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

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":"19 1","pages":"0"},"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

An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization 基于镜头对立学习和自适应β爬坡全局优化的改进大猩猩部队优化器

IF 2.4 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.019198

Yaning Xiao, Xue Sun, Yanling Guo, Sanping Li, Yapeng Zhang, Yangwei Wang

引用次数: 20

Identification of Denatured Biological Tissues Based on Improved Variational Mode Decomposition and Autoregressive Model during HIFU Treatment 基于改进变分模式分解和自回归模型的HIFU治疗过程中变性生物组织识别

IF 2.4 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.018130

Bei Liu, Xian Zhang

{"title":"Identification of Denatured Biological Tissues Based on Improved Variational Mode Decomposition and Autoregressive Model during HIFU Treatment","authors":"Bei Liu, Xian Zhang","doi":"10.32604/cmes.2022.018130","DOIUrl":"https://doi.org/10.32604/cmes.2022.018130","url":null,"abstract":"During high-intensity focused ultrasound (HIFU) treatment, the accurate identification of denatured biological tissue is an important practical problem. In this paper, a novel method based on the improved variational mode decomposition (IVMD) and autoregressive (AR) model was proposed, which identified denatured biological tissue according to the characteristics of ultrasonic scattered echo signals during HIFU treatment. Firstly, the IVMD method was proposed to solve the problem that the VMD reconstruction signal still has noise due to the limited number of intrinsic mode functions (IMF). The ultrasonic scattered echo signals were reconstructed by the IVMD to achieve denoising. Then, the AR model was introduced to improve the recognition rate of denatured biological tissues. The AR model order parameter was determined by the Akaike information criterion (AIC) and the characteristics of the AR coefficients were extracted. Finally, the optimal characteristics of the AR coefficients were selected according to the results of receiver operating characteristic (ROC). The experiments showed that the signal-to-noise ratio (SNR) and root mean square error (RMSE) of the reconstructed signal obtained by IVMD was better than those obtained by variational mode decomposition (VMD). The IVMD-AR method was applied to the actual ultrasonic scattered echo signals during HIFU treatment, and the support vector machine (SVM) was used to identify the denatured biological tissue. The results show that compared with sample entropy, information entropy, and energy methods, the proposed IVMD-AR method can more effectively identify denatured biological tissue. The recognition rate of denatured biological tissue was higher, up to 93.0%.","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":"17 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81381301","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

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

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

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

Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images 医学图像安全传输的低失真可逆水印方法

IF 2.4 4区工程技术

Cmes-computer Modeling in Engineering & Sciences Pub Date : 2022-01-01 DOI: 10.32604/cmes.2022.017650

Rizwan Taj, Feng Tao, S. Khurram, Ateeq ur Rehman, Syed Kamran Haider, Akber Abid Gardezi, S. Kanwal

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

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

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