Pub Date : 2024-09-14DOI: 10.3390/electronics13183663
Ozgur Alaca, Emilio Carlos Piesciorovsky, Ali Riza Ekti, Nils Stenvig, Yonghao Gui, Mohammed Mohsen Olama, Narayan Bhusal, Ajay Yadav
This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.
本研究介绍了基于包络和机器学习（ML）的配电网电气故障类型检测算法，超越了传统的基于逻辑的方法。所提出的检测模型包括三个阶段：异常区域检测、基于 ML 的故障存在检测和基于 ML 的故障类型检测。最初，改进了基于包络的检测器，以识别异常区域，从而处理来自电表的噪声较大的电网信号。第二阶段充当开关，从正常、电机、开关和故障四个类别中检测是否存在故障。最后，如果检测到故障，第三阶段将识别具体的故障类型。本研究探索了各种特征提取方法，并评估了不同的多线程算法，以最大限度地提高预测精度。使用不同采样率的电表/继电器，如 SEL735、SEL421、SEL734、SEL700GT 和 SEL351S，在离基于逆变器的光伏阵列农场较近和较远的地方，在模拟软硬件电网测试平台上测试了所提算法的性能。性能结果表明了所提出模型在现实条件下的稳健性和准确性。
{"title":"Assessment of Envelope- and Machine Learning-Based Electrical Fault Type Detection Algorithms for Electrical Distribution Grids","authors":"Ozgur Alaca, Emilio Carlos Piesciorovsky, Ali Riza Ekti, Nils Stenvig, Yonghao Gui, Mohammed Mohsen Olama, Narayan Bhusal, Ajay Yadav","doi":"10.3390/electronics13183663","DOIUrl":"https://doi.org/10.3390/electronics13183663","url":null,"abstract":"This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259554","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}
Burn wound depth is a significant determinant of patient treatment. Typically, the evaluation of burn depth relies heavily on the clinical experience of doctors. Even experienced surgeons may not achieve high accuracy and speed in diagnosing burn depth. Thus, intelligent burn depth classification is useful and valuable. Here, an intelligent classification method for burn depth based on machine learning techniques is proposed. In particular, this method involves extracting color, texture, and depth features from images, and sequentially cascading these features. Then, an iterative selection method based on random forest feature importance measure is applied. The selected features are input into the random forest classifier to evaluate this proposed method using the standard burn dataset. This method classifies burn images, achieving an accuracy of 91.76% when classified into two categories and 80.74% when classified into three categories. The comprehensive experimental results indicate that this proposed method is capable of learning effective features from limited data samples and identifying burn depth effectively.
{"title":"Multi-Feature Extraction and Selection Method to Diagnose Burn Depth from Burn Images","authors":"Xizhe Zhang, Qi Zhang, Peixian Li, Jie You, Jingzhang Sun, Jianhang Zhou","doi":"10.3390/electronics13183665","DOIUrl":"https://doi.org/10.3390/electronics13183665","url":null,"abstract":"Burn wound depth is a significant determinant of patient treatment. Typically, the evaluation of burn depth relies heavily on the clinical experience of doctors. Even experienced surgeons may not achieve high accuracy and speed in diagnosing burn depth. Thus, intelligent burn depth classification is useful and valuable. Here, an intelligent classification method for burn depth based on machine learning techniques is proposed. In particular, this method involves extracting color, texture, and depth features from images, and sequentially cascading these features. Then, an iterative selection method based on random forest feature importance measure is applied. The selected features are input into the random forest classifier to evaluate this proposed method using the standard burn dataset. This method classifies burn images, achieving an accuracy of 91.76% when classified into two categories and 80.74% when classified into three categories. The comprehensive experimental results indicate that this proposed method is capable of learning effective features from limited data samples and identifying burn depth effectively.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"25 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259589","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 : 2024-09-14DOI: 10.3390/electronics13183664
Yuheng Li, Zhiquan Zhou, Jinlong Wang, Lina Wang, Chenxu Wang
In recent years, with the burgeoning application of high voltage in various industrial sectors, the deployment of unmanned equipment, such as industrial heavy-load Unmanned Aerial Vehicles (UAVs), incorporating high-capacity Insulated-Gate Bipolar Transistors (IGBTs), has become increasingly prevalent. The demand for high-voltage IGBT modules in UAV is continuously growing; therefore, exploring methods to predict fault precursor parameters of multi-chip IGBT modules is crucial for the operational health management of unmanned equipment like UAVs. This paper analyzes the gate charge degradation in multi-chip IGBT modules after thermal cycling, which can be used to evaluate the operational state of these modules. Furthermore, to delve into the electrical response of a gate drive circuit caused by local damage within the IGBT module, an RLC model incorporating parasitic parameters of the gate drive circuit is established, and a sensitivity analysis of the peak current in the gate charge circuit is provided. Additionally, in the experimental circuit, an open sample of an IGBT module with partial bond wires lifted off is used to simulate actual faults. The analysis and experimental results indicate that the peak current of the gate charge is closely related to L and C. The significant deviation in the gate current, influenced by the partial bond wires lift-off, can provide a basis for the development of predictive methods for IGBT modules.
