2019 6th International Conference on Systems and Informatics (ICSAI)最新文献_第3页

An improved scan matching algorithm in SLAM 一种改进的SLAM扫描匹配算法

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010259

Hongkai Zhang, Niansheng Chen, Guangyu Fan, Dingyu Yang

引用次数: 2

Secure load-balanced scheme for cluster-based WSNs 基于集群的wsn安全负载均衡方案

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010499

Jiliang Zhou, Ziqiang Lin, Xi Jiang

引用次数: 1

The Outlier Elimination of Trajectory Data Based on Kernel Adaptive Filtering with Variable Step Size 基于变步长核自适应滤波的轨迹数据离群值消除

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010221

Zhen-xing Li, Biqiu Zhang

{"title":"The Outlier Elimination of Trajectory Data Based on Kernel Adaptive Filtering with Variable Step Size","authors":"Zhen-xing Li, Biqiu Zhang","doi":"10.1109/ICSAI48974.2019.9010221","DOIUrl":"https://doi.org/10.1109/ICSAI48974.2019.9010221","url":null,"abstract":"An outlier detection and elimination method based on kernel adaptive filtering with variable step size for trajectory data of vehicle test was proposed. The training sample of kernel adaptive filter is designed according to the effective trajectory data. After training, the residual error between the output of the kernel filter and the trajectory can be obtained. If the residual error at some time point is larger than 3 times of the standard deviation of the residual error, the corresponding data point can be considered to be the outlier data based on Wright guidelines, and then the data is instead of the output of the kernel adaptive filter to eliminate the outlier data. To further improve the precision of the outlier data elimination and interpolation, a variable step size algorithm was designed according to the output error of the kernel adaptive filter, in which the step size can be controlled during the iterative process. The proposed method can implement outlier data elimination and interpolation at the same time, which has good robustness and high precision. The simulation and test data processing results show the effectiveness.","PeriodicalId":270809,"journal":{"name":"2019 6th International Conference on Systems and Informatics (ICSAI)","volume":"12 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":"131338562","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

Sleep Breathing Data Analysis Based on Econometrics 基于计量经济学的睡眠呼吸数据分析

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010277

Cai Chen, Wei Li, Dedong Ma

引用次数: 0

Application Research of two-dimensional code in mobile augmented reality 二维码在移动增强现实中的应用研究

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010434

Mengde Zhao, Qing Li

{"title":"Application Research of two-dimensional code in mobile augmented reality","authors":"Mengde Zhao, Qing Li","doi":"10.1109/ICSAI48974.2019.9010434","DOIUrl":"https://doi.org/10.1109/ICSAI48974.2019.9010434","url":null,"abstract":"Augmented Reality (AR) is an emerging technology that adds computer-generated virtual information to real-world scenarios, thereby enhancing people's understanding of the surrounding real-world environment. With the development of network, augmented reality technology is gradually applied to mobile devices based on smart phones. Augmented reality technology has been widely used in foreign cultural circles, such as ancient architecture research, Museum exhibitions, enhanced travel and campus navigation. At present, our country's mobile augmented reality technology can only aim at a small number of targets, and can not meet the needs of target recognition and tracking for mobile users. On this basis, two-dimensional code is gradually developed in the mobile augmented reality system. A large number of two-dimensional code content coding technology and fast positioning technology can meet the needs of people. Target recognition requirements. Therefore, this paper expounds the general situation of two-dimensional code and mobile augmented reality technology, studies the application of two- dimensional code in mobile augmented reality, and points out its application in library.","PeriodicalId":270809,"journal":{"name":"2019 6th International Conference on Systems and Informatics (ICSAI)","volume":"65 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":"130278281","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

Detecting Vertebra Landmarks From Ultrasound Image Using Single Shot MultiBox Detector 利用单镜头多盒检测器检测超声图像中的椎体标志

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010161

Qifeng Deng, Qinghua Huang

引用次数: 1

Predicting Blood Glucose Levels with EMD and LSTM Based CGM Data 基于CGM数据的EMD和LSTM预测血糖水平

