Computers & Electrical Engineering最新文献

{"title":"Deep learning models security: A systematic review","authors":"Twinkle Tyagi,&nbsp;Amit Kumar Singh","doi":"10.1016/j.compeleceng.2024.109792","DOIUrl":"10.1016/j.compeleceng.2024.109792","url":null,"abstract":"<div><div>Deep learning models and the digital records they generate have remarkably increased their adoption of many practical applications. While the success of deep learning in multimedia applications, especially images, helps tackle some of the most challenging problems, one of its copyright violations, ownership conflict, poses a grave concern for many potential applications. Many works on intellectual property protection for such models have proposed to verify ownership. Therefore, it is necessary to conduct a comprehensive study on the security of deep learning models to evaluate their strong background, state-of-the-art solutions, possible attacks, current limitations and notable improvements. This survey attempts to systematically discuss and summarise the recent advanced security solutions for deep learning models through watermarking, encryption and fingerprinting. Our study explores the recent applications, possible attacks, current limitations and notable suggestions regarding deep learning. It also comprehensively evaluates the recent research gaps and opportunities in detail to empower researchers and practitioners to provide additional secure solutions for deep learning models. This extensive survey is the first to consider model security through several notable techniques.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109792"},"PeriodicalIF":4.0,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533713","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":"Mango leaf disease diagnosis using Total Variation Filter Based Variational Mode Decomposition","authors":"Rajneesh Kumar Patel ,&nbsp;Ankit Choudhary ,&nbsp;Siddharth Singh Chouhan ,&nbsp;Krishna Kumar Pandey","doi":"10.1016/j.compeleceng.2024.109795","DOIUrl":"10.1016/j.compeleceng.2024.109795","url":null,"abstract":"<div><div>Mango leaf diseases significantly threaten mango cultivation, impacting both yield and quality. Accurate and early diagnosis is essential for effectively managing and controlling these diseases. This study introduces a novel approach for diagnosing mango leaf diseases, leveraging Total Variation Filter-based Variational Mode Decomposition. The proposed method enhances the extraction of disease-specific features from leaf images by decomposing them into intrinsic mode functions while simultaneously reducing noise and preserving important edge information. Experimental results demonstrate that the proposed method effectively isolates relevant patterns associated with various mango leaf diseases, improving diagnostic accuracy compared to traditional methods. Deep learning models, DenseNet121 and VGG-19, are used for feature extraction from sub-band images, and extracted features are concatenated and fed to Random Forest for classification. Utilizing tenfold cross-validation, our model demonstrated enhanced classification accuracy (98.85 %), specificity (99.37 %), and sensitivity (98.0 %) in detecting diseases from Mango leaf images. Feature maps and Gradient-weighted Class Activation Mapping analysis was conducted to visualize and scrutinize the essential regions crucial for accurate predictions. Statistical analysis indicates that our proposed architecture outperforms pre-trained models and existing mango leaf disease detection methods. This diagnostic approach can be a rapid disease detection tool for imaging specialists utilizing leaf images. The robustness and efficiency of the presented work in handling complex and noisy image data make it a promising tool for automated agricultural disease diagnosis systems, facilitating timely and precise interventions in mango orchards.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109795"},"PeriodicalIF":4.0,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533653","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 energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability","authors":"Lei Xu","doi":"10.1016/j.compeleceng.2024.109779","DOIUrl":"10.1016/j.compeleceng.2024.109779","url":null,"abstract":"<div><div>This study introduces a novel application of modified particle swarm optimization (PSO) for optimizing multi-energy hub systems (EHSs) to enhance efficiency and sustainability. The proposed method leverages PSO to optimize the scheduling of various energy resources, including gas turbines, biomass units, and renewable sources such as solar and wind power. Unlike traditional optimization approaches that rely on genetic algorithm (GA) and complex encoding schemes, the PSO algorithm simplifies the process using real-valued vectors and direct communication within the swarm, which significantly reduces implementation complexity. Key contributions of this work include the development of a tailored PSO algorithm that integrates seamlessly with the multi-objective optimization of EHSs. The algorithm simultaneously targets a reduction in operational costs and carbon