Pub Date : 2024-11-05DOI: 10.1016/j.compeleceng.2024.109816
Huan Liu , Lunzhi Deng , Yiming Mou , Binhan Li , Jian Wen
In cloud-assisted telemedicine information system (CA-TMIS), patients upload data containing their health metrics to the cloud. Doctors can access the patient’s data through the cloud, thereby alleviating the geographical restrictions in the traditional medical system. To ensure the privacy of the patient, the patient will encrypt the data before uploading the data. How doctors can search through numerous encrypted files to locate specific patients’ relevant data is a topic worthy of research. Searchable encryption technology can fulfill the function of ciphertext retrieval. Some existing searchable encryption schemes use single-keyword for searching, resulting in insufficient search accuracy and increased search costs for doctors. Multi-keyword searchable encryption schemes can address this issue. Therefore, in this paper, we propose an identity-based authenticated multi-keyword searchable encryption scheme, which leverages multiple keywords for combined searching, enhancing search accuracy. The scheme is proven to be resistant to keyword guessing attacks in the standard model. Compared to four other existing schemes, our scheme does not utilize pairing operations and offers higher efficiency advantages. Consequently, our scheme is more suitable for CA-TMIS.
{"title":"Identity-based authenticated multi-keyword searchable encryption scheme for cloud-assisted telemedicine information system","authors":"Huan Liu , Lunzhi Deng , Yiming Mou , Binhan Li , Jian Wen","doi":"10.1016/j.compeleceng.2024.109816","DOIUrl":"10.1016/j.compeleceng.2024.109816","url":null,"abstract":"<div><div>In cloud-assisted telemedicine information system (CA-TMIS), patients upload data containing their health metrics to the cloud. Doctors can access the patient’s data through the cloud, thereby alleviating the geographical restrictions in the traditional medical system. To ensure the privacy of the patient, the patient will encrypt the data before uploading the data. How doctors can search through numerous encrypted files to locate specific patients’ relevant data is a topic worthy of research. Searchable encryption technology can fulfill the function of ciphertext retrieval. Some existing searchable encryption schemes use single-keyword for searching, resulting in insufficient search accuracy and increased search costs for doctors. Multi-keyword searchable encryption schemes can address this issue. Therefore, in this paper, we propose an identity-based authenticated multi-keyword searchable encryption scheme, which leverages multiple keywords for combined searching, enhancing search accuracy. The scheme is proven to be resistant to keyword guessing attacks in the standard model. Compared to four other existing schemes, our scheme does not utilize pairing operations and offers higher efficiency advantages. Consequently, our scheme is more suitable for CA-TMIS.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109816"},"PeriodicalIF":4.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587424","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}
Pub Date : 2024-11-05DOI: 10.1016/j.compeleceng.2024.109836
Ismail A. Soliman , Vladimir Tulsky , Hossam A. Abd el-Ghany , Ahmed E. ElGebaly
The widespread adoption of electric vehicles (EVs) helps improve air quality by minimizing pollutants and promoting sustainable transportation practices. The integration of a substantial fleet of EVs leads to an increase in the installation of charging stations. The unplanned allocation of these loads impacts the distribution systems, such as increasing power loss. This paper introduces an algorithm that proposes an optimal allocation strategy for charging stations (CSs) in a distribution system. The allocation process aims to minimize the total apparent energy losses and ensure that the system voltage profile remains within limits. Load profile, charging and discharging profiles of CSs are considered. Vehicle-to-Grid (V2G) mode is one of the merits of integrating EVs in the grid, so this feature has been used to maintain system voltage stability and minimize energy loss. Power rating and locations of V2G mode are optimally determined to guarantee power quality indices. A hybrid algorithm of genetic algorithm (GA) and Self-Adaptive Multi-Population Elitist JAYA (SAMPE-JAYA) is developed to simultaneously allocate CSs and V2G in the systems. The proposed algorithm is verified on standard systems, IEEE 33, and 69-bus systems. The proposed algorithm is verified on standard IEEE 33-bus and 69-bus systems. V2G integration with CSs results in a reduction of energy losses by 6.33% and 22.25%, respectively, and voltage deviation improvements to 7.61% and 7.88% for the two systems.
