Conference on Computer Graphics, Artificial Intelligence, and Data Processing最新文献_第6页

Effect of different armature sizes on the performance of electromagnetic rail guns 不同电枢尺寸对电磁轨道炮性能的影响

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674539

Jingpu Bian, T. Shu, T. Qin, Shuai Zhang, Ruicong Zhu, Meng Qian

引用次数: 0

Structural design and finite element analysis of a milling machine beam 铣床横梁结构设计及有限元分析

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674538

Weiwei Zhang, Xiaowei An

{"title":"Structural design and finite element analysis of a milling machine beam","authors":"Weiwei Zhang, Xiaowei An","doi":"10.1117/12.2674538","DOIUrl":"https://doi.org/10.1117/12.2674538","url":null,"abstract":"With the rapid development of the modern manufacturing industry, the requirements for milling machine products are getting higher and higher, and then the working accuracy of milling machines has higher requirements. The cross beam of the milling machine is a key component of the milling machine. The cross beam is connected to the top of the machine tool bed by bolts, which mainly play the role of connecting and supporting the saddle and the spindle sleeve. The beam of the milling machine bears most of the working load of the milling machine, so the rigidity and strength of the beam of the milling machine directly affect the working accuracy and service life of the milling machine. In this paper, three dimensional software is used to establish the geometric model of the milling machine beam and establish a finite element model. The stiffness and strength of the saddle are analyzed by the finite element method according to the load condition of the saddle in the middle of the beam. Through analysis, it can be seen that the sliding saddle of the milling machine is in the worst working condition in the middle of the beam. According to the strength theory, it can be checked that its strength meets the requirements. This static analysis can provide reliable data for the design optimization of the milling machine beam.","PeriodicalId":286364,"journal":{"name":"Conference on Computer Graphics, Artificial Intelligence, and Data Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115291932","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

Intelligent prediction and key factor analysis to lost circulation from drilling data based on machine learning 基于机器学习的钻井数据漏失智能预测和关键因素分析

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674534

Guangyao Wen, Huailong Chen, T. Zhou, Cheng Gao, B. Baletabieke, Haiqiu Zhou, Shan-shan Wang

{"title":"Intelligent prediction and key factor analysis to lost circulation from drilling data based on machine learning","authors":"Guangyao Wen, Huailong Chen, T. Zhou, Cheng Gao, B. Baletabieke, Haiqiu Zhou, Shan-shan Wang","doi":"10.1117/12.2674534","DOIUrl":"https://doi.org/10.1117/12.2674534","url":null,"abstract":"Lost circulation during drilling wells is very detrimental since it greatly increases the non-productive time and operational cost, also seriously lead to wellbore instability, pipe sticking, blow out, etc.. However, in the process of drilling wells, geological characteristics and operational drilling parameters all may have impacts to the lost circulation. This makes the establishment of the relations between the lost circulation and drilling factors very challenging. In this paper, we tested five different kernel function (linear, quadratic, cubic, medium Gaussian and fine Gaussian) derived support vector regression (SVR) models and four-layer artificial neural network (ANN). By combining their accuracy and time efficiency, the ANN is regarded as the optimal predictor of lost circulation. By training ANN using different combination of drilling features, we concluded that depth, torque, hanging weight, displacement, entrance density and export density are the key factors to accurate predict the lost circulation. The corresponding trained ANN network can achieve 99.2% accuracy and evaluate whether a drilling feature vector corresponds to lost circulation or not in milliseconds.","PeriodicalId":286364,"journal":{"name":"Conference on Computer Graphics, Artificial Intelligence, and Data Processing","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116301785","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

Study on load calculation of net clothes and applicability of hydrodynamic coefficient under screen model 筛网模型下网衣负荷计算及水动力系数适用性研究

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674661

Zhaolei Li, Fei Wang, Peng Li, Qiang Fu, Chunxiao Wang

引用次数: 0

Lightweight refueling behavior recognition algorithm based on sequence diagrams 基于序列图的轻型加油行为识别算法

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674654

Dasheng Guan, Lei Wang, Zhijun Zhang, Cong Liu

引用次数: 0

Operation and maintenance interactive system based on artificial intelligence and big data information system 基于人工智能和大数据信息系统的运维交互系统

