Computers & Electrical Engineering最新文献

{"title":"Orthogonal least square based feature selection for an automatic hate speech detection and classification","authors":"Srinivasulu Kothuru,&nbsp;A. Santhanavijayan","doi":"10.1016/j.compeleceng.2025.110131","DOIUrl":"10.1016/j.compeleceng.2025.110131","url":null,"abstract":"<div><div>Hate speech in social media is a growing issue nowadays that negatively affects the society and individuals within. Moreover, hate speech detection is a challenging task, due to the vast number of user data generated on a daily basis. This makes it difficult to review each comment made by a user. The major objective of this research is to effectively identify or detect hate speech, and classify the same, using Orthogonal Least Squares (OLS)-based feature selection. The different feature extraction approaches known as Term-Frequency-Inverse Data Frequency (TF-IDF), count vectorizer, global vector (GloVe) model, and aspect features, are used to extract frequent occurrences of the keywords, coverage of keywords and contextual meaning of words. The features chosen by the OLS are classified using Stacked Bidirectional Long Short-Term Memory with Multiple Attention Mechanism (SBiLSTM-MAM) to compute the attention among the hidden states and utterance embeddings. Thus, the developed OLS discards the unwanted features and concentrated on most significant features, that improves classification using SBiLSTM-MAM. Here, the OLS ranks the features according to its orthogonal significance, confirming that the chosen features are less collinear, thus minimizing the noise. From the result analysis, it clearly shows that the precisions gained by the proposed OLS-SBiLSTM-MAM, in the OLID and SOLID datasets, are 80.22% and 84.7% respectively, which are higher when compared to that of existing Softplus BiLSTM.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110131"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097545","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 dynamic range preprocessing, ParallelAttention Transformer and CoExpression analysis for facial expression recognition","authors":"Yuntao Zhou ,&nbsp;Thiyaporn Kantathanawat ,&nbsp;Somkiat Tuntiwongwanich ,&nbsp;Chunmao Liu","doi":"10.1016/j.compeleceng.2025.110110","DOIUrl":"10.1016/j.compeleceng.2025.110110","url":null,"abstract":"<div><div>Facial expression recognition (FER) aims to enable computers to automatically detect and recognize human facial expressions, thereby understanding their emotional states. Despite significant technological advancements in recent years, FER tasks still face several challenges, including expression diversity, individual differences, and the impact of lighting and detail variations on recognition accuracy. To address these challenges, a high-performance FER model is proposed that comprises three key components: High Dynamic Range (HDR) Preprocessing Module, ParallelAttention VisionTransformer structure, and CoExpression Head. In the preprocessing stage, the HDR Preprocessing Module optimizes input images through local contrast and detail enhancement techniques, improving the model’s adaptability to lighting and detail variations. During the feature processing stage, the ParallelAttention VisionTransformer structure employs a multi-head self-attention mechanism encoder to effectively capture and process facial expression features at various scales, allowing for a detailed understanding of subtle facial expression differences. Finally, the CoExpression Head utilizes a collaborative expression mechanism to efficiently handle and refine features across different expression states during the feature integration process. Combining these three stages significantly enhances the accuracy of facial expression recognition. Extensive experimental evaluations on public datasets, RAF-DB and AffectNet, demonstrate that the model achieves accuracy rates of 92.11%, 67.25%, and 63.40% on RAF-DB, AffectNet, and AffectNet-8, respectively, exhibiting outstanding performance comparable to other state-of-the-art models.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110110"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097544","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":"An optimization-based EVA-VCAS for effective voltage sag management","authors":"Adlan Pradana ,&nbsp;M．