Pub Date : 2024-09-18DOI: 10.3390/electronics13183693
Gabriele Ciravegna, Franco Galante, Danilo Giordano, Tania Cerquitelli, Marco Mellia
Resistance spot welding is widely adopted in manufacturing and is characterized by high reliability and simple automation in the production line. The detection of defective welds is a difficult task that requires either destructive or expensive and slow non-destructive testing (e.g., ultrasound). The robots performing the welding automatically collect contextual and process-specific data. In this paper, we test whether these data can be used to predict defective welds. To do so, we use a dataset collected in a real industrial plant that describes welding-related data labeled with ultrasonic quality checks. We use these data to develop several pipelines based on shallow and deep learning machine learning algorithms and test the performance of these pipelines in predicting defective welds. Our results show that, despite the development of different pipelines and complex models, the machine-learning-based defect detection algorithms achieve limited performance. Using a qualitative analysis of model predictions, we show that correct predictions are often a consequence of inherent biases and intrinsic limitations in the data. We therefore conclude that the automatically collected data have limitations that hamper fault detection in a running production plant.
{"title":"Fault Prediction in Resistance Spot Welding: A Comparison of Machine Learning Approaches","authors":"Gabriele Ciravegna, Franco Galante, Danilo Giordano, Tania Cerquitelli, Marco Mellia","doi":"10.3390/electronics13183693","DOIUrl":"https://doi.org/10.3390/electronics13183693","url":null,"abstract":"Resistance spot welding is widely adopted in manufacturing and is characterized by high reliability and simple automation in the production line. The detection of defective welds is a difficult task that requires either destructive or expensive and slow non-destructive testing (e.g., ultrasound). The robots performing the welding automatically collect contextual and process-specific data. In this paper, we test whether these data can be used to predict defective welds. To do so, we use a dataset collected in a real industrial plant that describes welding-related data labeled with ultrasonic quality checks. We use these data to develop several pipelines based on shallow and deep learning machine learning algorithms and test the performance of these pipelines in predicting defective welds. Our results show that, despite the development of different pipelines and complex models, the machine-learning-based defect detection algorithms achieve limited performance. Using a qualitative analysis of model predictions, we show that correct predictions are often a consequence of inherent biases and intrinsic limitations in the data. We therefore conclude that the automatically collected data have limitations that hamper fault detection in a running production plant.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"46 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.3390/electronics13183699
Bao Wu, Xingzhong Xiong, Yong Wang
In computer vision, the task of semantic segmentation is crucial for applications such as autonomous driving and intelligent surveillance. However, achieving a balance between real-time performance and segmentation accuracy remains a significant challenge. Although Fast-SCNN is favored for its efficiency and low computational complexity, it still faces difficulties when handling complex street scene images. To address this issue, this paper presents an improved Fast-SCNN, aiming to enhance the accuracy and efficiency of semantic segmentation by incorporating a novel attention mechanism and an enhanced feature extraction module. Firstly, the integrated SimAM (Simple, Parameter-Free Attention Module) increases the network’s sensitivity to critical regions of the image and effectively adjusts the feature space weights across channels. Additionally, the refined pyramid pooling module in the global feature extraction module captures a broader range of contextual information through refined pooling levels. During the feature fusion stage, the introduction of an enhanced DAB (Depthwise Asymmetric Bottleneck) block and SE (Squeeze-and-Excitation) attention optimizes the network’s ability to process multi-scale information. Furthermore, the classifier module is extended by incorporating deeper convolutions and more complex convolutional structures, leading to a further improvement in model performance. These enhancements significantly improve the model’s ability to capture details and overall segmentation performance. Experimental results demonstrate that the proposed method excels in processing complex street scene images, achieving a mean Intersection over Union (mIoU) of 71.7% and 69.4% on the Cityscapes and CamVid datasets, respectively, while maintaining inference speeds of 81.4 fps and 113.6 fps. These results indicate that the proposed model effectively improves segmentation quality in complex street scenes while ensuring real-time processing capabilities.