近年来，随着高压在各工业领域的蓬勃应用，采用大容量绝缘栅双极晶体管（IGBT）的工业重载无人机（UAV）等无人设备的部署也日益普及。无人机对高压 IGBT 模块的需求不断增长，因此，探索预测多芯片 IGBT 模块故障前兆参数的方法对于无人机等无人设备的运行健康管理至关重要。本文分析了多芯片 IGBT 模块在热循环后的栅极电荷衰减，可用于评估这些模块的运行状态。此外，为了深入研究 IGBT 模块内部局部损坏导致的栅极驱动电路的电气响应，本文建立了一个包含栅极驱动电路寄生参数的 RLC 模型，并提供了栅极电荷电路峰值电流的灵敏度分析。此外，在实验电路中，使用了部分键合线被掀开的 IGBT 模块开路样品来模拟实际故障。分析和实验结果表明，栅极电荷的峰值电流与 L 和 C 密切相关。栅极电流受部分键合线脱落的影响而出现显著偏差，这为开发 IGBT 模块的预测方法提供了依据。
{"title":"Research on Gate Charge Degradation of Multi-Chip IGBT Modules in Power Supply for Unmanned Aerial Vehicles","authors":"Yuheng Li, Zhiquan Zhou, Jinlong Wang, Lina Wang, Chenxu Wang","doi":"10.3390/electronics13183664","DOIUrl":"https://doi.org/10.3390/electronics13183664","url":null,"abstract":"In recent years, with the burgeoning application of high voltage in various industrial sectors, the deployment of unmanned equipment, such as industrial heavy-load Unmanned Aerial Vehicles (UAVs), incorporating high-capacity Insulated-Gate Bipolar Transistors (IGBTs), has become increasingly prevalent. The demand for high-voltage IGBT modules in UAV is continuously growing; therefore, exploring methods to predict fault precursor parameters of multi-chip IGBT modules is crucial for the operational health management of unmanned equipment like UAVs. This paper analyzes the gate charge degradation in multi-chip IGBT modules after thermal cycling, which can be used to evaluate the operational state of these modules. Furthermore, to delve into the electrical response of a gate drive circuit caused by local damage within the IGBT module, an RLC model incorporating parasitic parameters of the gate drive circuit is established, and a sensitivity analysis of the peak current in the gate charge circuit is provided. Additionally, in the experimental circuit, an open sample of an IGBT module with partial bond wires lifted off is used to simulate actual faults. The analysis and experimental results indicate that the peak current of the gate charge is closely related to L and C. The significant deviation in the gate current, influenced by the partial bond wires lift-off, can provide a basis for the development of predictive methods for IGBT modules.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"40 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259586","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}
Captured images often suffer from issues like color distortion, detail loss, and significant noise. Therefore, it is necessary to improve image quality for reliable threat detection. Balancing brightness enhancement with the preservation of natural colors and details is particularly challenging in low-light image enhancement. To address these issues, this paper proposes an unsupervised low-light image enhancement approach using a U-net neural network with Retinex theory and a Convolutional Block Attention Module (CBAM). This method leverages Retinex-based decomposition to separate and enhance the reflectance map, ensuring visibility and contrast without introducing artifacts. A local adaptive enhancement function improves the brightness of the reflection map, while the designed loss function addresses illumination smoothness, brightness enhancement, color restoration, and denoising. Experiments validate the effectiveness of our method, revealing improved image brightness, reduced color deviation, and superior color restoration compared to leading approaches.
