Ming Zhang, Xuan Yang, Zimu Wang, L. Mao, Yini Zhao
The capacitive voltage transformer operating condition monitoring method has the problem of excessive error, in order to design a digital twin-based capacitive voltage transformer operating condition monitoring method. The capacitive voltage transformer transmission characteristics are identified, the harmonic measurement signal is obtained by using a series-connected voltage divider, an equivalent circuit model is constructed based on digital twin, the capacitive transformer fault gas data is extracted and uploaded to the digital twin database, and the operating condition monitoring method is designed. The results show that the mean error value of this designed capacitive voltage transformer operating condition monitoring method is 24.334%, indicating that the capacitive voltage transformer operating condition monitoring method in the paper is more effective after combining digital twin technology.
{"title":"Research on digital twin-based capacitive voltage transformer operating condition monitoring method","authors":"Ming Zhang, Xuan Yang, Zimu Wang, L. Mao, Yini Zhao","doi":"10.1117/12.2672771","DOIUrl":"https://doi.org/10.1117/12.2672771","url":null,"abstract":"The capacitive voltage transformer operating condition monitoring method has the problem of excessive error, in order to design a digital twin-based capacitive voltage transformer operating condition monitoring method. The capacitive voltage transformer transmission characteristics are identified, the harmonic measurement signal is obtained by using a series-connected voltage divider, an equivalent circuit model is constructed based on digital twin, the capacitive transformer fault gas data is extracted and uploaded to the digital twin database, and the operating condition monitoring method is designed. The results show that the mean error value of this designed capacitive voltage transformer operating condition monitoring method is 24.334%, indicating that the capacitive voltage transformer operating condition monitoring method in the paper is more effective after combining digital twin technology.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115365075","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}
At present, the parallel computing theory based on spatial big data has problems such as difficult algorithms, difficult operations, and complex formulas, based on this, this paper proposes a p-Dot parallel computing model based on the traditional parallel computing model of BSP (Bulk Synchronous Parallel), and then tests the model effect by setting experiments. The results reveal that: (1) All curves are open up and have a minimum value. (2) The dataset with a capacity of 0.25GB is the benchmark dataset. (3) The expansion rate e(w) of the input data capacity of the model under different test procedures has a linear relationship with the expansion rate e(n* ) of the corresponding optimal number of machines. (4) When 𝑛→∞ in the partition equation p(n), p(n) tends to a certain value.
{"title":"Parallel computing of spatial big data and derivation of asymptotic behavior of statistical partition equation","authors":"Zeyu Long","doi":"10.1117/12.2671640","DOIUrl":"https://doi.org/10.1117/12.2671640","url":null,"abstract":"At present, the parallel computing theory based on spatial big data has problems such as difficult algorithms, difficult operations, and complex formulas, based on this, this paper proposes a p-Dot parallel computing model based on the traditional parallel computing model of BSP (Bulk Synchronous Parallel), and then tests the model effect by setting experiments. The results reveal that: (1) All curves are open up and have a minimum value. (2) The dataset with a capacity of 0.25GB is the benchmark dataset. (3) The expansion rate e(w) of the input data capacity of the model under different test procedures has a linear relationship with the expansion rate e(n* ) of the corresponding optimal number of machines. (4) When 𝑛→∞ in the partition equation p(n), p(n) tends to a certain value.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127101869","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}
This paper focuses on the recognition and classification of driver's dangerous driving actions through Blazepose algorithm and st-gru network to ensure that drivers can drive safely during the driving process and keep drivers safe at all times. blazepose is a lightweight human posture estimation model using blazepsoe method to replace the openpose method in human skeletal keypoints to improve the speed and reduce the model size. The st-gru network is one of the best action recognition models based on human skeletal keypoints, which is better than most of the current action recognition models in terms of model size, accuracy and recall value. Therefore, this project uses the st-gru network to classify the extracted human skeletal keypoint.
