Journal of Physics: Conference Series最新文献_第7页

A Registration Method Based on Planar Features Between BIM Model and Point Cloud 基于平面特征的 BIM 模型与点云注册方法

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012016

Qiwen Wu, Xi Zhao

{"title":"A Registration Method Based on Planar Features Between BIM Model and Point Cloud","authors":"Qiwen Wu, Xi Zhao","doi":"10.1088/1742-6596/2833/1/012016","DOIUrl":"https://doi.org/10.1088/1742-6596/2833/1/012016","url":null,"abstract":"In the digitization process of the construction industry, it is frequently necessary to use BIM as a digital model carrier, and the registration between BIM and point cloud is a crucial step in BIM applications. Most existing 3D registration methods, such as the ICP algorithm, are capable of aligning point clouds. However, these classical methods are subject to the influence of numerous points contained within the point cloud, relying heavily on point-to-point correlations. Consequently, it is challenging to extend these algorithms to register with other forms of spatial information, such as BIM models, beyond the point cloud. Considering that planes are basic geometric elements in building BIM models, this paper proposes a novel method for aligning BIM models with point clouds by matching planar features. The method extracts planes from point clouds using an enhanced region growing algorithm, directly parsing the planar geometric information from BIM in IFC format. After completing the matching of plane groups, the optimal solution is calculated using a weighted least squares method. The experimental results indicate that the proposed method can achieve successful registration between the BIM model and point cloud, with a lower RMSE of 5.73mm compared to the registration method using RANSAC+ICP on the same dataset.","PeriodicalId":16821,"journal":{"name":"Journal of Physics: Conference Series","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199372","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

A Geometric Approach to Textual Augmented Data Filtering 文本增强数据过滤的几何方法

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012007

Sherry J.H Feng, Edmund M-K Lai, Weihua Li

{"title":"A Geometric Approach to Textual Augmented Data Filtering","authors":"Sherry J.H Feng, Edmund M-K Lai, Weihua Li","doi":"10.1088/1742-6596/2833/1/012007","DOIUrl":"https://doi.org/10.1088/1742-6596/2833/1/012007","url":null,"abstract":"Data augmentation is necessary if the amount of training data is insufficient for supervised learning. For natural language processing tasks, obtaining good quality augmented data is not easy. This paper introduces GATFilter, a novel method for filtering out inappropriate augmented textual data for text classification (TC). Utilizing geometric concepts, more specifically the principle component and convex hull analyses, this method adeptly preserves the semantic integrity of words within augmented texts. GATFilter is versatile and applicable across various types of textual augmentation methods. Experiments using several datasets and augmentation strategies showed that classifiers trained with GATFilter-filtered augmented data sets showed improvements in key performance metrics, including accuracy, precision, recall, and F1 score. The method’s efficacy is notably influenced by the quality of the underlying augmentation techniques, indicating its potential to complement and refine various text augmentation strategies. Furthermore, our analysis showed that GATFilter is particularly able to amplify the effectiveness of methods that generate good quality augmented data. GATFilter is openly available online on Github<sup>1</sup>, and as a Python package<sup>2</sup>.","PeriodicalId":16821,"journal":{"name":"Journal of Physics: Conference Series","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199380","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

Research on failure simulation of the feeding system considering transmission gear wear 考虑传动齿轮磨损的喂料系统故障模拟研究

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012019

Deng Huiyong, Sun Haitao, Cui Kaibo, Zhao Yulong, Yin Fei

引用次数: 0

Research on the Application of Deep Learning in Human Spinal Image Segmentation 深度学习在人体脊柱图像分割中的应用研究

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012011

Zhao Feng, Qi Min, Xu Hua

引用次数: 0

Simulation Study of the Damage Effect of Typical Fragment on UAV Target 典型碎片对无人飞行器目标破坏效应的模拟研究

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012021

Kaibo Cui, Haitao Sun, Huiyong Deng

引用次数: 0

Modeling and Simulation of UAV Task Assignment and Execution in Designated Area Based on Order Strike Theory 基于阶跃打击理论的无人机任务分配与指定区域执行建模与仿真

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012022

Li Mingxuan, Cui Yanru, Zhu Yifan, Wang Weiping, Li Xiaobo, Wang Xinfeng

引用次数: 0

An Intrusion Detection Model Based on a Residual Memory Convolutional Neural Network with Attention Mechanism 基于带有注意机制的残差记忆卷积神经网络的入侵检测模型

