Pub Date : 2024-08-08DOI: 10.3390/electronics13163136
Kisung Park, Myeonghyun Kim, Youngho Park
In the Internet of Vehicles (IoV) environments, vehicles and roadside units (RSUs) communicate predominantly through public channels. These vehicles and RSUs exchange various data, such as traffic density, location, speed, etc. Therefore, secure and efficient authentication and key establishment (AKE) are needed to guarantee user privacy when exchanging data between vehicles and RSUs. Recently, a secure and computationally AKE scheme have been proposed to construct secure IoV environments. In their research, the authors asserted that their AKE scheme provides comprehensive security properties, protecting against various potential threats while simultaneously ensuring session key integrity, robust mutual authentication. This paper proved that the previous scheme does not prevent various attacks using logical and mathematical analyses. Moreover, we demonstrated that this scheme does not meet the essential security requirements and correctness of security assumptions. We perform the simulation proof using AVISPA, which is well known as a formal verification tool. To enhance the resilience of attacks, we propose solutions aimed at developing more robust and efficient AKE for IoV environments.
{"title":"On the Security of a Secure and Computationally Efficient Authentication and Key Agreement Scheme for Internet of Vehicles","authors":"Kisung Park, Myeonghyun Kim, Youngho Park","doi":"10.3390/electronics13163136","DOIUrl":"https://doi.org/10.3390/electronics13163136","url":null,"abstract":"In the Internet of Vehicles (IoV) environments, vehicles and roadside units (RSUs) communicate predominantly through public channels. These vehicles and RSUs exchange various data, such as traffic density, location, speed, etc. Therefore, secure and efficient authentication and key establishment (AKE) are needed to guarantee user privacy when exchanging data between vehicles and RSUs. Recently, a secure and computationally AKE scheme have been proposed to construct secure IoV environments. In their research, the authors asserted that their AKE scheme provides comprehensive security properties, protecting against various potential threats while simultaneously ensuring session key integrity, robust mutual authentication. This paper proved that the previous scheme does not prevent various attacks using logical and mathematical analyses. Moreover, we demonstrated that this scheme does not meet the essential security requirements and correctness of security assumptions. We perform the simulation proof using AVISPA, which is well known as a formal verification tool. To enhance the resilience of attacks, we propose solutions aimed at developing more robust and efficient AKE for IoV environments.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":"24 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925498","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 : 2024-08-08DOI: 10.3390/electronics13163141
Dongyoung Roh
CHAM is a family of lightweight block ciphers designed for resource-constrained environments like IoT devices and embedded systems, which require low power consumption and high performance. Despite numerous cryptanalytic evaluations, the security of CHAM remains robust. Differential–linear cryptanalysis, a method that combines two of the strongest attack methods on block ciphers—differential cryptanalysis and linear cryptanalysis—has been successfully applied to many block ciphers. This study introduces the first concrete differential–linear approximations of CHAM, marking a significant advancement in the cryptanalysis of this cipher family. Utilizing a Boolean satisfiability problem framework, we present a 46-round differential–linear approximation of CHAM-64/128 with a correlation of 2−31.08 and a 58-round approximation for CHAM-128/128 and CHAM-128/256 with correlations of 2−58.86 and 2−59.08, respectively. These findings significantly exceed the designers’ expectations for differential–linear approximations using CHAM. Furthermore, the 46-round differential–linear approximation of CHAM-64/128 is the best distinguisher of CHAM-64/128 to date in a single-key attack model. Notably, our findings do not threaten the security of CHAM but provide deeper insights into its cryptanalytic resistance.
