Applied and Computational Engineering最新文献_第2页

Precise positioning and prediction system for autonomous driving based on generative artificial intelligence 基于生成式人工智能的自动驾驶精确定位和预测系统

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241349

Beichang Liu, Guoqing Cai, Zhipeng Ling, Jili Qian, Quan Zhang

引用次数: 0

Deep learning DGA malicious domain name detection based on multi-stage feature fusion 基于多级特征融合的深度学习 DGA 恶意域名检测

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241334

Mingtian Xie, Ruifeng He, Aixing He

引用次数: 0

Multi-dimensional analysis of the impact of new energy vehicles on the urban ecological environment and prediction of future trends 新能源汽车对城市生态环境影响的多维分析及未来趋势预测

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241363

Xuanran Tang, Tianbing Yang, Chen Zhang, Zhenglin Xiong, Ruiqi Zhu

{"title":"Multi-dimensional analysis of the impact of new energy vehicles on the urban ecological environment and prediction of future trends","authors":"Xuanran Tang, Tianbing Yang, Chen Zhang, Zhenglin Xiong, Ruiqi Zhu","doi":"10.54254/2755-2721/64/20241363","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241363","url":null,"abstract":"This study examines the development indicators of China's new energy vehicle industry using clustering and multiple regression methods. The indicators are divided into internal and external aspects: external factors, such as the degree of completeness of charging facilities, market demand, policies and regulations, and internal factors, mainly brand types and power costs. By comparing the forecasting models of its industry data, including the exponential smoothing model, grey forecasting model and Brownian forecasting model. The forecast results show that this industry in China maintains a positive development trend in the next ten years. It shows that the development prospect of electric vehicles is very bright.The population competition model is used to model the competitive situation between new energy and traditional energy vehicles, and it is concluded that new energy vehicles are replacing traditional fuel vehicles and promoting the transformation of the automotive industry to be environmentally friendly and efficient.Collect the key measures and points in time that countries have taken to target the development of this industry in China. Analysing the data on the development of the industry before and after these events, it is found that external factors, such as other countries' policies, may inhibit the industry's growth. If other countries take action to thwart this industry in China, it may temporarily break its growth or even lead to a short-term industry recession.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"124 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140977651","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

Application of graph modeling and contrast learning in recommender system 图建模和对比学习在推荐系统中的应用

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241375

Wentao Zhang

{"title":"Application of graph modeling and contrast learning in recommender system","authors":"Wentao Zhang","doi":"10.54254/2755-2721/64/20241375","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241375","url":null,"abstract":"With the wide application of personalized recommender system in various fields, how to improve the accuracy and personalized level of recommender system has become a research hotspot. In this paper, a method of combining graph modeling and contrast learning is proposed to improve the performance of recommendation system by mining complex user project interaction and user preference. We first construct the user-project interaction graph, and extract the features of the graph structure by graph neural network (GNN) . In particular, graph convolution network (GCN) is used to update the node representation, and comparative learning is introduced to optimize the feature representation so as to improve the accuracy and personalization of recommendation. The experimental results show that the proposed method is superior to the traditional method in accuracy, recall and F 1 score. By analyzing the mechanism of combining graph modeling and contrast learning, this paper further expounds the theoretical basis and practical application of improving the performance of recommender system, and points out the limitations of existing methods and the future research direction.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975997","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

Implementation of seamless assistance with Google Assistant leveraging cloud computing 利用云计算与谷歌助手实现无缝协助

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241383

Jiaxin Huang, Yifan Zhang, Jingyu Xu, Binbin Wu, Bo Liu, Yulu Gong

{"title":"Implementation of seamless assistance with Google Assistant leveraging cloud computing","authors":"Jiaxin Huang, Yifan Zhang, Jingyu Xu, Binbin Wu, Bo Liu, Yulu Gong","doi":"10.54254/2755-2721/64/20241383","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241383","url":null,"abstract":"AI and cloud native are mutually reinforcing and inseparable. Due to the huge storage and computing power requirements, most AI applications need cloud support, especially large model applications If cloud native has influenced the software industry to a considerable extent in the past few years, the big model boom means that cloud native has become a standard option for developers.This paper describes the rise of AI model applications and their integration with traditional development workflows, pointing out the challenges that enterprises and developers face when integrating large models. With the rise of cloud-native technologies, the combination of artificial intelligence and cloud computing is becoming increasingly important. Cloud-native technologies provide the infrastructure needed to build and run resilient and scalable applications, while distributed infrastructure supports multi-cloud integration, enabling a unified foundation of \"one cloud, multiple computing.\" As an intelligent voice Assistant, Google Assistant achieves a more intelligent, convenient and efficient user experience through applications in smart home control, enterprise customer service and healthcare. Finally, this paper points out the advantages of combining Google Assistant with cloud computing, providing a more intelligent, convenient, and efficient user experience.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"23 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140972655","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

