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.
{"title":"Processing the Narrative: Innovative Graph Models and Queries for Textual Content Knowledge Extraction †","authors":"Genoveva Vargas-Solar","doi":"10.3390/electronics13183688","DOIUrl":"https://doi.org/10.3390/electronics13183688","url":null,"abstract":"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.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"6 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259393","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/electronics13183686
Zhongyang Mao, Zhilin Zhang, Faping Lu, Yaozong Pan, Tianqi Zhang, Jiafang Kang, Zhiyong Zhao, Yang You
As humans continue to exploit the ocean, the number of UAV nodes at sea and the demand for their services are increasing. Given the dynamic nature of marine environments, traditional resource allocation methods lead to inefficient service transmission and ping-pong effects. This study enhances the alignment between network resources and node services by introducing an attention mechanism and double deep Q-learning (DDQN) algorithm that optimizes the service-access strategy, curbs action outputs, and improves service-node compatibility, thereby constituting a novel method for UAV network resource allocation in marine environments. A selective suppression module minimizes the variability in action outputs, effectively mitigating the ping-pong effect, and an attention-aware module is designed to strengthen node-service compatibility, thereby significantly enhancing service transmission efficiency. Simulation results indicate that the proposed method boosts the number of completed services compared with the DDQN, soft actor–critic (SAC), and deep deterministic policy gradient (DDPG) algorithms and increases the total value of completed services.
{"title":"Sea-Based UAV Network Resource Allocation Method Based on an Attention Mechanism","authors":"Zhongyang Mao, Zhilin Zhang, Faping Lu, Yaozong Pan, Tianqi Zhang, Jiafang Kang, Zhiyong Zhao, Yang You","doi":"10.3390/electronics13183686","DOIUrl":"https://doi.org/10.3390/electronics13183686","url":null,"abstract":"As humans continue to exploit the ocean, the number of UAV nodes at sea and the demand for their services are increasing. Given the dynamic nature of marine environments, traditional resource allocation methods lead to inefficient service transmission and ping-pong effects. This study enhances the alignment between network resources and node services by introducing an attention mechanism and double deep Q-learning (DDQN) algorithm that optimizes the service-access strategy, curbs action outputs, and improves service-node compatibility, thereby constituting a novel method for UAV network resource allocation in marine environments. A selective suppression module minimizes the variability in action outputs, effectively mitigating the ping-pong effect, and an attention-aware module is designed to strengthen node-service compatibility, thereby significantly enhancing service transmission efficiency. Simulation results indicate that the proposed method boosts the number of completed services compared with the DDQN, soft actor–critic (SAC), and deep deterministic policy gradient (DDPG) algorithms and increases the total value of completed services.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"29 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259391","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}
Cargo handling issues affect the ability of large heavy-duty Unmanned Aerial Vehicles (UAVs) to transport cargo and limit the development of large UAVs. Compared to conventional landing gear, hydraulically controlled landing gear can tilt the drone within a specified angle, facilitating smoother loading and unloading of goods. Therefore, it is important to study the hydraulic landing gear control system for a UAV to make the UAV’s tilt possible. In this paper, an impedance-based parallel cooperative control method for front and rear landing gear hydraulic systems of large heavy-duty UAVs is presented, which can achieve UAV tilting within a reasonable angle during the loading and unloading of cargoes by large, heavy-duty UAVs. This paper establishes the physical model of the UAV’s landing gear, the mathematical model of the hydraulic system, and the kinematic model of the airframe. Through kinematic analysis, the correlation between each hydraulic dive unit’s (HDU’s) extension length in the landing gear and the UAV’s tilt angle is established. This paper introduces a two-fold based-loop parallel control technique, featuring angle based-loop control for the UAV’s front and position based-loop control for its rear landing gear. It aims to enable the UAV to freely tilt for loading and unloading cargo at a predetermined angle, by measuring the UAV’s tilting angle, the HDU’s force exerted on the landing gear, and its positional parameters. Ultimately, the practicality of this technique is confirmed through simulations and experiments.