{"title":"Differential–Linear Approximations of CHAM","authors":"Dongyoung Roh","doi":"10.3390/electronics13163141","DOIUrl":"https://doi.org/10.3390/electronics13163141","url":null,"abstract":"CHAM is a family of lightweight block ciphers designed for resource-constrained environments like IoT devices and embedded systems, which require low power consumption and high performance. Despite numerous cryptanalytic evaluations, the security of CHAM remains robust. Differential–linear cryptanalysis, a method that combines two of the strongest attack methods on block ciphers—differential cryptanalysis and linear cryptanalysis—has been successfully applied to many block ciphers. This study introduces the first concrete differential–linear approximations of CHAM, marking a significant advancement in the cryptanalysis of this cipher family. Utilizing a Boolean satisfiability problem framework, we present a 46-round differential–linear approximation of CHAM-64/128 with a correlation of 2−31.08 and a 58-round approximation for CHAM-128/128 and CHAM-128/256 with correlations of 2−58.86 and 2−59.08, respectively. These findings significantly exceed the designers’ expectations for differential–linear approximations using CHAM. Furthermore, the 46-round differential–linear approximation of CHAM-64/128 is the best distinguisher of CHAM-64/128 to date in a single-key attack model. Notably, our findings do not threaten the security of CHAM but provide deeper insights into its cryptanalytic resistance.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":"91 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926766","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 : 2024-08-08DOI: 10.3390/electronics13163138
Yunxin Tan, Guang-si Li, Chun Zhang, Weiming Gan
When performing high-resolution imaging with ground-based synthetic aperture radar (GB-SAR) systems, the data collected and processed are vast and complex, imposing higher demands on the real-time performance and processing efficiency of the imaging system. Yet a very limited number of studies have been conducted on the real-time processing method of GB-SAR monitoring data. This paper proposes a real-time imaging scheme based on parallel processing models, optimizing each step of the traditional ωK imaging algorithm in parallel. Several parallel optimization schemes are proposed for the computationally intensive and complex interpolation part, including dynamic parallelism, the Group-Nstream processing model, and the Fthread-Group-Nstream processing model. The Fthread-Group-Nstream processing model utilizes Fthread, Group, and Nstream for the finer-grained processing of monitoring data, reducing the impact of the nested depth on the algorithm’s performance in dynamic parallelism and alleviating the issue of serial execution within the Group-Nstream processing model. This scheme has been successfully applied in a synthetic aperture radar imaging system, achieving excellent imaging results and accuracy. The speedup ratio can reach 52.14, and the relative errors in amplitude and phase are close to 0, validating the effectiveness and practicality of the proposed schemes. This paper addresses the lack of research on the real-time processing of GB-SAR monitoring data, providing a reliable monitoring method for GB-SAR deformation monitoring.
{"title":"An Efficient and Accurate Ground-Based Synthetic Aperture Radar (GB-SAR) Real-Time Imaging Scheme Based on Parallel Processing Mode and Architecture","authors":"Yunxin Tan, Guang-si Li, Chun Zhang, Weiming Gan","doi":"10.3390/electronics13163138","DOIUrl":"https://doi.org/10.3390/electronics13163138","url":null,"abstract":"When performing high-resolution imaging with ground-based synthetic aperture radar (GB-SAR) systems, the data collected and processed are vast and complex, imposing higher demands on the real-time performance and processing efficiency of the imaging system. Yet a very limited number of studies have been conducted on the real-time processing method of GB-SAR monitoring data. This paper proposes a real-time imaging scheme based on parallel processing models, optimizing each step of the traditional ωK imaging algorithm in parallel. Several parallel optimization schemes are proposed for the computationally intensive and complex interpolation part, including dynamic parallelism, the Group-Nstream processing model, and the Fthread-Group-Nstream processing model. The Fthread-Group-Nstream processing model utilizes Fthread, Group, and Nstream for the finer-grained processing of monitoring data, reducing the impact of the nested depth on the algorithm’s performance in dynamic parallelism and alleviating the issue of serial execution within the Group-Nstream processing model. This scheme has been successfully applied in a synthetic aperture radar imaging system, achieving excellent imaging results and accuracy. The speedup ratio can reach 52.14, and the relative errors in amplitude and phase are close to 0, validating the effectiveness and practicality of the proposed schemes. This paper addresses the lack of research on the real-time processing of GB-SAR monitoring data, providing a reliable monitoring method for GB-SAR deformation monitoring.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":"48 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928201","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}
With the rapid development of renewable energy generation in recent years, microgrid technology has increasingly emerged as an effective means to facilitate the integration of renewable energy. To efficiently achieve optimal scheduling for microgrid cluster (MGC) systems while guaranteeing the safe and stable operation of a power grid, this study, drawing on actual electricity-consumption patterns and renewable energy generation in low-latitude coastal areas, proposes an integrated multi-objective coordinated optimization strategy. The objective function includes not only operational costs, environmental costs, and energy storage losses but also introduces penalty terms to comprehensively reflect the operation of the MGC system. To further enhance the efficiency of solving the economic dispatch model, this study combines chaotic mapping and dynamic opposition-based learning with the traditional Grey Wolf Optimization (GWO) algorithm, using the improved GWO (CDGWO) algorithm for optimization. Comparative experiments comprehensively validate the significant advantages of the proposed optimization algorithm in terms of economic benefits and scheduling efficiency. The results indicate that the proposed scheduling strategy, objective model, and solution algorithm can efficiently and effectively achieve multi-objective coordinated optimization scheduling for MGC systems, significantly enhancing the overall economic benefits of the MGC while ensuring a reliable power supply.
