Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of “few data, poor information”, this paper proposes a novel grey relational-based method for retinal vessel segmentation. Firstly, a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) is designed to enhance the image. Secondly, a threshold segmentation model based on grey relational analysis (TS-GRA) is designed to segment the enhanced vessel image. Finally, a post-processing stage involving hole filling and removal of isolated pixels is applied to obtain the final segmentation output. The performance of the proposed method is evaluated using multiple different measurement metrics on publicly available digital retinal DRIVE, STARE and HRF datasets. Experimental analysis showed that the average accuracy and specificity on the DRIVE dataset were 96.03% and 98.51%. The mean accuracy and specificity on the STARE dataset were 95.46% and 97.85%. Precision, F1-score, and Jaccard index on the HRF dataset all demonstrated high-performance levels. The method proposed in this paper is superior to the current mainstream methods.
{"title":"A Novel Single-Sample Retinal Vessel Segmentation Method Based on Grey Relational Analysis","authors":"Yating Wang, Hongjun Li","doi":"10.3390/s24134326","DOIUrl":"https://doi.org/10.3390/s24134326","url":null,"abstract":"Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of “few data, poor information”, this paper proposes a novel grey relational-based method for retinal vessel segmentation. Firstly, a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) is designed to enhance the image. Secondly, a threshold segmentation model based on grey relational analysis (TS-GRA) is designed to segment the enhanced vessel image. Finally, a post-processing stage involving hole filling and removal of isolated pixels is applied to obtain the final segmentation output. The performance of the proposed method is evaluated using multiple different measurement metrics on publicly available digital retinal DRIVE, STARE and HRF datasets. Experimental analysis showed that the average accuracy and specificity on the DRIVE dataset were 96.03% and 98.51%. The mean accuracy and specificity on the STARE dataset were 95.46% and 97.85%. Precision, F1-score, and Jaccard index on the HRF dataset all demonstrated high-performance levels. The method proposed in this paper is superior to the current mainstream methods.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A robust wood material crack detection algorithm, sensitive to small targets, is indispensable for production and building protection. However, the precise identification and localization of cracks in wooden materials present challenges owing to significant scale variations among cracks and the irregular quality of existing data. In response, we propose a crack detection algorithm tailored to wooden materials, leveraging advancements in the YOLOv8 model, named ICDW-YOLO (improved crack detection for wooden material-YOLO). The ICDW-YOLO model introduces novel designs for the neck network and layer structure, along with an anchor algorithm, which features a dual-layer attention mechanism and dynamic gradient gain characteristics to optimize and enhance the original model. Initially, a new layer structure was crafted using GSConv and GS bottleneck, improving the model’s recognition accuracy by maximizing the preservation of hidden channel connections. Subsequently, enhancements to the network are achieved through the gather–distribute mechanism, aimed at augmenting the fusion capability of multi-scale features and introducing a higher-resolution input layer to enhance small target recognition. Empirical results obtained from a customized wooden material crack detection dataset demonstrate the efficacy of the proposed ICDW-YOLO algorithm in effectively detecting targets. Without significant augmentation in model complexity, the mAP50–95 metric attains 79.018%, marking a 1.869% improvement over YOLOv8. Further validation of our algorithm’s effectiveness is conducted through experiments on fire and smoke detection datasets, aerial remote sensing image datasets, and the coco128 dataset. The results showcase that ICDW-YOLO achieves a mAP50 of 69.226% and a mAP50–95 of 44.210%, indicating robust generalization and competitiveness vis-à-vis state-of-the-art detectors.