{"title":"Hazardous action recognition system based on blazepose and ST-recurrent neural network","authors":"Zhengyi Ma, Hao Zhang, Yingshuo Feng, Chenyang Yang, Jiaying Zhu, Yaming Niu","doi":"10.1117/12.2671503","DOIUrl":"https://doi.org/10.1117/12.2671503","url":null,"abstract":"This paper focuses on the recognition and classification of driver's dangerous driving actions through Blazepose algorithm and st-gru network to ensure that drivers can drive safely during the driving process and keep drivers safe at all times. blazepose is a lightweight human posture estimation model using blazepsoe method to replace the openpose method in human skeletal keypoints to improve the speed and reduce the model size. The st-gru network is one of the best action recognition models based on human skeletal keypoints, which is better than most of the current action recognition models in terms of model size, accuracy and recall value. Therefore, this project uses the st-gru network to classify the extracted human skeletal keypoint.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128004132","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}
Qiping Yuan, Wei Dong, Y. Sun, Yong-Kee Kang, Tianxiang Wang, Ke Huang
With the development of the Internet of Things, the world is entering an era of interconnection. Some typical application scenarios, such as environmental monitoring, energy management, space equipment operation and maintenance, require gateways to integrate multiple heterogeneous networks, such as WiFi, Bluetooth, Zigbee, LoRa and other wireless LAN and wired LAN. However, the interfaces of existing gateways are different and incompatible with each other, which makes difficult to achieve the requirements of heterogeneous interconnection. Therefore, this paper presents a smart integrated access gateway with modular architecture, which consists of a motherboard and multi-type user cards with pluggable functions. Different user cards can provide different communication interfaces and adapt corresponding communication protocols, by which different network customizations can be achieved in combination. Compared with other research work, the gateway is more configurable customizable and flexible.
{"title":"Design and implementation of smart integrated access gateway for Internet of Things","authors":"Qiping Yuan, Wei Dong, Y. Sun, Yong-Kee Kang, Tianxiang Wang, Ke Huang","doi":"10.1117/12.2671353","DOIUrl":"https://doi.org/10.1117/12.2671353","url":null,"abstract":"With the development of the Internet of Things, the world is entering an era of interconnection. Some typical application scenarios, such as environmental monitoring, energy management, space equipment operation and maintenance, require gateways to integrate multiple heterogeneous networks, such as WiFi, Bluetooth, Zigbee, LoRa and other wireless LAN and wired LAN. However, the interfaces of existing gateways are different and incompatible with each other, which makes difficult to achieve the requirements of heterogeneous interconnection. Therefore, this paper presents a smart integrated access gateway with modular architecture, which consists of a motherboard and multi-type user cards with pluggable functions. Different user cards can provide different communication interfaces and adapt corresponding communication protocols, by which different network customizations can be achieved in combination. Compared with other research work, the gateway is more configurable customizable and flexible.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125952312","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}
Credit business income is the main source of income for banks, and effective prevention of credit risk is an important task for banks' operation and management. The application of various intelligent technologies in the financial field can provide strong technical support to the management of credit risk. How to use big data technology and artificial intelligence algorithms to improve risk control is an important research topic for commercial banks. To address the above issues, this paper studies the theories and technology applications related to artificial intelligence, risk management, and neural networks In this paper, by constructing a BP neural network model, determining evaluation indicators, and using model simulation and validation, risk assessment is performed on bank customer credit risk indicator data, and the validation reflects that the model has a better ability and high accuracy for customer risk prediction, which provides a reasonable determination of customer credit indicators and reduces It provides data basis for reasonable determination of customer credit indicators and reduction of bad debt losses of banks.
{"title":"Research on intelligent risk control of banks based on BP neural network","authors":"Zhengyan Wang, Shurui Jin, Wen Li","doi":"10.1117/12.2671494","DOIUrl":"https://doi.org/10.1117/12.2671494","url":null,"abstract":"Credit business income is the main source of income for banks, and effective prevention of credit risk is an important task for banks' operation and management. The application of various intelligent technologies in the financial field can provide strong technical support to the management of credit risk. How to use big data technology and artificial intelligence algorithms to improve risk control is an important research topic for commercial banks. To address the above issues, this paper studies the theories and technology applications related to artificial intelligence, risk management, and neural networks In this paper, by constructing a BP neural network model, determining evaluation indicators, and using model simulation and validation, risk assessment is performed on bank customer credit risk indicator data, and the validation reflects that the model has a better ability and high accuracy for customer risk prediction, which provides a reasonable determination of customer credit indicators and reduces It provides data basis for reasonable determination of customer credit indicators and reduction of bad debt losses of banks.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114237321","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}
Jing Yang, Caizeng Ye, Bei Han, Jilin Qin, Lei Peng
Cross-age image generation technology is to generate cross-age face images on the basis of the original face image. The synthetic face image can show facial details such as skin, wrinkles and hair at a certain age. The technology can be widely used in film and television, animation, public safety and other fields. Cross-age face synthesis techniques can be divided into traditional cross-age face synthesis techniques and cross-age face synthesis techniques based on generative adversarial network models. With the continuous development of GAN, the technologies based on generative adversarial network models have made more progress and advantages in the field of face synthesis. The model in this paper, based on the generation of the adversarial network model, combines the advantages of the conditional autoencoder and the StyleGAN model, and innovates in the use of the feature contrasting device, which can generate HD face images consistent with the change logic across ages, and effectively avoid the emergence of problems such as organ deformation and identity inconsistency.
