Ang Li, X. Ruan, Jing Huang, Xiao-qing Zhu, Fei Wang
{"title":"Review of vision-based Simultaneous Localization and Mapping","authors":"Ang Li, X. Ruan, Jing Huang, Xiao-qing Zhu, Fei Wang","doi":"10.1109/ITNEC.2019.8729285","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729285","url":null,"abstract":"Vision-based simultaneous localization and mapping (VSLAM) which uses visual sensor to make a robot locate itself in an unknown environment while simultaneously construct a map of the environment. With the continuous development of computer vision and robotics, VSLAM has become a supporting technology for popular fields such as unmanned aerial vehicle, virtual reality and unmanned driving. In this paper, the classical framework of visual SLAM is introduced briefly. On this basis, the key technologies and latest research progress of VSLAM from indirect and direct methods are surveyed. Then the research progress of deep learning techniques applied to VSLAM is reviewed. Finally, the development tendency of VSLAM is discussed.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127243023","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}
Liu Jingyuan, Fang Liyong, Hu Dongcai, Qi Xiaoshi, Deng Yangquan
{"title":"A new method to extract BGA solder balls in complex background X-ray image based on coordinate transformation","authors":"Liu Jingyuan, Fang Liyong, Hu Dongcai, Qi Xiaoshi, Deng Yangquan","doi":"10.1109/ITNEC.2019.8729540","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729540","url":null,"abstract":"In the detection of quality of BGA soldering based on AXI (Automated X-Ray Inspection), it’s critical important to extract solder balls accurately. Although the existing methods can extract most of the solder balls in X-ray images, they are sensitive to parameters and could only be used in the case of simple background. In order to solve this problem, a new method based on coordinate transformation is proposed in this paper. In this method, original X-ray images in Cartesian coordinate system are transformed in to polar coordinate firstly, so the edge of solder ball would be transformed from a curve to a straight line. Secondly a nonparametric edge detection could be used to extract the edge of solder balls. Result in Polar coordinate system would be transformed into original Cartesian coordinate system, finally. Through the comparison of two experiments, it is confirmed that solder balls in X-ray image could be extracted by proposed method precisely, while the proposed method has much more adaptability than other traditional methods. When proposed methods combined with other method such as the detection of voids in solder balls, the detection would be effective and improve the performance of AXI system ultimately.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127268141","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":"Hydrological time series forecast model based on wavelet de-noising and ARIMA-LSTM","authors":"Zheng Wang, Yuansheng Lou","doi":"10.1109/ITNEC.2019.8729441","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729441","url":null,"abstract":"Hydrological time series is affected by many factors and it is difficult to be forecasted accurately by traditional forecast models. In this paper, a hydrological time series forecast model based on wavelet de-noising and ARIMA-LSTM is proposed. The model first removes the interference factors in the hydrological time series by wavelet de-noising, and then uses ARIMA model to fit and forecast the de-noised data to obtain the fitting residuals and forecast results. Then we use the residuals to train LSTM network. Next, the forecast error of the ARIMA model is forecasted by LSTM network and used to correct the forecast result of ARIMA model. In this paper, we use the daily average water level time series of a hydrological station in Chuhe River Basin as the experimental data and compare this model with ARIMA model, LSTM network and BP-ANN-ARIMA model. Experiment shows that this model can be well adapted to the hydrological time series forecast and has the best forecast effect.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127664002","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":"Machine Learning in Software Defined Network","authors":"Jiamei Liu, Qiaozhi Xu","doi":"10.1109/ITNEC.2019.8729331","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729331","url":null,"abstract":"As a new network architecture, software defined network (SDN) separates the control plane from the forwarding plane which enables administrators to define and control the network through the method of software programming, provides a new research direction for the next generation of network architecture. At the same time, the machine learning technology has been developed rapidly in recent years and some studies have begun to introduce machine learning methods into SDN to improve the efficiency of network management and conformity, or to solve problems that cannot be solved easily by traditional methods. The paper analyses, summarizes and introduces these researches which used the supervised learning, unsupervised learning or semi-supervised learning methods to solve some specific problems on SDN, and it will help later researchers understand the filed more quickly and promote the development of the machine learning technology in SDN.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122479474","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":"SDN-based hybrid honeypot for attack capture","authors":"He Wang, Bin Wu","doi":"10.1109/ITNEC.2019.8729425","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729425","url":null,"abstract":"Honeypots have become an important tool for capturing attacks. Hybrid honeypots, including the front end and the back end, are widely used in research because of the scalability of the front end and the high interactivity of the back end. However, traditional hybrid honeypots have some problems that the flow control is difficult and topology simulation is not realistic. This paper proposes a new architecture based on SDN applied to the hybrid honeypot system for network topology simulation and attack traffic migration. Our system uses the good expansibility and controllability of the SDN controller to simulate a large and realistic network to attract attackers and redirect high-level attacks to a high-interaction honeypot for attack capture and further analysis. It improves the deficiencies in the network spoofing technology and flow control technology in the traditional honeynet. Finally, we set up the experimental environment on the mininet and verified the mechanism. The test results show that the system is more intelligent and the traffic migration is more stealthy.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128712605","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}
