Pub Date : 2024-07-19DOI: 10.3390/electronics13142855
Krishnakanth Mohanta, Saba Al-Rubaye
This paper presents an analytical exploration of sixth-generation (6G) satellite–terrestrial integrated networks, focusing specifically on their applications within air mobility operations, such as those involving unmanned aerial vehicles (UAVs). As the integration of satellite and terrestrial networks promises to revolutionize mobile communication by extending coverage and enhancing connectivity, this study delves into two critical aspects: link budget analysis and handover and mobility analysis for UAVs. The link budget analysis assesses the communication requirements necessary to ensure robust and consistent connectivity between satellites and UAVs, accounting for factors such as path loss, antenna gains, and power transmission. Meanwhile, the handover and mobility analysis investigates the challenges and solutions associated with UAVs transitioning between different network nodes and layers in a dynamic aerial environment. This paper utilizes theoretical models and simulations to provide insights into the design and optimization of these networks, aiming to enhance the reliability and efficiency of UAV operations in the context of the emerging 6G landscape. The findings propose not only technological advancements in network architecture but also practical guidelines for the deployment of UAVs in complex environments, marking a significant step toward the realization of a fully integrated, satellite-terrestrial ecosystem.
{"title":"Towards 6G Satellite–Terrestrial Networks: Analysis of Air Mobility Operations","authors":"Krishnakanth Mohanta, Saba Al-Rubaye","doi":"10.3390/electronics13142855","DOIUrl":"https://doi.org/10.3390/electronics13142855","url":null,"abstract":"This paper presents an analytical exploration of sixth-generation (6G) satellite–terrestrial integrated networks, focusing specifically on their applications within air mobility operations, such as those involving unmanned aerial vehicles (UAVs). As the integration of satellite and terrestrial networks promises to revolutionize mobile communication by extending coverage and enhancing connectivity, this study delves into two critical aspects: link budget analysis and handover and mobility analysis for UAVs. The link budget analysis assesses the communication requirements necessary to ensure robust and consistent connectivity between satellites and UAVs, accounting for factors such as path loss, antenna gains, and power transmission. Meanwhile, the handover and mobility analysis investigates the challenges and solutions associated with UAVs transitioning between different network nodes and layers in a dynamic aerial environment. This paper utilizes theoretical models and simulations to provide insights into the design and optimization of these networks, aiming to enhance the reliability and efficiency of UAV operations in the context of the emerging 6G landscape. The findings propose not only technological advancements in network architecture but also practical guidelines for the deployment of UAVs in complex environments, marking a significant step toward the realization of a fully integrated, satellite-terrestrial ecosystem.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.3390/electronics13142849
A. Manowska, Martin Boroš, Muhammad Waqar Hassan, A. Bluszcz, K. Tobór-Osadnik
Energy security is a crucial issue for political, environmental, and economic reasons. This article presents a modern approach to securing critical infrastructure in energy transmission networks, which are managed by advanced IT systems. This paper focuses on the integration of cryptographic mechanisms with biometric data, providing an additional layer of protection against cyber threats. The discussed solutions enable the protection of management systems in energy transmission networks, enhancing their resilience to cyberattacks. The use of the command-line interface (CLI) in combination with biometrics allows for precise execution of security tasks such as network monitoring, firewall management, and automation of security tasks. This makes these systems more reliable and secure, which is essential for the stability of energy systems.
