Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture最新文献

Intelligent pneumatic suction device for cabin of domestic vehicle 一种家用汽车驾驶室智能气动吸气装置

Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2020-10-15 DOI: 10.1145/3421766.3421878

Hui Huang, Yifa Sheng

{"title":"Intelligent pneumatic suction device for cabin of domestic vehicle","authors":"Hui Huang, Yifa Sheng","doi":"10.1145/3421766.3421878","DOIUrl":"https://doi.org/10.1145/3421766.3421878","url":null,"abstract":"In this paper, a smart pneumatic suction device for the cabin of a domestic car has been developed to solve the problem of hidden driving safety and vehicle performance degradation caused by changes in the working environment of the cabin caused by foreign objects trapped in the gap of the cabin. The device consists of a removable suction head, a universal hose, and a foreign body collection It consists of a silo and a negative pressure generator. The control system uses the single-chip K66 core board as the central calculation link, and the digital air pressure sensor is used as the feedback signal mechanism to convert the air pressure signal in the suction head into a digital signal and transmit it to the single chip through the IIC of the single chip; the PWM signal is converted and amplified to Drive the current of the RS-540 DC motor to achieve PID closed-loop control to achieve the effect of controlling the air pressure and stabilizing the air pressure; use the WH148 single-connect potentiometer to form a simple voltage divider circuit, and generate different voltage signals by rotating the potentiometer to achieve continuous adjustment Knob. The device has the characteristics of portability, high efficiency, simple operation, large market potential, and broad development and application prospects.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123205549","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

Design and Manufacturing Technology of Planar Coil for Magnetic Recoil Generator 磁后坐力发电机平面线圈的设计与制造技术

Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2020-10-15 DOI: 10.1145/3421766.3421804

Qiao Lu, Liming Li, Rui Zhang

引用次数: 0

Underwater Acoustic Target Classification Based on LOFAR Spectrum and Convolutional Neural Network 基于LOFAR谱和卷积神经网络的水声目标分类

Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2020-10-15 DOI: 10.1145/3421766.3421890

Xiaohan Yin, Xiaodong Sun, Peishun Liu, Liang Wang, Ruichun Tang

{"title":"Underwater Acoustic Target Classification Based on LOFAR Spectrum and Convolutional Neural Network","authors":"Xiaohan Yin, Xiaodong Sun, Peishun Liu, Liang Wang, Ruichun Tang","doi":"10.1145/3421766.3421890","DOIUrl":"https://doi.org/10.1145/3421766.3421890","url":null,"abstract":"The underwater acoustic target classification task has always been an important research direction of acoustic recognition and classification. The acoustic classification models include traditional models such as Gaussian Mixture Model (GMM), and deep learning models such as Convolutional Neural Network (CNN) and Long and Short Time Memory Network (LSTM). This paper proposes a deep sound feature extraction network based on VGGNet. An underwater acoustic target classification framework based on LOFAR spectrum and CNN is proposed. Although ordinary CNN can also extract underwater acoustic features, too few or too many network layers will cause problems such as insufficient features or increased calculations. Therefore, we draw on the excellent structure of VGGNet in feature extraction and delete several layers for feature extraction and classification of underwater acoustic targets. The accuracy are 94%, 98% and 96% respectively in three real data sets of civil ships, and the accuracy were improved com-pared with the traditional methods.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129515042","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: 7

Analysis of Visual Distribution Optimization of Power Grid Based on Data Mining 基于数据挖掘的电网分布可视化优化分析

Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2020-10-15 DOI: 10.1145/3421766.3421815

Chengsi Wang

引用次数: 0

Real-time Safety Helmet Detection System based on Improved SSD 基于改进SSD的安全帽实时检测系统

Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2020-10-15 DOI: 10.1145/3421766.3421774