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010318

Wei Song, Wanyuan Cai, Jing Li, Fusong Jiang, Shengqi He

{"title":"Predicting Blood Glucose Levels with EMD and LSTM Based CGM Data","authors":"Wei Song, Wanyuan Cai, Jing Li, Fusong Jiang, Shengqi He","doi":"10.1109/ICSAI48974.2019.9010318","DOIUrl":"https://doi.org/10.1109/ICSAI48974.2019.9010318","url":null,"abstract":"Blood glucose monitoring is essential for diabetes management. Applying deep learning technique for blood glucose monitoring is promising, given its success in a range of healthcare and medical tasks. In this paper, we proposed a method that combines Empirical Mode Decomposition (EMD) with Long-Short Term Memory (LSTM) to achieve good experimental results in predicting patient blood glucose. We used patients' real blood glucose levels time series data to train the method proposed in this paper and to predict blood glucose for 30 minutes to 120 minutes. First, we use only blood glucose readings and timestamps in the dataset. Meanwhile, we used ADF to verify the non-stationarity of blood glucose time series. Then, we use EMD to decompose the blood glucose time series and use LSTM to train the decomposed time series to obtain a blood glucose prediction model. Finally, Mean Absolute Error (MAE) and root mean squared error (RMSE) were used to evaluate the experimental results. On the test dataset, the mean values of the MAE and RMSE are 0.4458mmol/L and 1.08mmol/L for 30mins, 0.87 and 1.27 mmol/L for 60mins, 0.85mmol/L and 1.36 mmol/L for 120mins, respectively. Experimental results show that the EMD+LSTM had better predictive performance than the LSTM when blood glucose changed dramatically. Meanwhile, it is still challenging to reach a high accuracy of predicting the long-term blood glucose.","PeriodicalId":270809,"journal":{"name":"2019 6th International Conference on Systems and Informatics (ICSAI)","volume":"3 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":"131698437","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: 9

Low-Frequency Oscillation Mode Identification with OpenPDC Platform 基于OpenPDC平台的低频振荡模态识别

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010190

Jian Zuo, Jihong Tang, Hu Guo, Dijun Hu, Keren Zhang, Meng Xiang

{"title":"Low-Frequency Oscillation Mode Identification with OpenPDC Platform","authors":"Jian Zuo, Jihong Tang, Hu Guo, Dijun Hu, Keren Zhang, Meng Xiang","doi":"10.1109/ICSAI48974.2019.9010190","DOIUrl":"https://doi.org/10.1109/ICSAI48974.2019.9010190","url":null,"abstract":"Low frequency oscillation in power system may cause system failure and blackout. It is possible to avoid low frequency oscillation if we can detect the oscillation mode with low damping in early stage. This paper presents the Frequency Domain Decomposition (FDD) method, which can extract the low frequency oscillation modes from ambient measurement data of Phasor Measurement Units(PMU). The parameters of mode shape from poorly damped oscillation modes, such as damping ratio and modal frequency, can be directly determined from ambient PMU measurements. Then this paper presents the implementation of oscillation mode identification application with OpenPDC platform, which is an open-source platform and provides an easy-to-use test platform for phasor measurement application. The case study shows that the implementation of Oscillation Mode Identification module with FDD method and OpenPDC platform is very efficient for processing large quantity of PMU data and identify low frequency oscillation mode, which is key to take early actions avoiding low-frequency oscillation in power system.","PeriodicalId":270809,"journal":{"name":"2019 6th International Conference on Systems and Informatics (ICSAI)","volume":"21 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":"131727088","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

A Scoring System of Oral English-Chinese Translation Based on Frame Semantic Analysis 基于框架语义分析的英汉口语翻译评分系统

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010095

Xinguang Li, XiaoLan Long, Yan Long, ChuHua Liang

{"title":"A Scoring System of Oral English-Chinese Translation Based on Frame Semantic Analysis","authors":"Xinguang Li, XiaoLan Long, Yan Long, ChuHua Liang","doi":"10.1109/ICSAI48974.2019.9010095","DOIUrl":"https://doi.org/10.1109/ICSAI48974.2019.9010095","url":null,"abstract":"In recent years, there has been an increasing interest in speech evaluation. However, most of the automatic evaluation systems focus on evaluating the quality of retelling questions and reading questions. In our best knowledge, research evaluating the quality of English-Chinese oral translation questions have not been found. This paper proposed an automatic method for scoring English-Chinese oral translation. This method leveraged three indicators, including key words, general idea of sentences and fluency, as the basis of scoring. The key words indicator was evaluated by the synonym analysis at the lexical level to evaluate while the general idea of sentences indicator was evaluated by the framework semantic analysis at the sentence level. Then, fluency indicator was scored by the speed of the speech. The experimental results demonstrated that using Mel-frequency cepstral coefficients (MFCC) and the Hidden Markov Model (HMM) algorithm to identify the key words, the system achieved average recognition rate in answering keywords of 97.3%. There is a good possibility that the proposed method may be effective for key word recognition.","PeriodicalId":270809,"journal":{"name":"2019 6th International Conference on Systems and Informatics (ICSAI)","volume":"1 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":"131831702","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

Machine Learning Driven Network Routing 机器学习驱动的网络路由

2019 6th International Conference on Systems and Informatics (ICSAI) Pub Date : 2019-11-01 DOI: 10.1109/ICSAI48974.2019.9010507

Kun Yu, Li-Zhuang Tan, Xiao-jin Wu, Z. Gai

引用次数: 4