{"title":"A Novel Impedance-Based Parallel Cooperative Control Method for Front and Rear Landing Gear Hydraulic Systems of UAVs","authors":"Hua Qiu, Xinyu Wang, Guozhao Shi, Xinrong Li, Shuai Zhang, Xiangdong Kong, Kaixian Ba, Bin Yu","doi":"10.3390/electronics13183684","DOIUrl":"https://doi.org/10.3390/electronics13183684","url":null,"abstract":"Cargo handling issues affect the ability of large heavy-duty Unmanned Aerial Vehicles (UAVs) to transport cargo and limit the development of large UAVs. Compared to conventional landing gear, hydraulically controlled landing gear can tilt the drone within a specified angle, facilitating smoother loading and unloading of goods. Therefore, it is important to study the hydraulic landing gear control system for a UAV to make the UAV’s tilt possible. In this paper, an impedance-based parallel cooperative control method for front and rear landing gear hydraulic systems of large heavy-duty UAVs is presented, which can achieve UAV tilting within a reasonable angle during the loading and unloading of cargoes by large, heavy-duty UAVs. This paper establishes the physical model of the UAV’s landing gear, the mathematical model of the hydraulic system, and the kinematic model of the airframe. Through kinematic analysis, the correlation between each hydraulic dive unit’s (HDU’s) extension length in the landing gear and the UAV’s tilt angle is established. This paper introduces a two-fold based-loop parallel control technique, featuring angle based-loop control for the UAV’s front and position based-loop control for its rear landing gear. It aims to enable the UAV to freely tilt for loading and unloading cargo at a predetermined angle, by measuring the UAV’s tilting angle, the HDU’s force exerted on the landing gear, and its positional parameters. Ultimately, the practicality of this technique is confirmed through simulations and experiments.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"22 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259366","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 this study, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was used to improve the diagnosis of Alzheimer’s disease using medical imaging and the Alzheimer’s disease image dataset across four diagnostic classes. The WGAN-GP was employed for data augmentation. The original dataset, the augmented dataset and the combined data were mapped using Uniform Manifold Approximation and Projection (UMAP) in both a 2D and 3D space. The same combined interaction network analysis was then performed on the test data. The results showed that, for the test accuracy, the score was 30.46% for the original dataset (unbalanced), whereas for the WGAN-GP augmented dataset (balanced), it improved to 56.84%, indicating that the WGAN-GP augmentation can effectively address the unbalanced problem.
{"title":"Comprehensive Data Augmentation Approach Using WGAN-GP and UMAP for Enhancing Alzheimer’s Disease Diagnosis","authors":"Emi Yuda, Tomoki Ando, Itaru Kaneko, Yutaka Yoshida, Daisuke Hirahara","doi":"10.3390/electronics13183671","DOIUrl":"https://doi.org/10.3390/electronics13183671","url":null,"abstract":"In this study, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was used to improve the diagnosis of Alzheimer’s disease using medical imaging and the Alzheimer’s disease image dataset across four diagnostic classes. The WGAN-GP was employed for data augmentation. The original dataset, the augmented dataset and the combined data were mapped using Uniform Manifold Approximation and Projection (UMAP) in both a 2D and 3D space. The same combined interaction network analysis was then performed on the test data. The results showed that, for the test accuracy, the score was 30.46% for the original dataset (unbalanced), whereas for the WGAN-GP augmented dataset (balanced), it improved to 56.84%, indicating that the WGAN-GP augmentation can effectively address the unbalanced problem.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"3 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259474","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 : 2024-09-16DOI: 10.3390/electronics13183678
Aimin Wang, Yu Li, Wenxuan Yang, Guangxu Pan
Since rail-to-ground insulation decreases, large-level direct currents (DCs) leak from railways and form metro stray currents, corroding the buried metal. To locate the rail-to-ground insulation deterioration area, a location method is proposed based on parameter identification methods and the monitored information including the station rail potentials, currents at the traction power substations (TPSs), and train traction currents and train positions. According to the monitoring information of two adjacent TPSs, the section location model of the metro line is proposed, in which the rail-to-ground conductances of the test section are equivalent to the lumped parameters. Using the rail resistivity and traction currents as the known information, the rail-to-ground conductances are calculated with the least square method (LSM). The rail-to-ground insulation deterioration sections are identified by comparing the calculated conductances with thresholds determined by the standard requirements and section lengths. Then, according to the section location results, a detailed location model of the degradation section is proposed, considering the location distance accuracy. Using the genetic algorithm (GA) to calculate the rail-to-ground conductances, degradation positions are located by comparing the threshold calculated with the standard requirements and location distance accuracy. The location method is verified by comparing the calculation results under different degradation conditions. Moreover, the applications of the proposed method to different degradation lengths and different numbers of degradation sections are analyzed. The results show that the proposed method can locate rail-to-ground insulation deterioration areas.
