Pub Date : 2024-09-12DOI: 10.3390/electronics13183624
Yanfang Hou, Hui Tian
This paper develops a parameter tuning method for solving the set restabilization problem of perturbed Boolean control networks (BCNs). First, the absorbable attractor, which we previously proposed, is recalled. Based on the relationship between attractors, a necessary and sufficient restabilizability criterion is derived. This criterion is used to check whether a perturbed BCN can be stabilized to the original target set by modifying the least number of parameters to the old controller. Furthermore, a constructive method for fine-tuning the old controller is provided if the criterion condition derived above is satisfied. Compared with the existing relevant results, ours have clear advantages, since they can address the set restabilization problem of BCNs subject to multi-column function perturbations, which has not been solved yet. Finally, two examples are employed to show the effectiveness and advantages of our results.
{"title":"Set Restabilization of Perturbed Boolean Control Networks","authors":"Yanfang Hou, Hui Tian","doi":"10.3390/electronics13183624","DOIUrl":"https://doi.org/10.3390/electronics13183624","url":null,"abstract":"This paper develops a parameter tuning method for solving the set restabilization problem of perturbed Boolean control networks (BCNs). First, the absorbable attractor, which we previously proposed, is recalled. Based on the relationship between attractors, a necessary and sufficient restabilizability criterion is derived. This criterion is used to check whether a perturbed BCN can be stabilized to the original target set by modifying the least number of parameters to the old controller. Furthermore, a constructive method for fine-tuning the old controller is provided if the criterion condition derived above is satisfied. Compared with the existing relevant results, ours have clear advantages, since they can address the set restabilization problem of BCNs subject to multi-column function perturbations, which has not been solved yet. Finally, two examples are employed to show the effectiveness and advantages of our results.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"19 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208974","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 : 2024-09-12DOI: 10.3390/electronics13183640
Jiangang Zhu, Donglin Jing, Dapeng Gao
Object detection in aerial images has had a broader range of applications in the past few years. Unlike the targets in the images of horizontal shooting, targets in aerial photos generally have arbitrary orientation, multi-scale, and a high aspect ratio. Existing methods often employ a classification backbone network to extract translation-equivariant features (TEFs) and utilize many predefined anchors to handle objects with diverse appearance variations. However, they encounter misalignment at three levels, spatial, feature, and task, during different detection stages. In this study, we propose a model called the Staged Adaptive Alignment Detector (SAADet) to solve these challenges. This method utilizes a Spatial Selection Adaptive Network (SSANet) to achieve spatial alignment of the convolution receptive field to the scale of the object by using a convolution sequence with an increasing dilation rate to capture the spatial context information of different ranges and evaluating this information through model dynamic weighting. After correcting the preset horizontal anchor to an oriented anchor, feature alignment is achieved through the alignment convolution guided by oriented anchor to align the backbone features with the object’s orientation. The decoupling of features using the Active Rotating Filter is performed to mitigate inconsistencies due to the sharing of backbone features in regression and classification tasks to accomplish task alignment. The experimental results show that SAADet achieves equilibrium in speed and accuracy on two aerial image datasets, HRSC2016 and UCAS-AOD.
