2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)最新文献

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Enhanced Optimization of Computer Network Connection Based on Neural Network Algorithm 基于神经网络算法的计算机网络连接增强优化

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00019

Dai Liu

{"title":"Enhanced Optimization of Computer Network Connection Based on Neural Network Algorithm","authors":"Dai Liu","doi":"10.1109/FAIML57028.2022.00019","DOIUrl":"https://doi.org/10.1109/FAIML57028.2022.00019","url":null,"abstract":"In today's information age, computer networks play an increasingly important role in people's daily production and life. As an essential part of network management, network fault management is significant in ensuring network security, stability and reliability. From a certain perspective, the main task of fault management is to monitor, analyze and process network events. Therefore, fault management is the core of network management, and fault diagnosis is a critical technology in fault management. The introduction of artificial intelligence technology into the field of fault diagnosis makes it possible to locate faults and diagnose fault causes automatically. The research of this paper configures the appropriate network algorithm and network model to correctly process the network signal, so that the network fault system can be intelligentized. Through the research of this paper, it is concluded that the artificial neural network application system is more advanced. Using network algorithms and network models, it can reasonably process signals, identify an operation mode, and finally form an independent and perfect expert system, or an intelligent system robot.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130528989","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 Deep Learning-Based Soft Sensing Prediction Model for Tubular Furnace 基于深度学习的管式炉软测量预测模型

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00013

Xiaowen Wang, Yongjun Zhang, Qiang Guo, Fei Zhang, T. Yildirim

{"title":"A Deep Learning-Based Soft Sensing Prediction Model for Tubular Furnace","authors":"Xiaowen Wang, Yongjun Zhang, Qiang Guo, Fei Zhang, T. Yildirim","doi":"10.1109/FAIML57028.2022.00013","DOIUrl":"https://doi.org/10.1109/FAIML57028.2022.00013","url":null,"abstract":"Tubular furnaces are necessary in petrochemical industry, whose high-level automation has been hampered by the complicated inner thermal mechanism. To realize the high-accuracy prediction of key parameters of furnace thermal state, including thermal efficiency, which cannot be measured directly by sensors, in this paper, a soft sensing prediction model for tubular furnace is proposed. Based on the traditional CNN-GRU network, which is composed by the convolutional neural network (CNN) and the gated recurrent neural network (GRU), that the two designed feature extraction modules are embed, ultimately compose the proposed Conv-GRU network. Comparative experiments demonstrate that the proposed combinational network with two well-designed modules outperforms general convolution networks and shallow neural networks in terms of prediction accuracy. The results prove that the proposed GRU-Conv can accurately model the tubular furnace inner state with low computational cost, providing improvements room for the performance of combustion optimization control systems for tubular heating furnaces.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115168122","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

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