{"title":"An Improved Retinex-Based Approach Based on Attention Mechanisms for Low-Light Image Enhancement","authors":"Shan Jiang, Yingshan Shi, Yingchun Zhang, Yulin Zhang","doi":"10.3390/electronics13183645","DOIUrl":"https://doi.org/10.3390/electronics13183645","url":null,"abstract":"Captured images often suffer from issues like color distortion, detail loss, and significant noise. Therefore, it is necessary to improve image quality for reliable threat detection. Balancing brightness enhancement with the preservation of natural colors and details is particularly challenging in low-light image enhancement. To address these issues, this paper proposes an unsupervised low-light image enhancement approach using a U-net neural network with Retinex theory and a Convolutional Block Attention Module (CBAM). This method leverages Retinex-based decomposition to separate and enhance the reflectance map, ensuring visibility and contrast without introducing artifacts. A local adaptive enhancement function improves the brightness of the reflection map, while the designed loss function addresses illumination smoothness, brightness enhancement, color restoration, and denoising. Experiments validate the effectiveness of our method, revealing improved image brightness, reduced color deviation, and superior color restoration compared to leading approaches.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"7 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209014","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 : 2024-09-13DOI: 10.3390/electronics13183647
Yong Xiao, Xiaoming Lin, Yiyong Lei, Yanzhang Gu, Jianlin Tang, Fan Zhang, Bin Qian
Electricity markets are intricate systems that facilitate efficient energy exchange within interconnected grids. With the rise of low-carbon transportation driven by environmental policies and tech advancements, energy trading has become crucial. This trend towards Electric Vehicles (EVs) is bolstered by the pivotal role played by EV charging operators in providing essential charging infrastructure and services for widespread EV adoption. This paper introduces a blockchain-assisted secure electricity trading framework between EV charging operators and the electricity market with renewable energy sources. We propose a single-leader, multi-follower Stackelberg game between the electricity market and EV charging operators. In the two-stage Stackelberg game, the electricity market acts as the leader, deciding the price of electric energy. The EV charging aggregator leverages blockchain technology to record and verify energy trading transactions securely. The EV charging operators, acting as followers, then decide their demand for electric energy based on the set price. To find the Stackelberg equilibrium, we employ a Deep Reinforcement Learning (DRL) algorithm that tackles non-stationary challenges through policy, action space, and reward function formulation. To optimize efficiency, we propose the integration of pruning techniques into DRL, referred to as Tiny DRL. Numerical results demonstrate that our proposed schemes outperform traditional approaches.
{"title":"Blockchain-Assisted Secure Energy Trading in Electricity Markets: A Tiny Deep Reinforcement Learning-Based Stackelberg Game Approach","authors":"Yong Xiao, Xiaoming Lin, Yiyong Lei, Yanzhang Gu, Jianlin Tang, Fan Zhang, Bin Qian","doi":"10.3390/electronics13183647","DOIUrl":"https://doi.org/10.3390/electronics13183647","url":null,"abstract":"Electricity markets are intricate systems that facilitate efficient energy exchange within interconnected grids. With the rise of low-carbon transportation driven by environmental policies and tech advancements, energy trading has become crucial. This trend towards Electric Vehicles (EVs) is bolstered by the pivotal role played by EV charging operators in providing essential charging infrastructure and services for widespread EV adoption. This paper introduces a blockchain-assisted secure electricity trading framework between EV charging operators and the electricity market with renewable energy sources. We propose a single-leader, multi-follower Stackelberg game between the electricity market and EV charging operators. In the two-stage Stackelberg game, the electricity market acts as the leader, deciding the price of electric energy. The EV charging aggregator leverages blockchain technology to record and verify energy trading transactions securely. The EV charging operators, acting as followers, then decide their demand for electric energy based on the set price. To find the Stackelberg equilibrium, we employ a Deep Reinforcement Learning (DRL) algorithm that tackles non-stationary challenges through policy, action space, and reward function formulation. To optimize efficiency, we propose the integration of pruning techniques into DRL, referred to as Tiny DRL. Numerical results demonstrate that our proposed schemes outperform traditional approaches.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"101 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209017","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 : 2024-09-13DOI: 10.3390/electronics13183649