emissions, offering a comprehensive solution for energy hub design. The proposed PSO approach has demonstrated a 10.35 % reduction in operating costs and an 85.03 % decrease in CO2 emissions compared to traditional baseline setups. In comparative analysis, the integration of renewable sources using the PSO algorithm resulted in a 77.91 % reduction in total CO2 emissions and an 85.61 % decrease in operating costs, showcasing its effectiveness in advancing both economic and environmental objectives. Furthermore, the study provides a detailed evaluation of various scenarios, revealing that the PSO-optimized EHS configuration achieves a significant reduction in reliance on non-renewable energy sources (RES). For instance, the incorporation of photovoltaics and wind turbines in the EHS setup led to a 46.39 % increase in energy sold to the grid and a 26.82 % decrease in electricity purchased from external sources. These quantitative results underscore the robustness and practical benefits of the proposed PSO method in designing and optimizing energy systems for improved sustainability and cost-effectiveness.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109779"},"PeriodicalIF":4.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533710","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 enhanced diffusion-based network for efficient stamp removal","authors":"Guohao Cui,&nbsp;Cihui Yang","doi":"10.1016/j.compeleceng.2024.109738","DOIUrl":"10.1016/j.compeleceng.2024.109738","url":null,"abstract":"<div><div>Current diffusion models excel in computer vision tasks, but stamp removal from documents remains challenging, especially when stamps are light-colored and blend with text. Existing methods struggle to preserve background text and rely heavily on the training set, excelling in either text or table stamp removal, but not both. To address these problems, we propose an enhanced diffusion-based stamp removal model using a Spatial Attention Mechanism and a Simulate Rectified Linear Unit. Spatial Attention Mechanism combines the spatial transformation capabilities of the Spatial Transformer Network with the feature extraction of the Convolutional Block Attention Module for higher-quality images. Simulate Rectified Linear Unit mimics neuronal signal transmission in the human brain, enhancing feature extraction. Our diffusion model achieved a PSNR of 44.7, SSIM of 0.99, and RMSE of 3.47 on the stamp dataset, and performed optimally on the denoising-dirty-documents, CLWD, and DIBCO 2017 datasets. It also attained the highest PSNR of 26.8 on the DIBCO 2013 dataset, with other metrics close to the best. Code is available at <span><span>https://github.com/GuohaoCui/DiffusionModel</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109738"},"PeriodicalIF":4.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533709","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":"PSO-based Quaternion Fourier Transform steganography: Enhancing imperceptibility and robustness through multi-dimensional frequency embedding","authors":"Parsa Parsafar","doi":"10.1016/j.compeleceng.2024.109787","DOIUrl":"10.1016/j.compeleceng.2024.109787","url":null,"abstract":"<div><div>This paper presents a novel steganography technique using the Quaternion Fourier Transform (QFT) in the 4D frequency domain to enhance imperceptibility and robustness in digital image embedding. Steganography, the art of hiding information within media, faces challenges in balancing security, imperceptibility, and robustness. To address this, we leverage the multi-dimensional properties of quaternions, enabling the embedding of secret data in both grayscale and color images. For grayscale images, two quaternion dimensions are utilized for intensity and secret data, while for color images, all four dimensions are employed with one reserved for metadata. The research question centers on how to maximize spatial dispersion and color similarity while maintaining high imperceptibility and robustness against attacks. Experimental results show that the proposed method improves visual imperceptibility by more than 4 % and exhibits a 13 % increase in robustness against common steganalysis attacks compared to the best state-of-the-art existing technique. These advancements highlight the potential of this method for applications in secure communication, digital watermarking, and copyright protection. By combining the quaternion mathematical framework with a novel optimization strategy, this approach significantly improves upon traditional steganography methods.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109787"},"PeriodicalIF":4.