{"title":"Real-Time Semantic Segmentation Algorithm for Street Scenes Based on Attention Mechanism and Feature Fusion","authors":"Bao Wu, Xingzhong Xiong, Yong Wang","doi":"10.3390/electronics13183699","DOIUrl":"https://doi.org/10.3390/electronics13183699","url":null,"abstract":"In computer vision, the task of semantic segmentation is crucial for applications such as autonomous driving and intelligent surveillance. However, achieving a balance between real-time performance and segmentation accuracy remains a significant challenge. Although Fast-SCNN is favored for its efficiency and low computational complexity, it still faces difficulties when handling complex street scene images. To address this issue, this paper presents an improved Fast-SCNN, aiming to enhance the accuracy and efficiency of semantic segmentation by incorporating a novel attention mechanism and an enhanced feature extraction module. Firstly, the integrated SimAM (Simple, Parameter-Free Attention Module) increases the network’s sensitivity to critical regions of the image and effectively adjusts the feature space weights across channels. Additionally, the refined pyramid pooling module in the global feature extraction module captures a broader range of contextual information through refined pooling levels. During the feature fusion stage, the introduction of an enhanced DAB (Depthwise Asymmetric Bottleneck) block and SE (Squeeze-and-Excitation) attention optimizes the network’s ability to process multi-scale information. Furthermore, the classifier module is extended by incorporating deeper convolutions and more complex convolutional structures, leading to a further improvement in model performance. These enhancements significantly improve the model’s ability to capture details and overall segmentation performance. Experimental results demonstrate that the proposed method excels in processing complex street scene images, achieving a mean Intersection over Union (mIoU) of 71.7% and 69.4% on the Cityscapes and CamVid datasets, respectively, while maintaining inference speeds of 81.4 fps and 113.6 fps. These results indicate that the proposed model effectively improves segmentation quality in complex street scenes while ensuring real-time processing capabilities.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"3 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.3390/electronics13183702
Xingquan Li, Hongxia Zheng, Chunlong He, Yong Wang, Guoqing Wang
Data transmission is one of the critical factors in the future of the Internet of Things (IoT). The techniques of a reconfigurable intelligent surface (RIS) and backscatter communication (BackCom) are in need of a solution of realizing low-power sustainable transmission, which shows great potential in wireless communication. Hence, this paper introduces an RIS-based BackCom system, where the RIS receives energy from a base station (BS) and sends information by backscattering the signals from the BS. To maximize the sum rate of all IoT devices (IoTDs), we jointly optimized the time allocation, the RIS-reflecting phase shifts and the transmit power of the BS by exploiting an alternative optimization algorithm. The simulation results illustrate the effectiveness and the feasibility of the proposed wireless communication scheme and the proposed algorithm in IoT networks.
{"title":"Reconfigurable Intelligent Surface-Based Backscatter Communication for Data transmission","authors":"Xingquan Li, Hongxia Zheng, Chunlong He, Yong Wang, Guoqing Wang","doi":"10.3390/electronics13183702","DOIUrl":"https://doi.org/10.3390/electronics13183702","url":null,"abstract":"Data transmission is one of the critical factors in the future of the Internet of Things (IoT). The techniques of a reconfigurable intelligent surface (RIS) and backscatter communication (BackCom) are in need of a solution of realizing low-power sustainable transmission, which shows great potential in wireless communication. Hence, this paper introduces an RIS-based BackCom system, where the RIS receives energy from a base station (BS) and sends information by backscattering the signals from the BS. To maximize the sum rate of all IoT devices (IoTDs), we jointly optimized the time allocation, the RIS-reflecting phase shifts and the transmit power of the BS by exploiting an alternative optimization algorithm. The simulation results illustrate the effectiveness and the feasibility of the proposed wireless communication scheme and the proposed algorithm in IoT networks.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"118 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.3390/electronics13183690
Wei Cai, Weijie Gao, Xinhao Jiang, Xin Wang, Xingyu Di
Camouflaged object detection (COD) is a challenging task that involves identifying objects that closely resemble their background. In order to detect camouflaged objects more accurately, we propose a diffusion model for the COD network called DMNet. DMNet formulates COD as a denoising diffusion process from noisy boxes to prediction boxes. During the training stage, random boxes diffuse from ground-truth boxes, and DMNet learns to reverse this process. In the sampling stage, DMNet progressively refines random boxes to prediction boxes. In addition, due to the camouflaged object’s blurred appearance and the low contrast between it and the background, the feature extraction stage of the network is challenging. Firstly, we proposed a parallel fusion module (PFM) to enhance the information extracted from the backbone. Then, we designed a progressive feature pyramid network (PFPN) for feature fusion, in which the upsample adaptive spatial fusion module (UAF) balances the different feature information by assigning weights to different layers. Finally, a location refinement module (LRM) is constructed to make DMNet pay attention to the boundary details. We compared DMNet with other classical object-detection models on the COD10K dataset. Experimental results indicated that DMNet outperformed others, achieving optimal effects across six evaluation metrics and significantly enhancing detection accuracy.