Sofia Bakogianni, Aris Tsolis, Chrysanthi Angelaki, Antonis A. Alexandridis
A design framework for developing full-textile reconfigurable dipole antennas is proposed for wearable applications. To this end, a precise embroidery process using conductive threads is applied to properly manage the antenna structure. Further, mechanical reconfiguration to enhance antenna operation by using solely clothing components is outlined. As a proof-of-concept, we present a full-textile embroidered dipole antenna with mechanical frequency reconfiguration. Specifically, reconfiguration is achieved by folding the dipole arms through a triangular formation. Conductive Velcro strips are employed to guide the necessary dipole arrangement. As shown, the proposed design methodology enables frequency tunability that ranges from 780 to 1330 MHz for UHF and L bands, with satisfactory radiation performance. The measured and simulated results are in good agreement, in terms of achieving similar frequency reconfiguration concept, as predicted by the electromagnetic simulation models.
本文提出了一种针对可穿戴应用开发全织物可重构偶极子天线的设计框架。为此，采用了使用导电线的精确刺绣工艺来适当管理天线结构。此外，还概述了仅使用服装组件来增强天线运行的机械重配置。作为概念验证，我们展示了一种具有机械频率重新配置功能的全织物刺绣偶极子天线。具体来说，重新配置是通过将偶极子臂折叠成三角形来实现的。导电尼龙搭扣条用于引导必要的偶极子排列。如图所示，所提出的设计方法使频率可调范围达到 780 至 1330 MHz，适用于 UHF 和 L 波段，辐射性能令人满意。在实现类似频率重新配置概念方面，测量结果和模拟结果非常吻合，正如电磁模拟模型所预测的那样。
{"title":"On the Development of Embroidered Reconfigurable Dipole Antennas: A Textile Approach to Mechanical Reconfiguration","authors":"Sofia Bakogianni, Aris Tsolis, Chrysanthi Angelaki, Antonis A. Alexandridis","doi":"10.3390/electronics13183649","DOIUrl":"https://doi.org/10.3390/electronics13183649","url":null,"abstract":"A design framework for developing full-textile reconfigurable dipole antennas is proposed for wearable applications. To this end, a precise embroidery process using conductive threads is applied to properly manage the antenna structure. Further, mechanical reconfiguration to enhance antenna operation by using solely clothing components is outlined. As a proof-of-concept, we present a full-textile embroidered dipole antenna with mechanical frequency reconfiguration. Specifically, reconfiguration is achieved by folding the dipole arms through a triangular formation. Conductive Velcro strips are employed to guide the necessary dipole arrangement. As shown, the proposed design methodology enables frequency tunability that ranges from 780 to 1330 MHz for UHF and L bands, with satisfactory radiation performance. The measured and simulated results are in good agreement, in terms of achieving similar frequency reconfiguration concept, as predicted by the electromagnetic simulation models.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"8 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209018","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 : 2024-09-13DOI: 10.3390/electronics13183651
Paweł Pawłowski, Karol Piniarski
In this paper, we introduce an efficient lossy coding procedure specifically tailored for handling video sequences of automotive high-dynamic range (HDR) image sensors in advanced driver-assistance systems (ADASs) for autonomous vehicles. Nowadays, mainly for security reasons, lossless compression is used in the automotive industry. However, it offers very low compression rates. To obtain higher compression rates, we suggest using lossy codecs, especially when testing image processing algorithms in software in-the-loop (SiL) or hardware-in-the-loop (HiL) conditions. Our approach leverages the high-quality VP9 codec, operating in two distinct modes: grayscale image compression for automatic image analysis and color (in RGB format) image compression for manual analysis. In both modes, images are acquired from the automotive-specific RCCC (red, clear, clear, clear) image sensor. The codec is designed to achieve a controlled image quality and state-of-the-art compression ratios while maintaining real-time feasibility. In automotive applications, the inherent data loss poses challenges associated with lossy codecs, particularly in rapidly changing scenes with intricate details. To address this, we propose configuring the lossy codecs in variable bitrate (VBR) mode with a constrained quality (CQ) parameter. By adjusting the quantization parameter, users can tailor the codec behavior to their specific application requirements. In this context, a detailed analysis of the quality of lossy compressed images in terms of the structural similarity index metric (SSIM) and the peak signal-to-noise ratio (PSNR) metrics is presented. With this analysis, we extracted some codec parameters, which have an important impact on preservation of video quality and compression ratio. The proposed compression settings are very efficient: the compression ratios vary from 51 to 7765 for grayscale image mode and from 4.51 to 602.6 for RGB image mode, depending on the specified output image quality settings. We reached 129 frames per second (fps) for compression and 315 fps for decompression in grayscale mode and 102 fps for compression and 121 fps for decompression in the RGB mode. These make it possible to achieve a much higher compression ratio compared to lossless compression while maintaining control over image quality.
