2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture最新文献_第10页

Research on Experimental Optimization of Nanosecond Laser Cleaning Technology for Thin Oxide Layer on Aluminum Alloy Surface 铝合金表面氧化薄层纳秒激光清洗工艺的实验优化研究

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2021-10-23 DOI: 10.1145/3495018.3495073

Haibing Xiao, Encheng Zhang, Songling Zhang, Huarui Jiang, Lijun Wang

{"title":"Research on Experimental Optimization of Nanosecond Laser Cleaning Technology for Thin Oxide Layer on Aluminum Alloy Surface","authors":"Haibing Xiao, Encheng Zhang, Songling Zhang, Huarui Jiang, Lijun Wang","doi":"10.1145/3495018.3495073","DOIUrl":"https://doi.org/10.1145/3495018.3495073","url":null,"abstract":"In view of the traditional aluminum alloy cleaning processing problems such as dust and pollution, the surface of the aluminum alloy material thin oxide layer and micro dust particles were studied by laser cleaning process. The mechanism of laser cleaning aluminium and new cleaning process were proved by experimental optimization of nanosecond laser cleaning technology for thin oxide layer on aluminum alloy surface. The microstructure and roughness of the surface were detected by microscopy system and confocal microscope, and the optimal process parameters of laser cleaning aluminum alloy were determined. Aluminum alloy was taken as the experimental object, through nanosecond laser cleaning experimental, laser power, cleaning scanning speed, filling density and laser frequency are the main parameters that affect laser cleaning aluminum alloy. It can reduce the roughness of the oxidation layer on the surface greatly, which will not damage to the substrate, to achieve efficient laser cleaning, with 532 wavelength green nanosecond laser, 2500mm/s scanning speed and 36W laser power.","PeriodicalId":6873,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86602040","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

Solution of Mobile Base Station Based on Hybrid System of Wind Photovoltaic Energy Storage and Hydrogen Energy Storage 基于风能光伏储能和氢能储能混合系统的移动基站解决方案

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2021-10-23 DOI: 10.1145/3495018.3501150

Chao Gao, Xiuping Yao, Rixin Liu, Hao Sun

引用次数: 0

Research on Nonlinear Correlation Tracking Technology of Financial Data Mining Based on Cloud Computing 基于云计算的金融数据挖掘非线性相关跟踪技术研究

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2021-10-23 DOI: 10.1145/3495018.3501224

Haozhe Jiao, Juntao Lin, Shibo Xu, Mingyang Zhou

引用次数: 0

A New Single-Soldier Three-Phase Voltammeter with Intelligent Data Management 一种具有智能数据管理的新型单兵三相伏安表

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2021-10-23 DOI: 10.1145/3495018.3495420

Haihuan He, Sheng Xu, Rui Lu, Kun Wang

引用次数: 0

Security Issues in Commercial Application of Artificial Intelligence 人工智能商业应用中的安全问题

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2021-10-23 DOI: 10.1145/3495018.3495359

Hongzhen Lin, Zhipan Yu, Shiqi Peng, Bo Bian

引用次数: 0

Automatic Tracking Method of Unmanned Vehicle Trajectory Based on Reinforcement Learning 基于强化学习的无人驾驶车辆轨迹自动跟踪方法

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2021-10-23 DOI: 10.1145/3495018.3495448

Shouqing Lu

{"title":"Automatic Tracking Method of Unmanned Vehicle Trajectory Based on Reinforcement Learning","authors":"Shouqing Lu","doi":"10.1145/3495018.3495448","DOIUrl":"https://doi.org/10.1145/3495018.3495448","url":null,"abstract":"Up to now, unmanned driving is still a challenging research field in academia at home and abroad. The vehicle trajectory tracking technology is a very critical and urgent link, because it provides important information for intelligent traffic monitoring. The reinforcement learning method is an important method for learning in an unknown environment. In the field of artificial intelligence machine learning, reinforcement learning research has made great progress in theory, algorithm and application, and has become a current hotspot in research. Unmanned vehicle trajectory tracking is one of the key technologies in the field of unmanned driving research. It uses built-in sensors to perceive the environment, uses trajectory planning algorithms to generate the required path in real time, and the decision system selects the best path. Finally, the built-in path tracking controller implement it. This article mainly adopts the experimental analysis method to discuss how to break through the problem of automatic trajectory tracking technology in the support of enhanced learning by unmanned vehicles, and compare and analyze the expected yaw rate and actual yaw rate and frequency of the target vehicle. According to the experimental research results, the expected yaw rate and actual yaw rate of the unmanned vehicle trajectory automatic tracking test are relatively close, and the test system in this test has a certain tracking effect.","PeriodicalId":6873,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76042528","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 Quantitative Evaluation Method for the Whole Process Quality of Complex Products 复杂产品全过程质量的定量评价方法

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2021-10-23 DOI: 10.1145/3495018.3501040

Ting Zhang, Sheng Li, Jianchao Xia, Jixin Liu, Chuyao He

引用次数: 0

Temperature and Humidity Accelerated Model and Modeling Technology of Initiating Device 启动装置的温湿度加速模型及建模技术

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2021-10-23 DOI: 10.1145/3495018.3495099

Fang Li, Dongxiao Fu, Qiao Lu

引用次数: 0

Research and Countermeasures on coking and high temperature corrosion of boiler side wall water wall in power plant with opposed burner arrangement 电厂对置燃烧器锅炉侧壁水壁结焦及高温腐蚀的研究与对策

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2021-10-23 DOI: 10.1145/3495018.3501042

Wenjun Liu, Wuzhou Liang

引用次数: 0

Research on Network Security Situation Prediction Based on Improved GAN 基于改进GAN的网络安全态势预测研究

2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2021-10-23 DOI: 10.1145/3495018.3495128

Yue Zhao, G. Gan

{"title":"Research on Network Security Situation Prediction Based on Improved GAN","authors":"Yue Zhao, G. Gan","doi":"10.1145/3495018.3495128","DOIUrl":"https://doi.org/10.1145/3495018.3495128","url":null,"abstract":"This paper presents a model of situation prediction using generative adversarial network to simulate the situation development process. In order to solve the problem of GAN (Generative Adversarial Network) convergence difficulty, this paper takes the convergence distance sum function of Wasserstein distance as the theoretical basis for calculating GAN loss function, and the gradient penalty method is also used. Firstly, the simulation dataset to be used will be pre-processed to reduce the over-fitting of the training model due to the mismatch of feature types and the large difference of feature values. Then, a penalty item will be added every time the parameter is updated, aiming to punish the behaviour greater than 1 when the gradient is updated, which can solve the problem of gradient disappearance to some extent. After the improvement of the traditional GAN algorithm, the prediction accuracy and prediction accuracy of the situation value can be greatly improved, which proves the stability of the improved GAN network. Experimental results show that the proposed GAN model has significant advantages over the unimproved GAN model in terms of convergence and prediction accuracy.","PeriodicalId":6873,"journal":{"name":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74155188","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: 1