Electronic Research Archive最新文献_第4页

Structural optimization study based on crushing of semi-rigid base 基于半刚性基层破碎的结构优化研究

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023091

Liting Yu, Lenan Wang, J. Pei, Rui Li, Jiu-peng Zhang, Shihui Cheng

{"title":"Structural optimization study based on crushing of semi-rigid base","authors":"Liting Yu, Lenan Wang, J. Pei, Rui Li, Jiu-peng Zhang, Shihui Cheng","doi":"10.3934/era.2023091","DOIUrl":"https://doi.org/10.3934/era.2023091","url":null,"abstract":"Semi-rigid base materials are used in all pavement grades and are shown to have excellent stability. Furthermore, semi-rigid bases are known for their stiffness and strong frost resistance. However, these semi-rigid base materials are starting to require significant levels of maintenance and repair due to wear and tear. Maintenance is performed primarily to add pavement and resurfacing material, with the hope that the resurfacing layer can withstand large tensile and shear forces at the cracks of the base, thus increasing the overall durability of the pavement. Furthermore, crushing technology used for cement panel maintenance can be used to eliminate cracks in the overlay layer thus improving overall service life expectancy. The studies below study the application of crushing technology to a semi-rigid base and the subsequent alternation to the structural design of the pavement. Furthermore, the mechanical index sensitivities of pavement structures post-rubblization were analyzed using finite elements. Mechanical indexes with high sensitivity were subsequently adopted as structural damage control indexes. These indexes were used to optimize overlay structure and provide a reference for structural design post-rubblization.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70244242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Side-side-angle triangle congruence axiom and the complete quadrilaterals 边边角三角形同余公理与完全四边形

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023065

P. Csiba, L. Németh

引用次数: 0

Dynamic analysis of reaction-diffusion dual carbon model considering economic development in China 考虑中国经济发展的反应-扩散双碳模型动态分析

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023126

Yanchuang Hou, Chunyue Wei, Yuting Ding

引用次数: 1

Some identities of degenerate higher-order Daehee polynomials based on $ lambda $-umbral calculus 基于$ λ $-本影微积分的退化高阶Daehee多项式的一些恒等式

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023155

Dojin Kim, Sangbeom Park, J. Kwon

引用次数: 0

Tunicate swarm algorithm with deep convolutional neural network-driven colorectal cancer classification from histopathological imaging data 基于深卷积神经网络的结直肠癌组织病理影像分类的被囊虫群算法

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023141

A. A. AL-Ghamdi, Mahmoud Ragab

{"title":"Tunicate swarm algorithm with deep convolutional neural network-driven colorectal cancer classification from histopathological imaging data","authors":"A. A. AL-Ghamdi, Mahmoud Ragab","doi":"10.3934/era.2023141","DOIUrl":"https://doi.org/10.3934/era.2023141","url":null,"abstract":"Colorectal cancer (CRC) is one of the most popular cancers among both men and women, with increasing incidence. The enhanced analytical load data from the pathology laboratory, integrated with described intra- and inter-variabilities through the calculation of biomarkers, has prompted the quest for robust machine-based approaches in combination with routine practice. In histopathology, deep learning (DL) techniques have been applied at large due to their potential for supporting the analysis and forecasting of medically appropriate molecular phenotypes and microsatellite instability. Considering this background, the current research work presents a metaheuristics technique with deep convolutional neural network-based colorectal cancer classification based on histopathological imaging data (MDCNN-C3HI). The presented MDCNN-C3HI technique majorly examines the histopathological images for the classification of colorectal cancer (CRC). At the initial stage, the MDCNN-C3HI technique applies a bilateral filtering approach to get rid of the noise. Then, the proposed MDCNN-C3HI technique uses an enhanced capsule network with the Adam optimizer for the extraction of feature vectors. For CRC classification, the MDCNN-C3HI technique uses a DL modified neural network classifier, whereas the tunicate swarm algorithm is used to fine-tune its hyperparameters. To demonstrate the enhanced performance of the proposed MDCNN-C3HI technique on CRC classification, a wide range of experiments was conducted. The outcomes from the extensive experimentation procedure confirmed the superior performance of the proposed MDCNN-C3HI technique over other existing techniques, achieving a maximum accuracy of 99.45%, a sensitivity of 99.45% and a specificity of 99.45%.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70245236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Topological optimization algorithm for mechanical-electrical coupling of periodic composite materials 周期性复合材料机电耦合的拓扑优化算法