{"title":"Efficient Lossy Compression of Video Sequences of Automotive High-Dynamic Range Image Sensors for Advanced Driver-Assistance Systems and Autonomous Vehicles","authors":"Paweł Pawłowski, Karol Piniarski","doi":"10.3390/electronics13183651","DOIUrl":"https://doi.org/10.3390/electronics13183651","url":null,"abstract":"In this paper, we introduce an efficient lossy coding procedure specifically tailored for handling video sequences of automotive high-dynamic range (HDR) image sensors in advanced driver-assistance systems (ADASs) for autonomous vehicles. Nowadays, mainly for security reasons, lossless compression is used in the automotive industry. However, it offers very low compression rates. To obtain higher compression rates, we suggest using lossy codecs, especially when testing image processing algorithms in software in-the-loop (SiL) or hardware-in-the-loop (HiL) conditions. Our approach leverages the high-quality VP9 codec, operating in two distinct modes: grayscale image compression for automatic image analysis and color (in RGB format) image compression for manual analysis. In both modes, images are acquired from the automotive-specific RCCC (red, clear, clear, clear) image sensor. The codec is designed to achieve a controlled image quality and state-of-the-art compression ratios while maintaining real-time feasibility. In automotive applications, the inherent data loss poses challenges associated with lossy codecs, particularly in rapidly changing scenes with intricate details. To address this, we propose configuring the lossy codecs in variable bitrate (VBR) mode with a constrained quality (CQ) parameter. By adjusting the quantization parameter, users can tailor the codec behavior to their specific application requirements. In this context, a detailed analysis of the quality of lossy compressed images in terms of the structural similarity index metric (SSIM) and the peak signal-to-noise ratio (PSNR) metrics is presented. With this analysis, we extracted some codec parameters, which have an important impact on preservation of video quality and compression ratio. The proposed compression settings are very efficient: the compression ratios vary from 51 to 7765 for grayscale image mode and from 4.51 to 602.6 for RGB image mode, depending on the specified output image quality settings. We reached 129 frames per second (fps) for compression and 315 fps for decompression in grayscale mode and 102 fps for compression and 121 fps for decompression in the RGB mode. These make it possible to achieve a much higher compression ratio compared to lossless compression while maintaining control over image quality.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"10 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209019","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 : 2024-09-13DOI: 10.3390/electronics13183655
Miljana Shulajkovska, Maj Smerkol, Gjorgji Noveski, Marko Bohanec, Matjaž Gams
As urban populations rise globally, cities face increasing challenges in managing urban mobility. This paper addresses the question of identifying which modifications to introduce regarding city mobility by evaluating potential solutions using city-specific, subjective multi-objective criteria. The innovative AI-based recommendation engine assists city planners and policymakers in prioritizing key urban mobility aspects for effective policy proposals. By leveraging multi-criteria decision analysis (MCDA) and ±1/2 analysis, this engine provides a structured approach to systematically and simultaneously navigate the complexities of urban mobility planning. The proposed approach aims to provide an open-source interoperable prototype for all smart cities to utilize such recommendation systems routinely, fostering efficient, sustainable, and forward-thinking urban mobility strategies. Case studies from four European cities—Helsinki (tunnel traffic), Amsterdam (bicycle traffic for a new city quarter), Messina (adding another bus line), and Bilbao (optimal timing for closing the city center)—highlight the engine’s transformative potential in shaping urban mobility policies. Ultimately, this contributes to more livable and resilient urban environments, based on advanced urban mobility management.