{"title":"Stage-by-Stage Adaptive Alignment Mechanism for Object Detection in Aerial Images","authors":"Jiangang Zhu, Donglin Jing, Dapeng Gao","doi":"10.3390/electronics13183640","DOIUrl":"https://doi.org/10.3390/electronics13183640","url":null,"abstract":"Object detection in aerial images has had a broader range of applications in the past few years. Unlike the targets in the images of horizontal shooting, targets in aerial photos generally have arbitrary orientation, multi-scale, and a high aspect ratio. Existing methods often employ a classification backbone network to extract translation-equivariant features (TEFs) and utilize many predefined anchors to handle objects with diverse appearance variations. However, they encounter misalignment at three levels, spatial, feature, and task, during different detection stages. In this study, we propose a model called the Staged Adaptive Alignment Detector (SAADet) to solve these challenges. This method utilizes a Spatial Selection Adaptive Network (SSANet) to achieve spatial alignment of the convolution receptive field to the scale of the object by using a convolution sequence with an increasing dilation rate to capture the spatial context information of different ranges and evaluating this information through model dynamic weighting. After correcting the preset horizontal anchor to an oriented anchor, feature alignment is achieved through the alignment convolution guided by oriented anchor to align the backbone features with the object’s orientation. The decoupling of features using the Active Rotating Filter is performed to mitigate inconsistencies due to the sharing of backbone features in regression and classification tasks to accomplish task alignment. The experimental results show that SAADet achieves equilibrium in speed and accuracy on two aerial image datasets, HRSC2016 and UCAS-AOD.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"58 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208976","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}
This research introduces a novel approach to crisis management by implementing a multi-agent algorithm within a strategic decision system. The proposed system harnesses multiple agents’ collective intelligence and adaptive capabilities to enhance decision-making processes during critical situations. The study first investigates the theoretical foundations of crisis management and multi-agent systems, emphasizing the need for an integrated approach that combines strategic decision-making with autonomous agents. Subsequently, the research presents the design and implementation of the multi-agent algorithm, outlining its ability to gather, process, and analyze diverse data sources in real time. The multi-agent algorithm is specifically tailored to adapt to dynamic crisis scenarios, ensuring a resilient decision-making framework. Experimental simulations present the implementation of a panic simulator and prediction of evacuation and intervention routes using multi-agent artificial intelligence algorithms. The results demonstrate the multi-agent algorithm-driven decision system’s superiority in response time, resource allocation, and overall crisis mitigation. Furthermore, the research explores the system’s scalability and adaptability to different crisis types, illustrating its potential applicability across diverse domains.
{"title":"Crowd Panic Behavior Simulation Using Multi-Agent Modeling","authors":"Cătălin Dumitrescu, Valentin Radu, Radu Gheorghe, Alina-Iuliana Tăbîrcă, Maria-Cristina Ștefan, Liliana Manea","doi":"10.3390/electronics13183622","DOIUrl":"https://doi.org/10.3390/electronics13183622","url":null,"abstract":"This research introduces a novel approach to crisis management by implementing a multi-agent algorithm within a strategic decision system. The proposed system harnesses multiple agents’ collective intelligence and adaptive capabilities to enhance decision-making processes during critical situations. The study first investigates the theoretical foundations of crisis management and multi-agent systems, emphasizing the need for an integrated approach that combines strategic decision-making with autonomous agents. Subsequently, the research presents the design and implementation of the multi-agent algorithm, outlining its ability to gather, process, and analyze diverse data sources in real time. The multi-agent algorithm is specifically tailored to adapt to dynamic crisis scenarios, ensuring a resilient decision-making framework. Experimental simulations present the implementation of a panic simulator and prediction of evacuation and intervention routes using multi-agent artificial intelligence algorithms. The results demonstrate the multi-agent algorithm-driven decision system’s superiority in response time, resource allocation, and overall crisis mitigation. Furthermore, the research explores the system’s scalability and adaptability to different crisis types, illustrating its potential applicability across diverse domains.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"176 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208975","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 : 2024-09-11DOI: 10.3390/electronics13183606
Junxin Shen, Shuilan Zhou, Fanghao Xiao
Exploring the data quality problems in the context of federated cooperation and adopting corresponding governance countermeasures can facilitate the smooth progress of federated cooperation and obtain high-performance models. However, previous studies have rarely focused on quality issues in federated cooperation. To this end, this paper analyzes the quality problems in the federated cooperation scenario and innovatively proposes a “Two-stage” data quality governance framework for the federated collaboration scenarios. The first stage is mainly local data quality assessment and optimization, and the evaluation is performed by constructing a metrics scoring formula, and corresponding optimization measures are taken at the same time. In the second stage, the outlier processing mechanism is introduced, and the Data Quality Federated Averaging (Abbreviation DQ-FedAvg) aggregation method for model quality problems is proposed, so as to train high-quality global models and their own excellent local models. Finally, experiments are conducted in real datasets to compare the model performance changes before and after quality governance, and to validate the advantages of the data quality governance framework in a federated learning scenario, so that it can be widely applied to various domains. The governance framework is used to check and govern the quality problems in the federated learning process, and the accuracy of the model is improved.
