2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)最新文献

{"title":"Mudslide Disaster Monitoring and Early Warning System Based on ESP32","authors":"Fan Yang, Jing Zhang, Qian Lei, Guoping Li, Jianwei Liu, Zhicheng Wang","doi":"10.1109/AICIT55386.2022.9930199","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930199","url":null,"abstract":"Mudslide disasters have a great impact on human society, and severe mudslide disasters can cause significant casualties and property damage. In order to reduce the damage caused by natural disasters to human society, this paper designs a mudflow disaster monitoring and early warning system based on ESP32 module. The system uses rainfall sensor, fractometer, soil moisture content sensor and infrasound sensor to obtain corresponding data. The 4G module transmits these data to the cloud server for data processing and judgment of debris flow risk. The processed data is displayed in the WeChat applet. The results show that the system can monitor the environmental conditions of the target area from a long distance and provide a more accurate early warning of mudslide disasters.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132401753","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}

{"title":"Lithium battery SOC correction technology based on equivalent circuit + UKF filtering algorithm","authors":"Huang Chencheng, L. Jian","doi":"10.1109/AICIT55386.2022.9930284","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930284","url":null,"abstract":"In matlablSimulink environment, the first-order Thevenin equivalent circuit model and the traceless Kalman filtering algorithm are established, and theparameters of different SOCs and temperatures on the battery model are identified by establishing hybrid power pulse characteristic experiments, and the distinguished parameters are substituted into the UKF algorithm for simulation experiments. Experimental results show that the estimation of the state of charge has high accuracy.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131290274","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}

{"title":"Based on improved yolo_v3 for college students’ classroom behavior recognition","authors":"Zhipeng Li, Junqiao Xiong, Huafeng Chen","doi":"10.1109/AICIT55386.2022.9930274","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930274","url":null,"abstract":"The main purpose of the research on students’ classroom behavior recognition is to further systematically count all kinds of behavior data of students in class, and to provide a reliable technical support for education and teaching evaluation. Nowadays, the mainstream of target detection and recognition for multiple students in the classroom is to use various target detection and recognition technologies based on deep learning methods. These technologies optimize the model through self-learning of the data set through deep convolutional neural networks, thereby further Improve recognition efficiency. With the development of deep learning technology, the recognition efficiency has been greatly improved from the initial two-step detection to the current single-step detection algorithm. In the complex environment of the classroom, it is difficult to recognize students’ classroom behavior, which is effectively the problem of insufficient small target recognition accuracy. The original yolo-v3 network model is improved to make it suitable for students’ classrooms, which can solve this problem very well. According to the data fed back from the experimental results, the improved model has greatly improved the recognition efficiency.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115910047","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}

{"title":"MStereoNet: A Lightweight Stereo Matching Network Using MobileNet","authors":"Han Yu, Ke Wang, Lun Zhou, Zhen Wang","doi":"10.1109/AICIT55386.2022.9930293","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930293","url":null,"abstract":"Deep learning model-based approaches to stereo matching challenges are more accurate than conventional feature-based techniques created by hand. This leads to the issue that deploying applications on devices with restricted resources is not friendly to employing complicated networks and total cost space to increase performance. To minimize processing effort without sacrificing matching accuracy, we propose MStereoNet in this study, a more effective stereo network. It has been demonstrated experimentally that the network in this research significantly lowers the requirement for computing power. The network in this article also employs a multiscale loss to enhance the reliability of the detail. The approach in this study delivers a performance comparable to the best existing algorithms in the Sceneflow dataset when compared to other low-cost dense stereo depth estimation techniques. Research demonstrates that the network suggested in this study can reduce up to 72.5% and 87% of the parameters and operations than the largest volume of the methods involved in the comparison. Our network also marginally outperforms other lightweight binocular matching networks in terms of accuracy.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115589849","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}

{"title":"Semi-Instance Normalization Network for Turbulence Degraded Image Restoration","authors":"Junxiong Fei, Zezheng Li, Xia Hua, Yuerui Zhang, Mingxin Li, Zhigao Huang","doi":"10.1109/AICIT55386.2022.9930308","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930308","url":null,"abstract":"In computer vision tasks, a variety of normalization methods are widely used. Compared with other normalization methods, Instance Normalization (IN) performs better in turbulence degraded image restoration. However, the simple application of IN to a degraded image restoration network can be suboptimal. In this paper, we present a novel block named Semi Instance Normalization Block (SIN Block), which can improve the performance of the image restoration network. SIN Block incorporates original features in the normalization layer, which can preserve contextual information. Furthermore, we designed a semi-instance normalization Network (SINet) consisting of a series of the SIN Block for restoring turbulence degraded images. Extensive experiment on simulation dataset demonstrates that SINet can effectively restore details of the turbulence degraded image and sharpen its edges.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121526387","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}

{"title":"Multi-band Image Fusion With Infrared Broad Spectrum For Low And Slow Small Target Recognition","authors":"Jianwei Liu, Wei Gong, Tianxu Zhang, Yuhan Zhang, Wenbing Deng, Hanyu Liu","doi":"10.1109/AICIT55386.2022.9930170","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930170","url":null,"abstract":"While the widespread use of low and slow UAVs brings convenience to all areas of society, it also poses a serious threat to the safety of the low-altitude domain. In the current field, radar detection and identification technology and infrared image recognition technology are widely used in target detection and identification. The neural network designed in this paper adopts fully connected neural network and convolutional neural network to extract global feature information and local feature information from the infrared broad spectrum data of low and slow small targets respectively, and the extracted feature information is fed into the target detection networks of different time periods for recognition training to obtain image recognition models and spectral recognition models of different time periods, and finally, the image recognition and spectral recognition Finally, the recognition rates of image recognition and spectral recognition are fused to obtain the final recognition rate. By combining the strengths of infrared hyperspectral images, making up for the deficiencies of multi-band images for target hours which are not easy to recognize, and fusion processing at multiple levels, the multi-band images break through the limitations of airborne target recognition, improve the anti-interference ability of recognition network, and also improve the accuracy rate of airborne target recognition.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116975896","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}