{"title":"Optimizing Economic Dispatch for Microgrid Clusters Using Improved Grey Wolf Optimization","authors":"Xinchen Wang, Shaorong Wang, Jiaxuan Ren, Zhaoxia Song, Shun Zhang, Hupeng Feng","doi":"10.3390/electronics13163139","DOIUrl":"https://doi.org/10.3390/electronics13163139","url":null,"abstract":"With the rapid development of renewable energy generation in recent years, microgrid technology has increasingly emerged as an effective means to facilitate the integration of renewable energy. To efficiently achieve optimal scheduling for microgrid cluster (MGC) systems while guaranteeing the safe and stable operation of a power grid, this study, drawing on actual electricity-consumption patterns and renewable energy generation in low-latitude coastal areas, proposes an integrated multi-objective coordinated optimization strategy. The objective function includes not only operational costs, environmental costs, and energy storage losses but also introduces penalty terms to comprehensively reflect the operation of the MGC system. To further enhance the efficiency of solving the economic dispatch model, this study combines chaotic mapping and dynamic opposition-based learning with the traditional Grey Wolf Optimization (GWO) algorithm, using the improved GWO (CDGWO) algorithm for optimization. Comparative experiments comprehensively validate the significant advantages of the proposed optimization algorithm in terms of economic benefits and scheduling efficiency. The results indicate that the proposed scheduling strategy, objective model, and solution algorithm can efficiently and effectively achieve multi-objective coordinated optimization scheduling for MGC systems, significantly enhancing the overall economic benefits of the MGC while ensuring a reliable power supply.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":"13 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926313","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}
Pneumonia has long been a significant concern in global public health. With the advancement of convolutional neural networks (CNNs), new technological methods have emerged to address this challenge. However, the application of CNNs to pneumonia diagnosis still faces several critical issues. First, the datasets used for training models often suffer from insufficient sample sizes and imbalanced class distributions, leading to reduced classification performance. Second, although CNNs can automatically extract features and make decisions from complex image data, their interpretability is relatively poor, limiting their widespread use in clinical diagnosis to some extent. To address these issues, a novel weighted cross-entropy loss function is proposed, which calculates weights via an inverse proportion exponential function to handle data imbalance more efficiently. Additionally, we employ a transfer learning approach that combines pretrained CNN model parameter fine-tuning to improve classification performance. Finally, we introduce the gradient-weighted class activation mapping method to enhance the interpretability of the model’s decisions by visualizing the image regions of focus. The experimental results indicate that our proposed approach significantly enhances CNN performance in pneumonia diagnosis tasks. Among the four selected models, the accuracy rates improved to over 90%, and visualized results were provided.