{"title":"Research on Data Quality Governance for Federated Cooperation Scenarios","authors":"Junxin Shen, Shuilan Zhou, Fanghao Xiao","doi":"10.3390/electronics13183606","DOIUrl":"https://doi.org/10.3390/electronics13183606","url":null,"abstract":"Exploring the data quality problems in the context of federated cooperation and adopting corresponding governance countermeasures can facilitate the smooth progress of federated cooperation and obtain high-performance models. However, previous studies have rarely focused on quality issues in federated cooperation. To this end, this paper analyzes the quality problems in the federated cooperation scenario and innovatively proposes a “Two-stage” data quality governance framework for the federated collaboration scenarios. The first stage is mainly local data quality assessment and optimization, and the evaluation is performed by constructing a metrics scoring formula, and corresponding optimization measures are taken at the same time. In the second stage, the outlier processing mechanism is introduced, and the Data Quality Federated Averaging (Abbreviation DQ-FedAvg) aggregation method for model quality problems is proposed, so as to train high-quality global models and their own excellent local models. Finally, experiments are conducted in real datasets to compare the model performance changes before and after quality governance, and to validate the advantages of the data quality governance framework in a federated learning scenario, so that it can be widely applied to various domains. The governance framework is used to check and govern the quality problems in the federated learning process, and the accuracy of the model is improved.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"53 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209058","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 : 2024-09-11DOI: 10.3390/electronics13183614
Junfeng Liu, Qinghua Zhao
Technology mapping is an essential process in the Electronic Design Automation (EDA) flow which aims to find an optimal implementation of a logic network from a technology library. In application-specific integrated circuit (ASIC) designs, the non-linear delay behaviors of cells in the library essentially guide the search direction of technology mappers. Existing methods for cell delay estimation, however, rely on approximate simplifications that significantly compromise accuracy, thereby limiting the achievement of better Quality-of-Result (QoR). To address this challenge, we propose formulating cell delay estimation as a regression learning task by incorporating multiple perspective features, such as the structure of logic networks and non-linear cell delays, to guide the mapper search. We design a learning model that incorporates a customized attention mechanism to be aware of the pin delay and jointly learns the hierarchy between the logic network and library through a Neural Tensor Network, with the help of proposed parameterizable strategies to generate learning labels. Experimental results show that (i) our proposed method noticeably improves area by 9.3% and delay by 1.5%, and (ii) improves area by 12.0% for delay-oriented mapping, compared with the well-known mapper.
{"title":"AiMap+: Guiding Technology Mapping for ASICs via Learning Delay Prediction","authors":"Junfeng Liu, Qinghua Zhao","doi":"10.3390/electronics13183614","DOIUrl":"https://doi.org/10.3390/electronics13183614","url":null,"abstract":"Technology mapping is an essential process in the Electronic Design Automation (EDA) flow which aims to find an optimal implementation of a logic network from a technology library. In application-specific integrated circuit (ASIC) designs, the non-linear delay behaviors of cells in the library essentially guide the search direction of technology mappers. Existing methods for cell delay estimation, however, rely on approximate simplifications that significantly compromise accuracy, thereby limiting the achievement of better Quality-of-Result (QoR). To address this challenge, we propose formulating cell delay estimation as a regression learning task by incorporating multiple perspective features, such as the structure of logic networks and non-linear cell delays, to guide the mapper search. We design a learning model that incorporates a customized attention mechanism to be aware of the pin delay and jointly learns the hierarchy between the logic network and library through a Neural Tensor Network, with the help of proposed parameterizable strategies to generate learning labels. Experimental results show that (i) our proposed method noticeably improves area by 9.3% and delay by 1.5%, and (ii) improves area by 12.0% for delay-oriented mapping, compared with the well-known mapper.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"23 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209200","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 : 2024-09-11DOI: 10.3390/electronics13183611
Qimao Zhang, Keyu Zhou, Ming Deng, Qisheng Zhang, Yongqiang Feng, Leisong Liu
The high-precision magnetic survey system is crucial for ocean exploration. However, most existing systems face challenges such as high noise levels, low sensitivity, and inadequate magnetic compensation effects. To address these issues, we developed a high-precision magnetic survey system based on the manned submersible “Deep Sea Warrior” for deep-ocean magnetic exploration. This system incorporates a compact optically pumped cesium (Cs) magnetometer sensor to measure the total strength of the external magnetic field. Additionally, a magnetic compensation sensor is included at the front end to measure real-time attitude changes of the platform. The measured data are then transmitted to a magnetic signal processor, where an algorithm compensates for the platform’s magnetic interference. We also designed a deep pressure chamber to allow for a maximum working depth of 4500 m. Experiments conducted in both indoor and field environments verified the performance of the proposed magnetic survey system. The results showed that the system’s sensitivity is ≤0.5 nT, the noise level of the magnetometer sensor is ≤1 pT/√Hz at 1 Hz, and the sampling rate is 10 Hz. The proposed system has potential applications in ocean and geophysical exploration.