{"title":"ICDW-YOLO: An Efficient Timber Construction Crack Detection Algorithm","authors":"Jieyang Zhou, Jing Ning, Zhiyang Xiang, Pengfei Yin","doi":"10.3390/s24134333","DOIUrl":"https://doi.org/10.3390/s24134333","url":null,"abstract":"A robust wood material crack detection algorithm, sensitive to small targets, is indispensable for production and building protection. However, the precise identification and localization of cracks in wooden materials present challenges owing to significant scale variations among cracks and the irregular quality of existing data. In response, we propose a crack detection algorithm tailored to wooden materials, leveraging advancements in the YOLOv8 model, named ICDW-YOLO (improved crack detection for wooden material-YOLO). The ICDW-YOLO model introduces novel designs for the neck network and layer structure, along with an anchor algorithm, which features a dual-layer attention mechanism and dynamic gradient gain characteristics to optimize and enhance the original model. Initially, a new layer structure was crafted using GSConv and GS bottleneck, improving the model’s recognition accuracy by maximizing the preservation of hidden channel connections. Subsequently, enhancements to the network are achieved through the gather–distribute mechanism, aimed at augmenting the fusion capability of multi-scale features and introducing a higher-resolution input layer to enhance small target recognition. Empirical results obtained from a customized wooden material crack detection dataset demonstrate the efficacy of the proposed ICDW-YOLO algorithm in effectively detecting targets. Without significant augmentation in model complexity, the mAP50–95 metric attains 79.018%, marking a 1.869% improvement over YOLOv8. Further validation of our algorithm’s effectiveness is conducted through experiments on fire and smoke detection datasets, aerial remote sensing image datasets, and the coco128 dataset. The results showcase that ICDW-YOLO achieves a mAP50 of 69.226% and a mAP50–95 of 44.210%, indicating robust generalization and competitiveness vis-à-vis state-of-the-art detectors.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Linqiong Jia, Shicheng Feng, Yijin Zhang, Jin-Yuan Wang
Visible light communication (VLC) is a promising complementary technology to its radio frequency (RF) counterpart to satisfy the high quality-of-service (QoS) requirements of intelligent vehicular communications by reusing LED street lights. In this paper, a hybrid handover scheme for vehicular VLC/RF communication networks is proposed, to balance QoS and handover costs by considering the vertical handover and horizontal handover together, and judging from the mobile state of the vehicle. A Markov decision process (MDP) is formulated to describe this hybrid handover problem, with a cost function balancing the handover consumption, delay, and reliability. A value iteration algorithm was applied to solve the optimal handover policy. The simulation results demonstrated the performance of the proposed hybrid handover scheme in comparison to other benchmark schemes.
可见光通信（VLC）是射频通信（RF）的一种前景广阔的互补技术，可通过重复使用 LED 路灯来满足智能车辆通信对服务质量（QoS）的高要求。本文提出了一种车载 VLC/RF 通信网络的混合切换方案，通过同时考虑垂直切换和水平切换，并根据车辆的移动状态进行判断，来平衡 QoS 和切换成本。本文提出了一个马尔可夫决策过程（MDP）来描述这个混合切换问题，其成本函数平衡了切换消耗、延迟和可靠性。采用值迭代算法来求解最优切换策略。仿真结果表明，与其他基准方案相比，所提出的混合切换方案性能更佳。
{"title":"A Hybrid Handover Scheme for Vehicular VLC/RF Communication Networks","authors":"Linqiong Jia, Shicheng Feng, Yijin Zhang, Jin-Yuan Wang","doi":"10.3390/s24134323","DOIUrl":"https://doi.org/10.3390/s24134323","url":null,"abstract":"Visible light communication (VLC) is a promising complementary technology to its radio frequency (RF) counterpart to satisfy the high quality-of-service (QoS) requirements of intelligent vehicular communications by reusing LED street lights. In this paper, a hybrid handover scheme for vehicular VLC/RF communication networks is proposed, to balance QoS and handover costs by considering the vertical handover and horizontal handover together, and judging from the mobile state of the vehicle. A Markov decision process (MDP) is formulated to describe this hybrid handover problem, with a cost function balancing the handover consumption, delay, and reliability. A value iteration algorithm was applied to solve the optimal handover policy. The simulation results demonstrated the performance of the proposed hybrid handover scheme in comparison to other benchmark schemes.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiyu Chen, Min Zeng, Tao Wang, Wangze Ni, Jianhua Yang, Nantao Hu, Tong Zhang, Zhi Yang
Flexible ammonia (NH3) gas sensors have gained increasing attention for their potential in medical diagnostics and health monitoring, as they serve as a biomarker for kidney disease. Utilizing the pre-designable and porous properties of covalent organic frameworks (COFs) is an innovative way to address the demand for high-performance NH3 sensing. However, COF particles frequently encounter aggregation, low conductivity, and mechanical rigidity, reducing the effectiveness of portable NH3 detection. To overcome these challenges, we propose a practical approach using polyvinyl alcohol-carrageenan (κPVA) as a template for in the situ growth of two-dimensional COF film and particles to produce a flexible hydrogel gas sensor (COF/κPVA). The synergistic effect of COF and κPVA enhances the gas sensing, water retention, and mechanical properties. The COF/κPVA hydrogel shows a 54.4% response to 1 ppm NH3 with a root mean square error of less than 5% and full recovery compared to the low response and no recovery of bare κPVA. Owing to the dual effects of the COF film and the particles anchoring the water molecules, the COF/κPVA hydrogel remained stable after 70 h in atmospheric conditions, in contrast, the bare κPVA hydrogel was completely dehydrated. Our work might pave the way for highly sensitive hydrogel gas sensors, which have intriguing applications in flexible electronic devices for gas sensing.