出于政治、环境和经济原因，能源安全是一个至关重要的问题。本文介绍了一种保护能源传输网络关键基础设施安全的现代方法，该网络由先进的 IT 系统管理。本文的重点是将密码机制与生物识别数据相结合，为防范网络威胁提供额外的保护。所讨论的解决方案能够保护能源传输网络中的管理系统，增强其抵御网络攻击的能力。将命令行界面（CLI）与生物识别技术相结合，可精确执行网络监控、防火墙管理和安全任务自动化等安全任务。这使得这些系统更加可靠和安全，对能源系统的稳定性至关重要。
{"title":"A Modern Approach to Securing Critical Infrastructure in Energy Transmission Networks: Integration of Cryptographic Mechanisms and Biometric Data","authors":"A. Manowska, Martin Boroš, Muhammad Waqar Hassan, A. Bluszcz, K. Tobór-Osadnik","doi":"10.3390/electronics13142849","DOIUrl":"https://doi.org/10.3390/electronics13142849","url":null,"abstract":"Energy security is a crucial issue for political, environmental, and economic reasons. This article presents a modern approach to securing critical infrastructure in energy transmission networks, which are managed by advanced IT systems. This paper focuses on the integration of cryptographic mechanisms with biometric data, providing an additional layer of protection against cyber threats. The discussed solutions enable the protection of management systems in energy transmission networks, enhancing their resilience to cyberattacks. The use of the command-line interface (CLI) in combination with biometrics allows for precise execution of security tasks such as network monitoring, firewall management, and automation of security tasks. This makes these systems more reliable and secure, which is essential for the stability of energy systems.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multi-beam sonar imaging detection technology is increasingly becoming the mainstream technology in fields such as hydraulic safety inspection and underwater target detection due to its ability to generate clearer images under low-visibility conditions. However, during the multi-beam sonar detection process, issues such as low image resolution and blurred imaging edges lead to decreased target segmentation accuracy. Traditional filtering methods for echo signals cannot effectively solve these problems. To address these challenges, this paper introduces, for the first time, a multi-beam sonar dataset against the background of simulated crack detection for dam safety. This dataset included simulated cracks detected by multi-beam sonar from various angles. The width of the cracks ranged from 3 cm to 9 cm, and the length ranged from 0.2 m to 1.5 m. In addition, this paper proposes a BS-UNet semantic segmentation algorithm. The Swin-UNet model incorporates a dual-layer routing attention mechanism to enhance the accuracy of sonar image detail segmentation. Furthermore, an online convolutional reparameterization structure was added to the output end of the model to improve the model’s capability to represent image features. Comparisons of the BS-UNet model with commonly used semantic segmentation models on the multi-beam sonar dataset consistently demonstrated the BS-UNet model’s superior performance, as it improved semantic segmentation evaluation metrics such as Precision and IoU by around 0.03 compared to the Swin-UNet model. In conclusion, BS-UNet can effectively be applied in multi-beam sonar image segmentation tasks.
{"title":"Multi-Beam Sonar Target Segmentation Algorithm Based on BS-Unet","authors":"Wennuo Zhang, Xuewu Zhang, Yu Zhang, Pengyuan Zeng, Ruikai Wei, Junsong Xu, Yang Chen","doi":"10.3390/electronics13142841","DOIUrl":"https://doi.org/10.3390/electronics13142841","url":null,"abstract":"Multi-beam sonar imaging detection technology is increasingly becoming the mainstream technology in fields such as hydraulic safety inspection and underwater target detection due to its ability to generate clearer images under low-visibility conditions. However, during the multi-beam sonar detection process, issues such as low image resolution and blurred imaging edges lead to decreased target segmentation accuracy. Traditional filtering methods for echo signals cannot effectively solve these problems. To address these challenges, this paper introduces, for the first time, a multi-beam sonar dataset against the background of simulated crack detection for dam safety. This dataset included simulated cracks detected by multi-beam sonar from various angles. The width of the cracks ranged from 3 cm to 9 cm, and the length ranged from 0.2 m to 1.5 m. In addition, this paper proposes a BS-UNet semantic segmentation algorithm. The Swin-UNet model incorporates a dual-layer routing attention mechanism to enhance the accuracy of sonar image detail segmentation. Furthermore, an online convolutional reparameterization structure was added to the output end of the model to improve the model’s capability to represent image features. Comparisons of the BS-UNet model with commonly used semantic segmentation models on the multi-beam sonar dataset consistently demonstrated the BS-UNet model’s superior performance, as it improved semantic segmentation evaluation metrics such as Precision and IoU by around 0.03 compared to the Swin-UNet model. In conclusion, BS-UNet can effectively be applied in multi-beam sonar image segmentation tasks.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.3390/electronics13142844
Ying Zou, Zuguo Chen, Shangyang Zhu, Yingcong Li
Although the NSGA-III algorithm is able to find the global optimal solution and has a good effect on the workshop scheduling optimization, the limitations in population diversity, convergence ability and local optimal solutions make it not applicable to certain situations. Thus, an improved NSGA-III workshop scheduling optimization algorithm is proposed in this work. It aims to address these limitations of the NSGA-III algorithm in processing workshop scheduling optimization. To solve the problem of individual elimination in the traditional NSGA-III algorithm, chaotic mapping is introduced in the improved NSGA-III algorithm to generate new offspring individuals and add the selected winning individuals to the offspring population as the parent population for the next iteration. The proposed algorithm was applied to a pressure sensor calibration workshop. A comparison with the traditional NSGA-III algorithm was conducted through a simulation analysis. The results show that the proposed algorithm can obtain a better convergence performance, improve the optimization ability and avoid falling into local optimal solutions.
