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}
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}
Aiming at the problems of high modeling complexity and low rendering efficiency of existing visualization methods of real meteorological cloud data, a 3D visualization method of meteorological cloud data based on adaptive far-field grid structure of region of interest is proposed. Methods The region of interest was extracted to generate an adaptive far-field grid structure, which was applied to cloud particle modeling. The fine resolution of the region of interest was kept, and the number of particles in other regions was optimized. Finally, the rendering of 3D cloud images was completed. Simulation results based on WRF model meteorological cloud data show that the above grid structure can speed up rendering and rendering on the basis of ensuring the rendering quality, and can better display the morphology and structural characteristics of real clouds.
{"title":"Meteorological data modeling and 3D visualization based on adaptive grid structure","authors":"Liming Lin, Donghai Huang, Yuda Zhong","doi":"10.1117/12.2671570","DOIUrl":"https://doi.org/10.1117/12.2671570","url":null,"abstract":"Aiming at the problems of high modeling complexity and low rendering efficiency of existing visualization methods of real meteorological cloud data, a 3D visualization method of meteorological cloud data based on adaptive far-field grid structure of region of interest is proposed. Methods The region of interest was extracted to generate an adaptive far-field grid structure, which was applied to cloud particle modeling. The fine resolution of the region of interest was kept, and the number of particles in other regions was optimized. Finally, the rendering of 3D cloud images was completed. Simulation results based on WRF model meteorological cloud data show that the above grid structure can speed up rendering and rendering on the basis of ensuring the rendering quality, and can better display the morphology and structural characteristics of real clouds.","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":"123966752","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}
Pedestrian detection technology has high application value in various fields, and deep learning has become a key development direction in computer vision. Human object detection has also shifted from traditional detection algorithms to deep learning. Due to the influence of complex light and obstacles in the station, as well as the occlusions and size changes of passengers, the algorithm must be optimized for these complex scenes. This paper takes pedestrian detection technology as the goal, compares the methods based on human body parts recognition from the concepts and classification of artificial neural networks and deep learning, and profoundly discusses the convolutional neural network based on deep learning. Finally, pedestrian detection algorithms' problems and future trends are compared and discussed.
{"title":"Passenger and pedestrian recognition based on neural networks and deep learning in stations","authors":"Zhiyuan Zhang","doi":"10.1117/12.2672158","DOIUrl":"https://doi.org/10.1117/12.2672158","url":null,"abstract":"Pedestrian detection technology has high application value in various fields, and deep learning has become a key development direction in computer vision. Human object detection has also shifted from traditional detection algorithms to deep learning. Due to the influence of complex light and obstacles in the station, as well as the occlusions and size changes of passengers, the algorithm must be optimized for these complex scenes. This paper takes pedestrian detection technology as the goal, compares the methods based on human body parts recognition from the concepts and classification of artificial neural networks and deep learning, and profoundly discusses the convolutional neural network based on deep learning. Finally, pedestrian detection algorithms' problems and future trends are compared and discussed.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"96 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":"124053556","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}
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}
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}
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}
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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