{"title":"In Situ Growth of COF/PVA-Carrageenan Hydrogel Using the Impregnation Method for the Purpose of Highly Sensitive Ammonia Detection","authors":"Xiyu Chen, Min Zeng, Tao Wang, Wangze Ni, Jianhua Yang, Nantao Hu, Tong Zhang, Zhi Yang","doi":"10.3390/s24134324","DOIUrl":"https://doi.org/10.3390/s24134324","url":null,"abstract":"Flexible ammonia (NH3) gas sensors have gained increasing attention for their potential in medical diagnostics and health monitoring, as they serve as a biomarker for kidney disease. Utilizing the pre-designable and porous properties of covalent organic frameworks (COFs) is an innovative way to address the demand for high-performance NH3 sensing. However, COF particles frequently encounter aggregation, low conductivity, and mechanical rigidity, reducing the effectiveness of portable NH3 detection. To overcome these challenges, we propose a practical approach using polyvinyl alcohol-carrageenan (κPVA) as a template for in the situ growth of two-dimensional COF film and particles to produce a flexible hydrogel gas sensor (COF/κPVA). The synergistic effect of COF and κPVA enhances the gas sensing, water retention, and mechanical properties. The COF/κPVA hydrogel shows a 54.4% response to 1 ppm NH3 with a root mean square error of less than 5% and full recovery compared to the low response and no recovery of bare κPVA. Owing to the dual effects of the COF film and the particles anchoring the water molecules, the COF/κPVA hydrogel remained stable after 70 h in atmospheric conditions, in contrast, the bare κPVA hydrogel was completely dehydrated. Our work might pave the way for highly sensitive hydrogel gas sensors, which have intriguing applications in flexible electronic devices for gas sensing.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High temperature represents a critical constraint in the development of gas sensors. Therefore, investigating gas sensors operating at room temperature holds significant practical importance. In this study, coal-based porous carbon (C-700) and coal-based C/MoO2 nanohybrid materials were synthesized using a simple one-step vapor deposition and sintering method, and their gas-sensing performance was investigated. The gas-sensing performance for several VOC gases (phenol, ethyl acetate, ethanol, acetone, triethylamine, and toluene) and a 95% RH high-humidity environment were tested. The results indicated that the C/MoO2-450 sample sintered at 450 °C exhibited excellent specific selectivity towards acetone at room temperature, with a response value of 4153.09% and response/recovery times of 10.8 s and 2.9 s, respectively. Furthermore, the C/MoO2-450 sample also demonstrated good repeatability and long-term stability. The sensing mechanism of the synthesized materials was also explored. The superior gas-sensing performance can be attributed to the synergistic effect between the porous carbon and MoO2 nanoparticles. Given the importance of enhancing the high-tech and high-value-added utilization of coal, this study provides a viable approach for utilizing coal-based carbon materials in detecting volatile organic compounds at room temperature.