{"title":"NSGA-III-Based Production Scheduling Optimization Algorithm for Pressure Sensor Calibration Workshop","authors":"Ying Zou, Zuguo Chen, Shangyang Zhu, Yingcong Li","doi":"10.3390/electronics13142844","DOIUrl":"https://doi.org/10.3390/electronics13142844","url":null,"abstract":"Although the NSGA-III algorithm is able to find the global optimal solution and has a good effect on the workshop scheduling optimization, the limitations in population diversity, convergence ability and local optimal solutions make it not applicable to certain situations. Thus, an improved NSGA-III workshop scheduling optimization algorithm is proposed in this work. It aims to address these limitations of the NSGA-III algorithm in processing workshop scheduling optimization. To solve the problem of individual elimination in the traditional NSGA-III algorithm, chaotic mapping is introduced in the improved NSGA-III algorithm to generate new offspring individuals and add the selected winning individuals to the offspring population as the parent population for the next iteration. The proposed algorithm was applied to a pressure sensor calibration workshop. A comparison with the traditional NSGA-III algorithm was conducted through a simulation analysis. The results show that the proposed algorithm can obtain a better convergence performance, improve the optimization ability and avoid falling into local optimal solutions.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.3390/electronics13142856
Jia Yu, Gehao Lu, Jie Zhang
Infrared image and visible image fusion (IVIF) is a research direction that is currently attracting much attention in the field of image processing. The main goal is to obtain a fused image by reasonably fusing infrared images and visible images, while retaining the advantageous features of each source image. The research in this field aims to improve image quality, enhance target recognition ability, and broaden the application areas of image processing. To advance research in this area, we propose a breakthrough image fusion method based on the Residual Attention Network (RAN). By applying this innovative network to the task of image fusion, the mechanism of the residual attention network can better capture critical background and detail information in the images, significantly improving the quality and effectiveness of image fusion. Experimental results on public domain datasets show that our method performs excellently on multiple key metrics. For example, compared to existing methods, our method improves the standard deviation (SD) by 35.26%, spatial frequency (SF) by 109.85%, average gradient (AG) by 96.93%, and structural similarity (SSIM) by 23.47%. These significant improvements validate the superiority of our proposed residual attention network in the task of image fusion and open up new possibilities for enhancing the performance and adaptability of fusion networks.
