Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspect...Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.展开更多
In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe ope...In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines.Currently,assessment methods for fastener tightness include manual observation,acoustic wave detection,and image detection.There are limitations such as low accuracy and efficiency,easy interference and misjudgment,and a lack of accurate,stable,and fast detection methods.Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening,this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks.First,the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration.Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod.The point is projected onto the upper surface of the bolt to calculate the projection distance.Subsequently,the mapping relationship between the projection distance sequence and fastener tightness is established,and the influence of each parameter on the fastener tightness prediction is analyzed.Finally,by setting up a fastener detection scene in the track experimental base,collecting data,and completing the algorithm verification,the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm,which significantly improved the effect compared with other tightness detection methods,and realized an effective fastener tightness regression.展开更多
As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to r...As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.展开更多
It was found that the steel plate in the composite plate in the WJ-8 fastener used in high speed rail is rusty. The objective of this study is to test the zinc coating of the steel plate. A literature review was condu...It was found that the steel plate in the composite plate in the WJ-8 fastener used in high speed rail is rusty. The objective of this study is to test the zinc coating of the steel plate. A literature review was conducted to identify the zinc coating techniques, and the companies that can provide different coating service was identified. A salt fog chamber was built that was in compliance with the ANSI B117 code, and the steel plates that were coated by the identified companies were tested using the salt fog chamber. The results indicated that the coating technique that had the best performance in preventing corrosion was the Greenkote plates with passivation. The galvanized option had the roughest coating layer, and it was the most reactive in the salt water solution. This makes it non-ideal for the dynamic rail environment because the increased friction of the plate could damage the supports, especially during extreme temperatures that would cause the rail to expand or contract. Greenkote with Phosphate and ArmorGalv also provided increased corrosion prevention with a smooth, strong finish, but it had more rust on the surface area than the Greenkote with ELU passivation. The ArmorGalv sample had more rust on the surface area than the Greenkote samples. This may not be a weakness in the ArmorGalv process;rather, it likely was the result of this particular sample not having the added protection of a colored coating.展开更多
目的:基于网络药理学和分子对接方法,确定复方黄柏液治疗的Ⅲ度烧伤肉芽组织愈合的有效活性成分、关键靶点和潜在的药理学机制,并进行肉芽组织成纤维细胞的初步验证。方法:从公共数据库中药系统药理学分析平台(TCMSP)检索复方黄柏液组...目的:基于网络药理学和分子对接方法,确定复方黄柏液治疗的Ⅲ度烧伤肉芽组织愈合的有效活性成分、关键靶点和潜在的药理学机制,并进行肉芽组织成纤维细胞的初步验证。方法:从公共数据库中药系统药理学分析平台(TCMSP)检索复方黄柏液组成成分连翘、黄柏、金银花的有效成分和靶点;GeneCards、OMIM数据库检索“Ⅲ度烧伤”疾病相关靶点。通过生物信息学分析,包括蛋白质-蛋白质相互作用(Protein-proteininteraction,PPI)以及基因本体(Gene ontology,GO)和京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes,KEGG)分析,获得了关键的有效成分、核心靶点和相关信号通路;DiscoveryStudio分子对接分析有效成分化合物与靶蛋白的结合。0.5%的DMSO溶液处理的成纤维细胞记为对照组;槲皮素(40μmol/ml)处理的成纤维细胞记为槲皮素组。采用CCK8法、Transwell实验检测细胞增殖、迁移侵袭;WB试验检测细胞p-PI3K、p-Akt蛋白。结果:共筛选出74个有效成分,331个作用靶点,AKT1为潜在的治疗靶点,木犀草素、山柰酚、槲皮素、汉黄芩素、丹皮酚为潜在的候选药物。PI3K-AKT信号通路可能在复方黄柏液治疗Ⅲ度烧伤中发挥关键作用;分子对接表明槲皮素与AKT1结合最好。与对照组相比,槲皮素组成纤维细胞增殖、迁移侵袭均显著降低,p-PI3K、p-Akt蛋白表达也显著降低(P<0.05)。结论:复方黄柏液促进Ⅲ度烧伤患者肉芽组织形成的生物活性成分为槲皮素,潜在通路为PI3K-AKT信号通路,为复方黄柏液治疗Ⅲ度烧伤的研究提供了思路。展开更多
目的:探讨全髋关节置换术的CroweⅢ-Ⅳ型发育性髋关节发育不良(developmental dysplasia of the hip,DDH)患者的满意度及造成不满意的相关因素。方法:回顾性分析2013年3月至2018年3月行全髋关节置换术的169例CroweⅢ-Ⅳ型DDH患者,通过...目的:探讨全髋关节置换术的CroweⅢ-Ⅳ型发育性髋关节发育不良(developmental dysplasia of the hip,DDH)患者的满意度及造成不满意的相关因素。方法:回顾性分析2013年3月至2018年3月行全髋关节置换术的169例CroweⅢ-Ⅳ型DDH患者,通过微信进行调查问卷,调查患者对手术总体满意度、10项日常功能满意度和患者认为对自己日常生活影响比较大的前5个问题。手术前后采用髋关节Harris评分进行功能评价。结果:收到完整调查问卷145份,所有患者获随访,时间1~5(3.23±1.22)年。145例患者分成两组,其中对手术疗效满意的118例,不满意的27例,手术总体满意率81.38%(118/145)。患者认为对生活影响比较大的前5个问题分别是术后髋部疼痛,肢体明显不等长、行走、上下楼梯、蹲起。两组术前Harris评分比较,差异无统计学意义(P>0.05),不满意组术后Harris评分较低。术后髋关节疼痛、肢体不等长是影响手术不满意的直接因素。结论:采用全髋关节置换术治疗CroweⅢ-Ⅳ型DDH患者手术难度大;术后髋关节疼痛(轻度以上),肢体不等长(>2 cm)是术后不满意的独立危险因素。展开更多
基金supported in part by the National Natural Science Foundation of China (Grant Nos.51975347 and 51907117)in part by the Shanghai Science and Technology Program (Grant No.22010501600).
