-Scale Swin Transformer for fine-grained age estimation","authors":"Linbu Xu,&nbsp;Chunlong Hu,&nbsp;Xin Shu,&nbsp;Hualong Yu","doi":"10.1016/j.compeleceng.2025.110264","DOIUrl":"10.1016/j.compeleceng.2025.110264","url":null,"abstract":"<div><div>Facial age estimation is a classic problem in the field of computer vision. Previous studies have shown that learning discriminative features is crucial for accurate age estimation. Although Swin Transformer has been successfully applied on many computer vision tasks, it cannot effectively capture directional features during the aging process for age estimation task. Moreover, it still exhibits bias towards global features and cannot capture more fine-grained age-related features, ultimately leading to ambiguity in distinguishing adjacent ages. To address these issues, we propose <strong>Cross Spatial and Cross-Scale Swin Transformer (CSCS-Swin)</strong> that can extract fine-grained age-related features. Firstly, the <strong>Cross Spatial Feature Block (CSFB)</strong> module is constructed in CSCS-Swin, which extracts facial wrinkle features and craniofacial features along the horizontal and vertical directions, and models feature associations between different facial regions. Secondly, considering that the discrimination power of features at different scales differs in facial regions, <strong>Cross-Scale Feature Partition (CSFP)</strong> is proposed, which can precisely extract corss-scale fine-grained features. Lastly, the <strong>Feature Enhancement Module (FEM)</strong> is introduced to further enhance the ability of feature representation. These three modules in CSCS-Swin work together to improve the accuracy of age estimation. Extensive experiments on four popular datasets, namely, MORPH II, UTKFace, AFAD, and CACD, demonstrate the superiority of the proposed method.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110264"},"PeriodicalIF":4.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679512","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}

{"title":"Time–Frequency Domain Joint Noise Reduction Multi-Resolution Power System Time Series Prediction Network","authors":"Ziyang Cao ,&nbsp;Hangwei Tian ,&nbsp;Qihao Xu ,&nbsp;Jinzhu Wang","doi":"10.1016/j.compeleceng.2025.110255","DOIUrl":"10.1016/j.compeleceng.2025.110255","url":null,"abstract":"<div><div>This paper proposes an innovative time series prediction method designed for power systems to overcome the shortcomings of existing deep learning techniques in complex noise environments. The method, called <strong>T</strong>ime–Frequency <strong>D</strong>omain Joint Noise <strong>R</strong>eduction Multi-Resolution Power <strong>S</strong>ystem Time S<strong>e</strong>ries Prediction Network (TDRSE), investigates the impact of noise on prediction results and proposes a complete solution. TDRSE consists of two key components Exponential Decay-based Denoising Network (EDnet) and Dynamic Frequency-Domain Signal Enhancement Network (FDse). EDnet achieves dynamic attention to different time points in the time dimension by introducing exponential decay units to cope with the volatility of power loads and noise disturbances. At the same time, FDse employs frequency-domain enhancement techniques and adaptive thresholding strategies to remove the noise components in the frequency domain, thus further improving the model’s power data prediction accuracy. The experimental results show that TDRSE performs well on real data sets of multiple power systems, significantly improves the prediction accuracy under complex noise conditions, and reaches the industry-leading level.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110255"},"PeriodicalIF":4.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679040","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}

{"title":"Single stage transformer less multilevel inverter for solar PV application","authors":"Vinayak Kumar,&nbsp;Ruchi Agarwal","doi":"10.1016/j.compeleceng.2025.110243","DOIUrl":"10.1016/j.compeleceng.2025.110243","url":null,"abstract":"<div><div>The article presents a single stage transformer less multilevel inverter (SSTL-MLI) with common ground based inverter topology for grid tied PV application. It is designed with only 5 switches for 5 level generation. It uses switched capacitor approach therefore, needs single DC-source. Moreover, the topology has self voltage balancing feature, thereby no need of additional circuit. The peak current control (PCC) strategy is utilized to regulate switching pulse. MATLAB/Simulink is used to assess the proposed topology performance. It results voltage and current total harmonic distortion (THD) with values 32.49% and 1.66% respectively at the specified load at inverter output. It also satisfies IEEE 519 standard for power quality with grid current THD with value 2.12% for 20 harmonic order. The proposed system obtains maximum efficiency (<span><math><mi>η</mi></math></span>) with 98.30% value at input voltage (400V), and output power (4.72 kW). The cost function, composition with number of switch counts, DC source, weight coefficient and total standing voltage value has lowest value i.e. 16.75 in the proposed topology. Moreover, a scaled down prototype with 350 W rating is built of the proposed topology and validated under number of test condition such as change of load i.e resistive to inductive load, change of modulation index, and frequency change condition. The detailed comparative analysis is also presented in terms of switch count, TVS, MSV, efficiency, gain, cost function by comparing of number of existing topology so that the favorable feature of the proposed topology can be highlighted.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110243"},"PeriodicalIF":4.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679514","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}