{"title":"Face aging on SiGan","authors":"Jing Yang, Caizeng Ye, Bei Han, Jilin Qin, Lei Peng","doi":"10.1117/12.2671372","DOIUrl":"https://doi.org/10.1117/12.2671372","url":null,"abstract":"Cross-age image generation technology is to generate cross-age face images on the basis of the original face image. The synthetic face image can show facial details such as skin, wrinkles and hair at a certain age. The technology can be widely used in film and television, animation, public safety and other fields. Cross-age face synthesis techniques can be divided into traditional cross-age face synthesis techniques and cross-age face synthesis techniques based on generative adversarial network models. With the continuous development of GAN, the technologies based on generative adversarial network models have made more progress and advantages in the field of face synthesis. The model in this paper, based on the generation of the adversarial network model, combines the advantages of the conditional autoencoder and the StyleGAN model, and innovates in the use of the feature contrasting device, which can generate HD face images consistent with the change logic across ages, and effectively avoid the emergence of problems such as organ deformation and identity inconsistency.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129978574","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}
Yujing Wang, Ruida Ye, Tian Zhang, Yue Zhao, Shenghua Zhou, Zhitao Wang
In the satellite pose estimation problem, the deep learning method is used to train the network. The satellite pose needs to estimate the rotation (R) and translation (T), which are difficult to be well estimated simultaneously due to the internal coupling interaction. To solve the above problems, a dual-channel satellite pose estimation network based on ResNet50 is proposed to decouple the rotation and translation of satellite, effectively avoid the interaction, and estimate the translation and rotation of satellite respectively through the constructed network, which improves the recognition effect of satellite attitude. Through experimental verification, the network model constructed in this paper has better effect on the estimation of rotation and translation compared with other methods.
{"title":"Satellite pose estimation network based on dual-channel ResNet50","authors":"Yujing Wang, Ruida Ye, Tian Zhang, Yue Zhao, Shenghua Zhou, Zhitao Wang","doi":"10.1117/12.2671305","DOIUrl":"https://doi.org/10.1117/12.2671305","url":null,"abstract":"In the satellite pose estimation problem, the deep learning method is used to train the network. The satellite pose needs to estimate the rotation (R) and translation (T), which are difficult to be well estimated simultaneously due to the internal coupling interaction. To solve the above problems, a dual-channel satellite pose estimation network based on ResNet50 is proposed to decouple the rotation and translation of satellite, effectively avoid the interaction, and estimate the translation and rotation of satellite respectively through the constructed network, which improves the recognition effect of satellite attitude. Through experimental verification, the network model constructed in this paper has better effect on the estimation of rotation and translation compared with other methods.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134213052","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}
Based on the BP neural network in the category of artificial intelligence technology, this paper combined with the conventional expert system diagnosis and discrimination method, and completed the construction of automatic fault diagnosis system for rotating parts of mining machinery in ASP.NET environment. Taking the common rotating machinery in mining machinery and equipment as the research object, aiming at the fault characteristics of mining machinery and the difficulties faced by maintenance, such as high difficulty, high cost and high risk factor, the system provides a new comprehensive application solution for the fault diagnosis of rotating machinery with the help of the application advantages of various information technologies. Through data feature extraction, automatic diagnosis, manual diagnosis, data management and other modules in the system, the whole life cycle management of mining machinery and equipment, early warning and treatment of faults, historical data query and other functions are realized. It not only improves the level of health management of mining machinery and equipment, but also establishes a solid guarantee for the safe and stable production of enterprises, and further makes a positive and beneficial attempt for the construction of smart mines in China.