{"title":"RAN: Infrared and Visible Image Fusion Network Based on Residual Attention Decomposition","authors":"Jia Yu, Gehao Lu, Jie Zhang","doi":"10.3390/electronics13142856","DOIUrl":"https://doi.org/10.3390/electronics13142856","url":null,"abstract":"Infrared image and visible image fusion (IVIF) is a research direction that is currently attracting much attention in the field of image processing. The main goal is to obtain a fused image by reasonably fusing infrared images and visible images, while retaining the advantageous features of each source image. The research in this field aims to improve image quality, enhance target recognition ability, and broaden the application areas of image processing. To advance research in this area, we propose a breakthrough image fusion method based on the Residual Attention Network (RAN). By applying this innovative network to the task of image fusion, the mechanism of the residual attention network can better capture critical background and detail information in the images, significantly improving the quality and effectiveness of image fusion. Experimental results on public domain datasets show that our method performs excellently on multiple key metrics. For example, compared to existing methods, our method improves the standard deviation (SD) by 35.26%, spatial frequency (SF) by 109.85%, average gradient (AG) by 96.93%, and structural similarity (SSIM) by 23.47%. These significant improvements validate the superiority of our proposed residual attention network in the task of image fusion and open up new possibilities for enhancing the performance and adaptability of fusion networks.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.3390/electronics13142846
Christiam F. Frasser, Alejandro Morán, V. Canals, Joan Font, E. Isern, M. Roca, Josep L. Rosselló
Deploying modern neural networks on resource-constrained edge devices necessitates a series of optimizations to ready them for production. These optimizations typically involve pruning, quantization, and fixed-point conversion to compress the model size and enhance energy efficiency. While these optimizations are generally adequate for most edge devices, there exists potential for further improving the energy efficiency by leveraging special-purpose hardware and unconventional computing paradigms. In this study, we explore stochastic computing neural networks and their impact on quantization and overall performance concerning weight distributions. When arithmetic operations such as addition and multiplication are executed by stochastic computing hardware, the arithmetic error may significantly increase, leading to a diminished overall accuracy. To bridge the accuracy gap between a fixed-point model and its stochastic computing implementation, we propose a novel approximate arithmetic-aware training method. We validate the efficacy of our approach by implementing the LeNet-5 convolutional neural network on an FPGA. Our experimental results reveal a negligible accuracy degradation of merely 0.01% compared with the floating-point counterpart, while achieving a substantial 27× speedup and 33× enhancement in energy efficiency compared with other FPGA implementations. Additionally, the proposed method enhances the likelihood of selecting optimal LFSR seeds for stochastic computing systems.
{"title":"Optimizing Artificial Neural Networks to Minimize Arithmetic Errors in Stochastic Computing Implementations","authors":"Christiam F. Frasser, Alejandro Morán, V. Canals, Joan Font, E. Isern, M. Roca, Josep L. Rosselló","doi":"10.3390/electronics13142846","DOIUrl":"https://doi.org/10.3390/electronics13142846","url":null,"abstract":"Deploying modern neural networks on resource-constrained edge devices necessitates a series of optimizations to ready them for production. These optimizations typically involve pruning, quantization, and fixed-point conversion to compress the model size and enhance energy efficiency. While these optimizations are generally adequate for most edge devices, there exists potential for further improving the energy efficiency by leveraging special-purpose hardware and unconventional computing paradigms. In this study, we explore stochastic computing neural networks and their impact on quantization and overall performance concerning weight distributions. When arithmetic operations such as addition and multiplication are executed by stochastic computing hardware, the arithmetic error may significantly increase, leading to a diminished overall accuracy. To bridge the accuracy gap between a fixed-point model and its stochastic computing implementation, we propose a novel approximate arithmetic-aware training method. We validate the efficacy of our approach by implementing the LeNet-5 convolutional neural network on an FPGA. Our experimental results reveal a negligible accuracy degradation of merely 0.01% compared with the floating-point counterpart, while achieving a substantial 27× speedup and 33× enhancement in energy efficiency compared with other FPGA implementations. Additionally, the proposed method enhances the likelihood of selecting optimal LFSR seeds for stochastic computing systems.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.3390/electronics13142852
Wenbo Zhou
As a typical information system, a cloud file system enables the storage, retrieval, and management of data on remote servers or server clusters. The reliable design of such systems is critical to ensure the security of data and availability of services. However, designing correct-by-construction systems is challenging due to the complexity of and concurrency inherent in cloud file systems. Further, existing works on cloud file system analysis often focus on specific systems or lack formal modeling and verification, leading to potential design flaws and security vulnerabilities. To address these issues, we propose MSCFS-RP, which is a formal analysis model based on colored Petri nets. Leveraging the strengths of colored Petri nets in representing diverse information types with colored tokens and defining explicit rules for concurrent interactions, our model captures the writing and reading processes of clients, meta servers, and clusters. With strong formalism and support for verification using CPN Tools, we rigorously evaluate key properties such as replication consistency under various scenarios. The results demonstrate that MSCFS-RP satisfies these properties, validating its effectiveness and trustworthiness in managing information within cloud storage systems.
