Computers & Electrical Engineering最新文献

{"title":"Internet of Things-based Home Automation with Network Mapper and MQTT Protocol","authors":"Tahsin Alam ,&nbsp;Md. Rokonuzzaman ,&nbsp;Sohag Sarker ,&nbsp;A F M Zainul Abadin ,&nbsp;Tarun Debnath ,&nbsp;Md. Imran Hossain","doi":"10.1016/j.compeleceng.2024.109807","DOIUrl":"10.1016/j.compeleceng.2024.109807","url":null,"abstract":"<div><div>The increasing ability of internet-connected daily life electronic gadgets has propelled smart homes into a global trend. The Internet of Things (IoT) enables ambient devices to communicate and interact seamlessly through various sensors. Emerging technical concepts like Web3 and Industry 5.0 require decentralised and intelligent systems near the network's edge. Petabytes of IoT sensor-generated data cause a shortage of storage on the Cloud servers, adding a delay factor to the IoT system. Standard cloud-based IoT systems can't fully function in areas with unstable internet. This paper addresses these challenges and proposes a solution to integrate edge computing concepts. The proposed system is developed using a Raspberry Pi 3 Home Server (RHS) driven by the Support Vector Machine (SVM) algorithm. The designed prototype includes a fire and smoke detection system with MQ2 gas, dust, temperature, and flame sensors. The SVM and these sensors form a data fusion module integrating with Network Mapper (NMAP), Message Queuing Telemetry Transport (MQTT) broker, MariaDB SQL server, and InfluxDB time series database. The experiments demonstrate a fundamental edge operation with a latency of 2.45 ms (milliseconds), while NMAP integration ensures data security and device verification for sensor data storage. The synthetic simulations show positive outcomes for the data fusion-based monitoring system, where alerts are promptly triggered as sensor values change, with an overall system latency of approximately 24 ms. The developed system manages home automation, real-time monitoring for fire, smoke, gas leaks, network scans, anomaly detection, appliance usage tracking, and cloud data backup. A multi-level alert system ensures early threat mitigation, with alarms, SMS, notifications, and email alerts to maximize awareness.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109807"},"PeriodicalIF":4.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586781","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":"Data-driven approaches for generating probabilistic short-term renewable energy scenarios","authors":"Carlos D. Zuluaga-Ríos ,&nbsp;Cristian Guarnizo-Lemus","doi":"10.1016/j.compeleceng.2024.109817","DOIUrl":"10.1016/j.compeleceng.2024.109817","url":null,"abstract":"<div><div>Renewable energy sources (RES) are becoming increasingly prevalent in power systems, but their intermittent and unpredictable nature challenges deterministic optimal generation scheduling. Stochastic planning or operating methodologies offer superior performance compared to deterministic approaches, making renewable energy generation scenarios increasingly valuable inputs for multistage decision-making problems. In this paper, we introduce and compare three data-driven approaches for generating probabilistic renewable energy scenarios. Numerical results from both simulated and real-world datasets demonstrate the accuracy and computational efficiency of these methods. Our proposed approaches provide a powerful tool for creating precise and efficient probabilistic renewable energy scenarios, which can enhance optimal generation scheduling in power systems with high RES penetration.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109817"},"PeriodicalIF":4.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587425","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 front end PFC controller design for light electric vehicle charger application","authors":"Majhrul Israr,&nbsp;Paulson Samuel","doi":"10.1016/j.compeleceng.2024.109822","DOIUrl":"10.1016/j.compeleceng.2024.109822","url":null,"abstract":"<div><div>Power factor correction (PFC) boost converters operating in CCM (continuous conduction mode) typically utilize average current mode (ACM) control alongside a LPF (low-pass filter) to reduce the impact of double line frequency ripple on the current loop. However, the LPF limit the voltage loop bandwidth in ACM-regulated converters, resulting in sluggish dynamic response. Additionally, zero-crossing distortion (ZCD) often occurs in the current control loop due to inaccuracies in tracking the reference current at the zero crossing point of the waveform. To address these challenges, this paper proposes a feed-forward control strategy that utilizes supply voltage and output current, effectively eliminating the need for an LPF and enhancing transient response. The voltage loop is tuned using the conventional Z-N method, while the Grey Wolf Optimization (GWO) technique is employed to optimally tune the gain parameters of the current controller (K_Pi and K_Ii). This approach effectively reduces reference tracking errors and mitigates ZCD, offering a balance between simplicity and performance. The proposed method is simple, offering fast transient and steady-state response, low THD, near-unity PF, and tight voltage regulation under fluctuating conditions. The effectiveness of this approach is validated through MATLAB/Simulink simulations, and hardware verification is conducted using a 500 W laboratory prototype controlled by a dSPACE 1104 digital controller.