{"title":"Harnessing hybrid intelligence: Four vector metaheuristic and differential evolution for optimized photovoltaic parameter extraction","authors":"Charaf Chermite,&nbsp;Moulay Rachid Douiri","doi":"10.1016/j.compeleceng.2025.110276","DOIUrl":"10.1016/j.compeleceng.2025.110276","url":null,"abstract":"<div><div>Accurate parameter extraction in photovoltaic (PV) cells and modules is crucial for optimizing performance and ensuring efficient energy conversion in solar technologies. However, existing optimization methods exhibit inherent limitations. The Four Vector Intelligent Metaheuristic (FVIM) demonstrates strong local refinement but suffers from limited global exploration and premature convergence. Meanwhile, Differential Evolution (DE) offers effective global search but often struggles with stagnation in local optima. To overcome these challenges, we introduce a novel hybrid algorithm that synergistically combines FVIM's multi-vector refinement strategy with DE's robust mutation and crossover mechanisms. This hybridization ensures a balanced trade-off between local exploitation and global exploration, significantly reducing the Root Mean Square Error (RMSE) between measured and estimated current values, ensuring precise parameter estimation. The FVIM-DE algorithm is rigorously benchmarked against 15 state-of-the-art metaheuristic algorithms across three standard PV models: the Single Diode Model (SDM), Double Diode Model (DDM), and Photovoltaic Module Model (PMM). Additionally, it was evaluated on various PV technologies under different irradiance and temperature conditions. Additionally, it was evaluated on various PV technologies under different irradiance and temperature conditions. FVIM-DE consistently achieves the lowest RMSE values, with a minimum of 9.8602E-4 for SDM, 9.8248E-4 for DDM, and 2.4250E-3 for PMM, surpassing all competing algorithms. Furthermore, the Friedman test ranks FVIM-DE first across all PV models, highlighting its robustness and statistical superiority. Results consistently highlight FVIM-DE's superior accuracy, rapid convergence, and adaptability, outperforming other methods in minimizing RMSE. This positions FVIM-DE as a reliable and effective tool for PV parameter extraction, advancing solar energy applications under diverse environmental conditions.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110276"},"PeriodicalIF":4.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679508","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}

{"title":"Introduction to the special issue on application of multi-agent systems, AI and blockchain in smart energy systems (VSI-sea)","authors":"Dr. Mazdak Zamani ,&nbsp;Dr. Fernando De la Prieta Pintado ,&nbsp;Dr. Tiago Pinto","doi":"10.1016/j.compeleceng.2025.110274","DOIUrl":"10.1016/j.compeleceng.2025.110274","url":null,"abstract":"","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110274"},"PeriodicalIF":4.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679519","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}

{"title":"A federated learning model with the whale optimization algorithm for renewable energy prediction","authors":"Viorica Rozina Chifu,&nbsp;Tudor Cioara,&nbsp;Cristian Daniel Anitei,&nbsp;Cristina Bianca Pop,&nbsp;Ionut Anghel,&nbsp;Liana Toderean","doi":"10.1016/j.compeleceng.2025.110259","DOIUrl":"10.1016/j.compeleceng.2025.110259","url":null,"abstract":"<div><div>Federated prediction models for energy prosumers create a global model by combining insights from local machine learning models trained on-site without centralizing the data. For time series energy data, this collaborative approach faces challenges due to the non-IID nature of the data, variations in generation patterns, the high number of model parameters, and convergence issues, leading to poor prediction accuracy. This paper introduces a novel federated learning model, FedWOA, which uses the whale optimization algorithm to determine optimal aggregation coefficients based on the local model weight vectors by pondering the updates considering the model performance and data dimensionality construct the global shared model. To handle the non-IID data the prosumers were clustered based on the similarity of their energy profiles using K-Means. FedWOA improves the prediction quality at the prosumer site, with a 16 % average reduction of the mean absolute error compared to FedAVG while demonstrating good convergence and reduced loss.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110259"},"PeriodicalIF":4.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644962","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}

{"title":"Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing","authors":"Fardin Jalil Piran ,&nbsp;Zhiling Chen ,&nbsp;Mohsen Imani ,&nbsp;Farhad Imani","doi":"10.1016/j.compeleceng.2025.110261","DOIUrl":"10.1016/j.compeleceng.2025.110261","url":null,"abstract":"<div><div>Federated Learning (FL) has become a key method for preserving data privacy in Internet of Things (IoT) environments, as it trains Machine Learning (ML) models locally while transmitting only model updates. Despite this design, FL remains susceptible to threats such as model inversion and membership inference attacks, which can reveal private training data. Differential Privacy (DP) techniques are often introduced to mitigate these risks, but simply injecting DP noise into black-box ML models can compromise accuracy, particularly in dynamic IoT contexts, where continuous, lifelong learning leads to excessive noise accumulation. To address this challenge, we propose Federated HyperDimensional computing with Privacy-preserving (FedHDPrivacy), an eXplainable Artificial Intelligence (XAI) framework that integrates neuro-symbolic computing and DP. Unlike conventional approaches, FedHDPrivacy actively monitors the cumulative noise across learning rounds and adds only the additional noise required to satisfy privacy constraints. In a real-world application for monitoring manufacturing machining processes, FedHDPrivacy maintains high performance while surpassing standard FL frameworks — Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Normalized Averaging (FedNova), and Federated Optimization (FedOpt) — by up to 37%. Looking ahead, FedHDPrivacy offers a promising avenue for further enhancements, such as incorporating multimodal data fusion.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110261"},"PeriodicalIF":4.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644963","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}