{"title":"Denoising Diffusion Implicit Model for Camouflaged Object Detection","authors":"Wei Cai, Weijie Gao, Xinhao Jiang, Xin Wang, Xingyu Di","doi":"10.3390/electronics13183690","DOIUrl":"https://doi.org/10.3390/electronics13183690","url":null,"abstract":"Camouflaged object detection (COD) is a challenging task that involves identifying objects that closely resemble their background. In order to detect camouflaged objects more accurately, we propose a diffusion model for the COD network called DMNet. DMNet formulates COD as a denoising diffusion process from noisy boxes to prediction boxes. During the training stage, random boxes diffuse from ground-truth boxes, and DMNet learns to reverse this process. In the sampling stage, DMNet progressively refines random boxes to prediction boxes. In addition, due to the camouflaged object’s blurred appearance and the low contrast between it and the background, the feature extraction stage of the network is challenging. Firstly, we proposed a parallel fusion module (PFM) to enhance the information extracted from the backbone. Then, we designed a progressive feature pyramid network (PFPN) for feature fusion, in which the upsample adaptive spatial fusion module (UAF) balances the different feature information by assigning weights to different layers. Finally, a location refinement module (LRM) is constructed to make DMNet pay attention to the boundary details. We compared DMNet with other classical object-detection models on the COD10K dataset. Experimental results indicated that DMNet outperformed others, achieving optimal effects across six evaluation metrics and significantly enhancing detection accuracy.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"40 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.3390/electronics13183683
Alain Aoun, Nadine Kashmar, Mehdi Adda, Hussein Ibrahim
The idea of a decentralized electric grid has shifted from being a concept to a reality. The growing integration of distributed energy resources (DERs) has transformed the traditional centralized electric grid into a decentralized one. However, while most efforts to manage and optimize this decentralization focus on the electrical infrastructure layer, the operational and control layer, as well as the data management layer, have received less attention. Current electric grids rely on centralized control centers (CCCs) that serve as the electric grid’s brain, where operators monitor, control, and manage the entire grid infrastructure. Hence, any disruption caused by a cyberattack or a natural event, disconnecting the CCC, could have numerous negative effects on grid operations, including socioeconomic impacts, equipment damage, market repercussions, and blackouts. This article introduces the idea of a fully decentralized electric grid that leverages autonomous smart substations and blockchain integration for decentralized data management and control. The aim is to propose a blockchain-enabled decentralized electric grid model and its potential impact on energy markets, sustainability, and resilience. The model presented underlines the transformative potential of decentralized autonomous grids in revolutionizing energy systems for better operability, management, and flexibility.