{"title":"Development of a High-Precision Deep-Sea Magnetic Survey System for Human-Occupied Vehicles","authors":"Qimao Zhang, Keyu Zhou, Ming Deng, Qisheng Zhang, Yongqiang Feng, Leisong Liu","doi":"10.3390/electronics13183611","DOIUrl":"https://doi.org/10.3390/electronics13183611","url":null,"abstract":"The high-precision magnetic survey system is crucial for ocean exploration. However, most existing systems face challenges such as high noise levels, low sensitivity, and inadequate magnetic compensation effects. To address these issues, we developed a high-precision magnetic survey system based on the manned submersible “Deep Sea Warrior” for deep-ocean magnetic exploration. This system incorporates a compact optically pumped cesium (Cs) magnetometer sensor to measure the total strength of the external magnetic field. Additionally, a magnetic compensation sensor is included at the front end to measure real-time attitude changes of the platform. The measured data are then transmitted to a magnetic signal processor, where an algorithm compensates for the platform’s magnetic interference. We also designed a deep pressure chamber to allow for a maximum working depth of 4500 m. Experiments conducted in both indoor and field environments verified the performance of the proposed magnetic survey system. The results showed that the system’s sensitivity is ≤0.5 nT, the noise level of the magnetometer sensor is ≤1 pT/√Hz at 1 Hz, and the sampling rate is 10 Hz. The proposed system has potential applications in ocean and geophysical exploration.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"11 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209064","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 : 2024-09-11DOI: 10.3390/electronics13183604
Zijie Xu, Erfu Wang
In recent years, the advancement of digital image processing technology and the proliferation of image editing software have reduced the technical barriers to digital image processing, enabling individuals without professional training to modify and edit images at their discretion. Consequently, the integrity and authenticity of the original image content assume greater significance. The current techniques for detecting tampering in watermark embedding are inadequate in terms of security, efficiency, and image restoration quality. In light of the aforementioned considerations, this paper puts forth an algorithm for the detection and restoration of tampered images, which employs a chaotic watermark embedding technique. The algorithm employs a chaotic system to establish a mapping relationship between image sub-blocks, thereby ensuring the randomness of the watermark information with respect to the positioning of the original image block and enhancing the security of the algorithm. Furthermore, the detection algorithm utilizes layered tampering detection to enhance the overall accuracy of the detection process and facilitate the extraction of the fundamental information required for image restoration. The restoration algorithm partially designs a weight assignment function to distinguish between the original image block and the main restored image block, thereby enhancing restoration efficiency and quality. The experimental results demonstrate that the proposed algorithm exhibits superior tamper detection accuracy compared to traditional algorithms, and the quality of the restored images is also enhanced under various simulated tamper attacks.
