2019 6th International Conference on Systems and Informatics (ICSAI)最新文献_第4页

DOA estimation on CACIS-type array based on fast-Rvm algorithm 基于快速rvm算法的cacis型阵列DOA估计

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010295

B. Cheng, Ming-Wei Li, Mingyue Feng, Xiaojie Tang

引用次数: 2

Image Classification with Fisher Feature Analysis 基于Fisher特征分析的图像分类

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010355

P. Qin, Jun Chu, Yawei Su

引用次数: 0

Suboptimal Assignment when Creating a Formation of Mobile Objects 创建移动对象编队时的次优分配

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010493

A. Karkishchenko, V. Pshikhopov

引用次数: 0

Network Embedding Method Based On Extractive Summary 基于抽取摘要的网络嵌入方法

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010524

Yuanfa Ji, Yuzhu Liu, Xiaodong Cai, D. Huang, Yuelin Hu

引用次数: 0

Driver Action Recognition Based on Attention Mechanism 基于注意机制的驾驶员动作识别

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010589

Wenhao Wang, Xiaobo Lu, Pengguo Zhang, Huibin Xie, Wenbing Zeng

引用次数: 10

Genetic Algorithm to the Split Delivery Vehicle Routing Problem 分割配送车辆路径问题的遗传算法

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010082

Z. Gao, Guohua Sun, Zheng Yuan

引用次数: 0

An Improved ORB Algorithm of Extracting Features Based on Local Region Adaptive Threshold 基于局部自适应阈值的改进ORB特征提取算法

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010209

Kun Yang, Dan Yin, Jian Zhang, Hua Xiao, Kaiqing Luo

{"title":"An Improved ORB Algorithm of Extracting Features Based on Local Region Adaptive Threshold","authors":"Kun Yang, Dan Yin, Jian Zhang, Hua Xiao, Kaiqing Luo","doi":"10.1109/ICSAI48974.2019.9010209","DOIUrl":"https://doi.org/10.1109/ICSAI48974.2019.9010209","url":null,"abstract":"For the features extraction of image, an improved Oriented FAST and Rotated BRIEF (ORB) algorithm of extracting features based on local region adaptive threshold is proposed, which not only can it solve the problem that the traditional ORB feature extraction algorithm cannot adapt to the local brightness change of the image, but also solve the phenomenon that the extracted feature points exist clusters. Firstly, extracting local region adaptive threshold features on each pyramid image based on constructed an image pyramid. Then, the feature points are divided by the quad-tree algorithm and the direction and the descriptor of the feature points are calculated. After that, the Fast Library for Approximate Nearest Neighbors (FLANN) is used to the match feature points, the mismatch points are eliminated by Lowe's algorithm and rotation consistency. Finally, using the Random sample consensus (RANSAC) to get the fine matching image. The method proposed in this paper is carried out on the Oxford images. Experiments show that the proposed method can extract more stable feature points under different fuzzy, illumination and compression conditions, which improve the matching accuracy of feature points.","PeriodicalId":270809,"journal":{"name":"2019 6th International Conference on Systems and Informatics (ICSAI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115107438","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: 3

Multi-stage Real-time Human Head Pose Estimation 多阶段实时人体头部姿态估计

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010492

Xiangwei Zhang, Dongping Zhang, Jun Ge, Kui Hu, Li Yang, Ping Chen

{"title":"Multi-stage Real-time Human Head Pose Estimation","authors":"Xiangwei Zhang, Dongping Zhang, Jun Ge, Kui Hu, Li Yang, Ping Chen","doi":"10.1109/ICSAI48974.2019.9010492","DOIUrl":"https://doi.org/10.1109/ICSAI48974.2019.9010492","url":null,"abstract":"The paper explores a human head pose estimation method combining face detection and face dichotomy optimization algorithm. The estimation of head pose is mainly to obtain the angle information of face orientation. The human head pose estimation algorithm in this paper mainly estimates the 3D Euler Angle of the input face patch. That is to say, a regressor is trained in a data-driven way, which can directly predict the input face blocks. In this paper, we use three models to make the final prediction of human head posture. First, the first model is used to detect faces. So as to prevent the loss of face detection in realtime face detection, we set a small threshold on the face detection model. Second, on the basis of face detection, a face dichotomy model is added to optimize the detected face to determine whether it is a face. Third, if the result of the second step is a face, the head pose estimation of the detected face is carried out. This paper mainly aims at the change of head pose angle to judge people's attention, and puts forward multi-stage head pose estimation to meet the demand of real-time engineering calculation. In this paper, the first novelty is that the average speed on Windows with CPU(i5-7500 3.40GHz) is 35ms/frame, and the second novelty is that we have collected 64101 face images under the surveillance camera, and the third novelty is that we have mixed depthwise separable convolution for head pose recognition. The method is tested on the UMDFaces Dataset, and the consequences show that compared with extra approaches, the proposed method can obtain improvements in efficiency.","PeriodicalId":270809,"journal":{"name":"2019 6th International Conference on Systems and Informatics (ICSAI)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122064262","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

Mining Frequent Patterns in Data Using Apriori and Eclat: A Comparison of the Algorithm Performance and Association Rule Generation 利用Apriori和Eclat挖掘数据中的频繁模式:算法性能和关联规则生成的比较

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010367

Vlad Robu, V. Santos

引用次数: 8

Bond transaction link prediction based on dynamic network embedding and time series analysis 基于动态网络嵌入和时间序列分析的债券交易环节预测

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010471

Wei Hao, Hanglong Zhan, Xiaojing Bao, Yanmin Lu, Yue Zhou, Liang Dou, Jian Jin

{"title":"Bond transaction link prediction based on dynamic network embedding and time series analysis","authors":"Wei Hao, Hanglong Zhan, Xiaojing Bao, Yanmin Lu, Yue Zhou, Liang Dou, Jian Jin","doi":"10.1109/ICSAI48974.2019.9010471","DOIUrl":"https://doi.org/10.1109/ICSAI48974.2019.9010471","url":null,"abstract":"Trading behavior prediction is to estimate the possibility of the occurrence of links in a dynamic network of bond transactions. At present, most of the existing link prediction models are link predictions for static networks such as social networks that do not consider time dimension. Since the evolution of the network over time is not considered, it is difficult to meet the object of effective link prediction of bond transactions. In this paper, in order to meet the link forecasting demand of bond market risk warning, DNETSA's link prediction method is proposed to realize the link prediction task under dynamic network, which provides a basis for financial risk warning. The DNETSA method can effectively extract the advantage of the structural information of the network in each time period. Then combine it with the link number attribute information by means of the time series model, which realizes the prediction ability of the link in the dynamic network and overcomes the problem that the static network link prediction does not consider the shortcomings of the network evolution over time. The effective integration and utilization of the dynamic network structure information, time information and attribute information makes the DNETSA method increase the AUC value by 22% compared with the LMPF method, and the AUC value by 13% compared with the TS-sim method. Compared to the TS-occ method AUC, the value is increased by 12%, which is 9% higher than the AUC value of the SOTS method. In summary, the DNETSA method makes up for the shortcomings of other methods and can satisfy the prediction of bond trading behavior.","PeriodicalId":270809,"journal":{"name":"2019 6th International Conference on Systems and Informatics (ICSAI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125775396","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