Computers & Electrical Engineering最新文献

{"title":"A deep learning model based on multi-attention mechanism and gated recurrent unit network for photovoltaic power forecasting","authors":"Kuo Yang,&nbsp;Yanjie Cai,&nbsp;Jinrun Cheng","doi":"10.1016/j.compeleceng.2025.110250","DOIUrl":"10.1016/j.compeleceng.2025.110250","url":null,"abstract":"<div><div>Solar energy plays a crucial role in the power grid due to its clean, stable, and cost-effective nature, as well as its significant storage potential. Accurate short-term photovoltaic (PV) power forecasting is essential for effective grid management and dispatching decisions. This study introduces a hybrid deep learning model integrating multiple attention mechanisms and gated recurrent unit networks to forecast PV output power one day in advance. To address the impact of random weather variations and historical PV power data on forecasting accuracy, the model incorporates an input attention mechanism to process input features. Additionally, temporal and spatial attention mechanisms are embedded within the encoder-decoder framework to enhance prediction performance. These mechanisms effectively capture the relationships between historical PV power output and meteorological variables while identifying crucial time-dependent hidden states. The proposed model is validated on a real-world PV dataset, achieving a mean absolute error of 0.0903 under favorable weather conditions, demonstrating a 22.5 % improvement over traditional forecasting methods across various weather classifications. Comparative analyses with other state-of-the-art models confirm that the proposed approach offers superior predictive accuracy.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110250"},"PeriodicalIF":4.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679515","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":"Irradiance sensorless PSO-based Integral Backstepping and Immersion & invariance algorithm for robust MPPT control with real-climatic microcontroller-in-the-loop experimental validation","authors":"Jian Chen ,&nbsp;Ambe Harrison ,&nbsp;Njimboh Henry Alombah ,&nbsp;Wulfran Fendzi Mbasso ,&nbsp;Reagan Jean Jacques MOLU ,&nbsp;Abdullah M Alharbi ,&nbsp;Pradeep Jangir","doi":"10.1016/j.compeleceng.2025.110279","DOIUrl":"10.1016/j.compeleceng.2025.110279","url":null,"abstract":"<div><div>This paper presents a novel irradiance sensorless Maximum Power Point Tracking (MPPT) controller for photovoltaic (PV) systems using a Particle Swarm Optimization (PSO)-based Integral Backstepping (IBSC) and Immersion &amp; Invariance (I&amp;I) algorithm. The proposed controller addresses the limitations of traditional and contemporary MPPT methods, such as the need for costly irradiance sensors and suboptimal performance under dynamic environmental conditions. The integration of a higher-order sliding mode differentiator (HOSMD) with the IBSC enhances transient response by completely eliminating overshoots, achieving a 0 % overshoot compared to 4.8 % with the conventional IBSC under standard test conditions. The system exhibits rapid tracking convergence with a significantly reduced tracking time of 0.4 ms, approximately seven times faster than the traditional Perturb and Observe (P&amp;O) algorithm's 3 ms. Under real-world conditions, the proposed system's irradiance estimator maintains a mean absolute error below 15 W/m², with a maximum error of 69 W/m² at high irradiance levels. The system achieves an operating efficiency of 99.99 % with peak-to-peak power ripples of just 0.17 % under standard conditions, outperforming eight state-of-the-art MPPT techniques. This robust and efficient MPPT solution is validated through extensive simulations and real-climatic conditions. Additionally, real-climatic experimental implementations are carried out using Microcontroller-in-the-loop (MIL) integration. The acquired experimental results do not only corroborate the simulation outcomes but also endorses the reliability and practical robustness of the proposed MPPT controller</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110279"},"PeriodicalIF":4.