M Haque ,&nbsp;Mithulananthan Nadarajah ,&nbsp;AU Krismanto","doi":"10.1016/j.compeleceng.2025.110111","DOIUrl":"10.1016/j.compeleceng.2025.110111","url":null,"abstract":"<div><div>Voltage sags, which are temporary drops in voltage levels, pose significant challenges to power system stability and reliability, necessitating effective mitigation strategies. However, there is an opportunity to leverage energy stored in Electric Vehicles (EVs) to support the grid during disturbances through ancillary services. The entities that manage the energy flow from electric vehicles (EVs) to the grid are EV aggregators (EVAs). To measure the effectiveness of this approach, the Voltage Sag Score (VSS), a new single score designed to address the shortcomings of traditional metrics, is introduced. This score aims to complement aspects of the Rate of Change of Voltage (RoCoV) in the voltage disturbances. The proposed VSS integrates magnitude, duration, and minimum RoCoV with customizable weights depending on the element of importance. Five optimization algorithms—Particle Swarm Optimization (PSO), Nelder-Mead, Dividing Rectangles (DIRECT), Broyden-Fletcher-Goldfarb-Shanno (BFGS), and Legacy algorithms—are applied with two objective functions, Integral of the Squared Error (ISE) and Integral of the Time-weighted Squared Error (ITSE), across different power injection modes (active-reactive and reactive-only). These algorithms are implemented and compared using the System Parameter Identification toolbox in DIgSILENT PowerFactory.The results demonstrate that the PSO technique, combined with active and reactive power injection and the ISE performance measure, is the most effective solution for mitigating voltage sags, achieving a 63 % reduction in VSS. This approach significantly decreases the depth and duration of voltage drops, enhancing grid stability. Moreover, the method accounts for the mobility of EVs, proving that EVAs can effectively manage voltage issues even when EVs are not physically close to the affected areas. In such remote scenarios, the VSS was further reduced by 16.48 %, showcasing the robustness and adaptability of the proposed solution. When tested on a larger and more realistic grid, such as the IEEE 39-bus system, the VSS was reduced by 34.65 %, further validating the practical effectiveness and scalability of the approach in real-world grid environments.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110111"},"PeriodicalIF":4.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097540","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":"Compost maturity prediction and gas emissions monitoring: A sensor-based and interpretable machine learning approach","authors":"Amith Khandakar ,&nbsp;Azad Ashraf ,&nbsp;Mohamed Arselene Ayari ,&nbsp;Amin Esmaeili ,&nbsp;Mohannad Aljarrah ,&nbsp;Philips Michael ,&nbsp;Md. Nahiduzzaman ,&nbsp;Hafsa Binte Kibria ,&nbsp;Vasiliki Maria Gerokosta ,&nbsp;Abdul Ahad Shehbaz ,&nbsp;Maryam Abdulla R A Al-Mansoori ,&nbsp;Farah Khattab","doi":"10.1016/j.compeleceng.2025.110115","DOIUrl":"10.1016/j.compeleceng.2025.110115","url":null,"abstract":"<div><div>Here, a sensor-based machine learning approach has been presented to predict the maturity and monitor gas emissions during the composting process. By analyzing key environmental factors and emission data, our study aims to enhance the ecological responsibility of composting as a waste management solution. Our research combines a dedicated sensor system with machine learning. The sensor system, integrated with Arduino Mega 2560 R3 and ESP-32 microcontrollers, wirelessly transmits data for remote monitoring. Meanwhile, our machine learning framework analyzes features such as temperature, C/N ratio, ammonia concentration, pH levels, and nitrate content from ten datasets. After rigorous preprocessing and model training with a robust five-fold cross-validation, we optimize hyperparameters using GridSearchCV. The results highlight that both XGBOOST and CatBOOST excelled in achieving the highest predictive accuracy among the models, each attaining an impressive R² of 0.9912. In particular, XGBOOST demonstrated the lowest mean absolute error (MAE) at 1.1845, while CatBOOST exhibited the lowest mean squared error (MSE) at 1.8382. The interpretability of the model is ensured through LIME and SHAP, making complex models transparent and understandable. The results indicate that the XGBOOST model outperforms the others, achieving the highest predictive accuracy. This groundbreaking approach bridges scientific rigor with practical usability, ensuring responsible waste management for a sustainable future. Real-world applications of our research include more efficient and environmentally friendly waste management systems, reduced environmental impact, and improved compost quality for agricultural use.