{"title":"An Improved Weighted Cross-Entropy-Based Convolutional Neural Network for Auxiliary Diagnosis of Pneumonia","authors":"Zhenyu Song, Zhanling Shi, Xuemei Yan, Bin Zhang, Shuangbao Song, Cheng Tang","doi":"10.3390/electronics13152929","DOIUrl":"https://doi.org/10.3390/electronics13152929","url":null,"abstract":"Pneumonia has long been a significant concern in global public health. With the advancement of convolutional neural networks (CNNs), new technological methods have emerged to address this challenge. However, the application of CNNs to pneumonia diagnosis still faces several critical issues. First, the datasets used for training models often suffer from insufficient sample sizes and imbalanced class distributions, leading to reduced classification performance. Second, although CNNs can automatically extract features and make decisions from complex image data, their interpretability is relatively poor, limiting their widespread use in clinical diagnosis to some extent. To address these issues, a novel weighted cross-entropy loss function is proposed, which calculates weights via an inverse proportion exponential function to handle data imbalance more efficiently. Additionally, we employ a transfer learning approach that combines pretrained CNN model parameter fine-tuning to improve classification performance. Finally, we introduce the gradient-weighted class activation mapping method to enhance the interpretability of the model’s decisions by visualizing the image regions of focus. The experimental results indicate that our proposed approach significantly enhances CNN performance in pneumonia diagnosis tasks. Among the four selected models, the accuracy rates improved to over 90%, and visualized results were provided.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":"56 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807149","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 : 2024-07-24DOI: 10.3390/electronics13152928
Yuhong Tang, Guang Li, Ming Zhang, Jianjun Li
Few-shot learning (FSL) is a challenging problem. Transfer learning methods offer a straightforward and effective solution to FSL by leveraging pre-trained models and generalizing them to new tasks. However, pre-trained models often lack the ability to highlight and emphasize salient features, a gap that attention mechanisms can fill. Unfortunately, existing attention mechanisms encounter issues such as high complexity and incomplete attention information. To address these issues, we propose a dimensionally enhanced attention (DEA) module for FSL. This DEA module introduces minimal additional computational overhead while fully attending to both channel and spatial information. Specifically, the feature map is first decomposed into 1D tensors of varying dimensions using strip pooling. Next, a multi-dimensional collaborative learning strategy is introduced, enabling cross-dimensional information interactions through 1D convolutions with adaptive kernel sizes. Finally, the feature representation is enhanced by calculating attention weights for each dimension using a sigmoid function and weighting the original input accordingly. This approach ensures comprehensive attention to different dimensions of information, effectively characterizing data in various directions. Additionally, we have found that knowledge distillation significantly improves FSL performance. To this end, we implement a logit standardization self-distillation method tailored for FSL. This method addresses the issue of exact logit matching, which arises from the shared temperature in the self-distillation process, by employing logit standardization. We present experimental results on several benchmark datasets where the proposed method yields significant performance improvements.
{"title":"Few-Shot Learning Based on Dimensionally Enhanced Attention and Logit Standardization Self-Distillation","authors":"Yuhong Tang, Guang Li, Ming Zhang, Jianjun Li","doi":"10.3390/electronics13152928","DOIUrl":"https://doi.org/10.3390/electronics13152928","url":null,"abstract":"Few-shot learning (FSL) is a challenging problem. Transfer learning methods offer a straightforward and effective solution to FSL by leveraging pre-trained models and generalizing them to new tasks. However, pre-trained models often lack the ability to highlight and emphasize salient features, a gap that attention mechanisms can fill. Unfortunately, existing attention mechanisms encounter issues such as high complexity and incomplete attention information. To address these issues, we propose a dimensionally enhanced attention (DEA) module for FSL. This DEA module introduces minimal additional computational overhead while fully attending to both channel and spatial information. Specifically, the feature map is first decomposed into 1D tensors of varying dimensions using strip pooling. Next, a multi-dimensional collaborative learning strategy is introduced, enabling cross-dimensional information interactions through 1D convolutions with adaptive kernel sizes. Finally, the feature representation is enhanced by calculating attention weights for each dimension using a sigmoid function and weighting the original input accordingly. This approach ensures comprehensive attention to different dimensions of information, effectively characterizing data in various directions. Additionally, we have found that knowledge distillation significantly improves FSL performance. To this end, we implement a logit standardization self-distillation method tailored for FSL. This method addresses the issue of exact logit matching, which arises from the shared temperature in the self-distillation process, by employing logit standardization. We present experimental results on several benchmark datasets where the proposed method yields significant performance improvements.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":"36 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808529","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 : 2024-07-24DOI: 10.3390/electronics13152927