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023136

Ziqiang Wang, Chunyu Cen, Junying Cao

引用次数: 0

Spotted hyena optimizer with deep learning enabled vehicle counting and classification model for intelligent transportation systems 斑点鬣狗优化器与深度学习支持车辆计数和分类模型的智能交通系统

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023188

Manal Abdullah Alohali, M. Maashi, Raji Faqih, Hany Mahgoub, Abdullah Mohamed, Mohammed Assiri, Suhanda Drar

{"title":"Spotted hyena optimizer with deep learning enabled vehicle counting and classification model for intelligent transportation systems","authors":"Manal Abdullah Alohali, M. Maashi, Raji Faqih, Hany Mahgoub, Abdullah Mohamed, Mohammed Assiri, Suhanda Drar","doi":"10.3934/era.2023188","DOIUrl":"https://doi.org/10.3934/era.2023188","url":null,"abstract":"Traffic surveillance systems are utilized to collect and monitor the traffic condition data of the road networks. This data plays a crucial role in a variety of applications of the Intelligent Transportation Systems (ITSs). In traffic surveillance, it is challenging to achieve accurate vehicle detection and count the vehicles from traffic videos. The most notable difficulties include real-time system operations for precise classification, identification of the vehicles' location in traffic flows and functioning around total occlusions that hamper the vehicle tracking process. Conventional video-related vehicle detection techniques such as optical flow, background subtraction and frame difference have certain limitations in terms of efficiency or accuracy. Therefore, the current study proposes to design the spotted hyena optimizer with deep learning-enabled vehicle counting and classification (SHODL-VCC) model for the ITSs. The aim of the proposed SHODL-VCC technique lies in accurate counting and classification of the vehicles in traffic surveillance. To achieve this, the proposed SHODL-VCC technique follows a two-stage process that includes vehicle detection and vehicle classification. Primarily, the presented SHODL-VCC technique employs the RetinaNet object detector to identify the vehicles. Next, the detected vehicles are classified into different class labels using the deep wavelet auto-encoder model. To enhance the vehicle detection performance, the spotted hyena optimizer algorithm is exploited as a hyperparameter optimizer, which considerably enhances the vehicle detection rate. The proposed SHODL-VCC technique was experimentally validated using different databases. The comparative outcomes demonstrate the promising vehicle classification performance of the SHODL-VCC technique in comparison with recent deep learning approaches.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70245770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A computational approach to analyze the Hadamard quasigroup product 分析Hadamard拟群积的一种计算方法

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023164

Raúl M. Falcón, V. Álvarez, J. Armario, M. Frau, F. Gudiel, M. Güemes

引用次数: 0

Human-like car-following modeling based on online driving style recognition 基于在线驾驶风格识别的类人汽车跟随建模

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023165

Lijing Ma, Shiru Qu, Lijun Song, Junxi Zhang, J. Ren

{"title":"Human-like car-following modeling based on online driving style recognition","authors":"Lijing Ma, Shiru Qu, Lijun Song, Junxi Zhang, J. Ren","doi":"10.3934/era.2023165","DOIUrl":"https://doi.org/10.3934/era.2023165","url":null,"abstract":"Incorporating human driving style into car-following modeling is critical for achieving higher levels of driving automation. By capturing the characteristics of human driving, it can lead to a more natural and seamless transition from human-driven to automated driving. A clustering approach is introduced that utilized principal component analysis (PCA) and k-means clustering algorithm to identify driving style types such as aggressive, moderate and conservative at the timestep level. Additionally, an online driving style recognition technique is developed based on the memory effect in driving behavior, allowing for real-time identification of a driver's driving style and enabling adaptive control in automated driving. Finally, the Intelligent Driver Model (IDM) has been improved through the incorporation of an online driving style recognition strategy into car-following modeling, resulting in a human-like IDM that emulates real-world driving behaviors. This enhancement has important implications for the field of automated driving, as it allows for greater accuracy and adaptability in modeling human driving behavior and may ultimately lead to more effective and seamless transitions between human-driven and automated driving modes. The results show that the time-step level driving style recognition method provides a more precise understanding of driving styles that accounts for both inter-driver heterogeneity and intra-driver variation. The proposed human-like IDM performs well in capturing driving style characteristics and reproducing driving behavior. The stability of this improved human-like IDM is also confirmed, indicating its reliability and effectiveness. Overall, the research suggests that the proposed model has promising performance and potential applications in the field of automated driving.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70245920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Stochastic travelling wave solution of the $ N $-species cooperative systems with multiplicative noise 具有乘性噪声的N种合作系统的随机行波解

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023225

Hao Wen, Yantao Luo, Jianhua Huang, Yuhong Li

引用次数: 1