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.
{"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}
In order to improve the productivity efficiency of the magnetic recoil generator, the coil part of the device is optimized, and the traditional winding coil is replaced by the planar coil. The planar coil adopts the way of laser precision cutting after layer plating metal film to form a split conductor structure, so as to reduce the energy loss caused by skin effect. In this paper, the influence of magnetron sputtering parameters on the tensile strength and deposition rate of metal film on planar coil substrate was studied experimentally. The specific process parameters were determined as follows: target substrate distance was set at 8cm, working vacuum was 1Pa, argon gas flow rate was 100sccm when copper and nichrome films were plated. The target power is set at 188W for copper coating and 156W for nichrome coating. The resistance of the planar coil obtained by metal film plating and laser precision cutting is tested, which meets the product requirements.
{"title":"Design and Manufacturing Technology of Planar Coil for Magnetic Recoil Generator","authors":"Qiao Lu, Liming Li, Rui Zhang","doi":"10.1145/3421766.3421804","DOIUrl":"https://doi.org/10.1145/3421766.3421804","url":null,"abstract":"In order to improve the productivity efficiency of the magnetic recoil generator, the coil part of the device is optimized, and the traditional winding coil is replaced by the planar coil. The planar coil adopts the way of laser precision cutting after layer plating metal film to form a split conductor structure, so as to reduce the energy loss caused by skin effect. In this paper, the influence of magnetron sputtering parameters on the tensile strength and deposition rate of metal film on planar coil substrate was studied experimentally. The specific process parameters were determined as follows: target substrate distance was set at 8cm, working vacuum was 1Pa, argon gas flow rate was 100sccm when copper and nichrome films were plated. The target power is set at 188W for copper coating and 156W for nichrome coating. The resistance of the planar coil obtained by metal film plating and laser precision cutting is tested, which meets the product requirements.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"67 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":"121956340","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}
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.
{"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}
The continuous development of intelligent distribution technology in China has driven the vigorous construction of smart grid. Different data information systems play a vital role in the application of the distribution network, so the data scale of distribution network is expanding, and the complexity of data information is increasing. In order to accurately dig out the hidden laws from the operation and maintenance process of the smart grid, and ensure the stable operation of the smart grid, this study uses data mining related technologies to conduct an in-depth analysis of the visual power distribution technology of the grid. The results show that when the previous item is an uncertain situation such as unplanned power outages or faulty power outages, the data mining results of the latter item include light and no-load distribution transformers, heavy and planned power outages, which are caused by poor maintenance work, so more visual power distribution technology should be used. This study provides a certain reference for the optimization of power grid visualization distribution technology.
{"title":"Analysis of Visual Distribution Optimization of Power Grid Based on Data Mining","authors":"Chengsi Wang","doi":"10.1145/3421766.3421815","DOIUrl":"https://doi.org/10.1145/3421766.3421815","url":null,"abstract":"The continuous development of intelligent distribution technology in China has driven the vigorous construction of smart grid. Different data information systems play a vital role in the application of the distribution network, so the data scale of distribution network is expanding, and the complexity of data information is increasing. In order to accurately dig out the hidden laws from the operation and maintenance process of the smart grid, and ensure the stable operation of the smart grid, this study uses data mining related technologies to conduct an in-depth analysis of the visual power distribution technology of the grid. The results show that when the previous item is an uncertain situation such as unplanned power outages or faulty power outages, the data mining results of the latter item include light and no-load distribution transformers, heavy and planned power outages, which are caused by poor maintenance work, so more visual power distribution technology should be used. This study provides a certain reference for the optimization of power grid visualization distribution technology.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"58 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":"126372926","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}
B. Dai, Yuhu Nie, WenpengCui Cui, Rui Liu, Zhe Zheng
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.
{"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}
Jianping Wu, Xi Yang, Huan Wang, Jianping Chen, Quanzhong Zhao, B. Xiao
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.
{"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}
Random sample consensus (RANSAC) method is often utilized in the RGB-D Simultaneous localization and mapping (SLAM) systems and it is time-consuming because of more repeated fitting the transformation matrix. This paper aims to find a feature point matching method that can reduce computation time in the RGB-D SLAM system. We explore an approach based on four-point order-preserving constraint to determine inliers between two adjacent images. Firstly, the four-point order-preserving constraint between two frames is established to find the good inliers. Then, the 3D points corresponding to the good inliers are obtained to compute the transformation matrix in SLAM system. Finally, the localization and mapping in SLAM system are implemented from transformation matrix and the Global Graph Optimization (g2o) framework. The results indicate that our method is faster and more accurate than the RANSAC algorithm. The less computational time is significant for the real-time SLAM system, and the proposed method is clearly helpful for that.
