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2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)最新文献

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Research on a Weighted Least Squares Algorithm in Satellite Positioning 卫星定位中的加权最小二乘算法研究
2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) Pub Date : 2020-07-01 DOI: 10.1109/ICVRIS51417.2020.00233
Z. Zeng, Xiangrui Zhang, Liqin Jia
{"title":"Research on a Weighted Least Squares Algorithm in Satellite Positioning","authors":"Z. Zeng, Xiangrui Zhang, Liqin Jia","doi":"10.1109/ICVRIS51417.2020.00233","DOIUrl":"https://doi.org/10.1109/ICVRIS51417.2020.00233","url":null,"abstract":"In order to overcome the problem of the reliability of the pseudorange observation measurement in the satellite navigation system, the least-squares method used by common navigation receivers cannot solve the problem of the reliability of the pseudorange observation. The concept of weight gives greater credibility to pseudorange observations, allowing more reliable pseudorange observations to participate in the positioning solution algorithm, effectively solving the credibility of pseudorange observations. problem. The experimental simulation results prove that the positioning algorithm proposed in this paper is superior to the traditional least squares positioning algorithm in terms of positioning accuracy, meets the normal satellite positioning requirements, and improves the positioning accuracy by 1.503m.","PeriodicalId":162549,"journal":{"name":"2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125973227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Experimental study on vehicle extraction based on wv-2 image data 基于wv-2图像数据的车辆提取实验研究
2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) Pub Date : 2020-07-01 DOI: 10.1109/ICVRIS51417.2020.00167
Guo Dudu, Cai Shuaichao
{"title":"Experimental study on vehicle extraction based on wv-2 image data","authors":"Guo Dudu, Cai Shuaichao","doi":"10.1109/ICVRIS51417.2020.00167","DOIUrl":"https://doi.org/10.1109/ICVRIS51417.2020.00167","url":null,"abstract":"Real-time dynamic traffic information is an important basic information source for traffic monitoring and management. In view of the limitations of existing ground traffic detection equipment, people hope to be able to obtain a larger range of real-time dynamic traffic flow data with higher application value to monitor road traffic conditions far from the target. In this paper, features of wv-2 image data are analyzed. Firstly, gray scale transformation, filtering and mathematical morphology are used to preprocess the remote sensing image. Then, edge detection is used to extract the road and limit the area for vehicle identification. Then the method of double threshold and support vector machine is used to identify the vehicles on the road. Finally, the accuracy of the recognition results is analyzed. In the experimental results, the accuracy of the recognition using the double threshold method is 82.7%, and that of the support vector machine method is 95.2%. The latter has a better recognition rate.","PeriodicalId":162549,"journal":{"name":"2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123633177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Multi-objective Optimal Control of Train Operation Based on EOL-NSGA-III Algorithm 基于EOL-NSGA-III算法的列车运行多目标最优控制研究
2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) Pub Date : 2020-07-01 DOI: 10.1109/ICVRIS51417.2020.00178
Wenxiang Liu, Xiao Juan Liu, Chenge Song, Hong Zhi Lv
{"title":"Research on Multi-objective Optimal Control of Train Operation Based on EOL-NSGA-III Algorithm","authors":"Wenxiang Liu, Xiao Juan Liu, Chenge Song, Hong Zhi Lv","doi":"10.1109/ICVRIS51417.2020.00178","DOIUrl":"https://doi.org/10.1109/ICVRIS51417.2020.00178","url":null,"abstract":"In view of the functional demands of the automatic train operation (ATO) system of urban rail transit, under the conditions of train operation safety, speed limit and train dynamics performance constraints, the multi-objective optimization model for the train operation is built with low energy consumption, stopping accuracy, punctuality and passenger comfort as control objectives. Under the MATLAB environment, first comparing the Pareto optimal solution of NSGA-III algorithm with Non-dominated Sorting Genetic Algorithm III based on Elite opposition-based Learn (EOL- NSGA-III) algorithm, then based on the Beijing Yizhuang Line interval route data, the EOL-NSGA-III algorithm is applied to solve the multi-objective optimization model. The simulation results confirm the feasibility of the EOL-NSGA-III algorithm and the effectiveness of the multi-objective optimization model, thereby designing an efficient multi-objective operation of urban rail transit trains control strategy.","PeriodicalId":162549,"journal":{"name":"2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124934130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Pest Detection in Crop Images Based on OTSU Algorithm and Deep Convolutional Neural Network 基于OTSU算法和深度卷积神经网络的农作物图像害虫检测
2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) Pub Date : 2020-07-01 DOI: 10.1109/ICVRIS51417.2020.00111
Yongkang Yao, Yucheng Zhang, Wendu Nie