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012009

Yuankai Liu, Feng Guo, Qian Zhao, Chuankun Wu

{"title":"An Intrusion Detection Model Based on a Residual Memory Convolutional Neural Network with Attention Mechanism","authors":"Yuankai Liu, Feng Guo, Qian Zhao, Chuankun Wu","doi":"10.1088/1742-6596/2833/1/012009","DOIUrl":"https://doi.org/10.1088/1742-6596/2833/1/012009","url":null,"abstract":"As the utilization of IoT devices becomes more widespread, the variety of attacks targeting these devices is also increasing. Traditional intrusion detection systems in IoT environments often struggle to effectively recognize the diverse types of attacks. Therefore, this study proposes a Residual Memory Convolutional Neural Network (RMCNN) model incorporating an attention mechanism, aimed at improving the accuracy and efficiency of multi-class attack detection in IoT environments. The model begins by extracting spatial features from traffic data through Convolutional Neural Network (CNN) layers, and then captures dynamic changes in time series data using Gated Recurrent Unit (GRU). Subsequently, a multi-head attention mechanism is employed to reinforce focus on critical information. Finally, the outputs from the GRU are combined with those from the multi-head attention mechanism via residual connections, enhancing the model’s learning capabilities and improving the recognition accuracy of various attack types. Verified through experiments on the CICIOT2023 dataset, the model achieved an F1 score of 97.29%, indicating significant improvements in the detection performance of multi-class attacks and confirming its applicability and effectiveness in the field of IoT security.","PeriodicalId":16821,"journal":{"name":"Journal of Physics: Conference Series","volume":"144 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199379","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

Research on non-linear dynamic simulation model of feeding system 饲喂系统非线性动态模拟模型研究

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012020

Sun Haitao, Liang Hui, Deng Huiyong, Cui Kaibo, Jiang Youcai

引用次数: 0

Design and Implementation of TDMA Scheduling Based on BRKGA 基于 BRKGA 的 TDMA 调度设计与实现

Journal of Physics: Conference Series Pub Date : 2024-08-01 DOI: 10.1088/1742-6596/2833/1/012008

Shuai Xiaoying, Yin Yuxia

引用次数: 0

Carbon-Aramid Fiber/Epoxy Hybrid Composite Laminates with the Presence of Defect: An Experimental Study 存在缺陷的碳-芳纶纤维/环氧树脂混合复合材料层压板：实验研究

Journal of Physics: Conference Series Pub Date : 2024-07-31 DOI: 10.1088/1742-6596/2805/1/012008

H Junaedi and T A Sebaey

{"title":"Carbon-Aramid Fiber/Epoxy Hybrid Composite Laminates with the Presence of Defect: An Experimental Study","authors":"H Junaedi and T A Sebaey","doi":"10.1088/1742-6596/2805/1/012008","DOIUrl":"https://doi.org/10.1088/1742-6596/2805/1/012008","url":null,"abstract":"Carbon-Aramid fiber-reinforced epoxy has been used extensively in the aerospace and automobile industries. The combination of high-strength carbon fiber and the high toughness of aramid fiber is believed to be beneficial to the structural behavior of composites. In the current study, Aramid fiber was sandwiched between carbon fiber layers to maintain high strength and toughness simultaneously. The behavior of the laminate with the presence of an open hole and single-edge notch was investigated. For justification, the response of the hybrid laminate was compared with two other laminates, one is made totally from carbon fiber-reinforced epoxy (CFRP) and the other is made from aramid fiber-reinforced epoxy (AFRP). The effect of an open hole was assessed by a tension test, while the single-edge notch effect was evaluated by the flexural test. Tensile and flexural tests were also performed on the regular samples. As per the current results, the notch sensitivity of hybrid laminate was found to be less than that of CFRP laminate. The CFRP laminate failure type was dominated by delamination. AFRP composite laminate failure was dominated by fiber breakage and crack propagation through the matrix. The hybrid composite laminates were dominated by fiber breakage of the AFRP laminates and delamination of CFRP outer layers. The flexural modulus of hybrid laminate resulted in the greatest value, followed by CFRP and AFRP. The hybrid laminate’s fracture toughness is significantly higher than that of CFRP but lower than that of AFRP.","PeriodicalId":16821,"journal":{"name":"Journal of Physics: Conference Series","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934165","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