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674679

S. Wang, Tong Liang

{"title":"Operation and maintenance interactive system based on artificial intelligence and big data information system","authors":"S. Wang, Tong Liang","doi":"10.1117/12.2674679","DOIUrl":"https://doi.org/10.1117/12.2674679","url":null,"abstract":"In order to better meet the development needs of interconnection and further improve the core operation capability of hospitals, the research of operation and maintenance interactive system based on artificial intelligence and big data information system is proposed. In the process of building the hospital information operation and maintenance platform, there are still some problems such as insufficient support in technology and operation and maintenance resources. It is necessary to effectively optimize the operation and maintenance system with limited human resources to ensure the stable operation and development of the hospital information system. Under this background, this paper analyzes the problems existing in hospital information management and the current situation of operation and maintenance of information technology, and discusses the mode construction of operation and maintenance platform based on big data artificial intelligence architecture. In the construction of hospital information operation and maintenance platform, we should focus on improving the service level and meeting the needs of patients, so as to continuously enhance the security, integration and interactivity of the system platform, thus promoting the sustainable development of China's medical reform.","PeriodicalId":286364,"journal":{"name":"Conference on Computer Graphics, Artificial Intelligence, and Data Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121964250","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

A method for extracting correlative features of power grid big data based on improved deep learning 基于改进深度学习的电网大数据相关特征提取方法

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674980

Xinyan Wang, Ying Zhu, Yongjie Ning, Jiacheng Du, Jingli Jia

{"title":"A method for extracting correlative features of power grid big data based on improved deep learning","authors":"Xinyan Wang, Ying Zhu, Yongjie Ning, Jiacheng Du, Jingli Jia","doi":"10.1117/12.2674980","DOIUrl":"https://doi.org/10.1117/12.2674980","url":null,"abstract":"With the continuous maturity of big data, artificial intelligence, Internet of Things and other technologies, the rapid development of smart grid has been helped, but at the same time, the increasing line loss power has also attracted widespread attention. In the process of building a smart grid, each link of the grid operation generates a large number of multi-source heterogeneous data, including line loss data and line loss cause related data, which constitutes the big line loss data. First of all, considering the mining efficiency in big data, FP growth algorithm in association rule learning is selected to search the frequent item set of line loss features. Support, confidence and lift are used as evaluation indicators to analyze the association relationship between the causes of line loss; Secondly, a line loss prediction model based on deep learning is established. By eliminating the influence of line loss characteristics in turn, the correlation contribution of line loss causes to line loss is calculated to quantify the line loss caused by line loss causes. After verification, the depth confidence network and BP depth neural network as the prediction model of the depth learning method are superior to the shallow artificial neural network model in the prediction effect, and the prediction accuracy means the reliability of the contribution calculation. Finally, combined with the above two aspects of analysis, the causes of line loss in the substation area are comprehensively evaluated, and guidance suggestions are given to assist power enterprises in decision-making.","PeriodicalId":286364,"journal":{"name":"Conference on Computer Graphics, Artificial Intelligence, and Data Processing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123467123","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

Visual processing is specific to animals 视觉处理是动物特有的

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674776

Keling Huang, Weiqiang Peng, Dongjie Lan, Ran Huo

{"title":"Visual processing is specific to animals","authors":"Keling Huang, Weiqiang Peng, Dongjie Lan, Ran Huo","doi":"10.1117/12.2674776","DOIUrl":"https://doi.org/10.1117/12.2674776","url":null,"abstract":"It is well known that animals have a special meaning for humans, and in biology humans have coexisted with animals for a long time. From the ancestors of human beings there has been an inseparable relationship with animals. And this relationship also makes the human visual system seem to have a more special visual processing mechanism for animals than other targets. To find out whether this mechanism exists only in animals, we performed an experiment. The experiment was a two forced choice (2-AFC) task. Since the scene has an effect on object recognition, we will use animal stimuli without background for comparison experiments with non-animal stimuli. We mixed animal stimulus images and non-animal stimulus images in a disordered manner to form stimulus image sets (50 images each), all of which were without background. Our results showed that subjects had faster reaction times for the animal stimulus pictures than for the non-animal stimulus images, with 524 ms for the animal stimulus pictures and 547 ms for the nonanimal stimulus pictures. The subjects' correct judgment rate for animal stimulus images was higher than that for nonanimal stimulus images, with 96.1% for animal stimulus images and 91.6% for non-animal stimulus images.","PeriodicalId":286364,"journal":{"name":"Conference on Computer Graphics, Artificial Intelligence, and Data Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125544364","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

Analysis and prediction of factors influencing private car ownership in China based on XGBoost-neural network 基于xgboost -神经网络的中国私家车保有量影响因素分析与预测

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674568

Zhenyi Xu, Ping Lu

引用次数: 0

Improved adaptive template updating strategy based on correlation filter in tracking 跟踪中基于相关滤波的改进自适应模板更新策略

Conference on Computer Graphics, Artificial Intelligence, and Data Processing Pub Date : 2023-05-23 DOI: 10.1117/12.2674790

Jiuhong Jiang, An Zhe, Xiaodong Wang, Zhiqiang Zhou, Lingjuan Miao

引用次数: 0