{"title":"A Highly Selective Acetone Sensor Based on Coal-Based Carbon/MoO2 Nanohybrid Material","authors":"Min Zhang, Yi Han, Ting Liu, Hongguang Jia","doi":"10.3390/s24134320","DOIUrl":"https://doi.org/10.3390/s24134320","url":null,"abstract":"High temperature represents a critical constraint in the development of gas sensors. Therefore, investigating gas sensors operating at room temperature holds significant practical importance. In this study, coal-based porous carbon (C-700) and coal-based C/MoO2 nanohybrid materials were synthesized using a simple one-step vapor deposition and sintering method, and their gas-sensing performance was investigated. The gas-sensing performance for several VOC gases (phenol, ethyl acetate, ethanol, acetone, triethylamine, and toluene) and a 95% RH high-humidity environment were tested. The results indicated that the C/MoO2-450 sample sintered at 450 °C exhibited excellent specific selectivity towards acetone at room temperature, with a response value of 4153.09% and response/recovery times of 10.8 s and 2.9 s, respectively. Furthermore, the C/MoO2-450 sample also demonstrated good repeatability and long-term stability. The sensing mechanism of the synthesized materials was also explored. The superior gas-sensing performance can be attributed to the synergistic effect between the porous carbon and MoO2 nanoparticles. Given the importance of enhancing the high-tech and high-value-added utilization of coal, this study provides a viable approach for utilizing coal-based carbon materials in detecting volatile organic compounds at room temperature.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To solve the problem of aperture fill time (AFT) for wideband sparse arrays, variable fractional delay (VFD) FIR filters are applied to eliminate linear coupling between spatial and time domains. However, the large dimensions of the filter coefficient matrix result in high system complexity. To alleviate the computational burden of solving VFD filter coefficients, a novel multi–regultion minimax (MRMM) model utilizing the sparse representation technique has been presented. The error function is constrained by the introduction of L2–norm and L1–norm regularizations within the minimax criterion. The L2–norm effectively resolves the problems of overfitting and non–unique solutions that arise in the sparse optimization of traditional minimax (MM) models. Meanwhile, the use of multiple L1–norms enables the optimal design of the smallest sub–filter number and order of the VFD filter. To solve the established nonconvex model, an improved sequential–alternating direction method of multipliers (S–ADMM) algorithm for filter coefficients is proposed, which utilizes sequential alternation to iteratively update multiple soft–thresholding problems. The experimental results show that the optimized VFD filter reduces system complexity significantly and corrects AFT effectively in a wideband sparse array.
{"title":"Efficient Aperture Fill Time Correction for Wideband Sparse Array Using Improved Variable Fractional Delay Filters","authors":"Jie Gu, Min Xu, Wenjing Zhou, Mingwei Shen","doi":"10.3390/s24134327","DOIUrl":"https://doi.org/10.3390/s24134327","url":null,"abstract":"To solve the problem of aperture fill time (AFT) for wideband sparse arrays, variable fractional delay (VFD) FIR filters are applied to eliminate linear coupling between spatial and time domains. However, the large dimensions of the filter coefficient matrix result in high system complexity. To alleviate the computational burden of solving VFD filter coefficients, a novel multi–regultion minimax (MRMM) model utilizing the sparse representation technique has been presented. The error function is constrained by the introduction of L2–norm and L1–norm regularizations within the minimax criterion. The L2–norm effectively resolves the problems of overfitting and non–unique solutions that arise in the sparse optimization of traditional minimax (MM) models. Meanwhile, the use of multiple L1–norms enables the optimal design of the smallest sub–filter number and order of the VFD filter. To solve the established nonconvex model, an improved sequential–alternating direction method of multipliers (S–ADMM) algorithm for filter coefficients is proposed, which utilizes sequential alternation to iteratively update multiple soft–thresholding problems. The experimental results show that the optimized VFD filter reduces system complexity significantly and corrects AFT effectively in a wideband sparse array.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The quartz tuning fork (QTF) is a promising instrument for biosensor applications due to its advanced properties such as high sensitivity to physical quantities, cost-effectiveness, frequency stability, and high-quality factor. Nevertheless, the fork’s small size and difficulty in modifying the prongs’ surfaces limit its wide use in experimental research. Our study presents the development of a QTF immunosensor composed of three active layers: biocompatible natural melanin nanoparticles (MNPs), glutaraldehyde (GLU), and anti-IgG layers, for the detection of immunoglobulin G (IgG). Frequency shifts of QTFs after MNP functionalization, GLU activation, and anti-IgG immobilization were measured with an Asensis QTF F-master device. Using QTF immunosensors that had been modified under optimum conditions, the performance of QTF immunosensors for IgG detection was evaluated. Accordingly, a finite element method (FEM)-based model was produced using the COMSOL Multiphysics software program (COMSOL License No. 2102058) to simulate the effect of deposited layers on the QTF resonance frequency. The experimental results, which demonstrated shifts in frequency with each layer during QTF surface functionalization, corroborated the simulation model predictions. A modelling error of 0.05% was observed for the MNP-functionalized QTF biosensor compared to experimental findings. This study validated a simulation model that demonstrates the advantages of a simulation-based approach to optimize QTF biosensors, thereby reducing the need for extensive laboratory work.