作为一种典型的信息系统，云文件系统可以在远程服务器或服务器集群上存储、检索和管理数据。此类系统的可靠设计对于确保数据的安全性和服务的可用性至关重要。然而，由于云文件系统固有的复杂性和并发性，按构造设计正确的系统具有挑战性。此外，现有的云文件系统分析工作通常侧重于特定系统，或缺乏正式建模和验证，从而导致潜在的设计缺陷和安全漏洞。为了解决这些问题，我们提出了 MSCFS-RP，这是一种基于彩色 Petri 网的形式化分析模型。利用彩色 Petri 网在用彩色标记表示不同信息类型和为并发交互定义明确规则方面的优势，我们的模型捕获了客户端、元服务器和集群的写入和读取过程。有了强大的形式主义和 CPN 工具的验证支持，我们对各种情况下的复制一致性等关键属性进行了严格评估。结果表明，MSCFS-RP 满足这些属性，验证了其在云存储系统中管理信息的有效性和可信度。
{"title":"MSCFS-RP: A Colored-Petri-Net-Based Analysis Model for Master–Slave Cloud File Systems with Replication Pipelining","authors":"Wenbo Zhou","doi":"10.3390/electronics13142852","DOIUrl":"https://doi.org/10.3390/electronics13142852","url":null,"abstract":"As a typical information system, a cloud file system enables the storage, retrieval, and management of data on remote servers or server clusters. The reliable design of such systems is critical to ensure the security of data and availability of services. However, designing correct-by-construction systems is challenging due to the complexity of and concurrency inherent in cloud file systems. Further, existing works on cloud file system analysis often focus on specific systems or lack formal modeling and verification, leading to potential design flaws and security vulnerabilities. To address these issues, we propose MSCFS-RP, which is a formal analysis model based on colored Petri nets. Leveraging the strengths of colored Petri nets in representing diverse information types with colored tokens and defining explicit rules for concurrent interactions, our model captures the writing and reading processes of clients, meta servers, and clusters. With strong formalism and support for verification using CPN Tools, we rigorously evaluate key properties such as replication consistency under various scenarios. The results demonstrate that MSCFS-RP satisfies these properties, validating its effectiveness and trustworthiness in managing information within cloud storage systems.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.3390/electronics13142840
Junyan Qi, Yuhao Che, Lei Wang, Ruifu Yuan
Considering the shortcomings of the current monitoring system for tunnel anchor support systems, a tunnel anchor monitoring system based on LSTM-ARIMA prediction is proposed in this paper to prevent the deformation and collapse accidents that may occur in the underground mine tunnels during the backfilling process, which combines the Internet of Things and a neural network deep learning algorithm to achieve the real-time monitoring and prediction of the tunnel anchor pressure. To improve the prediction accuracy, a time series analysis algorithm is used in the prediction model of this system. In particular, an LSTM-ARIMA model is constructed to predict the tunnel anchor pressure by combining the Long Short-Term Memory (LSTM) model and the Autoregressive Integrated Moving Average (ARIMA) model. And a dynamic weighted combination method is designed based on model prediction confidence to acquire the optimal weight coefficients. This combined model enables the monitoring system to predict the anchor pressure more accurately, thereby preventing possible tunnel deformation and collapse accidents in advance. Finally, the overall system is verified using the anchor pressure dataset obtained from the 21,404 section of the Hulusu Coal Mine transportation tunnel in real-world engineering, whose results show that the pressure value predicted using the combined model is basically the same as the actual value on site, and the system has high real-time performance and stability, proving the effectiveness and reliability of the system.