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679516","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":"Design and development of SIW based H-plane horn applicator for sustainable agriculture applications","authors":"Kiran Kumari,&nbsp;Mayank Agarwal,&nbsp;Hari Shankar Singh,&nbsp;Rajesh Khanna","doi":"10.1016/j.compeleceng.2025.110254","DOIUrl":"10.1016/j.compeleceng.2025.110254","url":null,"abstract":"<div><div>Traditional weed control methods often depend on chemical herbicides, raising ecological and health hazards which are introducing unsustainable practices in agriculture. This study explores electromagnetic (EM) wave-based soil sterilization as a sustainable alternative. A substrate-integrated waveguide (SIW) horn applicator, operating at 2.45 GHz, is proposed for efficient weed mitigation. The applicator is designed on an ultrathin substrate with a thickness of λ₀/10 at 2.45 GHz, providing improved gain and maintaining a compact, low-profile design. The proposed antenna incorporates Vivaldi-shaped flaring to enhance radiation performance, specifically in terms of directivity and gain. Further optimizations include the introduction of reflector nails, substrate extension, and refined flaring geometry, which improve EM radiation performance. Experimental validation indicates that with 25 watts of power, the soil temperature can be raised to 68.6°C within 60 min, sufficient to thermally eradicate most weed species. Comprehensive thermal simulations were conducted to assess the antenna's efficacy in diverse soil conditions, such as wet and loamy soils, examining heat distribution by changing loss tangent of soil. The nutrient values such as NPK, PH and electric conductivity of soil also been measured with both controlled and treated soil. Results indicates a promising value across all the measured parameters. These analyses demonstrate the thermal impact of the SIW horn antenna and its potential for localized soil heating. The results provide critical insights into the use of EM waves for soil sterilization, offering a sustainable, non-chemical approach to weed management, with potential implications for both agricultural practices and environmental conservation.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110254"},"PeriodicalIF":4.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679047","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 section on the role of renewable energies in an efficient electric power system (VSI-irep3)","authors":"Ahmad Harb ,&nbsp;Fernando Tadeo","doi":"10.1016/j.compeleceng.2025.110273","DOIUrl":"10.1016/j.compeleceng.2025.110273","url":null,"abstract":"","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110273"},"PeriodicalIF":4.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679520","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":"High-performance and non-contact energy harvesters from high-voltage power lines magnetic fields","authors":"Bahram Rashidi","doi":"10.1016/j.compeleceng.2025.110267","DOIUrl":"10.1016/j.compeleceng.2025.110267","url":null,"abstract":"<div><div>In this paper, high-performance and non-contact energy harvesters from the magnetic fields of high-voltage power lines are presented. The energy harvesters are based on a rod ferrite core in the middle of the coil and two <span><math><mi>⊐</mi></math></span>-shaped ferrite cores at both ends of the rod ferrite core. The use of two <span><math><mi>⊐</mi></math></span>-shaped ferrite cores can provide several times improvement in power. Exposing these cores to magnetic flux increases the magnetic flux guiding efficiency and increases the energy harvesting rate. Here, by connecting a voltage rectifier circuit, we design a portable power supply based on energy harvesting from magnetic fields, which can provide the power consumption needs of some low-power circuits. The results of the capacitor charging ability, the open-circuit voltage across the harvesters, and the output power are measured to evaluate the performance of the proposed structures. The results show that the structures exhibit acceptable performance for practical requirements. Based on the results, a maximum open-circuit voltage of 13.619 V and an output power of 4.449 mW under a magnetic field of 7 <span><math><mi>μ</mi></math></span>T are achieved for the best proposed structure. Considering that the proposed energy harvesting structures are non-contact and low-cost, therefore, they can be used to provide power for some low-power wireless monitoring sensors in high-voltage power transmission systems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110267"},"PeriodicalIF":4.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679517","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":"TraceAwareness and dual-strategy fuzz testing: Enhancing path coverage and crash localization with stochastic science and large language models","authors":"Xiaoquan Chen ,&nbsp;Jian Liu ,&nbsp;Yingkai Zhang ,&nbsp;Qinsong Hu ,&nbsp;Yupeng Han ,&nbsp;Ruqi Zhang ,&nbsp;Jingqi Ran ,&nbsp;Lei Yan ,&nbsp;Baiqi Huang ,&nbsp;Shengtin Ma","doi":"10.1016/j.compeleceng.2025.110266","DOIUrl":"10.1016/j.compeleceng.2025.110266","url":null,"abstract":"<div><div>This paper proposes an innovative fuzzing technique to address path coverage and crash localization challenges inherent in traditional methods. We introduce