Application and development direction of deep learning in COVID-19 identification based on Computed Tomography images 基于计算机断层扫描图像的深度学习在 COVID-19 识别中的应用及发展方向

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241367

Haoran Chen

{"title":"Application and development direction of deep learning in COVID-19 identification based on Computed Tomography images","authors":"Haoran Chen","doi":"10.54254/2755-2721/64/20241367","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241367","url":null,"abstract":"Caused by the novel coronavirus SARS-CoV-2, COVID-19 is highly contagious via respiratory droplets from sneezing, coughing, or talking, and it can lead to severe respiratory issues, organ failure, and death. Early detection, treatment, and isolation of those at risk help slow its spread, it has challenged traditional diagnostic methods like RT-PCR due to limitations in sensitivity. CT imaging, aided by deep learning models, offers advantages in the early detection of lung abnormalities. This paper reviews the use of deep learning in analyzing CT images for COVID-19 diagnosis, highlighting advancements like image segmentation with U-Net and FPN, it also tracks the evolution of deep learning models in this domain, starting from initial applications focused on image classification and recognition to later advancements incorporating techniques like U-Net for image segmentation and feature pyramid networks. Novel techniques like multi-task learning and quantitative analysis show promise in improving accuracy. Future research focuses on enhancing training datasets, refining model architectures, and integrating methods to support clinical decision-making for COVID-19 management.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"45 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975740","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

Wind speed prediction 风速预测

Applied and Computational Engineering Pub Date : 2024-05-09 DOI: 10.54254/2755-2721/63/20240988

Alvin Xianghan Li

{"title":"Wind speed prediction","authors":"Alvin Xianghan Li","doi":"10.54254/2755-2721/63/20240988","DOIUrl":"https://doi.org/10.54254/2755-2721/63/20240988","url":null,"abstract":"With the help of wind farms, wind energy is a vital renewable energy source that contributes significantly to the worlds energy balance. The lifespan and maintenance costs of wind turbines will be reduced with an accurate wind speed prediction. On the other hand, wind speed is highly volatile and unpredictable. Thus, it is essential to do research into creating complex models and algorithms for precise wind speed prediction. So far, some of the most promising models include Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Autoregressive Moving Average (ARMA). Python, as an advanced and versatile programming language, is exceptionally suited for scripting the algorithms of these sophisticated models. This paper will use the data from Austin Texas and apply a Support Vector Machine (SVM) for wind speed prediction involves several stages, including data collection, data preprocessing, model selection, model training, parameter optimization, model validation, and prediction. Wind energy resource optimisation, maintenance cost reduction, and total wind farm efficiency can all be significantly improved by incorporating these models into predictive analytics and continuously improving them against changing data.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":" 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996262","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

Plane and vertical design research on end-around taxiways at high plateau airports 高原机场末端环形滑行道的平面和垂直设计研究

Applied and Computational Engineering Pub Date : 2024-05-09 DOI: 10.54254/2755-2721/63/20240993

Shitao Wu

引用次数: 0

Mechanistic study on the role of 3D-Printed biomimetic coral bone scaffolds in bone defect repair 三维打印生物仿生珊瑚骨支架在骨缺损修复中的作用机理研究

Applied and Computational Engineering Pub Date : 2024-05-09 DOI: 10.54254/2755-2721/63/20240995

Zeyu Liu, Wenjie Dong, Zihao Shi, Jie Pei, Tengfei Ma, Kun Fu

引用次数: 0

Numerical study of deposition rates of monodisperse particles in curved pipes with different expansion or shrinkage variables 对具有不同膨胀或收缩变量的弯曲管道中单分散颗粒沉积率的数值研究

Applied and Computational Engineering Pub Date : 2024-05-09 DOI: 10.54254/2755-2721/63/20241010

Yu Wang, Hao Lu

引用次数: 0