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533714","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":"Nonlinear integral backstepping control based on particle swarm optimization for a grid-connected variable wind energy conversion system during voltage dips","authors":"Elmostafa Chetouani ,&nbsp;Youssef Errami ,&nbsp;Abdellatif Obbadi ,&nbsp;Smail Sahnoun ,&nbsp;Elhadi Baghaz ,&nbsp;Hamid Chojaa ,&nbsp;Said Mahfoud","doi":"10.1016/j.compeleceng.2024.109790","DOIUrl":"10.1016/j.compeleceng.2024.109790","url":null,"abstract":"<div><div>Double-fed induction generator (DFIG) wind turbines connected to the grid are particularly subject to grid problems such as voltage dips. Because of this, it might be challenging to maintain system stability and prevent system disconnections when using a Proportional-Integral (PI) Controller to operate this system. This paper applies the Integral Backstepping Control for the wind power plant system connected to the power grid. The Integral Backstepping is a nonlinear and recursive method that employs the Lyapunov theory to ensure the system's stability. The best selection of gain values for the Lyapunov function guarantees improved system control. These gains are frequently adjusted using the trial-and-error strategy, which is time-consuming and inefficient. This technique becomes more complex when many parameters need to be determined. It also limits the system's performance and restricts this nonlinear approach's advantages. The objective is to apply the particle swarm optimization for computing several constant values of the nonlinear approach by minimizing an integral absolute error criterion index. The weighted sum of errors is employed to solve the multiple objective problems. The proposed controller tracks the maximum power point, maintains the voltage of the DC-Link constant, and controls active and reactive power. The robustness of this method is verified in critical conditions, including system parameter variation and asymmetrical and symmetrical grid faults. The simulation findings highlight the effectiveness and robustness of the combination of Integral Backstepping with Particle Swarm Optimization in terms of reducing response time from 10.6 (ms) to 2 (ms), canceling static error, and improving overshoot compared to the vector control based on PI regulator. Besides, the DC-Link voltage ripples during the asymmetrical grid faults are reduced to ±1 (V) using the suggested controller. The latter can be implemented thanks to advancements in Central Processor Unit technology.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109790"},"PeriodicalIF":4.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533705","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":"Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems","authors":"Yaping Fu ,&nbsp;Yifeng Wang ,&nbsp;Kaizhou Gao ,&nbsp;Min Huang","doi":"10.1016/j.compeleceng.2024.109780","DOIUrl":"10.1016/j.compeleceng.2024.109780","url":null,"abstract":"<div><div>With the development of Artificial Intelligence, Internet of Things and Big Data, intelligent manufacturing has become a new and popular trend in manufacturing industries. Manufacturing scheduling is one of the most critical components in intelligent manufacturing systems. It aims to optimize some specific objectives, e.g., production cost, customer satisfaction and energy efficiency, by making optimal decisions of processing routes, machine assignment, operation sequence, etc. Due to manufacturing scheduling problems featured with large scale, strong coupling and real-time optimization requirements, it is a huge challenge to effectively cope with them. As the extensive and successful applications of artificial intelligence in manufacturing areas, meta-heuristics and reinforcement learning methods achieve great breakthroughs in addressing manufacturing scheduling problems. It is noted that a hybridization of meta-heuristic and reinforcement learning algorithms has been recently proposed to solve such complicated problems. Firstly, this work summarizes the designs of meta-heuristics and reinforcement learning methods for dealing with manufacturing scheduling problems, respectively. Secondly, we review the hybridization of meta-heuristics and reinforcement learning methods in solving manufacturing scheduling problems, where the essential roles of reinforcement learning for meta-heuristics are analyzed and discussed from the views of ensemble methods, optimization criteria, scheduling models, performance evaluation metrics and stopping conditions. Finally, we conclude this work and sum up future research directions regarding the hybridization methods in handling manufacturing scheduling problems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109780"},"PeriodicalIF":4.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444890","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 size and location of UPFC for enhanced system dynamic stability using hybrid approach","authors":"N. Anil ,&nbsp;G. Balaji ,&nbsp;G. Sireesha ,&nbsp;S. Vijaya madhavi ,&nbsp;V. Naresh","doi":"10.1016/j.compeleceng.2024.109777","DOIUrl":"10.1016/j.compeleceng.2024.109777","url":null,"abstract":"<div><div>This paper proposed a novel hybrid optimization technique, combining the Enhanced Multi-Strategic Sparrow Search Algorithm (EMSA) and White Shark Optimizer (WSO), to determine the optimal location and size of a Unified Power Flow Controller (UPFC) for enhancing power system dynamic stability. The proposed method offers improved search capabilities, reduced randomness, and lower computational complexity compared to existing approaches. Generator faults can significantly impact system dynamic stability constraints, including voltage and power loss. EMSA algorithm is employed to identify optimal location for UPFC placement by selecting bus with minimum power loss. Subsequently, WSO algorithm is used to optimize UPFC's capacity, ensuring that affected system parameters and dynamic stability constraints are restored within safe limits. Optimized UPFC is then installed at identified location, and system's power flow is analyzed. Proposed method is implemented in MATLAB/Simulink environment and tested on both IEEE 30 and IEEE 14 standard benchmark systems. Proposed method's performance is evaluated by comparison with existing methods.