This paper describes the nearest neighbor (NN) search algorithm on the GBD(generalized BD) tree. The GBD tree is a spatial data structure suitable for two-or three-dimensional data and has good performance characteris...This paper describes the nearest neighbor (NN) search algorithm on the GBD(generalized BD) tree. The GBD tree is a spatial data structure suitable for two-or three-dimensional data and has good performance characteristics with respect to the dynamic data environment. On GIS and CAD systems, the R-tree and its successors have been used. In addition, the NN search algorithm is also proposed in an attempt to obtain good performance from the R-tree. On the other hand, the GBD tree is superior to the R-tree with respect to exact match retrieval, because the GBD tree has auxiliary data that uniquely determines the position of the object in the structure. The proposed NN search algorithm depends on the property of the GBD tree described above. The NN search algorithm on the GBD tree was studied and the performance thereof was evaluated through experiments.展开更多
We propose an influential set based moving k keyword query processing model, which avoids the shortcoming of safe region-based approaches that the update cost and update frequency cannot be optimized simultaneously. B...We propose an influential set based moving k keyword query processing model, which avoids the shortcoming of safe region-based approaches that the update cost and update frequency cannot be optimized simultaneously. Based on the model, we design a parallel query processing method and a parallel validation method for multicore processing platforms. The time complexity of the algorithms is O((log|D|+p.k)/p.k)?and O(log p.k), respectively, which are all O(1/k) times the time complexity of the state-of-the-art method. The experiment result confirms the superiority of our algorithms over the state-of-the-art method.展开更多
为解决运动想象脑电(electroencephalogram, EEG)信号多分类传输速率慢、准确率低的问题,本研究利用“一对多”滤波组共空间模式(one vs rest filter bank common spatial pattern, OVR-FBCSP)和稀疏嵌入(sparse embeddings, SE)提出了...为解决运动想象脑电(electroencephalogram, EEG)信号多分类传输速率慢、准确率低的问题,本研究利用“一对多”滤波组共空间模式(one vs rest filter bank common spatial pattern, OVR-FBCSP)和稀疏嵌入(sparse embeddings, SE)提出了一种基于SE的多分类EEG信号分类方法。为降低多类任务特征提取的复杂度,提高分类效率,本方法首先采用OVR-FBCSP进行EEG信号特征提取;然后对其相应的标签矩阵进行低维嵌入,构建稀疏嵌入模型,分别计算训练和测试数据的嵌入矩阵;最后在嵌入空间中对训练和测试数据执行k最近邻(k-nearest neighbor, kNN)分类。本研究在BCI Competition IV-2a公开数据集进行了实验测试,并与其他分类方法进行了对比。实验结果表明,本研究方法拥有较高的分类准确率和较短的分析时间。展开更多
文摘This paper describes the nearest neighbor (NN) search algorithm on the GBD(generalized BD) tree. The GBD tree is a spatial data structure suitable for two-or three-dimensional data and has good performance characteristics with respect to the dynamic data environment. On GIS and CAD systems, the R-tree and its successors have been used. In addition, the NN search algorithm is also proposed in an attempt to obtain good performance from the R-tree. On the other hand, the GBD tree is superior to the R-tree with respect to exact match retrieval, because the GBD tree has auxiliary data that uniquely determines the position of the object in the structure. The proposed NN search algorithm depends on the property of the GBD tree described above. The NN search algorithm on the GBD tree was studied and the performance thereof was evaluated through experiments.
文摘We propose an influential set based moving k keyword query processing model, which avoids the shortcoming of safe region-based approaches that the update cost and update frequency cannot be optimized simultaneously. Based on the model, we design a parallel query processing method and a parallel validation method for multicore processing platforms. The time complexity of the algorithms is O((log|D|+p.k)/p.k)?and O(log p.k), respectively, which are all O(1/k) times the time complexity of the state-of-the-art method. The experiment result confirms the superiority of our algorithms over the state-of-the-art method.
基金Supported by the Major State Basic Research Development Program of China (973Program), No.2006CB701305the Scientific Research Foundation of Jiangsu Key Laboratory of Resources and Environmental Information Engineering (China University of Mining and Technology)(Grant No.20080101)Open Research Fund Program of the Geomatics and Applications Laboratory,Liaoning Technical University,Grant No.2007001