{"title":"From Bottom-Up Towards a Completely Decentralized Autonomous Electric Grid Based on the Concept of a Decentralized Autonomous Substation","authors":"Alain Aoun, Nadine Kashmar, Mehdi Adda, Hussein Ibrahim","doi":"10.3390/electronics13183683","DOIUrl":"https://doi.org/10.3390/electronics13183683","url":null,"abstract":"The idea of a decentralized electric grid has shifted from being a concept to a reality. The growing integration of distributed energy resources (DERs) has transformed the traditional centralized electric grid into a decentralized one. However, while most efforts to manage and optimize this decentralization focus on the electrical infrastructure layer, the operational and control layer, as well as the data management layer, have received less attention. Current electric grids rely on centralized control centers (CCCs) that serve as the electric grid’s brain, where operators monitor, control, and manage the entire grid infrastructure. Hence, any disruption caused by a cyberattack or a natural event, disconnecting the CCC, could have numerous negative effects on grid operations, including socioeconomic impacts, equipment damage, market repercussions, and blackouts. This article introduces the idea of a fully decentralized electric grid that leverages autonomous smart substations and blockchain integration for decentralized data management and control. The aim is to propose a blockchain-enabled decentralized electric grid model and its potential impact on energy markets, sustainability, and resilience. The model presented underlines the transformative potential of decentralized autonomous grids in revolutionizing energy systems for better operability, management, and flexibility.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"25 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.3390/electronics13183687
José Saias, Jorge Bravo
One of the significant advantages of technological evolution is the greater ease of collecting and analyzing data. Miniaturization, wireless communication protocols and IoT allow the use of sensors to collect data, with all the potential to support decision making in real time. In this paper, we describe the design and implementation of a digital solution to guide the intensity of training or physical activity, based on heart rate wearable sensors applied to participants in group sessions. Our system, featuring a unified engine that simplifies sensor management and minimizes user disruption, has been proven effective for real-time monitoring. It includes custom alerts during variable-intensity workouts, and ensures data preservation for subsequent analysis by physiologists or clinicians. This solution has been used in sessions of up to six participants and sensors up to 12 m away from the gateway device. We describe some challenges and constraints we face in collecting data from multiple and possibly different sensors simultaneously via Bluetooth Low Energy, and the approaches we follow to overcome them. We conduct an in-depth questionnaire to identify potential obstacles and drivers for system acceptance. We also discuss some possibilities for extension and improvement of our system.
{"title":"Sensor-Based Real-Time Monitoring Approach for Multi-Participant Workout Intensity Management","authors":"José Saias, Jorge Bravo","doi":"10.3390/electronics13183687","DOIUrl":"https://doi.org/10.3390/electronics13183687","url":null,"abstract":"One of the significant advantages of technological evolution is the greater ease of collecting and analyzing data. Miniaturization, wireless communication protocols and IoT allow the use of sensors to collect data, with all the potential to support decision making in real time. In this paper, we describe the design and implementation of a digital solution to guide the intensity of training or physical activity, based on heart rate wearable sensors applied to participants in group sessions. Our system, featuring a unified engine that simplifies sensor management and minimizes user disruption, has been proven effective for real-time monitoring. It includes custom alerts during variable-intensity workouts, and ensures data preservation for subsequent analysis by physiologists or clinicians. This solution has been used in sessions of up to six participants and sensors up to 12 m away from the gateway device. We describe some challenges and constraints we face in collecting data from multiple and possibly different sensors simultaneously via Bluetooth Low Energy, and the approaches we follow to overcome them. We conduct an in-depth questionnaire to identify potential obstacles and drivers for system acceptance. We also discuss some possibilities for extension and improvement of our system.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"3 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In a hydropower-dominated power grid, the primary frequency regulation (PFR) capability of hydropower units is typically compromised to suppress ultra-low frequency oscillations (ULFOs). However, as renewable wind power is further integrated, a practicable solution to damp ULFOs has emerged, which is to adjust the frequency control parameters of wind turbine (WT) units. Driven by the goals of overall damping enhancement and ULFO suppression, this paper first establishes an extended unified frequency model (EUFM) of a hydro–wind power sending system. Based on EUFM, the damping torque of the hydro–wind power sending system is derived, and the specific impact of WT control parameters on ULFOs and PFR characteristics is investigated. Then, a novel optimization objective function considering damping in the ultra-low frequency band and PFR is formulated and solved using an intelligence algorithm. By optimizing the parameters of the WT to suppress ULFOs, the PFR capability of hydropower units can be released. Finally, simulation results verify that the optimized WT parameters can simultaneously address the ULFO problem and guarantee PFR performance, thereby enhancing the frequency dynamic stability of the sending system.