TraceAwareness, a technology for precise tracking and recording of program execution paths, significantly enhancing fuzzing efficiency and issue traceability. Additionally, we present a dual-strategy method (DSM-SST-LLMT) based on stochastic science theory and large language model technology, combining random exploration with intelligent analysis for effective test input generation. Experimental evaluations demonstrate that our technique achieves 85% edge coverage compared to AFL++’s 35%, discovers 3,000 new paths versus AFL++’s 800, and identifies 8 critical crashes where AFL++ found none. Our approach shows particular strength in handling complex and diverse inputs, reaching 2-3 times the maximum path depth of AFL++. This research offers new directions for improving software testing efficiency and reliability, with potential applications in critical infrastructure, cloud-based systems, and IoT environments.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110266"},"PeriodicalIF":4.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679513","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":"Embedded deep learning models for multilingual speech recognition","authors":"Mohamed Hedi Rahmouni ,&nbsp;Mohamed Salah Salhi ,&nbsp;Ezzeddine Touti ,&nbsp;Hatem Allagui ,&nbsp;Mouloud Aoudia ,&nbsp;Mohammad Barr","doi":"10.1016/j.compeleceng.2025.110271","DOIUrl":"10.1016/j.compeleceng.2025.110271","url":null,"abstract":"<div><div>This paper investigates the hybridization of Genetic Algorithms (GA) with Recurrent Self-Organizing Maps (RSOM) for speech recognition. It ensures the benchmarking of its performance against traditional and deep learning-based methods, including Hidden Markov Models (HMM), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), Gated Recurrent Units (GRU), and wave to vector 2.0 (wav2vec 2.0). The aim of this study is to demonstrate the performance of the hybrid GA-RSOM model implemented on an embedded system, such as a modern Digital Signal Processing (DSP). The evaluation is carried out in terms of reaction time and recognition accuracy for speech with very high variability and multilingual content. Experiments show that while the GARSOM model is slower than some models like CNN, it achieves a stable and precise recognition rate of up to 98 %, depending on the phonemes.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110271"},"PeriodicalIF":4.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679509","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 coupled framework for power load forecasting with Gaussian implicit spatio temporal block and attention mechanisms network","authors":"Dezhi Liu ,&nbsp;Xuan Lin ,&nbsp;Hanyang Liu ,&nbsp;Jiaming Zhu ,&nbsp;Huayou Chen","doi":"10.1016/j.compeleceng.2025.110263","DOIUrl":"10.1016/j.compeleceng.2025.110263","url":null,"abstract":"<div><div>The increasing demand for electricity underscores the need for accurate power load forecasting to optimize grid management and resource allocation. With the emergence of more complex multi-energy hybrid systems, the resulting multivariate power load data pose significant challenges for precise forecasting. To address this, we propose a novel framework that integrates Variational Mode Decomposition (VMD) with an Encoder–Decoder architecture featuring customized Gaussian Implicit Spatio-Temporal (GIST) blocks to uncover implicit spatial dependencies across temporal and multi-feature dimensions. Initially, VMD decomposes the original time series into multiple resolution components, effectively reducing noise and extracting intrinsic temporal patterns. These components are then processed by an Encoder–Decoder network for prediction. Within each GIST block, token embedding is applied to the input before being fed into a Gaussian Mixture Model (GMM)-based implicit spatio-temporal representation module. Unlike conventional expectation–maximization (EM) algorithms, our learned Gaussian modeling approach provides a more adaptive and computationally efficient alternative for residential power load forecasting. Temporal dependencies are further captured through Long Short-Term Memory (LSTM) units and attention mechanisms across subsequent blocks, enhancing the model’s predictive capability. Experimental validation demonstrates the superior performance of our proposed model, achieving reductions of 7.98% and 9.32% in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), respectively, compared to existing forecasting models. Notably, our GMM-based approach outperforms traditional two-dimensional convolution-based methods, yielding improvements of 11.3% and 5.72% in MAE and RMSE, highlighting the efficacy of our framework in handling complex multivariate power load data.