{"title":"Design of a Tunnel Anchor Monitoring System Based on Long Short-Term Memory–Autoregressive Integrated Moving Average Prediction","authors":"Junyan Qi, Yuhao Che, Lei Wang, Ruifu Yuan","doi":"10.3390/electronics13142840","DOIUrl":"https://doi.org/10.3390/electronics13142840","url":null,"abstract":"Considering the shortcomings of the current monitoring system for tunnel anchor support systems, a tunnel anchor monitoring system based on LSTM-ARIMA prediction is proposed in this paper to prevent the deformation and collapse accidents that may occur in the underground mine tunnels during the backfilling process, which combines the Internet of Things and a neural network deep learning algorithm to achieve the real-time monitoring and prediction of the tunnel anchor pressure. To improve the prediction accuracy, a time series analysis algorithm is used in the prediction model of this system. In particular, an LSTM-ARIMA model is constructed to predict the tunnel anchor pressure by combining the Long Short-Term Memory (LSTM) model and the Autoregressive Integrated Moving Average (ARIMA) model. And a dynamic weighted combination method is designed based on model prediction confidence to acquire the optimal weight coefficients. This combined model enables the monitoring system to predict the anchor pressure more accurately, thereby preventing possible tunnel deformation and collapse accidents in advance. Finally, the overall system is verified using the anchor pressure dataset obtained from the 21,404 section of the Hulusu Coal Mine transportation tunnel in real-world engineering, whose results show that the pressure value predicted using the combined model is basically the same as the actual value on site, and the system has high real-time performance and stability, proving the effectiveness and reliability of the system.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inverters are important interfaces between micropower sources and consuming loads. However, the varying inductors and capacitors, modeling errors, measurement errors, and external disturbances would lead to degradation of the inverters’ performances when conventional linear control is adopted, causing instability problems. To address it, a disturbance-rejection passivity-based nonlinear control strategy is proposed for the inverters of micropower sources. The proposed method innovatively introduces an extended high-gain state observer into the passivity-based controller to achieve online observation and elimination of complex influencing factors such as external disturbances, time-varying parameter uncertainties, and modeling errors, thus ensuring the global stability of the inverter under various disturbances. The design details on the passivity-based controller and the extended high-gain state observer are elaborated. The effectiveness and feasibility of the proposed control strategy are verified by the experiments performed by a 15 kVA inverter designed in the lab. The results show that the proposed control is able to ensure the inverter’s stable operation under the following conditions: constant power load, the filter inductance and capacitance reduce up to 33% and 96%, and the input voltage varies more than 22%.