Yanyan Dai, Deokgyu Kim, Kidong Lee
In addressing the complex challenges of path planning in multi-robot systems, this paper proposes a novel Hybrid Decentralized and Centralized Training and Execution (DCTE) Strategy, aimed at optimizing computational efficiency and system performance. The strategy solves the prevalent issues of collision and coordination through a tiered optimization process. The DCTE strategy commences with an initial decentralized path planning step based on Deep Q-Network (DQN), where each robot independently formulates its path. This is followed by a centralized collision detection the analysis of which serves to identify potential intersections or collision risks. Paths confirmed as non-intersecting are used for execution, while those in collision areas prompt a dynamic re-planning step using DQN. Robots treat each other as dynamic obstacles to circumnavigate, ensuring continuous operation without disruptions. The final step involves linking the newly optimized paths with the original safe paths to form a complete and secure execution route. This paper demonstrates how this structured strategy not only mitigates collision risks but also significantly improves the computational efficiency of multi-robot systems. The reinforcement learning time was significantly shorter, with the DCTE strategy requiring only 3 min and 36 s compared to 5 min and 33 s in the comparison results of the simulation section. The improvement underscores the advantages of the proposed method in enhancing the effectiveness and efficiency of multi-robot systems.
{"title":"Navigation Based on Hybrid Decentralized and Centralized Training and Execution Strategy for Multiple Mobile Robots Reinforcement Learning","authors":"Yanyan Dai, Deokgyu Kim, Kidong Lee","doi":"10.3390/electronics13152927","DOIUrl":"https://doi.org/10.3390/electronics13152927","url":null,"abstract":"In addressing the complex challenges of path planning in multi-robot systems, this paper proposes a novel Hybrid Decentralized and Centralized Training and Execution (DCTE) Strategy, aimed at optimizing computational efficiency and system performance. The strategy solves the prevalent issues of collision and coordination through a tiered optimization process. The DCTE strategy commences with an initial decentralized path planning step based on Deep Q-Network (DQN), where each robot independently formulates its path. This is followed by a centralized collision detection the analysis of which serves to identify potential intersections or collision risks. Paths confirmed as non-intersecting are used for execution, while those in collision areas prompt a dynamic re-planning step using DQN. Robots treat each other as dynamic obstacles to circumnavigate, ensuring continuous operation without disruptions. The final step involves linking the newly optimized paths with the original safe paths to form a complete and secure execution route. This paper demonstrates how this structured strategy not only mitigates collision risks but also significantly improves the computational efficiency of multi-robot systems. The reinforcement learning time was significantly shorter, with the DCTE strategy requiring only 3 min and 36 s compared to 5 min and 33 s in the comparison results of the simulation section. The improvement underscores the advantages of the proposed method in enhancing the effectiveness and efficiency of multi-robot systems.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":"11 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806424","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}
It is common to find severe perspective distortion in a pipeline’s image in medium-diameter pipeline defect detection by the panoramic image unwrapping method, resulting in low-quality image unwrapping and stitching, which is caused by the camera’s optical axis being completely deviated from the pipeline’s center. To solve this problem, a novel correction method for reducing perspective distortion in pipeline images was proposed for pipeline defect detection. Firstly, the method enhances the edges of unevenly illuminated regions within a pipeline to facilitate image segmentation and identify key points necessary for correcting perspective distortion. Then, a six-feature-point extraction method was proposed for a circle target to establish the projection relationship between the extracted feature and mapped points on the reference circle. Finally, a perspective matrix was constructed to complete the perspective transformation correction of the distorted images. The results show that the average correction rate and the average relative error of the proposed correction method can reach 90.85% and 1.31%, respectively. The study innovatively used the enhancement of uneven illumination to find distorted edge information. It proposed an extraction method using a reference circle and six key feature points to build a mapping model. It can provide a novel method which can be used to obtain a superior image for pipeline detection and lay a solid foundation for subsequent high-quality pipeline image stitching.