{"title":"Localization Method for Insulation Degradation Area of the Metro Rail-to-Ground Based on Monitor Information","authors":"Aimin Wang, Yu Li, Wenxuan Yang, Guangxu Pan","doi":"10.3390/electronics13183678","DOIUrl":"https://doi.org/10.3390/electronics13183678","url":null,"abstract":"Since rail-to-ground insulation decreases, large-level direct currents (DCs) leak from railways and form metro stray currents, corroding the buried metal. To locate the rail-to-ground insulation deterioration area, a location method is proposed based on parameter identification methods and the monitored information including the station rail potentials, currents at the traction power substations (TPSs), and train traction currents and train positions. According to the monitoring information of two adjacent TPSs, the section location model of the metro line is proposed, in which the rail-to-ground conductances of the test section are equivalent to the lumped parameters. Using the rail resistivity and traction currents as the known information, the rail-to-ground conductances are calculated with the least square method (LSM). The rail-to-ground insulation deterioration sections are identified by comparing the calculated conductances with thresholds determined by the standard requirements and section lengths. Then, according to the section location results, a detailed location model of the degradation section is proposed, considering the location distance accuracy. Using the genetic algorithm (GA) to calculate the rail-to-ground conductances, degradation positions are located by comparing the threshold calculated with the standard requirements and location distance accuracy. The location method is verified by comparing the calculation results under different degradation conditions. Moreover, the applications of the proposed method to different degradation lengths and different numbers of degradation sections are analyzed. The results show that the proposed method can locate rail-to-ground insulation deterioration areas.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"101 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259478","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 : 2024-09-16DOI: 10.3390/electronics13183672
Daniel Djolev, Milena Lazarova, Ognyan Nakov
In recent years, rapid technological advancements have propelled blockchain and artificial intelligence (AI) into prominent roles within the digital industry, each having unique applications. Blockchain, recognized for its secure and transparent data storage, and AI, a powerful tool for data analysis and decision making, exhibit common features that render them complementary. At the same time, machine learning has become a robust and influential technology, adopted by many companies to address non-trivial technical problems. This adoption is fueled by the vast amounts of data generated and utilized in daily operations. An intriguing intersection of blockchain and AI occurs in the realm of federated learning, a distributed approach allowing multiple parties to collaboratively train a shared model without centralizing data. This paper presents a decentralized platform FBLearn for the implementation of federated learning in blockchain, which enables us to harness the benefits of federated learning without the necessity of exchanging sensitive customer or product data, thereby fostering trustless collaboration. As the decentralized blockchain network is introduced in the distributed model training to replace the centralized server, global model aggregation approaches have to be utilized. This paper investigates several techniques for model aggregation based on the local model average and ensemble using either local or globally distributed validation data for model evaluation. The suggested aggregation approaches are experimentally evaluated based on two use cases of the FBLearn platform: credit risk scoring using a random forest classifier and credit card fraud detection using a logistic regression. The experimental results confirm that the suggested adaptive weight calculation and ensemble techniques based on the quality of local training data enhance the robustness of the global model. The performance evaluation metrics and ROC curves prove that the aggregation strategies successfully isolate the influence of the low-quality models on the final model. The proposed system’s ability to outperform models created with separate datasets underscores its potential to enhance collaborative efforts and to improve the accuracy of the final global model compared to each of the local models. Integrating blockchain and federated learning presents a forward-looking approach to data collaboration while addressing privacy concerns.