{"title":"Control Method for Ultra-Low Frequency Oscillation and Frequency Control Performance in Hydro–Wind Power Sending System","authors":"Renjie Wu, Qin Jiang, Baohong Li, Tianqi Liu, Xueyang Zeng","doi":"10.3390/electronics13183691","DOIUrl":"https://doi.org/10.3390/electronics13183691","url":null,"abstract":"In a hydropower-dominated power grid, the primary frequency regulation (PFR) capability of hydropower units is typically compromised to suppress ultra-low frequency oscillations (ULFOs). However, as renewable wind power is further integrated, a practicable solution to damp ULFOs has emerged, which is to adjust the frequency control parameters of wind turbine (WT) units. Driven by the goals of overall damping enhancement and ULFO suppression, this paper first establishes an extended unified frequency model (EUFM) of a hydro–wind power sending system. Based on EUFM, the damping torque of the hydro–wind power sending system is derived, and the specific impact of WT control parameters on ULFOs and PFR characteristics is investigated. Then, a novel optimization objective function considering damping in the ultra-low frequency band and PFR is formulated and solved using an intelligence algorithm. By optimizing the parameters of the WT to suppress ULFOs, the PFR capability of hydropower units can be released. Finally, simulation results verify that the optimized WT parameters can simultaneously address the ULFO problem and guarantee PFR performance, thereby enhancing the frequency dynamic stability of the sending system.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"17 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.3390/electronics13183685
Zhaorui Yang, Yu He, Jing Zhang, Zijian Zhang, Jie Luo, Guomin Gan, Jie Xiang, Yang Zou
The integration of large-scale wind power into power systems has exacerbated the challenges associated with peak load regulation. Concurrently, the ongoing advancement of electricity marketization reforms highlights the need to assess the impact of direct electricity procurement by large consumers on enhancing the flexibility of power systems. In this context, this paper introduces a Distributed Robust Optimal Scheduling (DROS) model, which addresses the uncertainties of wind power generation and direct electricity purchases by large consumers. Firstly, to mitigate the effects of wind power uncertainty on the power system, a first-order Markov chain model with interval characteristics is introduced. This approach effectively captures the temporal and variability aspects of wind power prediction errors. Secondly, building upon the day-ahead scenarios generated by the Markov chain, the model then formulates a data-driven optimization framework that spans from day-ahead to intra-day scheduling. In the day-ahead phase, the model leverages the price elasticity of the demand matrix to guide consumer behavior, with the primary objective of maximizing the total revenue of the wind farm. A robust scheduling strategy is developed, yielding an hourly scheduling plan for the day-ahead phase. This plan dynamically adjusts tariffs in the intra-day phase based on deviations in wind power output, thereby encouraging flexible user responses to the inherent uncertainty in wind power generation. Ultimately, the efficacy of the proposed DROS method is validated through extensive numerical simulations, demonstrating its potential to enhance the robustness and flexibility of power systems in the presence of significant wind power integration and market-driven direct electricity purchases.
大规模风力发电融入电力系统加剧了与高峰负荷调节相关的挑战。与此同时，电力市场化改革的不断推进凸显了评估大用户直接购电对提高电力系统灵活性的影响的必要性。在此背景下，本文引入了分布式鲁棒优化调度（DROS）模型，以解决风力发电和大用户直购电的不确定性问题。首先，为了减轻风力发电不确定性对电力系统的影响，本文引入了一个具有区间特性的一阶马尔可夫链模型。这种方法能有效捕捉风电预测误差的时间性和可变性。其次，在马尔科夫链生成的日前情景基础上，该模型制定了一个数据驱动的优化框架，从日前到日内调度。在日前阶段，模型利用需求矩阵的价格弹性来指导消费者行为，主要目标是实现风电场总收入的最大化。我们开发了一种稳健的调度策略，为日前阶段制定了一个小时调度计划。该计划可根据风电输出的偏差动态调整日内阶段的电价，从而鼓励用户灵活应对风力发电中固有的不确定性。最终，通过大量的数值模拟验证了所提出的 DROS 方法的有效性，证明了该方法在大量风电并网和市场驱动的直接购电情况下增强电力系统稳健性和灵活性的潜力。
{"title":"Two-Stage Distributed Robust Optimization Scheduling Considering Demand Response and Direct Purchase of Electricity by Large Consumers","authors":"Zhaorui Yang, Yu He, Jing Zhang, Zijian Zhang, Jie Luo, Guomin Gan, Jie Xiang, Yang Zou","doi":"10.3390/electronics13183685","DOIUrl":"https://doi.org/10.3390/electronics13183685","url":null,"abstract":"The integration of large-scale wind power into power systems has exacerbated the challenges associated with peak load regulation. Concurrently, the ongoing advancement of electricity marketization reforms highlights the need to assess the impact of direct electricity procurement by large consumers on enhancing the flexibility of power systems. In this context, this paper introduces a Distributed Robust Optimal Scheduling (DROS) model, which addresses the uncertainties of wind power generation