{"title":"Correction Method for Perspective Distortions of Pipeline Images","authors":"Zheng Zhang, Jiazheng Zhou, Xiuhong Li, Chaobin Xu, Xinyu Hu, Linhuang Wang","doi":"10.3390/electronics13152898","DOIUrl":"https://doi.org/10.3390/electronics13152898","url":null,"abstract":"It is common to find severe perspective distortion in a pipeline’s image in medium-diameter pipeline defect detection by the panoramic image unwrapping method, resulting in low-quality image unwrapping and stitching, which is caused by the camera’s optical axis being completely deviated from the pipeline’s center. To solve this problem, a novel correction method for reducing perspective distortion in pipeline images was proposed for pipeline defect detection. Firstly, the method enhances the edges of unevenly illuminated regions within a pipeline to facilitate image segmentation and identify key points necessary for correcting perspective distortion. Then, a six-feature-point extraction method was proposed for a circle target to establish the projection relationship between the extracted feature and mapped points on the reference circle. Finally, a perspective matrix was constructed to complete the perspective transformation correction of the distorted images. The results show that the average correction rate and the average relative error of the proposed correction method can reach 90.85% and 1.31%, respectively. The study innovatively used the enhancement of uneven illumination to find distorted edge information. It proposed an extraction method using a reference circle and six key feature points to build a mapping model. It can provide a novel method which can be used to obtain a superior image for pipeline detection and lay a solid foundation for subsequent high-quality pipeline image stitching.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":"118 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141811770","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 : 2024-07-23DOI: 10.3390/electronics13152896
Yaqiong Ding, Hanguang Jia, Yunshan Wei, Qingyuan Xu, Kai Wan
In the traditional iterative learning control (ILC) method, the operational time interval is conventionally fixed to facilitate a seamless learning process along the iteration axis. However, this condition may frequently be contravened in real-time applications owing to unknown uncertainties and unpredictable factors. In essence, replicating a control system at a consistent time interval proves challenging in practical scenarios. This paper proposes an adaptive iterative learning control (AILC) method for the multi-input–multi-output (MIMO) nonlinear system with nonuniform trial lengths and an invertible control gain matrix. Compared to the existing AILC research that features nonuniform trial lengths, the control gain matrix of the system in this paper is assumed to be invertible. Hence, the general requirement in the conventional AILC method that the control gain matrix of the system is positive-definite (or negative-definite) or even known is relaxed. Moreover, the tracking reference allows it to be iteration-varying. Finally, to prove the convergence of the system, the composite energy function is introduced and to verify the validity of the AILC method, a robot movement imitation with an uncalibrated camera system is used. The simulation results show that the actual output can track the desired reference trajectory well, and the tracking error converges to zero after 30 iterations.
