Pub Date : 2023-01-01DOI: 10.32604/cmes.2023.024189
Mi Zhou, Rui Liu, Pengfei Yi, Dongsheng Zhou
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.
{"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}
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.
{"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}
Pub Date : 2023-01-01DOI: 10.32604/cmes.2023.044709
Wangchen Yan, Jinbao Yang, Xin Luo
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.
{"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}
Pub Date : 2022-01-01DOI: 10.32604/cmes.2022.018134
K. Liu, Yanqiao Wang
{"title":"Influence of Soil Heterogeneity on the Behavior of Frozen Soil Slope under Freeze-Thaw Cycles","authors":"K. Liu, Yanqiao Wang","doi":"10.32604/cmes.2022.018134","DOIUrl":"https://doi.org/10.32604/cmes.2022.018134","url":null,"abstract":"","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72525296","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}
Pub Date : 2022-01-01DOI: 10.32604/cmes.2022.018201
Jong-In Choi, Sookyun Kim, Shinjin Kang
{"title":"Image Translation Method for Game Character Sprite Drawing","authors":"Jong-In Choi, Sookyun Kim, Shinjin Kang","doi":"10.32604/cmes.2022.018201","DOIUrl":"https://doi.org/10.32604/cmes.2022.018201","url":null,"abstract":"","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80293199","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}
Pub Date : 2022-01-01DOI: 10.32604/cmes.2022.019188
Kailei Wang, Mingjing Li, P. Yan, Leiting Dong
{"title":"An Experimental and Numerical Study on the Ballistic Performance of Multi-Layered Moderately-Thick Metallic Targets against 12.7-mm Projectiles","authors":"Kailei Wang, Mingjing Li, P. Yan, Leiting Dong","doi":"10.32604/cmes.2022.019188","DOIUrl":"https://doi.org/10.32604/cmes.2022.019188","url":null,"abstract":"","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84506062","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}
Pub Date : 2022-01-01DOI: 10.32604/cmes.2022.018518
Arif Mehmood, M. Aslam, Muhammad Imran Khan, Humera Qureshi, Choonkill Park, Jung Rye Lee
{"title":"A New Attempt to Neutrosophic Soft Bi-Topological Spaces","authors":"Arif Mehmood, M. Aslam, Muhammad Imran Khan, Humera Qureshi, Choonkill Park, Jung Rye Lee","doi":"10.32604/cmes.2022.018518","DOIUrl":"https://doi.org/10.32604/cmes.2022.018518","url":null,"abstract":"","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84972669","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}
Pub Date : 2022-01-01DOI: 10.32604/cmes.2022.018113
Yuhang Jin, E. Guo, Houli Wu, P. Yan
{"title":"Dynamic-Response Analysis of the Branch System of a Utility Tunnel Subjected to Near-Fault and Far-Field Ground Motions in Different Input Mechanisms","authors":"Yuhang Jin, E. Guo, Houli Wu, P. Yan","doi":"10.32604/cmes.2022.018113","DOIUrl":"https://doi.org/10.32604/cmes.2022.018113","url":null,"abstract":"","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81979084","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}
Pub Date : 2022-01-01DOI: 10.32604/cmes.2022.017313
Waris Ali, Asif Ali Shaikh, F. Shah, S. Hussain
{"title":"Melting Characteristics of a Phase Change Material Mixed with Nano Particles of Cobalt Oxide Bounded in a Trapezoidal Structure","authors":"Waris Ali, Asif Ali Shaikh, F. Shah, S. Hussain","doi":"10.32604/cmes.2022.017313","DOIUrl":"https://doi.org/10.32604/cmes.2022.017313","url":null,"abstract":"","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83003608","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}
Pub Date : 2022-01-01DOI: 10.32604/cmes.2022.018123
Debiao Meng, Hongtao Wang, S. Yang, Zhiyuan Lv, Z. Hu, Zihao Wang
{"title":"Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction","authors":"Debiao Meng, Hongtao Wang, S. Yang, Zhiyuan Lv, Z. Hu, Zihao Wang","doi":"10.32604/cmes.2022.018123","DOIUrl":"https://doi.org/10.32604/cmes.2022.018123","url":null,"abstract":"","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72803478","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}
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