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110263"},"PeriodicalIF":4.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679511","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":"Revolutionizing power electronics design through large language models: Applications and future directions","authors":"Khalifa Aliyu Ibrahim ,&nbsp;Patrick Chi-Kwong Luk ,&nbsp;Zhenhua Luo ,&nbsp;Seng Yim Ng ,&nbsp;Lee Harrison","doi":"10.1016/j.compeleceng.2025.110248","DOIUrl":"10.1016/j.compeleceng.2025.110248","url":null,"abstract":"<div><div>The design of electronic circuits is critical for a wide range of applications, from the electrification of transportation to the Internet of Things (IoT). It demands substantial resources, is time-intensive, and can be highly intricate. Current design methods often lead to inefficiencies, prolonged design cycles, and susceptibility to human error. Advancements in artificial intelligence (AI) play a crucial role in power electronics design by increasing efficiency, promoting automation, and enhancing sustainability of electrical systems. Research has demonstrated the applications of AI in power electronics to enhance system performance, optimization, and control strategy using machine learning, fuzzy logic, expert systems, and metaheuristic methods. However, a review that includes the recent AI advancements and potential of large language models (LLMs) like generative pre-train transformers (GPT) has not been reported. This paper presents an overview of applications of AI in power electronics (PE) including the potential of LLMs. The influence of LLMs-AI on the design process of PE and future research directions is also highlighted. The development of advanced AI algorithms such as pre-train transformers, real-time implementations, interdisciplinary collaboration, and data-driven approaches are also discussed. The proposed LLMs-AI is used to design parameters of high-frequency wireless power transfer (HFWPT) using MATLAB as a first case study, and high-frequency alternating current (HFAC) inverter using PSIM as a second case study. The proposed LLM-AI driven design is verified based on a similar design reported in the literature and Wilcoxon signed-rank test was conducted to further validate the result. Results show that the LLM-AI driven design based on the OpenAI foundation model has the potential to streamline the design process of power electronics. These findings provide a good reference on the feasibility of LLMs-AI on power electronic design.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110248"},"PeriodicalIF":4.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679510","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":"Towards a physical imaging-driven sparse attention dehazer for Internet of Things-aided Maritime Intelligent Transportation","authors":"Yuxuan Tian ,&nbsp;Yu Guo ,&nbsp;Yuxu Lu ,&nbsp;Yuan Gao ,&nbsp;Ryan Wen Liu","doi":"10.1016/j.compeleceng.2025.110257","DOIUrl":"10.1016/j.compeleceng.2025.110257","url":null,"abstract":"<div><div>In the field of Maritime Intelligent Transportation Systems (MITS), the integration of Internet of Things (IoT) technologies and intelligent algorithms has revolutionized visual IoT-aided MITS. This integration, enabled by advanced communication technologies, network infrastructures, sensor capabilities, and data science methodologies, has significantly enhanced monitoring, navigation, and collision avoidance systems, thus improving waterway transportation efficiency. However, the performance of these systems can be hampered by atmospheric conditions, leading to degraded imaging quality characterized by contrast reduction, color distortion, and object invisibility. Such challenges impede critical vision-based tasks like object detection, tracking, and scene understanding in MITS. To address the performance gap between clear and hazy scenes, we propose a novel framework called PSDformer. This framework integrates Top-K Sparse Attention with a Physics-Aware Feed-Forward Network to enhance performance under hazy conditions. Additionally, we introduce a novel paired data generation method to reduce the disparity between synthetic and real-world data. Experimental results on synthetic and real-world datasets demonstrate that PSDformer outperforms existing state-of-the-art methods in both qualitative and quantitative evaluations. Importantly, its exceptional dehazing capability significantly improves detection accuracy under adverse hazy conditions, thereby addressing a critical challenge in visual IoT-aided MITS.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110257"},"PeriodicalIF":4.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679518","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}