{"title":"Design and development of automatic fault diagnosis system for rotating parts of mining machinery based on artificial intelligence technology","authors":"Hui Song, Gerile Gerile, Shu Cai","doi":"10.1117/12.2671871","DOIUrl":"https://doi.org/10.1117/12.2671871","url":null,"abstract":"Based on the BP neural network in the category of artificial intelligence technology, this paper combined with the conventional expert system diagnosis and discrimination method, and completed the construction of automatic fault diagnosis system for rotating parts of mining machinery in ASP.NET environment. Taking the common rotating machinery in mining machinery and equipment as the research object, aiming at the fault characteristics of mining machinery and the difficulties faced by maintenance, such as high difficulty, high cost and high risk factor, the system provides a new comprehensive application solution for the fault diagnosis of rotating machinery with the help of the application advantages of various information technologies. Through data feature extraction, automatic diagnosis, manual diagnosis, data management and other modules in the system, the whole life cycle management of mining machinery and equipment, early warning and treatment of faults, historical data query and other functions are realized. It not only improves the level of health management of mining machinery and equipment, but also establishes a solid guarantee for the safe and stable production of enterprises, and further makes a positive and beneficial attempt for the construction of smart mines in China.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130965820","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 complex environment, the performance of traditional face recognition algorithm decreases greatly. In order to further improve the recognition accuracy of current face recognition algorithms, this paper proposes two face recognition algorithms based on improved convolutional neural networks through the analysis of the defects of traditional algorithms. Finally, we will build a new face recognition model to verify the effectiveness of the two new methods. The first method is to extract and classify face features by fusing convolution layer and pooling layer, train neural network by stochastic gradient descent method, recognize face by Softmax classifier, and finally solve the over-fitting problem by "Dropout" method. The second method is to use the network link structure of bisymmetric LetNet and DCT-LBP joint processing method to process the input image. The two algorithms have some similarities, and both can improve the accuracy of face recognition.
{"title":"Face recognition algorithm based on improved neural network","authors":"Chenyu Huang","doi":"10.1117/12.2671658","DOIUrl":"https://doi.org/10.1117/12.2671658","url":null,"abstract":"In complex environment, the performance of traditional face recognition algorithm decreases greatly. In order to further improve the recognition accuracy of current face recognition algorithms, this paper proposes two face recognition algorithms based on improved convolutional neural networks through the analysis of the defects of traditional algorithms. Finally, we will build a new face recognition model to verify the effectiveness of the two new methods. The first method is to extract and classify face features by fusing convolution layer and pooling layer, train neural network by stochastic gradient descent method, recognize face by Softmax classifier, and finally solve the over-fitting problem by \"Dropout\" method. The second method is to use the network link structure of bisymmetric LetNet and DCT-LBP joint processing method to process the input image. The two algorithms have some similarities, and both can improve the accuracy of face recognition.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134185744","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}
Route planning is an essential and important part of unmanned aerial vehicle (UAV) operations at sea. Therefore, this paper designs the trajectory planning for autonomous obstacle avoidance of unmanned ships in complex environments. Adopt the body coordinate system and inertial coordinate system to confirm the coordinates and heading angle of the unmanned ship; improve the inertia weight, determine the space constraints of the track planning, and accurately determine the autonomous obstacle avoidance path of the unmanned ship. Simulation experiments show that the trajectory planning method for autonomous obstacle avoidance of unmanned ships in complex environments designed in this paper reduces the time consumption of navigation, has stronger real-time performance, and can approximately represent the global optimal trajectory.
{"title":"Track planning and design of autonomous obstacle avoidance for unmanned ships in complex environments","authors":"Bei-lei Shi, Xiushan Zhang","doi":"10.1117/12.2671812","DOIUrl":"https://doi.org/10.1117/12.2671812","url":null,"abstract":"Route planning is an essential and important part of unmanned aerial vehicle (UAV) operations at sea. Therefore, this paper designs the trajectory planning for autonomous obstacle avoidance of unmanned ships in complex environments. Adopt the body coordinate system and inertial coordinate system to confirm the coordinates and heading angle of the unmanned ship; improve the inertia weight, determine the space constraints of the track planning, and accurately determine the autonomous obstacle avoidance path of the unmanned ship. Simulation experiments show that the trajectory planning method for autonomous obstacle avoidance of unmanned ships in complex environments designed in this paper reduces the time consumption of navigation, has stronger real-time performance, and can approximately represent the global optimal trajectory.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131425917","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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