Cmes-computer Modeling in Engineering & Sciences最新文献_第2页

ER-Net: Efficient Recalibration Network for Multi-View Multi-Person 3D Pose Estimation ER-Net:多视角多人三维姿态估计的高效再标定网络

4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-01-01 DOI: 10.32604/cmes.2023.024189

Mi Zhou, Rui Liu, Pengfei Yi, Dongsheng Zhou

{"title":"ER-Net: Efficient Recalibration Network for Multi-View Multi-Person 3D Pose Estimation","authors":"Mi Zhou, Rui Liu, Pengfei Yi, Dongsheng Zhou","doi":"10.32604/cmes.2023.024189","DOIUrl":"https://doi.org/10.32604/cmes.2023.024189","url":null,"abstract":"Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios. With the introduction of end-to-end direct regression methods, the field has entered a new stage of development. However, the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal method. In this paper, we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy, which is applied to the multi-view multi-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external factors. Specifically, it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy, which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding joints. We call this method as the Efficient Recalibration Network (ER-Net). Finally, experiments were conducted on two benchmark datasets for this task, Campus and Shelf, in which the PCP reached 97.3% and 98.3%, respectively.","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135470420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine 基于单类三角形全局对准核极值学习机的无人机状态数据异常检测

4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-01-01 DOI: 10.32604/cmes.2023.026732

Feisha Hu, Qi Wang, Haijian Shao, Shang Gao, Hualong Yu

{"title":"Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine","authors":"Feisha Hu, Qi Wang, Haijian Shao, Shang Gao, Hualong Yu","doi":"10.32604/cmes.2023.026732","DOIUrl":"https://doi.org/10.32604/cmes.2023.026732","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global Alignment Kernel (TGAK) instead of an RBF Kernel and introduce the Fast Independent Component Analysis (FastICA) algorithm to reconstruct UAV data. Based on the above improvements, we create a novel anomaly detection strategy FastICA-TGAK-OCELM. The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies (ALFA) dataset. The experimental results show that compared with other methods, the accuracy of this method is improved by more than 30%, and point anomalies are effectively detected.","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Quick Weighing of Passing Vehicles Using the Transfer-Learning-Enhanced Convolutional Neural Network 基于迁移学习增强卷积神经网络的过路车辆快速称重

4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-01-01 DOI: 10.32604/cmes.2023.044709

Wangchen Yan, Jinbao Yang, Xin Luo

{"title":"Quick Weighing of Passing Vehicles Using the Transfer-Learning-Enhanced Convolutional Neural Network","authors":"Wangchen Yan, Jinbao Yang, Xin Luo","doi":"10.32604/cmes.2023.044709","DOIUrl":"https://doi.org/10.32604/cmes.2023.044709","url":null,"abstract":"Transfer learning could reduce the time and resources required for the training of new models and be therefore important in generalized applications of the trained machine learning algorithms. In this study, a transfer learning-enhanced convolutional neural network (CNN) was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge. The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy. First of all, a CNN algorithm for bridge weigh-in-motion (B-WIM) technology was proposed to identify the axle weight and the gross weight of the typical two-axle, three-axle, and five-axle vehicles as they crossed the bridge with different loading routes and speeds. Then, the pre-trained CNN model was transferred by fine-tuning to weigh the moving vehicles on another bridge. Finally, the identification accuracy and the amount of training data required were compared between the two CNN models. Results showed that the pre-trained CNN model using transfer learning for B-WIM technology could be successfully used for the identification of the axle weight and the gross weight for moving vehicles on another bridge while reducing the training data by 63%. Moreover, the recognition accuracy of the pre-trained CNN model using transfer learning was comparable to that of the original model, showing its promising potentials in the actual applications.","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135839612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Influence of Soil Heterogeneity on the Behavior of Frozen Soil Slope under Freeze-Thaw Cycles 冻融循环作用下土壤非均质性对冻土边坡行为的影响

IF 2.4 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.018134

K. Liu, Yanqiao Wang

引用次数: 0

Image Translation Method for Game Character Sprite Drawing 游戏角色精灵绘制的图像翻译方法

IF 2.4 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.018201

Jong-In Choi, Sookyun Kim, Shinjin Kang

引用次数: 0

An Experimental and Numerical Study on the Ballistic Performance of Multi-Layered Moderately-Thick Metallic Targets against 12.7-mm Projectiles 多层中厚金属靶对12.7 mm弹丸弹道性能的实验与数值研究

IF 2.4 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.019188

Kailei Wang, Mingjing Li, P. Yan, Leiting Dong

引用次数: 0

A New Attempt to Neutrosophic Soft Bi-Topological Spaces 中性软双拓扑空间的新尝试

IF 2.4 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.018518

Arif Mehmood, M. Aslam, Muhammad Imran Khan, Humera Qureshi, Choonkill Park, Jung Rye Lee

引用次数: 1

Dynamic-Response Analysis of the Branch System of a Utility Tunnel Subjected to Near-Fault and Far-Field Ground Motions in Different Input Mechanisms 不同输入机制下公用隧道分支系统近断层和远场地震动的动力响应分析

IF 2.4 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.018113

Yuhang Jin, E. Guo, Houli Wu, P. Yan

引用次数: 4

Melting Characteristics of a Phase Change Material Mixed with Nano Particles of Cobalt Oxide Bounded in a Trapezoidal Structure 混合纳米氧化钴的相变材料的熔融特性

IF 2.4 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.017313

Waris Ali, Asif Ali Shaikh, F. Shah, S. Hussain

引用次数: 0

Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction 基于EMD特征提取的风电滚动轴承故障分析

IF 2.4 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.018123

Debiao Meng, Hongtao Wang, S. Yang, Zhiyuan Lv, Z. Hu, Zihao Wang

引用次数: 42