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110115"},"PeriodicalIF":4.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148972","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":"Robust operating strategy for voltage and frequency control in a non-linear hybrid renewable energy-based power system using communication time delay","authors":"Rasmia Irfan ,&nbsp;Muhammad Majid Gulzar ,&nbsp;Adnan Shakoor ,&nbsp;Salman Habib ,&nbsp;Hasnain Ahmad ,&nbsp;Shahid A. Hasib ,&nbsp;Huma Tehreem","doi":"10.1016/j.compeleceng.2025.110119","DOIUrl":"10.1016/j.compeleceng.2025.110119","url":null,"abstract":"<div><div>Nowadays modern power systems are of interconnected type having both conventional and renewable generation sources. Integration of renewable sources in these modern power systems causes serious stability issues specifically fluctuations of frequency and voltage are one of the major problems. So, to maintain the power quality we need to confront these issues of frequency and voltage fluctuations caused by the intermittent nature of renewable sources such as wind and solar. Addressing these challenges requires advanced control strategies based on real-time monitoring. In this paper, a sine cosine algorithm (SCA) tuned optimal dual mode PI controller with derivative control (DM-PI-DC) is proposed to mitigate frequency and voltage fluctuations. The investigated system comprises two areas having traditional power plants as well as renewable sources while taking into consideration the influence of communication time delays (CTDs). Confrontation of frequency fluctuation is handled by the load frequency control (LFC) loop and regulation of voltage in the power system is accomplished by the automatic voltage regulation (AVR) loop. In order to model a real system, the physical limitations of the power system are also taken into consideration. To manage the power flow, an interline power flow controller (IPFC) is incorporated and to keep the system stable during contingencies redox flow batteries (RFBs) are added to the system. Moreover, to evaluate the competence of the suggested controller it undergoes testing by variable loading, and also the comparison of performance is carried out with the advanced controllers. The detailed analysis showcases that the proposed controller demonstrates an oscillation-free response in 3.3 s whereas other controllers settle in 3.8 s, 6.45 s, 6.2 s, and 3.7 s. Moreover, the proposed controller achieves a 33.33 % improved response, particularly in terms of undershoot. The findings further show that the presented control strategy ensures power quality addressing all the key challenges.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110119"},"PeriodicalIF":4.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148977","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":"zk-DASTARK: A quantum-resistant, data authentication and zero-knowledge proof scheme for protecting data feed to smart contracts","authors":"Usama Habib Chaudhry ,&nbsp;Razi Arshad ,&nbsp;Ayesha Khalid ,&nbsp;Indranil Ghosh Ray ,&nbsp;Mehdi Hussain","doi":"10.1016/j.compeleceng.2025.110089","DOIUrl":"10.1016/j.compeleceng.2025.110089","url":null,"abstract":"<div><div>The emergence of blockchain technology and smart contracts revolutionize traditional digital applications such as identity management, supply chain management, banking and financial services with Decentralized Applications (DApps). When DApps are integrated with blockchain technology, blockchain validators can access user-sensitive off-chain data to execute a smart contract. On the one hand, DApps need authentic off-chain input user data to execute a given business scenario properly. On the other hand, users are more concerned about their privacy and are reluctant to share their sensitive data on the blockchain. For instance, healthcare insurance DApp requires sensitive user health data as input. DApp must ensure the privacy and authenticity of the user data given to the smart contract so that blockchain validators can perform operations on the user’s data without disclosing the user’s personal information. However, there is no complete solution to achieve both user privacy and data authenticity at the same time. To address this problem, we propose a highly efficient authenticated zero-knowledge proof scheme named zk-DASTARK by enhancing the standard zk-STARK scheme with a quantum