Zhaohui Meng, Peng Zhang, Hongli Zhang, Huawei Wang
Electric torque wrenches play a critical role in the assembly process of high‐strength bolts. This study focuses on three assembly processes for high‐strength bolts and introduces two statistical analysis methods to evaluate the quality of bolt assembly. To improve performance, a novel fully digital electric torque wrench, integrated with a smart meter chip, is designed. This wrench achieves integration between the current sensor control method and the torque sensor control method through a simplified hardware circuit, ensuring high precision and stability. Moreover, the AC voltage self‐stabilization method and Savitzky–Golay filtering method are employed in this study to enhance the accuracy and robustness of the wrench. Simulation experiments using Simulink and Matlab validate the effectiveness of these two methods. Additionally, a cloud platform is utilized to facilitate seamless connectivity and data transmission, enabling rapid Internet of Things access for the wrench.
{"title":"New concepts in the design of an IoT electric torque wrench‐based on a smart meter chip","authors":"Zhaohui Meng, Peng Zhang, Hongli Zhang, Huawei Wang","doi":"10.1002/eng2.12941","DOIUrl":"https://doi.org/10.1002/eng2.12941","url":null,"abstract":"Electric torque wrenches play a critical role in the assembly process of high‐strength bolts. This study focuses on three assembly processes for high‐strength bolts and introduces two statistical analysis methods to evaluate the quality of bolt assembly. To improve performance, a novel fully digital electric torque wrench, integrated with a smart meter chip, is designed. This wrench achieves integration between the current sensor control method and the torque sensor control method through a simplified hardware circuit, ensuring high precision and stability. Moreover, the AC voltage self‐stabilization method and Savitzky–Golay filtering method are employed in this study to enhance the accuracy and robustness of the wrench. Simulation experiments using Simulink and Matlab validate the effectiveness of these two methods. Additionally, a cloud platform is utilized to facilitate seamless connectivity and data transmission, enabling rapid Internet of Things access for the wrench.","PeriodicalId":502604,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141345985","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}
Muhammad Rizwan, Samina Bibi, S. Haq, Muhammad Asif, Tariqullah Jan, M. H. Zafar
Agricultural plants are the fundamental source of nutrients worldwide. The attack of diseases on these plants leads to food scarcity and results in a catastrophic situation. These diseases can be prevented by using manual or automatic approaches. The manual approach, where plant pathologists inspect fields, is costly, error‐prone, and time‐consuming. Alternatively, automatic approaches utilize 2D plant images processed through machine learning. The current study opts for the later approach due to its advantages in terms of speed, efficiency, and convenience. Convolutional neural network (CNN)‐based prominent models, such as MobileNet, ResNet50, Inception, and Xception, are preferred for automatic plant disease detection due to their high performance, but they demand substantial computational resources, limiting their use to a class of large‐scale farmers. The proposed study developed a novel CNN model that is suitable for small‐scale farmers. The numerical outcomes indicate that the proposed model surpassed the state‐of‐the‐art models by achieving an average accuracy of 96.86%. The proposed model utilized comparatively limited computational resources as analyzed through floating‐point operations (FLOPs), number of parameters, computation time, and model's size. Furthermore, a statistical approach was proposed to analyze a model while collectively accounting for its performance and computational complexity. It is observed from the results that the proposed model outperformed the state‐of‐the‐art techniques in terms of both average recognition accuracy and computational complexity.