{"title":"An Adaptive Learning Control for MIMO Nonlinear System with Nonuniform Trial Lengths and Invertible Control Gain Matrix","authors":"Yaqiong Ding, Hanguang Jia, Yunshan Wei, Qingyuan Xu, Kai Wan","doi":"10.3390/electronics13152896","DOIUrl":"https://doi.org/10.3390/electronics13152896","url":null,"abstract":"In the traditional iterative learning control (ILC) method, the operational time interval is conventionally fixed to facilitate a seamless learning process along the iteration axis. However, this condition may frequently be contravened in real-time applications owing to unknown uncertainties and unpredictable factors. In essence, replicating a control system at a consistent time interval proves challenging in practical scenarios. This paper proposes an adaptive iterative learning control (AILC) method for the multi-input–multi-output (MIMO) nonlinear system with nonuniform trial lengths and an invertible control gain matrix. Compared to the existing AILC research that features nonuniform trial lengths, the control gain matrix of the system in this paper is assumed to be invertible. Hence, the general requirement in the conventional AILC method that the control gain matrix of the system is positive-definite (or negative-definite) or even known is relaxed. Moreover, the tracking reference allows it to be iteration-varying. Finally, to prove the convergence of the system, the composite energy function is introduced and to verify the validity of the AILC method, a robot movement imitation with an uncalibrated camera system is used. The simulation results show that the actual output can track the desired reference trajectory well, and the tracking error converges to zero after 30 iterations.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":"27 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813158","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 : 2024-07-23DOI: 10.3390/electronics13152890
Fan Xu, Shun Zhang, Xiangyu Gao, Wei Wang, Wencheng Yue, Qiang Xu, Shuxiao Wang, Yan Cai
With the advantages of high speed, small size, and easy integration, the silicon photonic resonant ring modulator has gradually become a critical device for emerging integrated optical platforms. Ring modulators are primarily used in optical communications, optical computing, artificial intelligence, and other fields. In this work, the proposed ring modulator can operate in both the O- and C-bands. The 3 dB electro-optical (EO) bandwidth of the ring modulator is 39 GHz and 34 GHz at −4 V in the O-band and C-band, respectively. The modulation efficiency of the device is 0.92 V·cm and 0.95 V·cm in the O-band and C-band, respectively. The eye diagram of an optical output signal from the device is tested using a 100 Gbit/s non-return-to-zero (NRZ) input signal with a 2.5 Vpp in both the O-band and C-band. The modulation speed can reach 140 Gb/s and 120 Gb/s in the O-band and C-band with four-level pulse amplitude modulation (PAM-4) formats at a voltage swing of 2.5 Vpp, respectively.
硅光子谐振环调制器具有速度快、体积小、易于集成等优点，已逐渐成为新兴集成光学平台的关键器件。环形调制器主要应用于光通信、光计算、人工智能等领域。在这项工作中，所提出的环形调制器可以在 O 波段和 C 波段工作。在-4 V电压下，环形调制器在O波段和C波段的3 dB光电（EO）带宽分别为39 GHz和34 GHz。该器件在 O 波段和 C 波段的调制效率分别为 0.92 V 厘米和 0.95 V 厘米。使用 100 Gbit/s 非归零（NRZ）输入信号测试了该器件输出光信号的眼图，O 波段和 C 波段的眼图均为 2.5 Vpp。在电压摆幅为 2.5 Vpp 时，采用四级脉冲幅度调制（PAM-4）格式，O 波段和 C 波段的调制速度可分别达到 140 Gb/s 和 120 Gb/s。
{"title":"A High-Speed Silicon Ring Modulator with a Large Working Wavelength Range","authors":"Fan Xu, Shun Zhang, Xiangyu Gao, Wei Wang, Wencheng Yue, Qiang Xu, Shuxiao Wang, Yan Cai","doi":"10.3390/electronics13152890","DOIUrl":"https://doi.org/10.3390/electronics13152890","url":null,"abstract":"With the advantages of high speed, small size, and easy integration, the silicon photonic resonant ring modulator has gradually become a critical device for emerging integrated optical platforms. Ring modulators are primarily used in optical communications, optical computing, artificial intelligence, and other fields. In this work, the proposed ring modulator can operate in both the O- and C-bands. The 3 dB electro-optical (EO) bandwidth of the ring modulator is 39 GHz and 34 GHz at −4 V in the O-band and C-band, respectively. The modulation efficiency of the device is 0.92 V·cm and 0.95 V·cm in the O-band and C-band, respectively. The eye diagram of an optical output signal from the device is tested using a 100 Gbit/s non-return-to-zero (NRZ) input signal with a 2.5 Vpp in both the O-band and C-band. The modulation speed can reach 140 Gb/s and 120 Gb/s in the O-band and C-band with four-level pulse amplitude modulation (PAM-4) formats at a voltage swing of 2.5 Vpp, respectively.","PeriodicalId":504598,"journal":{"name":"Electronics","volume":"97 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812345","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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