attack-resistant data authentication scheme (CRYSTALS Dilithium digital signature scheme, now FIPS-204 or ML-DSA by the National Institute of Standards and Technology, NIST in the USA). Based on zk-DASTARK, we design a zk-STARKFeed, a zero-knowledge authenticated off-chain data feed mechanism that provides user data privacy and authentication for blockchain-based DApps. The blockchain validators’ computation costs can be significantly reduced using zk-STARKFeed with the integration of the ”compute off-chain and verify on-chain” approach. We have implemented zk-STARKFeed on the IOTA blockchain and performed extensive testing on the healthcare insurance DApp. Our proposed zk-STARKFeed is highly efficient on the IOTA blockchain in such a way that its proof generation takes less than 60 ms (ms) and its proof verification takes less than 10 ms.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110089"},"PeriodicalIF":4.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148975","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":"DBC-MulBiLSTM: A DistilBERT-CNN Feature Fusion Framework enhanced by multi-head self-attention and BiLSTM for smart contract vulnerability detection","authors":"Shujiang Xu ,&nbsp;Haochen He ,&nbsp;Miodrag J. Mihaljević ,&nbsp;Shuhui Zhang ,&nbsp;Wei Shao ,&nbsp;Qizheng Wang","doi":"10.1016/j.compeleceng.2025.110096","DOIUrl":"10.1016/j.compeleceng.2025.110096","url":null,"abstract":"<div><div>With the burgeoning of blockchain technology, particularly the Ethereum platform, smart contracts, serving as the core technology of blockchain, have demonstrated immense potential in numerous fields. However, vulnerabilities in smart contracts have also become targets for cyberattacks, potentially leading to significant economic losses. This study introduces a DBC-MulBiLSTM framework designed for the detection of vulnerabilities in smart contracts. The framework first utilizes the lightweight pre-trained model DistilBERT to extract contextual features from smart contracts, while simultaneously utilizing Convolutional Neural Networks (CNN) to identify local features. Through feature fusion, a multi-dimensional feature representation is formed to improve the model’s capabilities to recognize complex vulnerability patterns. Furthermore, the framework incorporates a multi-head self-attention mechanism within the BiLSTM architecture, thereby establishing the MulBiLSTM training framework. This design enables the simultaneous capture of long-range dependencies throughout the entire dataset, enhancing the model’s ability to represent intricate dependencies and contextual information effectively. Experimental results demonstrate that DBC-MulBiLSTM exhibits substantial efficacy in the detection of vulnerabilities within smart contracts, achieving an F1 score of 95.44%, an accuracy rate of 96.57%, and a recall of 95.36%. For various vulnerability types, the model consistently achieves accuracy and F1-scores over 96%, and recall rates above 95%, showcasing efficient and accurate smart contract vulnerability detection capabilities.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110096"},"PeriodicalIF":4.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148976","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":"Spatial pyramid attention and affinity inference embedding for unsupervised person re-identification","authors":"Qianyue Duan ,&nbsp;Huanjie Tao","doi":"10.1016/j.compeleceng.2025.110126","DOIUrl":"10.1016/j.compeleceng.2025.110126","url":null,"abstract":"<div><div>Unsupervised person re-identification (<em>Re</em>-ID) aims to learn discriminative features for retrieving person utilizing unlabeled data. Most existing unsupervised person <em>Re</em>-ID methods adopt the generic backbone to extract features for clustering to generate pseudo labels and utilize the pseudo labels to train the model. However, due to the lack of accurate category supervision, the generic backbone inevitably extracts interfering features, which degrade the quality of pseudo-labels. Besides, many methods only utilize the similarity between query and gallery images for matching person and ignore the use of affinity information between gallery images. To solve the above issues, we propose a spatial pyramid attention and affinity inference embedding network for unsupervised person <em>Re</em>-ID. We explore the benefit of attention mechanisms in unsupervised person <em>Re</em>-ID, where research is currently limited. We adopt the spatial pyramid attention (SPA) to aggregate structural