Engineering Reports最新文献_第2页

New concepts in the design of an IoT electric torque wrench‐based on a smart meter chip 基于智能电表芯片的物联网电动扭矩扳手设计新理念

Engineering Reports Pub Date : 2024-06-13 DOI: 10.1002/eng2.12941

Zhaohui Meng, Peng Zhang, Hongli Zhang, Huawei Wang

引用次数: 0

Automatic plant disease detection using computationally efficient convolutional neural network 利用计算效率高的卷积神经网络自动检测植物病害

Engineering Reports Pub Date : 2024-06-13 DOI: 10.1002/eng2.12944

Muhammad Rizwan, Samina Bibi, S. Haq, Muhammad Asif, Tariqullah Jan, M. H. Zafar

{"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}

引用次数: 0

Crack detection based on attention mechanism with YOLOv5 基于 YOLOv5 注意力机制的裂缝检测

Engineering Reports Pub Date : 2024-04-24 DOI: 10.1002/eng2.12899

Min‐Li Lan, Dan Yang, Shuang‐Xi Zhou, Yang Ding

引用次数: 0

Mapping damages from inspection images to 3D digital twins of large‐scale structures 从检测图像到大型结构三维数字孪生的损坏映射

Engineering Reports Pub Date : 2024-01-01 DOI: 10.1002/eng2.12837

Hans‐Henrik von Benzon, Xiao Chen

{"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}

引用次数: 0

Redesigning elastic full‐waveform inversion on the new Sunway architecture 在新的 Sunway 架构上重新设计弹性全波形反演

Engineering Reports Pub Date : 2023-11-23 DOI: 10.1002/eng2.12819

Mengyuan Hua, Wubing Wan, Zhaoqi Sun, Zekun Yin, Puyu Xiong, Xiaohui Liu, Haodong Tian, Ping Gao, Weiguo Liu, Hua Wang, Wenlai Zhao, Zhenchun Huang

{"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}

引用次数: 0