Computers & Electrical Engineering最新文献

{"title":"MIASS: A multi-interactive attention model for sleep staging via EEG and EOG signals","authors":"Xuhui Wang,&nbsp;Yuanyuan Zhu,&nbsp;Wenxin Lai","doi":"10.1016/j.compeleceng.2024.109852","DOIUrl":"10.1016/j.compeleceng.2024.109852","url":null,"abstract":"<div><div>Sleep staging is essential for sleep analysis. Recent studies have attempted to integrate multi-modal signals such as electroencephalogram (EEG) and electrooculogram (EOG) to enhance model sensitivity. However, these attempts still face limitations in effectively fusing multi-modal signals, particularly in capturing both global and fine-grained interaction information in sleep epochs simultaneously. To address this, we propose a multi-interactive model (MIASS) that integrates two core modules, the global information interaction (GII) module and the fine-grained information interaction (FII) module. The GII module can effectively capture the global correlation paradigm in EEG and EOG at the epoch level by combining the global channel and spatial attentions with a residual network. The FII module explores the fine-grained correlation paradigm between small EEG and EOG segments within epochs using the cross-attention mechanism to achieve more fine-grained interaction information. The combination of these modules increased the accuracy of the model up to 89.2%, 86.6% and 89.7% on the SleepEDF-20, SleepEDF-78 and SHHS datasets, respectively, which outperforms the comparison models by 0.2–5.7%. The ablation study confirmed the benefits of integrating global and fine-grained correlation paradigms to enhance sleep staging performance, and the model input study demonstrated that MIASS maintains good performance under various input conditions.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"121 ","pages":"Article 109852"},"PeriodicalIF":4.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701815","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":"Improved beluga whale optimization-based variable universe fuzzy controller for brushless direct current motors of electric tractors","authors":"Xionglin He ,&nbsp;Qiang Yu ,&nbsp;Xinjia Pan ,&nbsp;Longze Liu ,&nbsp;Zihong Jiang ,&nbsp;Wenyao Zhao ,&nbsp;Rui Fan","doi":"10.1016/j.compeleceng.2024.109866","DOIUrl":"10.1016/j.compeleceng.2024.109866","url":null,"abstract":"<div><div>Brushless direct current (BLDC) motors are widely used in electric tractor powertrains, but torque ripple remains a challenge. Proportional Integral Derivative (PID) controllers are effective in steady-state regulation but struggle with load-induced uncertainties. A new method for tuning sensorless BLDC motors by integrating improved Beluga Whale Optimization (IBWO) with an optimal variable universe fuzzy (VUF) controller is proposed. The enhanced IBWO addresses limitations in solving nonlinear systems, optimizing the VUF controller for precise torque control. A fast non-singular terminal sliding mode observer is also introduced for accurate state estimation. The IBWO adjusts the VUF controller parameters in real time, enabling adaptive torque and speed regulation, thereby reducing overshoot and torque ripple. To validate the proposed approach, a dual closed-loop control model is designed to simulate motor behavior under no load, variable load, and variable speed conditions during plowing operations. The results show that the proposed controller reduces torque ripple by at least 75 % and 60 % compared to PID and fuzzy controllers, respectively, and improves speed regulation time by over 26 %, with steady-state errors of 0.6, 0.7, and 0.12 rpm (rpm) under different conditions.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109866"},"PeriodicalIF":4.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698897","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 novel proactive frequency control based on 4-DoF-TMPC-1+PI-FOPI for a high order power system with communication delays and uncertainties","authors":"Daud Sibtain ,&nbsp;Riaz Ahmed Rana ,&nbsp;Ali Faisal Murtaza","doi":"10.1016/j.compeleceng.2024.109876","DOIUrl":"10.1016/j.compeleceng.2024.109876","url":null,"abstract":"<div><div>The stability of the electric power systems (EPS) is essential for the continuous flow of electricity in an extensive distributed area network (EDAN). Mitigating frequency fluctuations is one of the chief challenges in complex power system networks (PSN). The nonlinear dynamics of the electric power systems and the high penetration of renewable energy sources (RESs) will impose additional challenges on the EDAN by deteriorating the system frequency. To affirm better stability of the EPS under several contingencies of load perturbation, communication time delay (CTD), system parameters uncertainties, high renewable penetration, and faults between areas. A proactive frequency control (PFC) is formulated with the ability to counter these challenges by introducing novel 4-degrees of freedom (4-DOF) based hybrid tilt model predictive control (TMPC), and 1+ proportional integral-fractional order proportional integral (1+PI-FOPI) controller is deployed by taking into account frequency deviation, area control error (ACE), power tie and power grid. Utilizes an outer loop to minimize errors and a quicker inner loop to counter act the impacts of disturbances. The 4-DoF-TMPC-1+PI-FOPI is optimized by tunicate searching algorithm (TSA) for high order interconnected power system (HOIPS). The proposed controller shows efficient resilience in reducing frequency fluctuations by depicting a frequency regulation in 1.772 sec, 1.598 sec, 1.950 sec and 2.665 sec for area-1, area-2, area-3 and area-4 respectively.