2018 IEEE International Conference on Progress in Informatics and Computing (PIC)最新文献_第8页

Concurrency Bug-Oriented Mutation Operators Design for Java 面向并发错误的Java变异操作符设计

2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706335

Xiaoxue Wu, Wei Zheng, Zhao Shi, Zehai Wang, Lixin Cao, Dejun Mu

引用次数: 0

Fine-grained Transmission Optimization of Large-scale Web VR Scenes 大规模Web VR场景的细粒度传输优化

2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706277

Changqing Yin, Zhaohui Chen, Yonghao Hu, Kexin Yu

{"title":"Fine-grained Transmission Optimization of Large-scale Web VR Scenes","authors":"Changqing Yin, Zhaohui Chen, Yonghao Hu, Kexin Yu","doi":"10.1109/PIC.2018.8706277","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706277","url":null,"abstract":"The latency of transmitting large-scale WebVR scenes over mobile Internet is known as the bottleneck problem. This paper tries to challenge this problem by combining adaptive packaging transmission, use UDP and QUIC protocol for transmission instead of TCP and HTTP2. In addition, we also propose to use p2p for transmission to reduce the load pressure of the server during data transmission. Different with those pure research DVE (Distributed Virtual Environment) P2P works built on simulation platform, a novel WebVR-P2P framework is realized based on WebTorrent and WebGL. On server side, large- scale WebVR scenes are divided into smaller fine-grained subspaces in terms of closeness and visibility to lower networking congestions. These two preprocessing steps are integrated to decrease less bandwidth occupation at utmost. Then, each fine-grained subspace is packaged adaptively in terms of Frustum Fill Ratio (FFR) for smooth and efficient transmission. A new WebTorrent framework is extended to transmit Web3D files and all packaged WebVR subspaces are transferred in the peer-to-peer style. At the same time, on server side, we introduced the support of QUIC, and improved the support of QUIC in the project. Finally, WebVR-P2P and QUIC supported platform is implemented based on all above key technologies, a large-scale WebVR scene (an industrial park: 1325M) is chosen to test for P2P transmission and QUIC transmission performance in this WebVR-P2P platform, the practical experimenting results are conducted to show the effectiveness and potentiality of our proposed solution.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126737222","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

An Improved Firefly Algorithm Enhanced by Negatively Correlated Search Mechanism 基于负相关搜索机制的改进萤火虫算法

2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706281

Shi Wang, X. Yang, Zonghui Cai, Lin Zou, Shangce Gao

引用次数: 6

A Fault Diagnosis Scheme for High-Speed Train Bogie based on Depth-wise Convolution 基于深度卷积的高速列车转向架故障诊断方案

2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706307

Yunpu Wu, Wei-dong Jin

引用次数: 1

A GMM and SVM Combined Approach for Automatically Software Fault Localization 基于GMM和SVM的软件故障自动定位方法

2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706285

Xiaoxue Wu, Wei Zheng, Junzheng Chen, Han Bai, Desheng Hu, Dejun Mu

引用次数: 1

Research on Hyperspectral Image Target Detection By the Convergence Neural Network Based on Transfer Learning 基于迁移学习的收敛神经网络高光谱图像目标检测研究

2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706329

Liang Bao, Yaoqin Zhu

引用次数: 0

A System to Localize and Recognize Texts in Oriented ID Card Images 面向身份证图像文本定位与识别系统

2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706303

Jianxing Xu, Xing Wu

{"title":"A System to Localize and Recognize Texts in Oriented ID Card Images","authors":"Jianxing Xu, Xing Wu","doi":"10.1109/PIC.2018.8706303","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706303","url":null,"abstract":"ID card recognition is an application scenario in image recognition. As an important identity certificate for citizens, ID card identification and information management has also become a hot issue of concern. Chinese ID card images recognition mainly faces the following problems: Firstly, the shadow grid lines and the noise interference caused by the camera hardware conditions and illumination, together with the useful information in the picture, cause a huge problem of extracting useful information in the ID card. Secondly, the way to extract the foreground part in a complex context is also important for recognition. In response to these problems, this paper proposes an ID card region location method based on face detection and national emblem detection. This method can locate the foreground target area of the ID card in a complex context. In addition, the method of rotation correction of ID card region based on Hough transform is proposed, which can correct the tilt target within the range of tilt angle less than 45 degrees. In the text localization stage, the morphological method of the image is used to locate the text area. The deep convolutional neural network is then used to implement character segmentation and recognition. The recognition process proposed in this paper achieves a better recognition effect, and the recognition of Chinese and digital characters reaches 99.7% recognition rate, and can cope with the rotation tilt and the image containing the background.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"355 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122338003","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: 5

Network Node Resource Risk Assessment Based on Bayesian Belief Network Model 基于贝叶斯信念网络模型的网络节点资源风险评估

2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706297

Jun Li, YuQiang Liu, Yan Niu, Hui Zhang

引用次数: 0

Weighted fuzzy time series forecasting based on improved fuzzy C-means clustering algorithm 基于改进模糊c均值聚类算法的加权模糊时间序列预测

2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706278

Xiaoshuang Sang, Qinghua Zhao, Hong Lu, Jianfeng Lu

{"title":"Weighted fuzzy time series forecasting based on improved fuzzy C-means clustering algorithm","authors":"Xiaoshuang Sang, Qinghua Zhao, Hong Lu, Jianfeng Lu","doi":"10.1109/PIC.2018.8706278","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706278","url":null,"abstract":"A novel method for fuzzy time series (FTS) forecasting is presented based on improved fuzzy C-means clustering algorithm (IFCM) and first-order difference. Traditional forecasting approaches have weighted the central values of fuzzy intervals corresponding to fuzzy sets, but the central values may not be accurate enough since the assumed membership functions may be different. To avoid the problem of even distribution, in this paper, we weight the cluster centers derived from IFCM that defines the initial cluster centers of traditional fuzzy C-means clustering algorithm (FCM). There are many unstable characteristics in the time series forecasting model. To eliminate the fluctuation tendency of unstable characteristics, the first-order difference is used as the smooth time sequence to observe. Our experimental results on Alabama University enrollments and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) demonstrate that the effectiveness and superiority of the proposed forecasting approach, in this paper, which gets higher forecasting accuracy than state-of-the-art methods.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128149376","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: 6

[Front matter] (前页)

2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/pic.2018.8706294

引用次数: 0