{"title":"An Algorithm for Detecting and Restoring Tampered Images Using Chaotic Watermark Embedding","authors":"Zijie Xu, Erfu Wang","doi":"10.3390/electronics13183604","DOIUrl":"https://doi.org/10.3390/electronics13183604","url":null,"abstract":"In recent years, the advancement of digital image processing technology and the proliferation of image editing software have reduced the technical barriers to digital image processing, enabling individuals without professional training to modify and edit images at their discretion. Consequently, the integrity and authenticity of the original image content assume greater significance. The current techniques for detecting tampering in watermark embedding are inadequate in terms of security, efficiency, and image restoration quality. In light of the aforementioned considerations, this paper puts forth an algorithm for the detection and restoration of tampered images, which employs a chaotic watermark embedding technique. The algorithm employs a chaotic system to establish a mapping relationship between image sub-blocks, thereby ensuring the randomness of the watermark information with respect to the positioning of the original image block and enhancing the security of the algorithm. Furthermore, the detection algorithm utilizes layered tampering detection to enhance the overall accuracy of the detection process and facilitate the extraction of the fundamental information required for image restoration. The restoration algorithm partially designs a weight assignment function to distinguish between the original image block and the main restored image block, thereby enhancing restoration efficiency and quality. The experimental results demonstrate that the proposed algorithm exhibits superior tamper detection accuracy compared to traditional algorithms, and the quality of the restored images is also enhanced under various simulated tamper attacks.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"270 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209066","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 : 2024-09-11DOI: 10.3390/electronics13183610
Li Yang, Qiaoni Zhao
Flywheel energy storage systems (FESSs) are widely used for power regulation in wind farms as they can balance the wind farms’ output power and improve the wind power grid connection rate. Due to the complex environment of wind farms, it is costly and time-consuming to repeatedly debug the system on-site. To save research costs and shorten research cycles, a hardware-in-the-loop (HIL) testing system was built to provide a convenient testing environment for the research of FESSs on wind farms. The focus of this study is the construction of mathematical models in the HIL testing system. Firstly, a mathematical model of the FESS main circuit is established using a hierarchical method. Secondly, the principle of the permanent magnet synchronous motor (PMSM) is analyzed, and a nonlinear dq mathematical model of the PMSM is established by referring to the relationship among d-axis inductance, q-axis inductance, and permanent magnet flux change with respect to the motor’s current. Then, the power grid and wind farm test models are established. Finally, the established mathematical models are applied to the HIL testing system. The experimental results indicated that the HIL testing system can provide a convenient testing environment for the optimization of FESS control algorithms.
飞轮储能系统（FESS）可平衡风电场的输出功率，提高风电并网率，因此被广泛用于风电场的功率调节。由于风电场环境复杂，现场反复调试成本高、耗时长。为了节约研究成本，缩短研究周期，我们建立了硬件在环（HIL）测试系统，为风电场 FESS 的研究提供了便捷的测试环境。本研究的重点是在 HIL 测试系统中构建数学模型。首先，采用分层方法建立了 FESS 主电路的数学模型。其次，分析了永磁同步电机（PMSM）的原理，并参考 d 轴电感、q 轴电感和永磁磁通随电机电流变化的关系，建立了 PMSM 的非线性 dq 数学模型。然后，建立电网和风电场测试模型。最后，将建立的数学模型应用于 HIL 测试系统。实验结果表明，HIL 测试系统可为 FESS 控制算法的优化提供便利的测试环境。
{"title":"Hardware-in-the-Loop Simulation of Flywheel Energy Storage Systems for Power Control in Wind Farms","authors":"Li Yang, Qiaoni Zhao","doi":"10.3390/electronics13183610","DOIUrl":"https://doi.org/10.3390/electronics13183610","url":null,"abstract":"Flywheel energy storage systems (FESSs) are widely used for power regulation in wind farms as they can balance the wind farms’ output power and improve the wind power grid connection rate. Due to the complex environment of wind farms, it is costly and time-consuming to repeatedly debug the system on-site. To save research costs and shorten research cycles, a hardware-in-the-loop (HIL) testing system was built to provide a convenient testing environment for the research of FESSs on wind farms. The focus of this study is the construction of mathematical models in the HIL testing system. Firstly, a mathematical model of the FESS main circuit is established using a hierarchical method. Secondly, the principle of the permanent magnet synchronous motor (PMSM) is analyzed, and a nonlinear dq mathematical model of the PMSM is established by referring to the relationship among d-axis inductance, q-axis inductance, and permanent magnet flux change with respect to the motor’s current. Then, the power grid and wind farm test models are established. Finally, the established mathematical models are applied to the HIL testing system. The experimental results indicated that the HIL testing system can provide a convenient testing environment for the optimization of FESS control algorithms.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"53 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209114","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 : 2024-09-11DOI: 10.3390/electronics13183603
Peiming Zhang, Jie Zhao, Qiaohong Liu, Xiao Liu, Xinyu Li, Yimeng Gao, Weiqi Li
To detect fundus diseases, for instance, diabetic retinopathy (DR) at an early stage, thereby providing timely intervention and treatment, a new diabetic retinopathy grading method based on a convolutional neural network is proposed. First, data cleaning and enhancement are conducted to improve the image quality and reduce unnecessary interference. Second, a new conditional generative adversarial network with a self-attention mechanism named SACGAN is proposed to augment the number of diabetic retinopathy fundus images, thereby addressing the problems of insufficient and imbalanced data samples. Next, an improved convolutional neural network named DRMC Net, which combines ResNeXt-50 with the channel attention mechanism and multi-branch convolutional residual module, is proposed to classify diabetic retinopathy. Finally, gradient-weighted class activation mapping (Grad-CAM) is utilized to prove the proposed model’s interpretability. The outcomes of the experiment illustrates that the proposed method has high accuracy, specificity, and sensitivity, with specific results of 92.3%, 92.5%, and 92.5%, respectively.