{"title":"Artificial Intelligence-Based Decision Support System for Sustainable Urban Mobility","authors":"Miljana Shulajkovska, Maj Smerkol, Gjorgji Noveski, Marko Bohanec, Matjaž Gams","doi":"10.3390/electronics13183655","DOIUrl":"https://doi.org/10.3390/electronics13183655","url":null,"abstract":"As urban populations rise globally, cities face increasing challenges in managing urban mobility. This paper addresses the question of identifying which modifications to introduce regarding city mobility by evaluating potential solutions using city-specific, subjective multi-objective criteria. The innovative AI-based recommendation engine assists city planners and policymakers in prioritizing key urban mobility aspects for effective policy proposals. By leveraging multi-criteria decision analysis (MCDA) and ±1/2 analysis, this engine provides a structured approach to systematically and simultaneously navigate the complexities of urban mobility planning. The proposed approach aims to provide an open-source interoperable prototype for all smart cities to utilize such recommendation systems routinely, fostering efficient, sustainable, and forward-thinking urban mobility strategies. Case studies from four European cities—Helsinki (tunnel traffic), Amsterdam (bicycle traffic for a new city quarter), Messina (adding another bus line), and Bilbao (optimal timing for closing the city center)—highlight the engine’s transformative potential in shaping urban mobility policies. Ultimately, this contributes to more livable and resilient urban environments, based on advanced urban mobility management.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"5 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209025","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}
The existing collaborative channel perception suffers from unreasonable data fusion weight allocation, which mismatches the channel perception capability of the node devices. This often leads to significant deviations between the channel perception results and the actual channel state. To solve this issue, this paper integrates the data fusion algorithm from evidence fusion theory with data link channel state perception. It applies the data fusion advantages of evidence fusion theory to evaluate the traffic pulse statistical capability of network node devices. Specifically, the typical characteristic parameters describing the channel perception capability of node devices are regarded as evidence parameter sets under the recognition framework. By calculating the credibility and falsity of the characteristic parameters, the differences and conflicts between nodes are measured to achieve a comprehensive evaluation of the traffic pulse statistical capabilities of node devices. Based on this evaluation, the geometric mean method is adopted to calculate channel state perception weights for each node within a single-hop range, and a weight allocation strategy is formulated to improve the accuracy of channel state perception.
{"title":"Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion","authors":"Zhiyong Zhao, Zhongyang Mao, Zhilin Zhang, Yaozong Pan, Jianwu Xu","doi":"10.3390/electronics13183643","DOIUrl":"https://doi.org/10.3390/electronics13183643","url":null,"abstract":"The existing collaborative channel perception suffers from unreasonable data fusion weight allocation, which mismatches the channel perception capability of the node devices. This often leads to significant deviations between the channel perception results and the actual channel state. To solve this issue, this paper integrates the data fusion algorithm from evidence fusion theory with data link channel state perception. It applies the data fusion advantages of evidence fusion theory to evaluate the traffic pulse statistical capability of network node devices. Specifically, the typical characteristic parameters describing the channel perception capability of node devices are regarded as evidence parameter sets under the recognition framework. By calculating the credibility and falsity of the characteristic parameters, the differences and conflicts between nodes are measured to achieve a comprehensive evaluation of the traffic pulse statistical capabilities of node devices. Based on this evaluation, the geometric mean method is adopted to calculate channel state perception weights for each node within a single-hop range, and a weight allocation strategy is formulated to improve the accuracy of channel state perception.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"25 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209011","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 : 2024-09-13DOI: 10.3390/electronics13183656