{"title":"Automatic plant disease detection using computationally efficient convolutional neural network","authors":"Muhammad Rizwan, Samina Bibi, S. Haq, Muhammad Asif, Tariqullah Jan, M. H. Zafar","doi":"10.1002/eng2.12944","DOIUrl":"https://doi.org/10.1002/eng2.12944","url":null,"abstract":"Agricultural plants are the fundamental source of nutrients worldwide. The attack of diseases on these plants leads to food scarcity and results in a catastrophic situation. These diseases can be prevented by using manual or automatic approaches. The manual approach, where plant pathologists inspect fields, is costly, error‐prone, and time‐consuming. Alternatively, automatic approaches utilize 2D plant images processed through machine learning. The current study opts for the later approach due to its advantages in terms of speed, efficiency, and convenience. Convolutional neural network (CNN)‐based prominent models, such as MobileNet, ResNet50, Inception, and Xception, are preferred for automatic plant disease detection due to their high performance, but they demand substantial computational resources, limiting their use to a class of large‐scale farmers. The proposed study developed a novel CNN model that is suitable for small‐scale farmers. The numerical outcomes indicate that the proposed model surpassed the state‐of‐the‐art models by achieving an average accuracy of 96.86%. The proposed model utilized comparatively limited computational resources as analyzed through floating‐point operations (FLOPs), number of parameters, computation time, and model's size. Furthermore, a statistical approach was proposed to analyze a model while collectively accounting for its performance and computational complexity. It is observed from the results that the proposed model outperformed the state‐of‐the‐art techniques in terms of both average recognition accuracy and computational complexity.","PeriodicalId":502604,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348006","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}
In order to reduce the manual workload and reduce the maintenance cost, it is particularly important to realize automatic detection of cracks. Aiming at the problems of poor real‐time performance and low precision of traditional pavement crack detection, a crack detection method based on improved YOLOv5 one‐step target detection algorithm of convolutional neural network is proposed by using the advantages of depth learning network in target detection. The images were manually marked with LabelImg annotation software, and then the network model parameters were obtained through improving the YOLOv5 network training. Finally, the cracks are verified and detected by the established model. In addition, the precision and speed of crack detection using YOLOv3, YOLOv5s, and YOLOv5s‐attention models are compared by using Precision, Recall, and F1. After comparison, it is found that the detection precision of YOLOv5s‐attention is improved by 1.0%, F1 by 0.9%, and mAP@.5 by 1.8%.
{"title":"Crack detection based on attention mechanism with YOLOv5","authors":"Min‐Li Lan, Dan Yang, Shuang‐Xi Zhou, Yang Ding","doi":"10.1002/eng2.12899","DOIUrl":"https://doi.org/10.1002/eng2.12899","url":null,"abstract":"In order to reduce the manual workload and reduce the maintenance cost, it is particularly important to realize automatic detection of cracks. Aiming at the problems of poor real‐time performance and low precision of traditional pavement crack detection, a crack detection method based on improved YOLOv5 one‐step target detection algorithm of convolutional neural network is proposed by using the advantages of depth learning network in target detection. The images were manually marked with LabelImg annotation software, and then the network model parameters were obtained through improving the YOLOv5 network training. Finally, the cracks are verified and detected by the established model. In addition, the precision and speed of crack detection using YOLOv3, YOLOv5s, and YOLOv5s‐attention models are compared by using Precision, Recall, and F1. After comparison, it is found that the detection precision of YOLOv5s‐attention is improved by 1.0%, F1 by 0.9%, and mAP@.5 by 1.8%.","PeriodicalId":502604,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140663030","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}
This study develops a methodology to create detailed visual Digital Twins of large‐scale structures with their realistic damages detected from visual inspection or nondestructive testing. The methodology is demonstrated with a transition piece of an offshore wind turbine and a composite rotor blade, with surface paint damage and subsurface delamination damage, respectively. Artificial Intelligence and color threshold segmentation are used to classify and localize damages from optical images taken by drones. These damages are digitalized and mapped to a 3D geometry reconstruction of the large‐scale structure or a CAD model of the structure. To map the images from 2D to 3D, metadata information is combined with the geo placement of the large‐scale structure's 3D model. The 3D model can here both be a CAD model of the structure or a 3D reconstruction based on photogrammetry. After mapping the damage, the Digital Twin gives an accurate representation of the structure. The location, shape, and size of the damage are visible on the Digital Twin. The demonstrated methodology can be applied to industrial sectors such as wind energy, the oil and gas industry, marine and aerospace to facilitate asset management.