{"title":"FBLearn: Decentralized Platform for Federated Learning on Blockchain","authors":"Daniel Djolev, Milena Lazarova, Ognyan Nakov","doi":"10.3390/electronics13183672","DOIUrl":"https://doi.org/10.3390/electronics13183672","url":null,"abstract":"In recent years, rapid technological advancements have propelled blockchain and artificial intelligence (AI) into prominent roles within the digital industry, each having unique applications. Blockchain, recognized for its secure and transparent data storage, and AI, a powerful tool for data analysis and decision making, exhibit common features that render them complementary. At the same time, machine learning has become a robust and influential technology, adopted by many companies to address non-trivial technical problems. This adoption is fueled by the vast amounts of data generated and utilized in daily operations. An intriguing intersection of blockchain and AI occurs in the realm of federated learning, a distributed approach allowing multiple parties to collaboratively train a shared model without centralizing data. This paper presents a decentralized platform FBLearn for the implementation of federated learning in blockchain, which enables us to harness the benefits of federated learning without the necessity of exchanging sensitive customer or product data, thereby fostering trustless collaboration. As the decentralized blockchain network is introduced in the distributed model training to replace the centralized server, global model aggregation approaches have to be utilized. This paper investigates several techniques for model aggregation based on the local model average and ensemble using either local or globally distributed validation data for model evaluation. The suggested aggregation approaches are experimentally evaluated based on two use cases of the FBLearn platform: credit risk scoring using a random forest classifier and credit card fraud detection using a logistic regression. The experimental results confirm that the suggested adaptive weight calculation and ensemble techniques based on the quality of local training data enhance the robustness of the global model. The performance evaluation metrics and ROC curves prove that the aggregation strategies successfully isolate the influence of the low-quality models on the final model. The proposed system’s ability to outperform models created with separate datasets underscores its potential to enhance collaborative efforts and to improve the accuracy of the final global model compared to each of the local models. Integrating blockchain and federated learning presents a forward-looking approach to data collaboration while addressing privacy concerns.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"118 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259473","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 : 2024-09-16DOI: 10.3390/electronics13183673
Sofia Kouah, Asma Saighi, Maryem Ammi, Aymen Naït Si Mohand, Marwa Ines Kouah, David Megías
The Internet of Things refers to a network of interconnected devices, objects, and systems, that can interact with one another without human intervention. The adoption of IoT technology has expanded rapidly, significantly impacting various fields, including smart healthcare, intelligent transportation, agriculture, and smart homes. This paper focuses on smart street lighting, which represents the core piece of the smart city and the key public service for citizens’ safety. Nevertheless, it poses substantial challenges related to energy consumption, especially during energy crises. This work aims to provide an advanced solution that enables intelligent control of street lighting, enhances human safety, reduces CO2 emissions and light pollution, and optimizes energy consumption, as well as facilitates maintenance of the lighting network. The solution is twofold: First, it introduces IoT-based smart street lighting referential models; second, it presents a framework for controlling smart street lighting based on the referential models. The proposal uses an IoT-based fuzzy multi-agent systems approach to address the challenges of smart street lighting. The approach leverages the strengths and properties of fuzzy logic and multi-agent systems to address the system requirements. This is illustrated through a testbed case study conducted on a concrete IoT prototype.
{"title":"Internet of Things-Based Multi-Agent System for the Control of Smart Street Lighting","authors":"Sofia Kouah, Asma Saighi, Maryem Ammi, Aymen Naït Si Mohand, Marwa Ines Kouah, David Megías","doi":"10.3390/electronics13183673","DOIUrl":"https://doi.org/10.3390/electronics13183673","url":null,"abstract":"The Internet of Things refers to a network of interconnected devices, objects, and systems, that can interact with one another without human intervention. The adoption of IoT technology has expanded rapidly, significantly impacting various fields, including smart healthcare, intelligent transportation, agriculture, and smart homes. This paper focuses on smart street lighting, which represents the core piece of the smart city and the key public service for citizens’ safety. Nevertheless, it poses substantial challenges related to energy consumption, especially during energy crises. This work aims to provide an advanced solution that enables intelligent control of street lighting, enhances human safety, reduces CO2 emissions and light pollution, and optimizes energy consumption, as well as facilitates maintenance of the lighting network. The solution is twofold: First, it introduces IoT-based smart street lighting referential models; second, it presents a framework for controlling smart street lighting based on the referential models. The proposal uses an IoT-based fuzzy multi-agent systems approach to address the challenges of smart street lighting. The approach leverages the strengths and properties of fuzzy logic and multi-agent systems to address the system requirements. This is illustrated through a testbed case study conducted on a concrete IoT prototype.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"6 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259477","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}
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