文摘Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.
基金Supported by Fundamental Research Funds for the Central Universities of China(Grant No.2023JBMC014).
文摘In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines.Currently,assessment methods for fastener tightness include manual observation,acoustic wave detection,and image detection.There are limitations such as low accuracy and efficiency,easy interference and misjudgment,and a lack of accurate,stable,and fast detection methods.Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening,this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks.First,the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration.Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod.The point is projected onto the upper surface of the bolt to calculate the projection distance.Subsequently,the mapping relationship between the projection distance sequence and fastener tightness is established,and the influence of each parameter on the fastener tightness prediction is analyzed.Finally,by setting up a fastener detection scene in the track experimental base,collecting data,and completing the algorithm verification,the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm,which significantly improved the effect compared with other tightness detection methods,and realized an effective fastener tightness regression.
文摘As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.
文摘It was found that the steel plate in the composite plate in the WJ-8 fastener used in high speed rail is rusty. The objective of this study is to test the zinc coating of the steel plate. A literature review was conducted to identify the zinc coating techniques, and the companies that can provide different coating service was identified. A salt fog chamber was built that was in compliance with the ANSI B117 code, and the steel plates that were coated by the identified companies were tested using the salt fog chamber. The results indicated that the coating technique that had the best performance in preventing corrosion was the Greenkote plates with passivation. The galvanized option had the roughest coating layer, and it was the most reactive in the salt water solution. This makes it non-ideal for the dynamic rail environment because the increased friction of the plate could damage the supports, especially during extreme temperatures that would cause the rail to expand or contract. Greenkote with Phosphate and ArmorGalv also provided increased corrosion prevention with a smooth, strong finish, but it had more rust on the surface area than the Greenkote with ELU passivation. The ArmorGalv sample had more rust on the surface area than the Greenkote samples. This may not be a weakness in the ArmorGalv process;rather, it likely was the result of this particular sample not having the added protection of a colored coating.
文摘目的:基于网络药理学和分子对接方法,确定复方黄柏液治疗的Ⅲ度烧伤肉芽组织愈合的有效活性成分、关键靶点和潜在的药理学机制,并进行肉芽组织成纤维细胞的初步验证。方法:从公共数据库中药系统药理学分析平台(TCMSP)检索复方黄柏液组成成分连翘、黄柏、金银花的有效成分和靶点;GeneCards、OMIM数据库检索“Ⅲ度烧伤”疾病相关靶点。通过生物信息学分析,包括蛋白质-蛋白质相互作用(Protein-proteininteraction,PPI)以及基因本体(Gene ontology,GO)和京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes,KEGG)分析,获得了关键的有效成分、核心靶点和相关信号通路;DiscoveryStudio分子对接分析有效成分化合物与靶蛋白的结合。0.5%的DMSO溶液处理的成纤维细胞记为对照组;槲皮素(40μmol/ml)处理的成纤维细胞记为槲皮素组。采用CCK8法、Transwell实验检测细胞增殖、迁移侵袭;WB试验检测细胞p-PI3K、p-Akt蛋白。结果:共筛选出74个有效成分,331个作用靶点,AKT1为潜在的治疗靶点,木犀草素、山柰酚、槲皮素、汉黄芩素、丹皮酚为潜在的候选药物。PI3K-AKT信号通路可能在复方黄柏液治疗Ⅲ度烧伤中发挥关键作用;分子对接表明槲皮素与AKT1结合最好。与对照组相比,槲皮素组成纤维细胞增殖、迁移侵袭均显著降低,p-PI3K、p-Akt蛋白表达也显著降低(P<0.05)。结论:复方黄柏液促进Ⅲ度烧伤患者肉芽组织形成的生物活性成分为槲皮素,潜在通路为PI3K-AKT信号通路,为复方黄柏液治疗Ⅲ度烧伤的研究提供了思路。
文摘目的:探讨全髋关节置换术的CroweⅢ-Ⅳ型发育性髋关节发育不良(developmental dysplasia of the hip,DDH)患者的满意度及造成不满意的相关因素。方法:回顾性分析2013年3月至2018年3月行全髋关节置换术的169例CroweⅢ-Ⅳ型DDH患者,通过微信进行调查问卷,调查患者对手术总体满意度、10项日常功能满意度和患者认为对自己日常生活影响比较大的前5个问题。手术前后采用髋关节Harris评分进行功能评价。结果:收到完整调查问卷145份,所有患者获随访,时间1~5(3.23±1.22)年。145例患者分成两组,其中对手术疗效满意的118例,不满意的27例,手术总体满意率81.38%(118/145)。患者认为对生活影响比较大的前5个问题分别是术后髋部疼痛,肢体明显不等长、行走、上下楼梯、蹲起。两组术前Harris评分比较,差异无统计学意义(P>0.05),不满意组术后Harris评分较低。术后髋关节疼痛、肢体不等长是影响手术不满意的直接因素。结论:采用全髋关节置换术治疗CroweⅢ-Ⅳ型DDH患者手术难度大;术后髋关节疼痛(轻度以上),肢体不等长(>2 cm)是术后不满意的独立危险因素。