{"title":"Mapping damages from inspection images to 3D digital twins of large‐scale structures","authors":"Hans‐Henrik von Benzon, Xiao Chen","doi":"10.1002/eng2.12837","DOIUrl":"https://doi.org/10.1002/eng2.12837","url":null,"abstract":"This study develops a methodology to create detailed visual Digital Twins of large‐scale structures with their realistic damages detected from visual inspection or nondestructive testing. The methodology is demonstrated with a transition piece of an offshore wind turbine and a composite rotor blade, with surface paint damage and subsurface delamination damage, respectively. Artificial Intelligence and color threshold segmentation are used to classify and localize damages from optical images taken by drones. These damages are digitalized and mapped to a 3D geometry reconstruction of the large‐scale structure or a CAD model of the structure. To map the images from 2D to 3D, metadata information is combined with the geo placement of the large‐scale structure's 3D model. The 3D model can here both be a CAD model of the structure or a 3D reconstruction based on photogrammetry. After mapping the damage, the Digital Twin gives an accurate representation of the structure. The location, shape, and size of the damage are visible on the Digital Twin. The demonstrated methodology can be applied to industrial sectors such as wind energy, the oil and gas industry, marine and aerospace to facilitate asset management.","PeriodicalId":502604,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139125485","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}
IFOS3D is a three‐dimensional elastic full‐waveform inversion (EFWI) tool designed for high‐resolution estimation of the Earth's material properties within 3D subsurface structures. However, due to the significant computational costs associated with 3D EFWI, leveraging the computing power of a supercomputer for implementation is a logical choice. In this article, we introduce several innovative process‐level and thread‐level optimizations based on heterogeneous many‐core architectures in the new Sunway supercomputer, which is a powerful system globally. These optimizations encompass a process‐level communication overlapping strategy, thread‐level data partitioning and layout approaches, a remote memory access optimized master‐slave communication scheme, and a thread‐level data reuse and overlapping strategy. Through these optimizations, we achieve significant improvements in each iteration, with a kernel function speedup of approximately 59 and an overall program speedup of about 14. Our findings demonstrate the ability of our proposed optimization strategies to overcome the computational challenges associated with 3D EFWI, providing a promising framework for future advancements in the field of subsurface imaging.
{"title":"Redesigning elastic full‐waveform inversion on the new Sunway architecture","authors":"Mengyuan Hua, Wubing Wan, Zhaoqi Sun, Zekun Yin, Puyu Xiong, Xiaohui Liu, Haodong Tian, Ping Gao, Weiguo Liu, Hua Wang, Wenlai Zhao, Zhenchun Huang","doi":"10.1002/eng2.12819","DOIUrl":"https://doi.org/10.1002/eng2.12819","url":null,"abstract":"IFOS3D is a three‐dimensional elastic full‐waveform inversion (EFWI) tool designed for high‐resolution estimation of the Earth's material properties within 3D subsurface structures. However, due to the significant computational costs associated with 3D EFWI, leveraging the computing power of a supercomputer for implementation is a logical choice. In this article, we introduce several innovative process‐level and thread‐level optimizations based on heterogeneous many‐core architectures in the new Sunway supercomputer, which is a powerful system globally. These optimizations encompass a process‐level communication overlapping strategy, thread‐level data partitioning and layout approaches, a remote memory access optimized master‐slave communication scheme, and a thread‐level data reuse and overlapping strategy. Through these optimizations, we achieve significant improvements in each iteration, with a kernel function speedup of approximately 59 and an overall program speedup of about 14. Our findings demonstrate the ability of our proposed optimization strategies to overcome the computational challenges associated with 3D EFWI, providing a promising framework for future advancements in the field of subsurface imaging.","PeriodicalId":502604,"journal":{"name":"Engineering Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139246331","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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