Yasheng Sun, Tao He, Jie Hu, Haiqing Huang, Biao Chen
{"title":"Socially-Aware Graph Convolutional Network for Human Trajectory Prediction","authors":"Yasheng Sun, Tao He, Jie Hu, Haiqing Huang, Biao Chen","doi":"10.1109/ITNEC.2019.8729387","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729387","url":null,"abstract":"Learning to understand human behaviors and predict their trajectories is a prerequisite for an automated car to navigate through the crowd safely and efficiently. This problem is particularly challenging as it requires the car to jointly reason about multiple pedestrians in a scenario where every one cooperates with each other to avoid collisions. To model the interactions among them, we propose a socially-aware graph convolutional network (SAGCN) which solves this problem in a graph learning framework. An attention graph is first built where each of its node carries the pedestrian temporal information and its edge represents the correspondence between pairwise pedestrians. To extract the temporal features, we implement temporal convolutional network (TCN) on each node. By utilization of relative motion between pairwise pedestrians, another TCN is employed to learn the correspondence of them which is formulated to an adjacency matrix of the attention graph. With the learned temporal features and adjacency matrix, a graph convolutional network (GCN) is exploited to aggregate the node information and jointly predict future trajectories of multiple pedestrians. Through experiments in several publicly available datasets, we demonstrate that our model effectively learns the comprehensive spatial-temporal representation and outperforms state-of-art methods in terms of prediction accuracy.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129120817","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}
Xiong Fu, Jian Li, Wenjie Liu, Song Deng, Junchang Wang
{"title":"Data Replica Placement Policy Based on Load Balance in Cloud Storage System","authors":"Xiong Fu, Jian Li, Wenjie Liu, Song Deng, Junchang Wang","doi":"10.1109/ITNEC.2019.8728995","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8728995","url":null,"abstract":"Data replication technology has been widely used in cloud storage system. How to select the appropriate data center to place data replication to effectively improve the access performance of cloud storage system became a problem which is worthy of study. To solve this problem we presented a replica placement algorithm based on load balance in this paper. According to the capital value of the data center, the algorithm selects a new replica node and can ensure load balance between the data centers. Related simulation experiments show that the algorithm can quickly and effectively respond to user requests and it is reliable and efficient.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116934827","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":"Optic Disc and Cup Segmentation Based on Deep Learning","authors":"Pengzhi Qin, Linyan Wang, Hongbing Lv","doi":"10.1109/ITNEC.2019.8729455","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729455","url":null,"abstract":"Glaucoma is a disease that damages eye’s optic nerve, and it is the leading cause of global irreversible blindness. Optic nerve head (ONH) assessment is a convenient way to detect glaucoma early and cup to disc ratio (CDR) is an important index for ONH evaluation. Thus, it is a fundamental task to segment OD and OC from the fundus images automatically and accurately. Most existing method segment them separately, and rely on hand-crafted visual feature from fundus image. This paper presents universal approach for automatic optic disc and cup segmentation, which is based on deep learning, namely, modification of fully convolutional network (FCN) and the Inception building blocks in GoogleNet. For improving the segmentation performance further, we also introduce new optic disc localization and pre-processing method. Our experiments show that our method achieves quality comparable to current state-of-the-art methods.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114461791","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":"Location Model of Pallet Service Centers Based on the Pallet Pool Mode","authors":"K. Zhou, R. Song","doi":"10.1109/ITNEC.2019.8729152","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729152","url":null,"abstract":"Containerized transport using pallets can largely improve transport efficiency, reduce damage and mistake rate of goods, it is conducive to improve the logistics service quality. Based on the pallet pool mode, a comprehensive optimization method of pallet service centers location is proposed in the paper, in order to achieve the optimal pallet service network structure. Supply and demand time of each agency in the pallet pool system is fully considered, it aims to realize the pallets circulation through the reasonable arrangement of pallet service centers and reduce inventory. According to the demand of enterprises for pallets and return of pallets from freight terminals, the minimum total cost including the fixed construction cost, inventory cost, delivery and recovery costs and transportation cost is taken as the objective to construct optimization model. Considering the uncertainty of enterprises for the demand of different type pallets, random demand is added and chance constraints with random variables is converted into determinate form. Based on the characteristics of model, the ILOG Cplex is used.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125769617","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}
Hu Huang, Ding Yang, Xue Yang, Yuhui Lei, Yang Chen
{"title":"Portable multifunctional electronic stethoscope","authors":"Hu Huang, Ding Yang, Xue Yang, Yuhui Lei, Yang Chen","doi":"10.1109/ITNEC.2019.8729172","DOIUrl":"https://doi.org/10.1109/ITNEC.2019.8729172","url":null,"abstract":"In view of the limitation of current medical stethoscope function simple, and cannot store weak signal of the human body. Designed a portable multi-function wireless transmission electronic stethoscope, this design used British CSR’s CSR8670 Bluetooth chip as the master control chip, combined with Filtering algorithms and SD card storage technologies. It implemented many functions such as real-time heart sound, core heart sound, lung sound, core lung sound, bowel sound and fetal sound auscultation, recording and playback. Through experiments, the system can accurately separate the sound signal of different frequency bands and highlight the sound signal of the selected frequency bands.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121923803","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}