and direct electricity purchases by large consumers. Firstly, to mitigate the effects of wind power uncertainty on the power system, a first-order Markov chain model with interval characteristics is introduced. This approach effectively captures the temporal and variability aspects of wind power prediction errors. Secondly, building upon the day-ahead scenarios generated by the Markov chain, the model then formulates a data-driven optimization framework that spans from day-ahead to intra-day scheduling. In the day-ahead phase, the model leverages the price elasticity of the demand matrix to guide consumer behavior, with the primary objective of maximizing the total revenue of the wind farm. A robust scheduling strategy is developed, yielding an hourly scheduling plan for the day-ahead phase. This plan dynamically adjusts tariffs in the intra-day phase based on deviations in wind power output, thereby encouraging flexible user responses to the inherent uncertainty in wind power generation. Ultimately, the efficacy of the proposed DROS method is validated through extensive numerical simulations, demonstrating its potential to enhance the robustness and flexibility of power systems in the presence of significant wind power integration and market-driven direct electricity purchases.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"79 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.3390/electronics13183689
Yongjoon Lee, Jaeil Lee, Dojin Ryu, Hansol Park, Dongkyoo Shin
Recently, Clop ransomware attacks targeting non-IT fields such as distribution, logistics, and manufacturing have been rapidly increasing. These advanced attacks are particularly concentrated on Active Directory (AD) servers, causing significant operational and financial disruption to the affected organizations. In this study, the multi-step behavior of Clop ransomware was deeply investigated to decipher the sequential techniques and strategies of attackers. One of the key insights uncovered is the vulnerability in AD administrator accounts, which are often used as a primary point of exploitation. This study aims to provide a comprehensive analysis that enables organizations to develop a deeper understanding of the multifaceted threats posed by Clop ransomware and to build more strategic and robust defenses against them.
最近，针对分销、物流和制造等非 IT 领域的 Clop 勒索软件攻击迅速增加。这些高级攻击尤其集中在活动目录（AD）服务器上，对受影响的组织造成了严重的运营和财务破坏。本研究深入研究了 Clop 勒索软件的多步骤行为，以破解攻击者的连续技术和策略。发现的一个关键问题是 AD 管理员账户的漏洞，该漏洞通常被用作主要的攻击点。本研究旨在提供全面的分析，使企业能够更深入地了解 Clop 勒索软件带来的多方面威胁，并针对这些威胁建立更具战略性和更强大的防御。
{"title":"Clop Ransomware in Action: A Comprehensive Analysis of Its Multi-Stage Tactics","authors":"Yongjoon Lee, Jaeil Lee, Dojin Ryu, Hansol Park, Dongkyoo Shin","doi":"10.3390/electronics13183689","DOIUrl":"https://doi.org/10.3390/electronics13183689","url":null,"abstract":"Recently, Clop ransomware attacks targeting non-IT fields such as distribution, logistics, and manufacturing have been rapidly increasing. These advanced attacks are particularly concentrated on Active Directory (AD) servers, causing significant operational and financial disruption to the affected organizations. In this study, the multi-step behavior of Clop ransomware was deeply investigated to decipher the sequential techniques and strategies of attackers. One of the key insights uncovered is the vulnerability in AD administrator accounts, which are often used as a primary point of exploitation. This study aims to provide a comprehensive analysis that enables organizations to develop a deeper understanding of the multifaceted threats posed by Clop ransomware and to build more strategic and robust defenses against them.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"17 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.3390/electronics13183688
Genoveva Vargas-Solar
The internet contains vast amounts of text-based information across various domains, such as commercial documents, medical records, scientific research, engineering tests, and events affecting urban and natural environments. Extracting knowledge from these texts requires a deep understanding of natural language nuances and accurately representing content while preserving essential information. This process enables effective knowledge extraction, inference, and discovery. This paper proposes a critical study of state-of-the-art contributions exploring the complexities and emerging trends in representing, querying, and analysing content extracted from textual data. This study’s hypothesis states that graph-based representations can be particularly effective when annotated with sophisticated querying and analytics techniques. This hypothesis is discussed through the lenses of contributions in linguistics, natural language processing, graph theory, databases, and artificial intelligence.
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