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109876"},"PeriodicalIF":4.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699499","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":"Optimizing random forests to detect intrusion in the Internet of Things","authors":"Seyede Zohre Majidian ,&nbsp;Shiva TaghipourEivazi ,&nbsp;Bahman Arasteh ,&nbsp;Ali Ghaffari","doi":"10.1016/j.compeleceng.2024.109860","DOIUrl":"10.1016/j.compeleceng.2024.109860","url":null,"abstract":"<div><div>The Internet of Things (IoT) has created new security challenges by connecting billions of smart devices to each other. One of these challenges is detecting attacks in IoT networks. Traditional attack detection methods are usually not suitable for large and complex networks such as IoT networks. In this research, a new model for detecting intrusion in IoT networks using Software-Defined Networking (SDN) is introduced. The main goal of the current research was to improve the stability of IoT networks against various attacks using an optimized machine learning model in a distributed manner. The presented approach uses the advantages of SDN, such as flexibility and centralized control, to improve intrusion detection performance. The proposed method includes two phases: first, the topology of the network is divided into a set of subdomains, and a controller node is assigned to each subdomain. Then, in the second phase, an ensemble classification model based on a random forest is utilized for detecting intrusion in each subdomain. This learning model is a forest of classification and regression trees (CARTs), each component of which is optimized by genetic algorithm (GA). Controller nodes can use this classification model to identify intrusion independently or cooperatively. The main novelty of the current work lies in optimizing multiple learning models and cooperatively utilizing them for intrusion detection goals. In an experimental environment based on MATLAB software, the effectiveness of this model for detecting intrusions on two databases, NSW-NB15 and NSLKDD, was evaluated. The findings of the experiments showed that this model can identify the attacks in these two databases with 98.06 % and 99.67 % accuracy respectively, which is significantly higher than the compared models.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109860"},"PeriodicalIF":4.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699501","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 efficient multi-criteria cell selection handover mechanism for Vehicle-to-Everything (V2X)","authors":"Faiza Rashid Ammar Al Harthi,&nbsp;Abderezak Touzene,&nbsp;Nasser Alzidi,&nbsp;Faiza Al Salti","doi":"10.1016/j.compeleceng.2024.109884","DOIUrl":"10.1016/j.compeleceng.2024.109884","url":null,"abstract":"<div><div>The deployment of cost-effective small cells to create ultra-high-density (UDN) heterogeneous networks in 5 G networks has emerged as a potentially effective strategy for enhancing network coverage and optimising resource allocation. However, UDN makes network selection more challenging due to the densification of small cells in 5 G and their heterogeneity. This research presents an efficient small cell selection handover mechanism for 5 G V2X networks. The proposed mechanism uses a Multiple Criteria Decision Making (MCDM) technique for the handover best cell selection to improve the overall performance. The proposed handover mechanism is context sensitive and it adapts to changing network conditions, ensuring efficient handovers during high-speed vehicular movement. Furthermore, the mechanism incorporates the concept of small cell Stay Time, which may reduce unnecessary handovers. The simulation results reveal that the proposed mechanism outperforms traditional handover techniques and Handover Decision-making Algorithm (HDMA) mechanisms significantly in terms of reducing the number of frequent handovers, minimizing link failures, and minimizing ping-pong with an average of 66 % reduction for unnecessary handovers.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109884"},"PeriodicalIF":4.