B. Dai, Yuhu Nie, WenpengCui Cui, Rui Liu, Zhe Zheng

{"title":"Real-time Safety Helmet Detection System based on Improved SSD","authors":"B. Dai, Yuhu Nie, WenpengCui Cui, Rui Liu, Zhe Zheng","doi":"10.1145/3421766.3421774","DOIUrl":"https://doi.org/10.1145/3421766.3421774","url":null,"abstract":"The detection of the safety helmet is difficulties due to the ariable ighting, weather changes and complex background. We proposed a deep learning detection method to detect safety helmet to solve the problems of low accuracy and poor robustness of traditional detection methods. This method is based on the SSD (Single Shot MultiBox Detector) object detection and improved the network. First, we used the fusion of multi-layer to consideration of shallow low sematic information and deep semantic information, which improves the sensitivity of the network to small target detection. Second, we proposed lightweight network structure of compresses the network, reducing the amount of parameters and calculations of the model. Third, we made safety helmet datasets to train and test the improved network model, and the model is compared with the original SSD. The results show that the detection accuracy of the model is 86.75%, which is similar to SSD, but the detection speed has been improved significantly, which is 295% higher than SSD, up to 83 frame/s. Experiments show that the improved network model can significantly improve the detection speed while ensuring the detection accuracy and meet the real-time detection requirements.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128845419","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: 9

Study on Prediction Model of Magnetic Field Intensity of Submarine Power Cable Based on LSTM 基于LSTM的海底电力电缆磁场强度预测模型研究

Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2020-10-15 DOI: 10.1145/3421766.3421783

Jianping Wu, Xi Yang, Huan Wang, Jianping Chen, Quanzhong Zhao, B. Xiao

{"title":"Study on Prediction Model of Magnetic Field Intensity of Submarine Power Cable Based on LSTM","authors":"Jianping Wu, Xi Yang, Huan Wang, Jianping Chen, Quanzhong Zhao, B. Xiao","doi":"10.1145/3421766.3421783","DOIUrl":"https://doi.org/10.1145/3421766.3421783","url":null,"abstract":"After the submarine power cables are laid, the parameters such as distance between cables and surface of the sea (H), distance between two submarine power cables placed in parallel (d) are constant. It is difficult to study the magnetic field intensity distribution of submarine power cable under different H and d, because the submarine cable can't be placed randomly. In order to solve this problem, firstly the influence of different groups of H and d on the distribution of submarine cable magnetic field intensity is studied, and the magnetic field intensity of submarine cable generated by different groups of H and d are used as training samples and testing samples. Then the training samples are used to construct the submarine power cable magnetic field prediction model based on Long Short Term Memory (LSTM) neural network, and the submarine power cable magnetic field is predicted with the testing samples as the input. Finally, the simulation results show that there is good prediction ability of LSTM prediction model for submarine cable magnetic field data.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"4 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124301680","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

Feature Point Matching Based on Four-point Order Consistency in the RGB-D SLAM System 基于四点顺序一致性的RGB-D SLAM系统特征点匹配

Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2020-10-15 DOI: 10.1145/3421766.3421776

Xingwang Liu, Haijiang Zhu, Zhicheng Wang

引用次数: 0

Research on Pollution Flashover Detection of Power Insulator Based on High Frequency Electromagnetic Wave 基于高频电磁波的电力绝缘子污闪检测研究

Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2020-10-15 DOI: 10.1145/3421766.3421833

Qiu Xia, Chenyu He, Yifan Liu, Zihao Shi, Yu Chen

引用次数: 1

Tooth Point Cloud Segmentation of Dental Model Based on Region Growing 基于区域生长的牙齿模型牙点云分割

Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2020-10-15 DOI: 10.1145/3421766.3421802

Jiawen He, Shigang Wang, Jian Li

引用次数: 2

Design of Software and Hardware for Large Bridge Deformation Monitoring System 大型桥梁变形监测系统的软硬件设计

Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2020-10-15 DOI: 10.1145/3421766.3421831

Qing An, Yanhua Chen, Shusen Wu

引用次数: 0