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109822"},"PeriodicalIF":4.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578660","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":"Suspicious action recognition in surveillance based on handcrafted and deep learning methods: A survey of the state of the art","authors":"Shaista Khanam ,&nbsp;Muhammad Sharif ,&nbsp;Xiaochun Cheng ,&nbsp;Seifedine Kadry","doi":"10.1016/j.compeleceng.2024.109811","DOIUrl":"10.1016/j.compeleceng.2024.109811","url":null,"abstract":"<div><div>Suspicious action recognition is a captivating and testing task in the realm of surveillance. An anomaly recognition framework recognizes abnormal happenings uniquely in contrast to existing examples because any anomaly is an example that is not the same as a bunch of standard examples. Security is a fundamental need in each space, whether it is public or private. The utilization of feature extraction techniques, both from hand-crafted and deep learning methods, significantly influences the comprehensive methodology discussed in detail within this paper. This survey paper comprehensively covers multiple areas of advancements in surveillance. Starting with the importance and application of anomaly recognition in surveillance which leads to a comparison of different survey papers is also presented for reference which also includes the areas that are covered in this survey paper. Available datasets in the realm of surveillance are also explored in this survey paper leading to feature extraction methods of both handcrafted and deep learning. This paper also summarizes different methods available for suspicious action recognition in surveillance. The paper delves into the challenges faced when addressing this vital issue, presents valuable findings, and outlines limitations associated with the topic. It provides extensive analysis and ends by outlining potential future trends.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109811"},"PeriodicalIF":4.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578659","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":"Optimal configuration for improved system performance of droop-controlled DC microgrid with distributed energy resources and storage","authors":"Dinto Mathew,&nbsp;Prajof Prabhakaran","doi":"10.1016/j.compeleceng.2024.109809","DOIUrl":"10.1016/j.compeleceng.2024.109809","url":null,"abstract":"<div><div>The placement of sources and loads in DC microgrids (DCMGs) influences the system’s voltage regulation, span, and losses. In order to minimize losses and enhance voltage regulation, a unique algorithm for configuring a radial DCMG under droop control in an optimal way is presented in this paper. The suggested approach solves the optimal design problem by applying the power flow analysis technique. The genetic algorithm (GA), a heuristic method, is used to determine the ideal configuration because of the complexity of the optimization problem. An improved particle swarm optimization (IPSO)-based technique is also proposed for resolving the optimization issue to improve the convergence rate and computing efficiency. Appropriate modifications are proposed to yield an optimal configuration that results in the maximum achievable span for the radial, droop-controlled DCMG. To limit the bus voltage variations within the bounds, the objective functions of the optimization problem are appropriately formulated. In addition, the proposed algorithm is used to find the best position and power rating of a new distributed energy resource (DER) or load in the DCMG, in order to reduce system losses. A 5-bus, 500 W, radial, droop-controlled DCMG system’s comprehensive numerical and simulation results are presented to validate the effectiveness of the proposed approaches. The findings are significant and useful for DCMG consumers as well as system designers.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109809"},"PeriodicalIF":4.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573401","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 ghost-free multi-exposure image fusion using adaptive alignment for static and dynamic images","authors":"Jishnu C．R．