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698898","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":"Uneven clustering in wireless sensor networks: A comprehensive review","authors":"Yogender Kumar Sharma ,&nbsp;Gulrej Ahmed ,&nbsp;Dinesh Kumar Saini","doi":"10.1016/j.compeleceng.2024.109844","DOIUrl":"10.1016/j.compeleceng.2024.109844","url":null,"abstract":"<div><div>The key component of Wireless Sensor Networks (WSNs) is the sensor node, which has a battery with limited energy, therefore the power utilization of the batteries must be optimized. Optimization in WSNs is required for energy efficiency and life span improvement. Several optimization techniques are proposed by researchers and clustering is one of the prominent techniques, in the power management of wireless sensor networks. Clustered WSNs provide advantages over normal WSNs such as improved bandwidth utilization, less overhead, enhancement in connectivity of links, efficiently balanced sensor nodes, stability in network topology, lesser delay, and reduced routing tables. There are two ways of clustering: even clustering and uneven clustering. In even clustering, the hotspot problem is caused by the inequality of the power consumed by the WSN's member nodes, which reduces the lifetime of the WSNs. To address the issue of hot spots, uneven clustering types are employed to balance the load among the cluster heads (CHs). Uneven cluster sizes have a significant impact on the communication range and reliability of the networks. Diversified clustering properties and methods of uneven clustering are rigorously reviewed. Uneven clustering characteristics and algorithms are classified and explained in the paper. In this paper, the authors reviewed all the algorithms for making clusters to balance uneven energy consumption and increase the lifespan of WSNs.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109844"},"PeriodicalIF":4.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698896","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":"Static security assessment of renewable integrated power systems using ensemble of modified ELM with unsupervised feature learning technique","authors":"Mukesh Singh,&nbsp;Sushil Chauhan","doi":"10.1016/j.compeleceng.2024.109881","DOIUrl":"10.1016/j.compeleceng.2024.109881","url":null,"abstract":"<div><div>The integration of renewable energy sources into power systems poses various challenges for static security assessment, including intermittency and variability of renewable generation, uncertainty in forecasting and impact on grid stability. Overcoming these challenges involves utilizing advanced modelling methods, refining forecasting algorithms, enhancing monitoring and control systems for the grid, and developing robust static security assessment approaches specifically designed for power systems integrated with renewable energy generation. A modified Extreme learning machine (ELM) based ensemble approach is proposed in this study, where ELM is combined with Levenberg-Marquardt (LM) backpropagation technique to improve the accuracy and robustness of prediction. Further, computational efficiency is improved through an unsupervised feature learning technique in the form of autoencoder to reduce the curse of dimensionality. The ensemble technique provides a comprehensive solution for evaluating the static security of power systems in the presence of uncertainties introduced by renewable energy sources. The uncertainties are incorporated into the test systems by simulating random solar and wind scenarios using a well-established Monte Carlo (MC) simulation method. The effectiveness of this approach is demonstrated through numerical testing on modified IEEE 14-bus, 30-bus, 118-bus, and an Indian practical 75-bus systems. Results show that the proposed model outperforms base learners in terms of reliability and efficiency.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109881"},"PeriodicalIF":4.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698899","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 domain correlation entropy image conversion: A new method for fault diagnosis of vehicle-mounted cable terminals","authors":"Kai Liu ,&nbsp;Like Fan ,&nbsp;Guangbo Nie ,&nbsp;Kai Wang ,&nbsp;Bo Gao ,&nbsp;Jianmin Fu ,&nbsp;Junbin Mu ,&nbsp;Guangning Wu","doi":"10.1016/j.compeleceng.2024.109865","DOIUrl":"10.1016/j.compeleceng.2024.109865","url":null,"abstract":"<div><div>The identification of partial discharge (PD) in cable terminals is crucial for the safe operation of trains. However, the complexity of the operational environment and the similarity of PD signals make defect identification challenging. Consequently, this paper proposes a Time-domain Local Correlation Entropy Image (T-LCEI) transformation method, which constructs an entropy matrix to convert raw PD signals into images. These images embed feature and bandwidth information from the original PD data, significantly enhancing the ability to differentiate between similar PD signals. Furthermore, the method combines a Dual Attention Convolutional Neural Network (DA_CNN) for the effective classification of correlation entropy images. Experimental results demonstrate that this approach achieves an average classification accuracy of 99.69% across four typical PD defect datasets, with a testing accuracy of 97.75% in practical scenarios. Compared to existing PD detection methods, T-LCEI offers significant improvements in effectiveness and discriminability. The integration of DA_CNN further enhances recognition accuracy. The study demonstrates that the proposed method excels in PD defect identification, providing reliable technical support for on-site fault detection and maintenance, thereby significantly improving the operational safety of cable terminals.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109865"},"PeriodicalIF":4.