{"title":"Pest Detection in Crop Images Based on OTSU Algorithm and Deep Convolutional Neural Network","authors":"Yongkang Yao, Yucheng Zhang, Wendu Nie","doi":"10.1109/ICVRIS51417.2020.00111","DOIUrl":"https://doi.org/10.1109/ICVRIS51417.2020.00111","url":null,"abstract":"With the development of computer image detection technology in agriculture, the accurate detection of crop pests under complex background has become an important issue in agriculture. Due to various forms and complex environments, some pest images cannot be accurately detected by existing detection algorithms. In order to improve the detection accuracy, a deep convolutional neural network based on feature fusion is proposed. This algorithm is based on Mask R-CNN network and OSTU, introduces automatic threshold segmentation algorithm. In the feature extraction stage, an improved threshold segmentation algorithm is introduced, and then the feature data generated by segmentation is used to replace the original feature maps. The experiment on crop pest detection shows that this detection algorithm proposed in this paper can effectively detect crop pests and achieve the effect of identification and instance segmentation.","PeriodicalId":162549,"journal":{"name":"2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129859300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Fault diagnosis of rolling bearing based on generalized S-transform and dropout CNN 基于广义s变换和dropout CNN的滚动轴承故障诊断
2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) Pub Date : 2020-07-01 DOI: 10.1109/ICVRIS51417.2020.00222
Lei Yang, Qing-rong Wang
{"title":"Fault diagnosis of rolling bearing based on generalized S-transform and dropout CNN","authors":"Lei Yang, Qing-rong Wang","doi":"10.1109/ICVRIS51417.2020.00222","DOIUrl":"https://doi.org/10.1109/ICVRIS51417.2020.00222","url":null,"abstract":"In order to solve the problems of feature extraction in the field of fault diagnosis, such as insufficient feature extraction and too complex classifier training, this paper takes rolling bearing, the key component of mechanical transmission device, as an example, and proposes to combine the feature extraction based on time-frequency analysis of generalized S-transform with dropout CNN to realize the fault detection of rolling bearing. In the diagnosis model, the time-frequency map of the original bearing data is obtained by the generalized S-transform, then the secondary feature is extracted by convolution neural network, and then the fault is classified by the classifier, so as to carry out the fault diagnosis of rolling bearing. The experimental results show that the accuracy of the diagnosis model can reach 99.6%, and the extracted features are highly differentiated. Compared with support vector machine (SVM) and convolutional neural network (CNN), this model has higher diagnostic accuracy and stability.","PeriodicalId":162549,"journal":{"name":"2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128016086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Extraction of Malignant Tumor Diagnostic Text Information Based on Named Entity Recognition 基于命名实体识别的恶性肿瘤诊断文本信息提取
2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) Pub Date : 2020-07-01 DOI: 10.1109/ICVRIS51417.2020.00140
Qingwei Chen, Huang Xu, Guanlin Chen
{"title":"Extraction of Malignant Tumor Diagnostic Text Information Based on Named Entity Recognition","authors":"Qingwei Chen, Huang Xu, Guanlin Chen","doi":"10.1109/ICVRIS51417.2020.00140","DOIUrl":"https://doi.org/10.1109/ICVRIS51417.2020.00140","url":null,"abstract":"In order to extract named entity recognition from malignant tumor medical medical text data, an algorithm that combines the bidirectional long short-term memory network and the conditional random field (Bi-LSTM-CRF) is proposed. This method adds the CRF layer processing after Bi-LSTM network output, so that the model has better comprehensive performance since the CRF can take orders of words into consideration. The experimental results show that comparing to the algorithm that combines the maximum entropy Markov model and the conditional random field (MEMM+CRF) and the algorithm of the bidirectional long short-term memory network (Bi-LSTM), our method is more excellent in entity recognition for comprehensive practical applications, and can basically identify the corresponding medical entity.","PeriodicalId":162549,"journal":{"name":"2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121134761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Study on Application of Zero-Velocity Update Technology to Tracked Vehicle Inertial Navigation 零速度更新技术在履带车辆惯性导航中的应用研究
2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) Pub Date : 2020-07-01 DOI: 10.1109/ICVRIS51417.2020.00040
Yang Zhang, Qi Feng, Mei Gao
{"title":"Study on Application of Zero-Velocity Update Technology to Tracked Vehicle Inertial Navigation","authors":"Yang Zhang, Qi Feng, Mei Gao","doi":"10.1109/ICVRIS51417.2020.00040","DOIUrl":"https://doi.org/10.1109/ICVRIS51417.2020.00040","url":null,"abstract":"Tracked vehicle navigation technology provides an important guarantee for military and civilian tracked vehicles to perform their tasks. In this paper, the zero-velocity update technology (ZUPT) is used to optimize the inertial navigation algorithm of the crawler and improve the navigation accuracy based on the motion constraints of the crawler. Through uphill & downhill experiment, it is verified that the optimized zero-velocity detection algorithm has better detection effect. The closed-loop experiment proves that the ZUPT can effectively suppress the navigation errors of the tracked vehicle, and combined with the zero-angular-rate update technology (ZARU) can achieve higher navigation accuracy.","PeriodicalId":162549,"journal":{"name":"2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)","volume":"309 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115212944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Study on the effect of temperature on the ratio of signal to noise for NDIR gas analyzer 温度对NDIR气体分析仪信噪比影响的研究
