2016 International Conference on Control, Automation and Information Sciences (ICCAIS)最新文献_第5页

Multiple model box-particle cardinality balanced multi-target multi-Bernoulli filter for multiple maneuvering targets tracking 多模型盒粒子基数平衡多目标多伯努利滤波用于多机动目标跟踪

2016 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2016-10-01 DOI: 10.1109/ICCAIS.2016.7822438

Feng Yang, Wanying Zhang, Yan Liang, Xiaoxu Wang, Linfeng Xu

{"title":"Multiple model box-particle cardinality balanced multi-target multi-Bernoulli filter for multiple maneuvering targets tracking","authors":"Feng Yang, Wanying Zhang, Yan Liang, Xiaoxu Wang, Linfeng Xu","doi":"10.1109/ICCAIS.2016.7822438","DOIUrl":"https://doi.org/10.1109/ICCAIS.2016.7822438","url":null,"abstract":"Cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has been proved as a promising method in the context of multi-target tracking with an unknown number of targets, clutter and false alarms. For tracking maneuvering targets, the CBMeMBer filter has been extended by using jump Markov models (JMM). However, the standard particle implementation of the multiple model CBMeMBer (MM-CBMeMBer) filter requires a large number of particles in order to obtain a satisfactory performance. Based on the capability of box-particle filter to process measurements which are affected by bounded errors of unknown distributions and biases, a box-particle implementation of the MM-CBMeMBer filter is proposed. Simulation result shows that the proposed MM-Box-CBMeMBer filter can obtain similar accuracy results with a MM-Particle-CBMeMBer filter but considerably reduce the computational costs. Meanwhile, in the presence of strongly biased measurements, it is shown that the MM-Box-CBMeMBer filter is superior to the MM-Particle-CBMeMBer filter.","PeriodicalId":407031,"journal":{"name":"2016 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114286829","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

Applying active learning strategy to classify large scale data with imbalanced classes 应用主动学习策略对类别不平衡的大规模数据进行分类

2016 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2016-10-01 DOI: 10.1109/ICCAIS.2016.7822443

Phairod Tuntiwachiratrakun, P. Vateekul

{"title":"Applying active learning strategy to classify large scale data with imbalanced classes","authors":"Phairod Tuntiwachiratrakun, P. Vateekul","doi":"10.1109/ICCAIS.2016.7822443","DOIUrl":"https://doi.org/10.1109/ICCAIS.2016.7822443","url":null,"abstract":"Nowadays, classification tasks are very challenging because data is usually large and imbalanced. They can cause low prediction accuracy and high computation costs. Active Learning is a technique that employs only a small set of data to construct an initial classification model. Then, it iteratively improves the model by incrementally learning from the misclassified examples. In this paper, we aim to improve prediction accuracy by applying Active Learning. To solve the imbalance issue, the active model was iteratively updated based on the G-mean, and the under sampling sampling was also applied. The proposed algorithm was suitable for large scale data since it did not need to use the whole data set to construct a model. The experiment was conducted on two standard corpuses, one of which contained more than 100,000 examples. The result showed that a prediction performance of standard technique (Neural Network) can be improved by applying the Active Learning strategy for 5%–13%. Furthermore, this technique also outperformed other classical classification algorithms including K-nearest neighbors (kNN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB) and Artificial Neural Network (ANN).","PeriodicalId":407031,"journal":{"name":"2016 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124138663","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

Multidimensional cube for representing flight data in visualization-based system for tracking flyer 基于可视化的飞行跟踪系统中飞行数据的多维立方体表示

2016 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2016-10-01 DOI: 10.1109/ICCAIS.2016.7822446

H. T. Nguyen, P. Tran

{"title":"Multidimensional cube for representing flight data in visualization-based system for tracking flyer","authors":"H. T. Nguyen, P. Tran","doi":"10.1109/ICCAIS.2016.7822446","DOIUrl":"https://doi.org/10.1109/ICCAIS.2016.7822446","url":null,"abstract":"Flyer is a means utilized popularly and effectively in recording landscape from above, collecting data of environment and weather, contributing to field works, especially conveying samples and medications from or to patients living in areas isolated by flood. It is necessary to track flyer's locations and characteristics for its activities. Visualization-based system for tracking flyer enables user to monitor flyer by analyzing visually multivariable flight data. Visualization-based system for tracking flyer using 3D cube represents flight data including ground position and elevation, using space-time cube (STC) represents ground position and time, using 4D cube represents time, ground position, and elevation. Meanwhile, multipurpose flyer needs to be tracked not only time, ground position, elevation, but also characteristics. The paper proposes multidimensional cube (mD cube) for visualization-based tracking system to represent multivariable flight data including time, location, and characteristics. Multidimensional cube results from the combination of a 4D cube with a multivariate cube representing characteristics changing over time. The mD cube represents visually multivariable flight data in visualization-based tracking system to enable user to monitor flyer. With mathematical reasoning, user can understand the significance of multivariable flight data by responding several analytical tasks.","PeriodicalId":407031,"journal":{"name":"2016 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129964891","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: 4

Multi region segmentation algorithm based on edge preserving for molten pool image 基于边缘保持的熔池图像多区域分割算法

2016 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2016-10-01 DOI: 10.1109/ICCAIS.2016.7822462

Fei Gao, Mingli Lu, Benlian Xu, Qian Zhang

引用次数: 2