{"title":"Feature Point Matching Based on Four-point Order Consistency in the RGB-D SLAM System","authors":"Xingwang Liu, Haijiang Zhu, Zhicheng Wang","doi":"10.1145/3421766.3421776","DOIUrl":"https://doi.org/10.1145/3421766.3421776","url":null,"abstract":"Random sample consensus (RANSAC) method is often utilized in the RGB-D Simultaneous localization and mapping (SLAM) systems and it is time-consuming because of more repeated fitting the transformation matrix. This paper aims to find a feature point matching method that can reduce computation time in the RGB-D SLAM system. We explore an approach based on four-point order-preserving constraint to determine inliers between two adjacent images. Firstly, the four-point order-preserving constraint between two frames is established to find the good inliers. Then, the 3D points corresponding to the good inliers are obtained to compute the transformation matrix in SLAM system. Finally, the localization and mapping in SLAM system are implemented from transformation matrix and the Global Graph Optimization (g2o) framework. The results indicate that our method is faster and more accurate than the RANSAC algorithm. The less computational time is significant for the real-time SLAM system, and the proposed method is clearly helpful for that.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"224 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":"114429092","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}
In power system accidents, power insulators account for a large proportion of pollution flashover accidents caused by pollution. Real-time detection of pollution flashovers is an important guarantee for reducing power system accidents. This paper proposes a principle of using partial discharge of polluted insulators to generate electromagnetic waves to achieve pollution flashover detection. According to the characteristics of electromagnetic waves, this method selects the corresponding frequency band antenna to receive and amplify electromagnetic wave signals, filter out signal interference, and perform data analysis and processing to detect pollution flashover. Experiments show that this method has high detection accuracy and strong anti-interference ability. It can judge the severity of pollution and the type of pollution in real time. It has strong practicability and is widely used.
{"title":"Research on Pollution Flashover Detection of Power Insulator Based on High Frequency Electromagnetic Wave","authors":"Qiu Xia, Chenyu He, Yifan Liu, Zihao Shi, Yu Chen","doi":"10.1145/3421766.3421833","DOIUrl":"https://doi.org/10.1145/3421766.3421833","url":null,"abstract":"In power system accidents, power insulators account for a large proportion of pollution flashover accidents caused by pollution. Real-time detection of pollution flashovers is an important guarantee for reducing power system accidents. This paper proposes a principle of using partial discharge of polluted insulators to generate electromagnetic waves to achieve pollution flashover detection. According to the characteristics of electromagnetic waves, this method selects the corresponding frequency band antenna to receive and amplify electromagnetic wave signals, filter out signal interference, and perform data analysis and processing to detect pollution flashover. Experiments show that this method has high detection accuracy and strong anti-interference ability. It can judge the severity of pollution and the type of pollution in real time. It has strong practicability and is widely used.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"1 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":"125951761","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}
Single tooth segmentation is an important technique for computer-assisted orthodontic restoration. Aiming at the problem of interdental region fusion in digital 3D dental model and the limitations of traditional tooth segmentation methods such as complex interactive operations and high manual interference, a tooth point cloud segmentation method based on region growing is proposed. First, the curvature information is used to identify and extract the Gum-tooth boundary feature area, and the points of the gum area and the tooth area are segmented by the region growing method. Then, the local distribution density is used to extract the interdental fusion region, and any point of each tooth is selected as the seed point for region growing. Finally, the point cloud segmentation result of each tooth is obtained. The experimental results show that the proposed algorithm can effectively reduce manual intervention and realize the point cloud segmentation of each tooth of the dental model more accurately.
{"title":"Tooth Point Cloud Segmentation of Dental Model Based on Region Growing","authors":"Jiawen He, Shigang Wang, Jian Li","doi":"10.1145/3421766.3421802","DOIUrl":"https://doi.org/10.1145/3421766.3421802","url":null,"abstract":"Single tooth segmentation is an important technique for computer-assisted orthodontic restoration. Aiming at the problem of interdental region fusion in digital 3D dental model and the limitations of traditional tooth segmentation methods such as complex interactive operations and high manual interference, a tooth point cloud segmentation method based on region growing is proposed. First, the curvature information is used to identify and extract the Gum-tooth boundary feature area, and the points of the gum area and the tooth area are segmented by the region growing method. Then, the local distribution density is used to extract the interdental fusion region, and any point of each tooth is selected as the seed point for region growing. Finally, the point cloud segmentation result of each tooth is obtained. The experimental results show that the proposed algorithm can effectively reduce manual intervention and realize the point cloud segmentation of each tooth of the dental model more accurately.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"6 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":"128521686","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}
Aiming at the technical problems of realizing all-weather, full-automatic, high-precision and high-reliability on-line 3D deformation monitoring and early-warning of bridges, taking Wuhan Baishazhou Yangtze River Bridge as an example, a 3D deformation monitoring and early-warning system for bridges with real-time monitoring, intelligent alarm, advanced prediction and dynamic evaluation based on GNSS / GIS high-precision fusion positioning technology is developed. The experimental results show that the monitoring system can realize the functions of unmanned collection, fast and safe transmission, automatic solution processing and visual early warning of 3D deformation information of Baishazhou bridge, and achieve the deformation positioning accuracy of real-time centimeter level and afterwards millimeter level, which can provide decision support for bridge management.
{"title":"Design of Software and Hardware for Large Bridge Deformation Monitoring System","authors":"Qing An, Yanhua Chen, Shusen Wu","doi":"10.1145/3421766.3421831","DOIUrl":"https://doi.org/10.1145/3421766.3421831","url":null,"abstract":"Aiming at the technical problems of realizing all-weather, full-automatic, high-precision and high-reliability on-line 3D deformation monitoring and early-warning of bridges, taking Wuhan Baishazhou Yangtze River Bridge as an example, a 3D deformation monitoring and early-warning system for bridges with real-time monitoring, intelligent alarm, advanced prediction and dynamic evaluation based on GNSS / GIS high-precision fusion positioning technology is developed. The experimental results show that the monitoring system can realize the functions of unmanned collection, fast and safe transmission, automatic solution processing and visual early warning of 3D deformation information of Baishazhou bridge, and achieve the deformation positioning accuracy of real-time centimeter level and afterwards millimeter level, which can provide decision support for bridge management.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"1 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":"129954322","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}
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