2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) Pub Date : 2020-07-01 DOI: 10.1109/ICVRIS51417.2020.00121
Hongmei Liu, Chuanwu Tan
{"title":"Study on the effect of temperature on the ratio of signal to noise for NDIR gas analyzer","authors":"Hongmei Liu, Chuanwu Tan","doi":"10.1109/ICVRIS51417.2020.00121","DOIUrl":"https://doi.org/10.1109/ICVRIS51417.2020.00121","url":null,"abstract":"In order to improve the signal-to-noise ratio of the non spectroscopic infrared gas analyzer and analyze the influence of the temperature stability in the infrared pool on the absorption of the gas, this paper makes a comparative test on the non spectroscopic infrared gas analyzer in the room temperature and constant temperature control environment. In the experiment, the high-precision constant temperature system with incremental PID algorithm is used to control the constant temperature point at 48 ± 0.1 °C, which is shown by the test comparison under the high-precision constant temperature system, The signal-to-noise ratio of the gas analyzer is improved obviously.","PeriodicalId":162549,"journal":{"name":"2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126998307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research On Security Warning Model Of Intelligent Optical Cable Based On YOLOv3-tiny 基于YOLOv3-tiny的智能光缆安全预警模型研究
2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) Pub Date : 2020-07-01 DOI: 10.1109/ICVRIS51417.2020.00268
Zhuzhen He, Jinhua Hu, Shuai Ren, Yinghui Xue, Feng Wang, Jingke Wan
{"title":"Research On Security Warning Model Of Intelligent Optical Cable Based On YOLOv3-tiny","authors":"Zhuzhen He, Jinhua Hu, Shuai Ren, Yinghui Xue, Feng Wang, Jingke Wan","doi":"10.1109/ICVRIS51417.2020.00268","DOIUrl":"https://doi.org/10.1109/ICVRIS51417.2020.00268","url":null,"abstract":"In order to ensure the unbolcked communication of optical cable lines need to be regularly inspected.Most of the existing UAV(Unmanned Aerial Vehicle) patrol systems work by collecting images from the aircraft and sending them back to the ground station for processing.It will take a long time and may be affected by the wireless channel, which makes it impossible to stop and intervene in time. This paper proposes an intelligent security warning system for optical cable line based on YOLOv3-tiny.By enlarging the pre-processed images of engineering vehicles, the data set is expanded and the generalization ability of the model is improved. The target box of the data set is reset by k-means clustering algorithm, which improves the parameter performance of YOLOv3-tiny. Finally, the model system was tested on the Nvidia jetson tx2 platform, and the results showed that the system could quickly carry out real-time detection of construction vehicles, with the Mean Average Precision (mAP) up to 0.33 and the detection speed up to 24fps.","PeriodicalId":162549,"journal":{"name":"2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125366748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Data Encryption of Big Data Redundancy Elimination Algorithm Combining Bloom Filter Technology 结合布隆滤波技术的大数据冗余消除算法的数据加密
2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) Pub Date : 2020-07-01 DOI: 10.1109/ICVRIS51417.2020.00135
Gen Li
{"title":"Data Encryption of Big Data Redundancy Elimination Algorithm Combining Bloom Filter Technology","authors":"Gen Li","doi":"10.1109/ICVRIS51417.2020.00135","DOIUrl":"https://doi.org/10.1109/ICVRIS51417.2020.00135","url":null,"abstract":"With the continuous expansion of various businesses, data have presented explosive growth. The previous data redundancy elimination algorithm had high storage consumption, prolonged time consumption, and unsatisfactory detection effect of repetition rate. This paper introduced the Bloom filter data structure to reduce the dimension of big data and proposed a new data redundancy elimination algorithm. Firstly, the complete file detection algorithm is used in this algorithm to test and match the data. For the data blocks passing the test, the CDC block detection algorithm is used for further testing and matching. The cosine similarity equation and Hamming distance value are used to calculate the data similarity, complete the final redundancy elimination of data, and perform data encryption on this basis equation. The simulation experiment results suggest that the data redundancy elimination algorithm proposed in this paper has excellent comprehensive performance, which has not only ensured the detection accuracy of data repetition rate but also improved the data detection speed, while reducing the storage overhead and protecting the data security at the same time.","PeriodicalId":162549,"journal":{"name":"2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114490628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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