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655572","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":"Efficient Bayesian ECG denoising using adaptive covariance estimation and nonlinear Kalman Filtering","authors":"Hamed Danandeh Hesar ,&nbsp;Amin Danandeh Hesar","doi":"10.1016/j.compeleceng.2024.109869","DOIUrl":"10.1016/j.compeleceng.2024.109869","url":null,"abstract":"<div><div>Model-based Bayesian methods for denoising electrocardiogram (ECG) signals have demonstrated promise in preserving ECG morphology and diagnostic properties. These methods are effective for preserving and enhancing the features of ECG signals. However, their performance heavily relies on accurately selecting model parameters, particularly the state and measurement noise covariance matrices. Some of these frameworks also involve computationally intensive computations and loops for state estimation. To address these problems, in this study, we propose a novel approach to improve the performance of several model-based Bayesian frameworks, including the extended Kalman filter/smoother (EKF/EKS), unscented Kalman filter/smoother (UKF/UKS), cubature Kalman filter/smoother (CKF/CKS), and ensemble Kalman filter/smoother (EnKF/EnKS), specifically for ECG denoising tasks. Our methodology dynamically adjusts the state and measurement covariance matrices of the filters using outputs from nonlinear Kalman-based filtering methods. For each filter, we develop a unique approach based on the theoretical foundations of that filter. Additionally, we introduce two distinct strategies for updating these matrices, considering whether the noise in the signals is stationary or nonstationary. Furthermore, we propose a computationally efficient method that significantly reduces the calculation time required for implementing CKF/CKS, UKF/UKS, and EnKF/EnKS frameworks, while maintaining their denoising performance. Our approach can achieve a 50 % reduction in computation time for these frameworks, effectively making them twice as fast as their original implementations We thoroughly evaluated our approach by comparing denoising performance between the original filters and their adaptive versions, as well as against the state-of-the-art marginalized particle extended Kalman filter (MP-EKF). The evaluation utilized various normal ECG segments obtained from different records. The results demonstrate that the adaptive adjustment of covariance matrices significantly improves the denoising performance of nonlinear Kalman-based frameworks in both stationary and non-stationary environments, achieving performance comparable to that of the MP-EKF framework.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109869"},"PeriodicalIF":4.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655457","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":"The coupled Kaplan–Yorke-Logistic map for the image encryption applications","authors":"Puneet Kumar Pal,&nbsp;Dhirendra Kumar","doi":"10.1016/j.compeleceng.2024.109850","DOIUrl":"10.1016/j.compeleceng.2024.109850","url":null,"abstract":"<div><div>Chaos has practical significance in various domains, including the stock market, quantum physics, communication networks, disease diagnosis, cosmic events, and digital data security. Chaotic maps are widely utilised for encrypting multimedia data for secure communication due to their sensitivity to initial conditions and unpredictability. However, some chaotic maps suffer from weak chaotic dynamics that can make them vulnerable to certain types of attacks, limiting their effectiveness in sensitive applications such as encryption or secure communication in military operations and personal data. This research study proposes a novel nonlinear discrete chaotic map termed a coupled Kaplan–Yorke-Logistic map. By coupling chaotic maps, the Kaplan–Yorke map and the Logistic map, we have significantly enhanced key features such as the length of chaotic orbits, output distribution, and the security of chaotic sequences. An empirical assessment of the proposed coupled Kaplan–Yorke-Logistic map in terms of several measures such as bifurcation diagrams, phase diagrams, Lyapunov exponent analysis, permutation entropy, and sample entropy shows promising ergodicity and a diverse range of hyperchaotic behaviours compared to several recent chaotic maps. Consequently, the proposed map is utilised to develop an efficient image encryption algorithm. The encryption algorithm employs a methodology that utilises simultaneous confusion and diffusion processes aiming to significantly reduce the computation time for encryption and decryption processes for real-time applications without compromising the security parameters. A thorough assessment of the proposed image encryption algorithm is performed on a variety of image datasets by utilising multiple cryptanalysis methods, including key space analysis, information entropy, correlation coefficient evaluation, differential attack, key sensitivity testing, histogram analysis, computational time analysis, and occlusion and noise attacks. Comparative analysis with the state-of-the-art methods indicates the superiority of the proposed algorithm.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109850"},"PeriodicalIF":4.0,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655458","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}