{"title":"Fundus Image Generation and Classification of Diabetic Retinopathy Based on Convolutional Neural Network","authors":"Peiming Zhang, Jie Zhao, Qiaohong Liu, Xiao Liu, Xinyu Li, Yimeng Gao, Weiqi Li","doi":"10.3390/electronics13183603","DOIUrl":"https://doi.org/10.3390/electronics13183603","url":null,"abstract":"To detect fundus diseases, for instance, diabetic retinopathy (DR) at an early stage, thereby providing timely intervention and treatment, a new diabetic retinopathy grading method based on a convolutional neural network is proposed. First, data cleaning and enhancement are conducted to improve the image quality and reduce unnecessary interference. Second, a new conditional generative adversarial network with a self-attention mechanism named SACGAN is proposed to augment the number of diabetic retinopathy fundus images, thereby addressing the problems of insufficient and imbalanced data samples. Next, an improved convolutional neural network named DRMC Net, which combines ResNeXt-50 with the channel attention mechanism and multi-branch convolutional residual module, is proposed to classify diabetic retinopathy. Finally, gradient-weighted class activation mapping (Grad-CAM) is utilized to prove the proposed model’s interpretability. The outcomes of the experiment illustrates that the proposed method has high accuracy, specificity, and sensitivity, with specific results of 92.3%, 92.5%, and 92.5%, respectively.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"9 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209060","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 : 2024-09-11DOI: 10.3390/electronics13183617
Daniel Spiekermann, Tobias Eggendorfer, Jörg Keller
As organizations increasingly adopt virtualized environments for enhanced flexibility and scalability, securing virtual networks has become a critical part of current infrastructures. This research paper addresses the challenges related to intrusion detection in virtual networks, with a focus on various deep learning techniques. Since physical networks do not use encapsulation, but virtual networks do, packet analysis based on rules or machine learning outcomes for physical networks cannot be transferred directly to virtual environments. Encapsulation methods in current virtual networks include VXLAN (Virtual Extensible LAN), an EVPN (Ethernet Virtual Private Network), and NVGRE (Network Virtualization using Generic Routing Encapsulation). This paper analyzes the performance and effectiveness of network intrusion detection in virtual networks. It delves into challenges inherent in virtual network intrusion detection with deep learning, including issues such as traffic encapsulation, VM migration, and changing network internals inside the infrastructure. Experiments on detection performance demonstrate the differences between intrusion detection in virtual and physical networks.
{"title":"Deep Learning for Network Intrusion Detection in Virtual Networks","authors":"Daniel Spiekermann, Tobias Eggendorfer, Jörg Keller","doi":"10.3390/electronics13183617","DOIUrl":"https://doi.org/10.3390/electronics13183617","url":null,"abstract":"As organizations increasingly adopt virtualized environments for enhanced flexibility and scalability, securing virtual networks has become a critical part of current infrastructures. This research paper addresses the challenges related to intrusion detection in virtual networks, with a focus on various deep learning techniques. Since physical networks do not use encapsulation, but virtual networks do, packet analysis based on rules or machine learning outcomes for physical networks cannot be transferred directly to virtual environments. Encapsulation methods in current virtual networks include VXLAN (Virtual Extensible LAN), an EVPN (Ethernet Virtual Private Network), and NVGRE (Network Virtualization using Generic Routing Encapsulation). This paper analyzes the performance and effectiveness of network intrusion detection in virtual networks. It delves into challenges inherent in virtual network intrusion detection with deep learning, including issues such as traffic encapsulation, VM migration, and changing network internals inside the infrastructure. Experiments on detection performance demonstrate the differences between intrusion detection in virtual and physical networks.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"26 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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