逆变器是微电源和消费负载之间的重要接口。然而，在采用传统线性控制时，电感器和电容器的变化、建模误差、测量误差以及外部干扰都会导致逆变器性能下降，造成不稳定问题。为此，针对微功率源逆变器提出了一种基于干扰抑制被动性的非线性控制策略。该方法创新性地在基于被动性的控制器中引入了扩展的高增益状态观测器，实现了对外部干扰、时变参数不确定性和建模误差等复杂影响因素的在线观测和消除，从而保证了逆变器在各种干扰下的全局稳定性。详细阐述了基于被动性的控制器和扩展的高增益状态观测器的设计细节。在实验室设计的 15 kVA 逆变器上进行的实验验证了所提控制策略的有效性和可行性。结果表明，所提出的控制能确保逆变器在以下条件下稳定运行：恒定功率负载、滤波器电感和电容分别降低 33% 和 96%、输入电压变化超过 22%。
{"title":"Disturbance-Rejection Passivity-Based Control for Inverters of Micropower Sources","authors":"Chao Luo, Liang Tu, Haiqing Cai, Haohan Gu, Jiawei Chen, G. Jia, Xinke Zhu","doi":"10.3390/electronics13142851","DOIUrl":"https://doi.org/10.3390/electronics13142851","url":null,"abstract":"Inverters are important interfaces between micropower sources and consuming loads. However, the varying inductors and capacitors, modeling errors, measurement errors, and external disturbances would lead to degradation of the inverters’ performances when conventional linear control is adopted, causing instability problems. To address it, a disturbance-rejection passivity-based nonlinear control strategy is proposed for the inverters of micropower sources. The proposed method innovatively introduces an extended high-gain state observer into the passivity-based controller to achieve online observation and elimination of complex influencing factors such as external disturbances, time-varying parameter uncertainties, and modeling errors, thus ensuring the global stability of the inverter under various disturbances. The design details on the passivity-based controller and the extended high-gain state observer are elaborated. The effectiveness and feasibility of the proposed control strategy are verified by the experiments performed by a 15 kVA inverter designed in the lab. The results show that the proposed control is able to ensure the inverter’s stable operation under the following conditions: constant power load, the filter inductance and capacitance reduce up to 33% and 96%, and the input voltage varies more than 22%.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.3390/electronics13142845
Eduard Zadobrischi, Ștefan Havriliuc
In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration of these communication protocols within smart intersections, emphasizing their capabilities and synergies. DSRC, based on IEEE 802.11p, provides reliable short-range communication with data rates up to 27 Mbps and latencies below 50 ms, ideal for real-time safety applications. C-V2X leverages LTE and 5G networks, offering broader coverage up to 10 km and supporting data rates up to 100 Mbps, with latencies as low as 20 ms in direct communication mode (PC5). LoRa 2.4 GHz, known for its long-range (up to 15 km in rural areas, 1–2 km in urban settings) and low-power characteristics, offers data rates between 0.3 and 37.5 kbps, suitable for non-critical data exchange and infrastructure monitoring. The study evaluates the performance and interoperability of these technologies in urban environments, focusing on data latency, transmission reliability, and scalability. Experimental results from simulated and real-world scenarios show that DSRC maintains reliable communication within 1 km with minimal interference. C-V2X demonstrates superior scalability and coverage, maintaining robust communication over several kilometers in high-density urban settings. LoRa 2.4 GHz exhibits excellent penetration through urban obstacles, maintaining connectivity and efficient data transmission with packet error rates below 10%.
{"title":"Enhancing Scalability of C-V2X and DSRC Vehicular Communication Protocols with LoRa 2.4 GHz in the Scenario of Urban Traffic Systems","authors":"Eduard Zadobrischi, Ștefan Havriliuc","doi":"10.3390/electronics13142845","DOIUrl":"https://doi.org/10.3390/electronics13142845","url":null,"abstract":"In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration of these communication protocols within smart intersections, emphasizing their capabilities and synergies. DSRC, based on IEEE 802.11p, provides reliable short-range communication with data rates up to 27 Mbps and latencies below 50 ms, ideal for real-time safety applications. C-V2X leverages LTE and 5G networks, offering broader coverage up to 10 km and supporting data rates up to 100 Mbps, with latencies as low as 20 ms in direct communication mode (PC5). LoRa 2.4 GHz, known for its long-range (up to 15 km in rural areas, 1–2 km in urban settings) and low-power characteristics, offers data rates between 0.3 and 37.5 kbps, suitable for non-critical data exchange and infrastructure monitoring. The study evaluates the performance and interoperability of these technologies in urban environments, focusing on data latency, transmission reliability, and scalability. Experimental results from simulated and real-world scenarios show that DSRC maintains reliable communication within 1 km with minimal interference. C-V2X demonstrates superior scalability and coverage, maintaining robust communication over several kilometers in high-density urban settings. LoRa 2.4 GHz exhibits excellent penetration through urban obstacles, maintaining connectivity and efficient data transmission with packet error rates below 10%.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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