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109777"},"PeriodicalIF":4.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533707","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":"vDefender: An explainable and introspection-based approach for identifying emerging malware behaviour at hypervisor-layer in virtualization environment","authors":"Avantika Gaur ,&nbsp;Preeti Mishra ,&nbsp;Vinod P. ,&nbsp;Arjun Singh ,&nbsp;Vijay Varadharajan ,&nbsp;Uday Tupakula ,&nbsp;Mauro Conti","doi":"10.1016/j.compeleceng.2024.109742","DOIUrl":"10.1016/j.compeleceng.2024.109742","url":null,"abstract":"<div><div>Virtualization can be defined as the backbone of cloud computing services, which has gathered significant attention from organizations and users. Due to the increasing number of cyberattacks, virtualization security has become a crucial area of study. In this paper, we propose an explainable and introspection-based malware detection approach called <em>vDefender</em> for fine-grain monitoring of virtual machine (VM) processes at the hypervisor to identify the malicious behaviour of 17 different malware families of Windows exhibiting new evolving behaviour. Initially, it performs a basic security check to detect hidden processes and ensures the presence of security-critical processes. Then, deep memory introspection is performed using a software breakpoints injection approach to intercept the execution of processes. Various process activity logs are captured that include process-related, file manipulation, kernel heap object creation, exception-related activities, etc. Hybrid feature vectors are derived from these logs, which are reconstructed using the proposed mechanism to eliminate the redundant behaviour. The features are then learnt using Random Forest (RF) algorithm to classify distinct malware families. The interpretation and analysis of RF results involve the use of explainability techniques. The proposed approach achieves an accuracy of 95.49%, F1-score of 95.82% with 0.05% false alarms when evaluated using an emerging malware dataset. The contribution includes a comprehensive discussion of results, accompanied by a comparative analysis of current approaches that gives readers insight towards future research directions.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109742"},"PeriodicalIF":4.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534367","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":"Placement and routing approach for MQCA-based designs with BFS and A* algorithms","authors":"Vineet Jaiswal ,&nbsp;Trailokya Nath Sasamal","doi":"10.1016/j.compeleceng.2024.109771","DOIUrl":"10.1016/j.compeleceng.2024.109771","url":null,"abstract":"<div><div>Magnetic Quantum-dot Cellular Automata (MQCA) based technologies hold significant promise in outperforming CMOS technology due to their reduced power consumption and increased device density. This new technology has several challenges in carrying tasks like circuit mapping, placement, and routing. This study presents a method for automatically mapping and routing a gate-level circuit using a Nanomagnetic Logic (NML) layout. Our approach leverages the Breadth-First Search algorithm for placement and the A* algorithm for each node’s circuit traversal and route generation. Clock synchronization, layout area, and other essential circuit design elements are skilfully integrated into the proposed algorithms. To validate the effectiveness of the proposed algorithms, we implemented various circuits, including 2:1 &amp; 4:1 multiplexers, 1-bit &amp; 2-bit full adders, XOR gate, and the C17 ISCAS 85 benchmark circuit. Moreover, to demonstrate the scalability of the algorithms, we also present ripple carry adders (RCAs) of different sizes. For a 64-bit RCA, our algorithms achieve significant improvements, with reductions of <span><math><mo>∼</mo></math></span>91%–98% in clock zones, <span><math><mo>∼</mo></math></span>91%–99% in nanomagnet counts, and a <span><math><mo>∼</mo></math></span>99% reduction in the total bounded area compared to the state-of-the-art designs. Furthermore, to ensure the correctness of the proposed algorithms, we provide a detailed simulation analysis of implemented circuits using the NMLSim 2.0 micromagnetic simulator.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109771"},"PeriodicalIF":4.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533706","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}