{"title":"Garbage classification system based on artificial intelligence and Internet of Things","authors":"Xianjun Yi, Yinyi Liang, Hongchi Peng","doi":"10.1109/AICIT55386.2022.9930306","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930306","url":null,"abstract":"The existing garbage classification system has some problems such as poor classification effect, high classification cost and difficult management. In order to solve these problems, this paper proposes a garbage classification system that can manage multiple garbage cans simultaneously and classify them accurately in real time. The system deploys the improved MobileNet V2 network model on the low-cost K210 module, and adopts the method of first object recognition and then garbage classification to classify the garbage images read by the camera. At the same time, the system collects environmental information such as temperature and humidity, overflow situation, and air quality of the garbage cans through sensors, then summarizes the classification information and environmental information at the terminal, and finally uploads the aggregated information to the cloud through the NB module. After physical tests, the system can classify garbage with an accuracy rate of more than 90%, run at a maximum speed of 12 frames per second, and can monitor environmental information in real time, which has certain practical value.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115123146","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}

{"title":"Research on Indoor Positioning Based on Smart Home Bluetooth Networking","authors":"XueZhou Tong, Liheng Wang, YanCheng Cui","doi":"10.1109/AICIT55386.2022.9930283","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930283","url":null,"abstract":"With the rapid development of the Internet of Things, most of the smart home is equipped with Bluetooth above 5.0 system. With the wide use of smart home such as Tmall Spirit and Mijia, it provides a new way for indoor positioning. Therefore, this paper designs an indoor positioning system based on Bluetooth networking. The system uses the characteristics of low power consumption and communication stability of Bluetooth Mesh networking, combines with Gaussian filtering and Kalman filtering to filter the Bluetooth signal, and finally calculates the Bluetooth signal through the RSSI algorithm and realizes the positioning in the server through the three-sided center of mass algorithm.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131650515","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}

{"title":"Detection Of Hypersonic Targets In Space And Near Space By A Constellation Of Space Satellites","authors":"Xiaotai Liu, Jing Deng, Tianxu Zhang, Hanyu Liu, Kechao Wang, Wenbing Deng","doi":"10.1109/AICIT55386.2022.9930312","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930312","url":null,"abstract":"Currently, most of the detection methods for hypersonic moving targets in space and near space at home and abroad are based on space-based detection systems and ground-based detection systems, etc. for the detection of hypersonic moving targets. The space-based detection system consists mainly of space-based infra-red and space-based radar detection systems, which capture and track and intercept the flight and climb segments of targets via medium and high orbiting satellites. Ground-based detection systems are mainly used to detect airborne targets by means of long-range early warning phased array radars, ground-based multifunctional radars, gaze radars and electromagnetic fences in conjunction with ground-based infrared detection systems. Instead, we use the all-weather, real-time global coverage of satellite signals as a source of radiation to detect hypersonic targets in space and near space. Calculating the power loss of the satellite transmitting electromagnetic wave signals and subsequently receiving the return signals of its detection of space and near-space targets, based on the transmitting and receiving power of the satellite transmitting and receiving antennas, amplification, electromagnetic wave frequency and the propagation distance from the satellite to space and near-space moving targets, to obtain the minimum power value required to ensure that a single satellite can properly transmit and receive its detection of electromagnetic wave signals of space and near-space targets and their return signals. The satellite ephemeris and other data parameters of the world’s currently launched satellites in low, medium and high orbits, as well as satellites in synchronous orbits, were obtained and combined with the satellite simulation software STK to build a simulation model describing the distribution and operation of the space satellite constellation. Effective detection of hypersonic targets is achieved through computational analysis of their coverage by a constellation of space satellites.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131314554","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}

{"title":"Path Planning of Unmanned Surface Vehicle Integrating Optimized A* and Dynamic Window Approach","authors":"Xiong Nan","doi":"10.1109/AICIT55386.2022.9930238","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930238","url":null,"abstract":"There are problems such as poor real-time performance, low efficiency of path planning, and easy to fall into local optimum when using traditional path planning algorithms for unmanned surface vehicle path planning. Aiming at the above problems, this paper proposes a path planning algorithm for unmanned surface vehicles that integrates the optimized A* algorithm and the dynamic window approach. The algorithm firstly optimizes and adjusts the cost function and path search method of the traditional A* algorithm, and secondly adopts the double broken line optimization strategy to greatly reduce the number of path inflection points and improve the smoothness of the global path. Finally, by introducing the path evaluation sub-function into the evaluation function of the dynamic window approach, the optimized A* algorithm is integrated with the dynamic window approach. The simulation results show that the path search efficiency of the algorithm in the static environment is significantly improved compared with the traditional A* algorithm, the smoothness of the path is better than that of the traditional A* algorithm, and it has a good dynamic obstacle avoidance effect in the dynamic environment.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133600948","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}