35588617
Predictive Skyline Queries for Moving Objects
The existing works on skyline queries mainly assume static datasets. However, querying skylines on a database of moving objects is of equal importance, if not more. We propose a framework for processing predictive skyline queries for moving objects which also contain other dynamic and static attributes. We present two schemes, namely RBBS and TPBBS, to answer predictive skyline queries. Experimental results show that TPBBS considerably outperforms RBBS.
5747087
Efficient Processing of Warping Time Series Join of Motion Capture Data
Discovering non-trivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We first address this problem by defining it as a problem of l-e-join over two time series. Given two time series, the goal of l-e-join is to find those non-trivial matching subsequences by detecting maximal l-connections from the e-matching matrix of the two time series. Given a querying motion sequence, the l-e-join can be applied to retrieve all connectable motion sequences from a database of motion sequences. To support efficient l-e-join of time series, we propose a two-step filter-and-refine algorithm, called Warping Time Series Join (WTSJ) algorithm. The filtering step serves to prune those sparse regions of the e-matching matrix where there are no maximal l-connections without incurring costly computation. The refinement step serves to detect closed l-connections within regions that cannot be pruned by the filtering step. To speed up the computation of e-matching matrix, we propose a block-based time series summarization method, based on which the block-wise e-matching matrix is first computed. Lots of pairwise distance computation of elements can then be avoided by applying the filtering algorithm on the block-wise e-matching matrix.Extensive experiments on l-e-join of motion capture sequences are conducted. The results confirm the efficiency and effectiveness of our proposed algorithm in processing l-e-join of motion capture time series.
8051159
A Web-based Framework For On-line Collaborative Learning
This paper presents a framework for design of online courses and collaborative learning based on Web technologies. It provides the following features: construction of structured learning materials in a distributed environment; personalization of course contents in the run-time that adapts to the progress of individual students; it can supply relevant Web resources to the students by analyzing the learning history dynamically; students in a group could exchange their learning experiences and discover relevant knowledge via the communication channels provided by the framework. The proposed techniques could be deployed to a variety of online computer-assisted modules to improve the effectiveness of Web-based e-learning
116588251
On-line evolutionary exponential family mixture
This paper deals with evolutionary clustering, which refers to the problem of clustering data with distribution drifting along time. Starting from a density estimation view to clustering problems, we propose two general on-line frameworks. In the first framework, i.e., historical data dependent (HDD), current model distribution is designed to approximate both current and historical data distributions. In the second framework, i.e., historical model dependent (HMD), current model distribution is designed to approximate both current data distribution and historical model distribution. Both frameworks are based on the general exponential family mixture (EFM) model. As a result, all conventional clustering algorithms based on EFMs can be extended to evolutionary setting under the two frameworks. Empirical results validate the two frameworks.
8340253
UGE4B: An Universal Grid Environment for Bioinformatics Research
Recent years, grid technology has been used widely in the fields of scientific research. In this paper, we present a universal grid environment for bioinformatics research (UGE4B). Although there have been several similar projects successfully constructed in the world, we need to meet some unique requirements from bioinformatics research in China. UGE4B consists of 6 components, including portal, task management, data management, information server, security and computing node. As a national-wide bioinformatics research platform, UGE4B is built through interconnecting multiple sub grids provided by each bioinformatics institutes. Besides provides a universal portal to end users for bioinformatics computing and data accessing, UGE4B also supported some special bioinformatics applications
26669443
Bottom-up discovery of frequent rooted unordered subtrees
In the past decade, XML has emerged as the standard language for information exchanging over the Internet. Due to its tree-structure paradigm, XML is superior for its capability of storing, querying, and manipulating complex data. Therefore, discovering frequent tree patterns over tree-structured data has become an interesting topic for XML data management. In this paper, we propose a tree mining algorithm, named BUXMiner, for finding a special class of frequent trees, called rooted unordered trees, from a tree-structured database. BUXMiner employs an efficient bottom-up approach to enumerate all candidate trees over a compact global tree guide and computes the frequent trees based on the tree guide. In addition to BUXMiner, we also propose a mining approach called BUMXMiner to discover the maximal frequent rooted unordered trees. We compare BUXMiner with previous tree-structure mining algorithms, namely XQPMinerTID and FastXMiner, which were also proposed to discover rooted unordered trees. The experimental results show that our algorithm outperforms XQPMinerTID and FastXMiner in terms of efficiency. The performance results from real-world applications also indicate the usefulness of our proposed tree mining algorithms in a variety of web applications, such as analysis of web page access patterns and mining frequent XML query patterns for caching.
11514442
A test pattern ordering algorithm for diagnosis with truncated fail data
In this paper, we propose a test pattern ordering algorithm for fault diagnosis. Test pattern ordering is effective in situations where the fail log is truncated and contains a limited number of fail data. In such cases, higher diagnostic resolution can be achieved with the test set appropriately ordered. Test pattern ordering is independent of the diagnosis algorithm used. The higher resolution achieved by test pattern ordering is obtained at no additional cost once the test patterns have been appropriately ordered. Experimental results on two industrial designs are presented to demonstrate the effectiveness of the proposed method
6824407
Defect aware test patterns
A method to generate test patterns referred to as defect aware test patterns is proposed. Defect aware test patterns increase the ability to detect unmodeled defects. The proposed method can be used with any test generation procedure to improve the effectiveness of the tests in detecting unmodeled defects. Experimental results on several industrial designs show the effectiveness of defect aware tests. We also propose a measure to estimate the effectiveness of given test sets in detecting unmodeled defects.
15486362
Simulation Training Platform for Large Tactical Communication Equipment Based on VR
A simulation training platform aiming at the difficult problem of training is presented. Platform is built based on HLA and its framework is designed to hierarchical structure. Models of training equipment are created with software named GL Studio and Multigen Creator. Key technologies of modeling process are detailed. Equipment operations are layered and its rules are described with XML document. Driving framework programming with Vega Prime API can integrate 3D models, operation rules, special effect and whole scene to a platform system based on VR. Finally, plug-in soft framework is adopted to implement the system and simulation examples are introduced to prove the feasibility of the platform.
13987553
Assessment of Color Video Quality with Singular Value Decomposition of Complex Matrix
Most proposed video quality assessment approaches in the literature are only applied to the luminance layer of frame. Others are applied to luminance and chrominance layers respectively and then pooling by means of grayscale image quality metrics. In this paper, we proposed a full-reference objective video quality assessment called CMSVD,which treats luminance and chrominance parts as a whole and considers multiple factors of the human visual system(HVS)and video features. The algorithm is tested on the video quality experts group (VQEG) phase I FR-TV test data set.Experiments show that it has good correlation with perceived video quality.
39222179
A New ART Neural Networks for Remote Sensing Image Classification
A new ART2A-C algorithm based on fuzzy operators to cluster the remote sensing images and aerials is proposed in this paper. By combining two ART ANNs with higher performance, the traditional ART2A-C is developed with the fuzzy operators introduced in matching rule. Then the proposed method is applied to the classification and the new network is implemented as well as two other existed ARTs respectively. Experimental results show that the new method outperforms the traditional ones.