Zejun Huang, Hao Bai, Min Xu, Yuchao Hou, Ruotian Yao, Yipeng Liu, Qi Guo, Chunming Tu
To resolve the issue of the difficultly in effectively balancing the output performance improvement, cost reduction, and efficiency improvement of a medium-voltage modular multilevel converter (MMC), a novel MMC (NMMC) topology based on high- and low-frequency hybrid modulation is proposed in this study. Each arm of the NMMC contains a high-frequency sub-module composed of a heterogeneous cross-connect module (HCCM) and N − 1 low-frequency sub-modules composed of half-bridge converters. The high-frequency bridge arm of the HCCM in this study adopts SiC MOSFET devices, while the commutation bridge arm and low-frequency sub-module of the HCCM adopt Si IGBT devices. For the NMMC topology, this study adopts a high/low-frequency hybrid modulation strategy, which gives full play to the advantages of low switching loss in SiC MOSFET devices and low on-state loss in Si IGBT devices. In addition, a specific capacitor voltage balance strategy is proposed for the HCCM, and the working state of the HCCM is analyzed in detail. Furthermore, the feasibility and effectiveness of the proposed topology, modulation strategy, and voltage balancing strategy are verified by experiments. Finally, the proposed topology is compared with the existing MMC topology in terms of device cost and operating loss, which proves that the proposed topology can better balance the cost and efficiency indicators of the device.
为了解决中压模块化多电平转换器（MMC）在提高输出性能、降低成本和提高效率之间难以有效平衡的问题，本研究提出了一种基于高低频混合调制的新型 MMC（NMMC）拓扑结构。NMMC 的每个臂包含一个由异质交叉连接模块 (HCCM) 组成的高频子模块和 N - 1 个由半桥转换器组成的低频子模块。本研究中 HCCM 的高频桥臂采用了 SiC MOSFET 器件，而 HCCM 的换向桥臂和低频子模块则采用了 Si IGBT 器件。对于 NMMC 拓扑，本研究采用了高/低频混合调制策略，充分发挥了 SiC MOSFET 器件开关损耗低和 Si IGBT 器件导通损耗低的优势。此外，还针对 HCCM 提出了具体的电容器电压平衡策略，并详细分析了 HCCM 的工作状态。此外，还通过实验验证了所提出的拓扑结构、调制策略和电压平衡策略的可行性和有效性。最后，将所提出的拓扑结构与现有的 MMC 拓扑结构在器件成本和工作损耗方面进行了比较，证明所提出的拓扑结构能更好地平衡器件的成本和效率指标。
{"title":"A Novel Modular Multilevel Converter Topology with High- and Low-Frequency Modules and Its Modulation Strategy","authors":"Zejun Huang, Hao Bai, Min Xu, Yuchao Hou, Ruotian Yao, Yipeng Liu, Qi Guo, Chunming Tu","doi":"10.3390/electronics13183656","DOIUrl":"https://doi.org/10.3390/electronics13183656","url":null,"abstract":"To resolve the issue of the difficultly in effectively balancing the output performance improvement, cost reduction, and efficiency improvement of a medium-voltage modular multilevel converter (MMC), a novel MMC (NMMC) topology based on high- and low-frequency hybrid modulation is proposed in this study. Each arm of the NMMC contains a high-frequency sub-module composed of a heterogeneous cross-connect module (HCCM) and N − 1 low-frequency sub-modules composed of half-bridge converters. The high-frequency bridge arm of the HCCM in this study adopts SiC MOSFET devices, while the commutation bridge arm and low-frequency sub-module of the HCCM adopt Si IGBT devices. For the NMMC topology, this study adopts a high/low-frequency hybrid modulation strategy, which gives full play to the advantages of low switching loss in SiC MOSFET devices and low on-state loss in Si IGBT devices. In addition, a specific capacitor voltage balance strategy is proposed for the HCCM, and the working state of the HCCM is analyzed in detail. Furthermore, the feasibility and effectiveness of the proposed topology, modulation strategy, and voltage balancing strategy are verified by experiments. Finally, the proposed topology is compared with the existing MMC topology in terms of device cost and operating loss, which proves that the proposed topology can better balance the cost and efficiency indicators of the device.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"19 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209024","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：