information at different scales and ensures enough utilization of structural information during attention learning. With the help of SPA, the model reduces the extraction of interfering features, ensuring that it can learn more discriminative for clustering to improve pseudo-label quality. In addition, the affinity inference module (AIM) is utilized to optimize the distance between the query images and the gallery images by additionally using affinity information between gallery images. Extensive experiments on three datasets demonstrate that our method achieves competitive performance. Especially, our method achieves Rank-1 accuracy of 77.1 % on the MSMT17 dataset, outperforming the recent unsupervised work DCMIP by 7+%. Our code will be released at: <span><span>https://github.com/wanderer1230/SPAENet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110126"},"PeriodicalIF":4.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097541","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":"Navigating cybersecurity training: A comprehensive review","authors":"Saif Al-Dean Qawasmeh ,&nbsp;Ali Abdullah S. AlQahtani ,&nbsp;Muhammad Khurram Khan","doi":"10.1016/j.compeleceng.2025.110097","DOIUrl":"10.1016/j.compeleceng.2025.110097","url":null,"abstract":"<div><div>In the dynamic realm of cybersecurity, awareness training is crucial for strengthening defenses against cyber threats. This survey examines a spectrum of cybersecurity awareness training methods, analyzing traditional, technology-based, and innovative strategies. It evaluates the principles, efficacy, and constraints of each method, presenting a comparative analysis that highlights their pros and cons. The study also investigates emerging trends like artificial intelligence and extended reality, discussing their prospective influence on the future of cybersecurity training. Additionally, it addresses implementation challenges and proposes solutions, drawing on insights from real-world case studies. The goal is to bolster the understanding of cybersecurity awareness training’s current landscape, offering valuable perspectives for both practitioners and scholars.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110097"},"PeriodicalIF":4.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148973","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":"MPPT controller improvement for a PEM fuel cell system based on Gaussian Process Regression with a digital twin","authors":"Jokin Uralde ,&nbsp;Oscar Barambones ,&nbsp;Jesus Sanchez ,&nbsp;Isidro Calvo ,&nbsp;Asier del Rio","doi":"10.1016/j.compeleceng.2025.110101","DOIUrl":"10.1016/j.compeleceng.2025.110101","url":null,"abstract":"<div><div>Hydrogen, due to its high energy density, stands out as an energy storage method for renewable energies in order to reduce the impact of the energy sector on global warming. Proton Exchange Membrane Fuel Cells (PEMFC) are responsible for converting the stored hydrogen into electrical energy and in order to obtain the highest energy conversion efficiency, the maximum power point (MPP) of the voltage-power curve of the fuel cell must be reached. Traditional Maximum Power Point Tracking (MPPT) algorithms, such as Perturb and Observe (P&amp;O) or controllers such us Proportional Integral Derivative (PID) controller, are easy to implement, but must strike a balance between fast response and accurate control. Other more complex controllers such as Fuzzy Logic Control (FLC) or neural networks achieve better performance but at a higher computational cost. This paper presents a combination of a conventional Sliding Mode Control (SMC) and a machine learning Gaussian Process Regression (GPR) algorithm that provides a reference duty cycle reaching a point close to the MPP which is then used by the SMC to obtain the actual MPP. A Digital Twin of the PEMFC and a DC/DC converter, which allow a fast and large data-set generation, are used for the generation of the GPR algorithm. The proposed control is compared with a conventional SMC and performance improvements are observed using the Integral of the Absolute Error (IAE) metric. The results show, in a control initiation test, a 67% improvement in the IAE metric of the proposed control over the conventional SMC. In a load change test, the proposed control also outperforms the conventional SMC by 42.9%.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110101"},"PeriodicalIF":4.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149961","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}