{"title":"Anti-IgG Doped Melanin Nanoparticles Functionalized Quartz Tuning Fork Immunosensors for Immunoglobulin G Detection: In Vitro and In Silico Study","authors":"Dilhan Gürcan, Engin Baysoy, Gizem Kaleli-Can","doi":"10.3390/s24134319","DOIUrl":"https://doi.org/10.3390/s24134319","url":null,"abstract":"The quartz tuning fork (QTF) is a promising instrument for biosensor applications due to its advanced properties such as high sensitivity to physical quantities, cost-effectiveness, frequency stability, and high-quality factor. Nevertheless, the fork’s small size and difficulty in modifying the prongs’ surfaces limit its wide use in experimental research. Our study presents the development of a QTF immunosensor composed of three active layers: biocompatible natural melanin nanoparticles (MNPs), glutaraldehyde (GLU), and anti-IgG layers, for the detection of immunoglobulin G (IgG). Frequency shifts of QTFs after MNP functionalization, GLU activation, and anti-IgG immobilization were measured with an Asensis QTF F-master device. Using QTF immunosensors that had been modified under optimum conditions, the performance of QTF immunosensors for IgG detection was evaluated. Accordingly, a finite element method (FEM)-based model was produced using the COMSOL Multiphysics software program (COMSOL License No. 2102058) to simulate the effect of deposited layers on the QTF resonance frequency. The experimental results, which demonstrated shifts in frequency with each layer during QTF surface functionalization, corroborated the simulation model predictions. A modelling error of 0.05% was observed for the MNP-functionalized QTF biosensor compared to experimental findings. This study validated a simulation model that demonstrates the advantages of a simulation-based approach to optimize QTF biosensors, thereby reducing the need for extensive laboratory work.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scene graphs can enhance the understanding capability of intelligent ships in navigation scenes. However, the complex entity relationships and the presence of significant noise in contextual information within navigation scenes pose challenges for navigation scene graph generation (NSGG). To address these issues, this paper proposes a novel NSGG network named SGK-Net. This network comprises three innovative modules. The Semantic-Guided Multimodal Fusion (SGMF) module utilizes prior information on relationship semantics to fuse multimodal information and construct relationship features, thereby elucidating the relationships between entities and reducing semantic ambiguity caused by complex relationships. The Graph Structure Learning-based Structure Evolution (GSLSE) module, based on graph structure learning, reduces redundancy in relationship features and optimizes the computational complexity in subsequent contextual message passing. The Key Entity Message Passing (KEMP) module takes full advantage of contextual information to refine relationship features, thereby reducing noise interference from non-key nodes. Furthermore, this paper constructs the first Ship Navigation Scene Graph Simulation dataset, named SNSG-Sim, which provides a foundational dataset for the research on ship navigation SGG. Experimental results on the SNSG-sim dataset demonstrate that our method achieves an improvement of 8.31% (R@50) in the PredCls task and 7.94% (R@50) in the SGCls task compared to the baseline method, validating the effectiveness of our method in navigation scene graph generation.
{"title":"SGK-Net: A Novel Navigation Scene Graph Generation Network","authors":"Wenbin Yang, Hao Qiu, Xiangfeng Luo, Shaorong Xie","doi":"10.3390/s24134329","DOIUrl":"https://doi.org/10.3390/s24134329","url":null,"abstract":"Scene graphs can enhance the understanding capability of intelligent ships in navigation scenes. However, the complex entity relationships and the presence of significant noise in contextual information within navigation scenes pose challenges for navigation scene graph generation (NSGG). To address these issues, this paper proposes a novel NSGG network named SGK-Net. This network comprises three innovative modules. The Semantic-Guided Multimodal Fusion (SGMF) module utilizes prior information on relationship semantics to fuse multimodal information and construct relationship features, thereby elucidating the relationships between entities and reducing semantic ambiguity caused by complex relationships. The Graph Structure Learning-based Structure Evolution (GSLSE) module, based on graph structure learning, reduces redundancy in relationship features and optimizes the computational complexity in subsequent contextual message passing. The Key Entity Message Passing (KEMP) module takes full advantage of contextual information to refine relationship features, thereby reducing noise interference from non-key nodes. Furthermore, this paper constructs the first Ship Navigation Scene Graph Simulation dataset, named SNSG-Sim, which provides a foundational dataset for the research on ship navigation SGG. Experimental results on the SNSG-sim dataset demonstrate that our method achieves an improvement of 8.31% (R@50) in the PredCls task and 7.94% (R@50) in the SGCls task compared to the baseline method, validating the effectiveness of our method in navigation scene graph generation.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoteng Dai, Yiqiang Chen, Jie Chen, Ruichang Qiu
Capacitors are crucial components in power electronic converters, responsible for harmonic elimination, energy buffering, and voltage stabilization. However, they are also the most susceptible to damage due to their operational environment. Accurate temperature estimation of capacitors is essential for monitoring their condition and ensuring the reliability of the converter system. This paper presents a novel method for estimating the core temperature of capacitors using a long short-term memory (LSTM) algorithm. The approach incorporates a continued training mechanism to adapt to variable load conditions in converters. Experimental results demonstrate the proposed method’s high accuracy and robustness, making it suitable for real-time capacitor temperature monitoring in practical applications.