,&nbsp;Vishnukumar S．","doi":"10.1016/j.compeleceng.2024.109808","DOIUrl":"10.1016/j.compeleceng.2024.109808","url":null,"abstract":"<div><div>Multi-Exposure image Fusion (MEF) blends images with varying exposures to construct a well-exposed outcome that retains all essential details. While many MEF techniques are effective, the dynamic image sets, where movements are present, pose challenges during fusion, leading to severe artifacts. Existing approaches inherently rely on the median image to align image sets before fusion for rectifying this crisis. However, the uncertainty caused by limited datasets and distorted median image during alignment is an ongoing critical issue in the domain. The proposed method presents a novel MEF framework, introducing a newly developed adaptive alignment technique and a unique Singular Value Decomposition (SVD) weight map, specifically designed to handle dynamic image sets. This strategy efficiently aligns the input images using a qualified reference image and performs pyramidal fusion using SVD along with adaptive well-exposedness, and contrast weight maps. This effectively handles both dynamic and static images, outperforming existing MEF techniques in visual analysis and empirical tests. Furthermore, significant performances from the execution time, pixel intensity analysis, and infrared-visible image fusion analysis confirm the practicality of our approach. The proposed methodology reinforces MEF's vital role in image processing applications such as medical imaging, surveillance, and remote sensing.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109808"},"PeriodicalIF":4.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573464","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":"Adapting visible-light-image diffusion model for infrared image restoration in rainy weather","authors":"Zhaofei Xu ,&nbsp;Yuanshuo Cheng ,&nbsp;Yuanjian Qiao ,&nbsp;Yecong Wan ,&nbsp;Mingwen Shao ,&nbsp;Chong Kang","doi":"10.1016/j.compeleceng.2024.109814","DOIUrl":"10.1016/j.compeleceng.2024.109814","url":null,"abstract":"<div><div>Infrared images captured in rainy conditions always suffer from significant quality degradation, limiting the utilization of infrared equipment in rainy weather. However, the problems mentioned above have not been effectively solved yet. On the one hand, no research has been devoted to developing methods for rainy weather infrared image restoration. On the other hand, there is no available paired infrared image restoration dataset for training. To tackle the aforementioned issues, we propose a novel framework, named InfDiff, to restore low-quality infrared images via High-Quality Visible-light image Prior. Meanwhile, we establish a realistic paired infrared rainy weather dataset for model training. Specifically, the proposed InfDiff consists of an Infrared Restoration Transformer and a Prior Generation Module. InfRestormer achieves degradation removal by modeling the inverse process of infrared degradation generating and can efficiently improve image quality using High-Quality Infrared image Prior. Correspondingly, the Prior Generation Module generates High-Quality Visible-light image Prior employing a diffusion model pre-trained on abundant visible-light images, and converts it into High-Quality Infrared image Prior via adapter fine-tuning for exploitation by InfRestormer. The above approach allows employing abundant visible-light data to effectively improve the quality of infrared images with the limited amount and diversity of infrared training data. In addition, to train the InfRestormer and fine-tune the adapter, we propose a realistic degradation simulation scheme and synthesize a paired clean-degraded infrared image dataset for the first time. In summary, we find that information in high-quality visible-light images can help restore corrupted content in low-quality infrared images. Based on the above finding, we propose the first rainy weather infrared image restoration framework, named InfDiff. Additionally, we synthesized the first rainy weather infrared image restoration dataset for model training. Extensive experiments demonstrate that our method significantly outperforms the existing image restoration scheme.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109814"},"PeriodicalIF":4.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573400","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":"Advancements in Alzheimer's disease classification using deep learning frameworks for multimodal neuroimaging: A comprehensive review","authors":"Prashant Upadhyay ,&nbsp;Pradeep Tomar ,&nbsp;Satya Prakash Yadav","doi":"10.1016/j.compeleceng.2024.109796","DOIUrl":"10.1016/j.compeleceng.2024.109796","url":null,"abstract":"<div><div>Over the past years, Alzheimer's disease has emerged as a serious concern for people's health. Researchers are facing challenges in effectively categorizing and diagnosing the different stages of Alzheimer's disease (AD). Current promising studies have shown that multimodal Neuroimaging has the potential to offer vital information about the