{"title":"TFMSNet: A time series forecasting framework with time–frequency analysis and multi-scale processing","authors":"Xin Song ,&nbsp;Xianglong Zhang ,&nbsp;Wang Tian ,&nbsp;Qiqi Zhu","doi":"10.1016/j.compeleceng.2025.110260","DOIUrl":"10.1016/j.compeleceng.2025.110260","url":null,"abstract":"<div><div>Time series forecasting is crucial in various fields. When dealing with complex time series data, existing methods often focus on a single scale or overlook frequency domain information, leading to the loss of critical information. To address this, this paper proposes TFMSNet, a novel time series forecasting framework combining time–frequency analysis with multi-scale processing. The framework decomposes the data into seasonal and trend components. For the seasonal component, TFMSNet utilizes Discrete Wavelet Transform (DWT) to decompose the data into subsequences of different frequencies, combining this with patch-based encoding layers and Inverse DWT to finely capture and reconstruct time–frequency features. It then performs multi-scale analysis and forecasting. For the trend component, the framework achieves multi-resolution representations through downsampling and uses Multilayer Perceptrons (MLPs) for prediction. By integrating both frequency and time domain information and leveraging the multi-scale characteristics of the data, TFMSNet significantly enhances prediction accuracy and robustness. Across 70 results from seven datasets, TFMSNet achieves 48 best and 20 second-best results, demonstrating the best overall performance.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110260"},"PeriodicalIF":4.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644961","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}

{"title":"COLO: Combined osprey and lyrebird optimization for optimal antenna selection for massive MIMO system","authors":"Raghunath Mandipudi,&nbsp;Chandra Shekhar Kotikalapudi","doi":"10.1016/j.compeleceng.2025.110245","DOIUrl":"10.1016/j.compeleceng.2025.110245","url":null,"abstract":"<div><div>Massive MIMO (M-MIMO) design is essential for enhancing spatial multiplexing gains in modern communication systems, but it often compromises energy efficiency (EE). Selecting the optimal antenna subset is crucial for boosting EE without negatively impacting spectrum efficiency (SE). However, due to the exponential increase in processing time as antenna count rises, exhaustive search methods become impractical for large MIMO systems. To address this, a novel optimization approach for optimal antenna selection (OAS) is proposed, combining the Osprey Optimization Algorithm (OOA) and Lyrebird Optimization Algorithm (LOA) into a hybrid COLO algorithm. COLO introduces key innovations, including a Feature Dependency-based (FDB) selection technique, a Global Positioning Strategy (GPS) for better search guidance, and OOA integration for enhanced exploration and exploitation. This approach aims to maximize SE while improving system efficiency. The suggested COLO for the maximal scenario has a lower fitness of 4.55×10–09, whereas the traditional LOA, RPO, OOA, EHO, COA, CB-PSO, and GA+CSO+PSO models achieve a higher fitness than COLO. The performance of COLO-based OAS is evaluated against existing methods in terms of efficiency, antenna count, statistical analysis, and convergence, demonstrating its superiority in maximizing SE.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110245"},"PeriodicalIF":4.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636272","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}

{"title":"A multi-perturbation consistency framework for semi-supervised person re-identification","authors":"Xinyuan Chen ,&nbsp;Yi Niu ,&nbsp;Mingwen Shao ,&nbsp;Weikuan Jia","doi":"10.1016/j.compeleceng.2025.110246","DOIUrl":"10.1016/j.compeleceng.2025.110246","url":null,"abstract":"<div><div>The semi-supervised person re-identification(Re-ID) task only manually annotates a small portion of person identities to reduce costs, but existing methods suffer from insufficient and incomplete utilization of hard unlabeled data, which leads to performance bottleneck. In this paper, we propose a new semi-supervised Re-ID framework to address this issue. In this framework, hard unlabeled samples participate in dual feature consistency learning by generating Multi-perturbation views. The proposed multi-perturbations include three different image-level perturbations and one feature-level perturbation, and the combination of these perturbations can fully simulate the complex changes of persons. To further improve the disturbance quality, a semi-supervised image generation network Semi-DGNet and a Perturbation Scheme Generator (PSG) are proposed to enhance the disturbance effect and control the disturbance intensity. Furthermore, a new Quintuplet loss is proposed to further reduce intra-class distance and increase inter-class distance through a metric learning strategy that involves the joint participation of labeled and unlabeled samples. The above work effectively explores the guiding role of labeled samples in training hard unlabeled data, which has inspiring value for future weakly supervised learning research. Extensive experiments on two datasets and sufficient comparisons with other existing state-of-art methods validate the effectiveness of the proposed framework, and verify its successful integration of multiple training strategies and process, modules, and optimization techniques.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110246"},"PeriodicalIF":4.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636273","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}