{"title":"Converter Capacitor Temperature Estimation Based on Continued Training LSTM under Variable Load Conditions","authors":"Xiaoteng Dai, Yiqiang Chen, Jie Chen, Ruichang Qiu","doi":"10.3390/s24134304","DOIUrl":"https://doi.org/10.3390/s24134304","url":null,"abstract":"Capacitors are crucial components in power electronic converters, responsible for harmonic elimination, energy buffering, and voltage stabilization. However, they are also the most susceptible to damage due to their operational environment. Accurate temperature estimation of capacitors is essential for monitoring their condition and ensuring the reliability of the converter system. This paper presents a novel method for estimating the core temperature of capacitors using a long short-term memory (LSTM) algorithm. The approach incorporates a continued training mechanism to adapt to variable load conditions in converters. Experimental results demonstrate the proposed method’s high accuracy and robustness, making it suitable for real-time capacitor temperature monitoring in practical applications.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhan Shi, Yanhu Zhang, Jiawei Gu, Bao Liu, Hao Fu, Hongyu Liang, Jinghu Ji
The triboelectric nanogenerator (TENG), as a novel energy harvesting technology, has garnered widespread attention. As a relatively young field in nanogenerator research, investigations into various aspects of the TENG are still ongoing. This review summarizes the development and dissemination of the fundamental principles of triboelectricity generation. It outlines the evolution of triboelectricity principles, ranging from the fabrication of the first TENG to the selection of triboelectric materials and the confirmation of the electron cloud overlapping model. Furthermore, recent advancements in TENG application scenarios are discussed from four perspectives, along with the research progress in performance optimization through three primary approaches, highlighting their respective strengths and limitations. Finally, the paper addresses the major challenges hindering the practical application and widespread adoption of TENGs, while also providing insights into future developments. With continued research on the TENG, it is expected that these challenges can be overcome, paving the way for its extensive utilization in various real-world scenarios.
三电纳米发电机（TENG）作为一种新型能量收集技术，已引起广泛关注。作为纳米发电机研究中一个相对年轻的领域，对 TENG 各方面的研究仍在进行中。本综述总结了三光电发电基本原理的发展和传播。它概述了三电原理的演变过程，从第一台 TENG 的制造到三电材料的选择以及电子云重叠模型的确认。此外，论文还从四个方面讨论了 TENG 应用场景的最新进展，以及通过三种主要方法进行性能优化的研究进展，强调了它们各自的优势和局限性。最后，本文探讨了阻碍 TENG 实际应用和广泛采用的主要挑战，同时也对未来的发展提出了见解。随着对 TENG 研究的不断深入，这些挑战有望被克服，为其在各种实际应用场景中的广泛应用铺平道路。
{"title":"Triboelectric Nanogenerators: State of the Art","authors":"Zhan Shi, Yanhu Zhang, Jiawei Gu, Bao Liu, Hao Fu, Hongyu Liang, Jinghu Ji","doi":"10.3390/s24134298","DOIUrl":"https://doi.org/10.3390/s24134298","url":null,"abstract":"The triboelectric nanogenerator (TENG), as a novel energy harvesting technology, has garnered widespread attention. As a relatively young field in nanogenerator research, investigations into various aspects of the TENG are still ongoing. This review summarizes the development and dissemination of the fundamental principles of triboelectricity generation. It outlines the evolution of triboelectricity principles, ranging from the fabrication of the first TENG to the selection of triboelectric materials and the confirmation of the electron cloud overlapping model. Furthermore, recent advancements in TENG application scenarios are discussed from four perspectives, along with the research progress in performance optimization through three primary approaches, highlighting their respective strengths and limitations. Finally, the paper addresses the major challenges hindering the practical application and widespread adoption of TENGs, while also providing insights into future developments. With continued research on the TENG, it is expected that these challenges can be overcome, paving the way for its extensive utilization in various real-world scenarios.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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