structural and functional alterations associated with Alzheimer's. Using advanced computational techniques, Machine Learning calculations have been demonstrated to be highly precise in deciphering patterns and connections within the multimodal Neuroimaging data, eventually aiding in the arrangement of Alzheimer's illness stages. This research aimed to survey the adequacy of Machine Learning techniques in correctly categorizing stages of Alzheimer's disease by working on multiple neuroimaging modalities. In this review, a detailed analysis was carried out on the classification algorithms included. The study specifically examines publications published between 2016 and 2024. From the review, it was found that deep learning frameworks are more robust in Alzheimer's disease classification.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109796"},"PeriodicalIF":4.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573071","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":"Function offloading approaches in serverless computing: A Survey","authors":"Mohsen Ghorbian,&nbsp;Mostafa Ghobaei-Arani","doi":"10.1016/j.compeleceng.2024.109832","DOIUrl":"10.1016/j.compeleceng.2024.109832","url":null,"abstract":"<div><div>In recent years, serverless computing has become one of the popular approaches to developing and running applications, allowing developers to run their code directly in the cloud without worrying about managing server infrastructure. One of the critical aspects of serverless computing is offloading approaches, which refers to transferring computing tasks or data to other locations to reduce the processing load of local devices. Considering the use of different approaches and strategies in the offloading process in serverless computing, not choosing the right approach can cause the unloading process to face challenges such as network delay, security problems, and complexity of resource management. Therefore, a detailed understanding of the loading approaches used in serverless computing can significantly reduce the challenges in this process. This paper provides a comprehensive and systematic review of various commonly used offloading approaches in serverless computing in the form of a taxonomy. The applied approaches are based on machine learning (ML), frameworks, in-network computing (INC), and heuristics. This classification is done to identify the strengths and weaknesses of each of these approaches to help developers improve the productivity and efficiency of their systems by choosing the best offloading strategies. Another goal of this article is to identify and analyze open challenges and issues related to the offloading process in serverless computing to propose effective solutions to these challenges and provide future research directions. Finally, this article expands the existing knowledge in the offloading field and creates new fields for research and development.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109832"},"PeriodicalIF":4.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573466","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 improved arithmetic method for determining the optimum placement and size of EV charging stations","authors":"Georgios Fotis","doi":"10.1016/j.compeleceng.2024.109840","DOIUrl":"10.1016/j.compeleceng.2024.109840","url":null,"abstract":"<div><div>The increasing number of electric vehicles (EVs) will result in a rise in electric vehicle charging stations (EVCSs), which will have a significant effect on the electrical grid. One major issue is deciding where to place EVCSs in the power grid in the most optimal way. The distribution network is greatly impacted by inadequate EVCS prediction, which results in issues with frequency and voltage stability. This paper suggests an optimization method called Binary Random Dynamic Arithmetic Optimization Algorithm (BRDAOA) that is applied on an IEEE 33 bus network to determine the best position for EVCSs as efficiently as possible, and the Loss Sensitivity Factor (LSF) was used in the analysis. Considering the system voltage, the load (actual power), and the system losses, LSF was calculated for a variety of buses. The efficacy of the suggested method is demonstrated by a final comparison of its findings with those of the Arithmetic Optimization Algorithm (AOA) and two additional metaheuristic algorithms. In addition to reducing line losses by 2% compared to the AOA method and 4% compared to the other two metaheuristic optimization methods, the suggested optimization approach known as BRDAOA requires less computing time than the other three methods. Finally, a reliability test was conducted to determine the best location for EVCS in the IEEE 33 BUS system.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109840"},"PeriodicalIF":4.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573465","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}