{"title":"A comprehensive simultaneous allocation algorithm of charging stations and vehicle to grid operation in radial networks","authors":"Ismail A. Soliman , Vladimir Tulsky , Hossam A. Abd el-Ghany , Ahmed E. ElGebaly","doi":"10.1016/j.compeleceng.2024.109836","DOIUrl":"10.1016/j.compeleceng.2024.109836","url":null,"abstract":"<div><div>The widespread adoption of electric vehicles (EVs) helps improve air quality by minimizing pollutants and promoting sustainable transportation practices. The integration of a substantial fleet of EVs leads to an increase in the installation of charging stations. The unplanned allocation of these loads impacts the distribution systems, such as increasing power loss. This paper introduces an algorithm that proposes an optimal allocation strategy for charging stations (CSs) in a distribution system. The allocation process aims to minimize the total apparent energy losses and ensure that the system voltage profile remains within limits. Load profile, charging and discharging profiles of CSs are considered. Vehicle-to-Grid (V2G) mode is one of the merits of integrating EVs in the grid, so this feature has been used to maintain system voltage stability and minimize energy loss. Power rating and locations of V2G mode are optimally determined to guarantee power quality indices. A hybrid algorithm of genetic algorithm (GA) and Self-Adaptive Multi-Population Elitist JAYA (SAMPE-JAYA) is developed to simultaneously allocate CSs and V2G in the systems. The proposed algorithm is verified on standard systems, IEEE 33, and 69-bus systems. The proposed algorithm is verified on standard IEEE 33-bus and 69-bus systems. V2G integration with CSs results in a reduction of energy losses by 6.33% and 22.25%, respectively, and voltage deviation improvements to 7.61% and 7.88% for the two systems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109836"},"PeriodicalIF":4.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587422","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}
Pub Date : 2024-11-05DOI: 10.1016/j.compeleceng.2024.109815
Yaobin Zou , Qingqing Huang , Huikang Qi
Many images exhibit non-modal, unimodal, bimodal, or multimodal gray level distributions. Current thresholding methods often struggle with images whose gray level distributions do not conform to a bimodal or unimodal pattern. We propose a novel bi-level threshold selection technique guided by maximizing Pearson correlation, addressing these four distribution types within a unified framework. Our method entails a multiscale multiplicative transformation of the image to create a template, extracting contours from binary images at different thresholds, and using Pearson correlation to assess the similarity between these contours and the template. The threshold with the highest similarity is chosen as the final threshold. Tested against seven methods on 20 synthetic images and 50 real-world images with non-modal, unimodal, bimodal or multimodal distribution patterns, our method showed more flexible adaptability of threshold selection and lower misclassification error, although it did not exhibit an advantage in computational efficiency.
{"title":"Automatic threshold selection guided by maximizing Pearson correlation","authors":"Yaobin Zou , Qingqing Huang , Huikang Qi","doi":"10.1016/j.compeleceng.2024.109815","DOIUrl":"10.1016/j.compeleceng.2024.109815","url":null,"abstract":"<div><div>Many images exhibit non-modal, unimodal, bimodal, or multimodal gray level distributions. Current thresholding methods often struggle with images whose gray level distributions do not conform to a bimodal or unimodal pattern. We propose a novel bi-level threshold selection technique guided by maximizing Pearson correlation, addressing these four distribution types within a unified framework. Our method entails a multiscale multiplicative transformation of the image to create a template, extracting contours from binary images at different thresholds, and using Pearson correlation to assess the similarity between these contours and the template. The threshold with the highest similarity is chosen as the final threshold. Tested against seven methods on 20 synthetic images and 50 real-world images with non-modal, unimodal, bimodal or multimodal distribution patterns, our method showed more flexible adaptability of threshold selection and lower misclassification error, although it did not exhibit an advantage in computational efficiency.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109815"},"PeriodicalIF":4.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586782","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}
Pub Date : 2024-11-05DOI: 10.1016/j.compeleceng.2024.109807
Tahsin Alam , Md. Rokonuzzaman , Sohag Sarker , A F M Zainul Abadin , Tarun Debnath , Md. Imran Hossain
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.
{"title":"Internet of Things-based Home Automation with Network Mapper and MQTT Protocol","authors":"Tahsin Alam , Md. Rokonuzzaman , Sohag Sarker , A F M Zainul Abadin , Tarun Debnath , 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}
Pub Date : 2024-11-05DOI: 10.1016/j.compeleceng.2024.109817
Carlos D. Zuluaga-Ríos , Cristian Guarnizo-Lemus
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.
{"title":"Data-driven approaches for generating probabilistic short-term renewable energy scenarios","authors":"Carlos D. Zuluaga-Ríos , 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}
Pub Date : 2024-11-04DOI: 10.1016/j.compeleceng.2024.109822
Majhrul Israr, Paulson Samuel
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 (KPi and KIi). 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.
{"title":"High-performance front end PFC controller design for light electric vehicle charger application","authors":"Majhrul Israr, 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<sub>Pi</sub> and K<sub>Ii</sub>). 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}
Pub Date : 2024-11-04DOI: 10.1016/j.compeleceng.2024.109811
Shaista Khanam , Muhammad Sharif , Xiaochun Cheng , Seifedine Kadry
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.
{"title":"Suspicious action recognition in surveillance based on handcrafted and deep learning methods: A survey of the state of the art","authors":"Shaista Khanam , Muhammad Sharif , Xiaochun Cheng , 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}
Pub Date : 2024-11-02DOI: 10.1016/j.compeleceng.2024.109809
Dinto Mathew, Prajof Prabhakaran
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.
{"title":"Optimal configuration for improved system performance of droop-controlled DC microgrid with distributed energy resources and storage","authors":"Dinto Mathew, 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}
Pub Date : 2024-11-02DOI: 10.1016/j.compeleceng.2024.109808
Jishnu C．R．, Vishnukumar S．
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.
{"title":"A ghost-free multi-exposure image fusion using adaptive alignment for static and dynamic images","authors":"Jishnu C．R．, 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}
Pub Date : 2024-11-01DOI: 10.1016/j.compeleceng.2024.109814
Zhaofei Xu , Yuanshuo Cheng , Yuanjian Qiao , Yecong Wan , Mingwen Shao , Chong Kang
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.
{"title":"Adapting visible-light-image diffusion model for infrared image restoration in rainy weather","authors":"Zhaofei Xu , Yuanshuo Cheng , Yuanjian Qiao , Yecong Wan , Mingwen Shao , 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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号