13951708
OBSI: Object Based Storage System for Massive Image Databases
The popularity of online free album services in the recent prevailing Web2.0 paradigm has posed great challenges to the existing image storage systems. In this paper, we analyze the characteristics of online image storage techniques from a system perspective. We present a novel framework called OBSI, which stands for an object based storage system for storing and retrieving images of online album services. In the design of OBSI we employ a novel divide and conquer approach which subdivides OBSI into two layers: an object layer and a chunk layer. The chunk layer handles the "dirty work", such as disk space management and reliability guarantee. The object layer provides a uniform object storage and retrieval interface to applications. OBSI reduces disk access by employing a simple and yet effective storage allocation algorithm which allocates storage objects continuously as clustered to their respective metadata. Experimental results show that OBSI provides high efficiency and throughput in image storage and retrieval services.
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.
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.
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.
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
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
14934044
ANSYS-Based Dynamic Analysis of High-Speed Motorized Spindle
In the paper, the characteristics of mode and harmonic response of the axis of the high-speed motorized spindle are presented with the FEM (Finite Element Method). A dual-dimension model with the FEM is developed and simulated using ANSYS commercial software. The result shows that the rated rotating speed of the high-speed motorized spindle is far smaller than the first-order natural resonance speed, and the dynamic displacement and stiffness do not change significantly compared with the static performance of working of the motorized spindle. The dynamic characteristics of the motorized spindle are accord with the requirements of design.
5453876
Coordinating multi-agents using JavaSpaces
In recent years, multiagent systems have become a new attractive paradigm for developing Internet-based enterprise applications. In this paper, we investigate the agent coordination issue of multiagent systems. We explore the emerging JavaSpace technology for achieving coordination within a multi-level supply chain management environment. JavaSpace is a recent realization of the classic Linda model. The tuple space of the Linda model provides a convenient way for agent communication and coordination. The coordination protocol for the supply chain management system is presented. The protocol is designed based on JavaSpace, and is described using colored Petri nets. We argue that the emerging JavaSpaces technology provides a convenient, yet flexible approach to agent coordination in multiagent system environment.
7031383
Image retrieval based on Sugeno fuzzy integral
In this paper, we propose a fuzzy similarity measure based on the Sugeno fuzzy integral and apply it to the content-based image retrieval. Fuzzy measure is used to formulate the subjective feedback information. Experimental results show that the proposed method can improve the efficiency and reliability of image retrieval system. Our method of formulating the subjective feedback information performs better than the weighted average operator (WAO) and a Choquet-integral-based similarity measure.
120558441
Statistical Error Analysis for the Direct Simulation Monte Carlo Technique
The statistical error associated with the direct simulation Monte Carlo technique is studied when it is applied to nonequilibrium hypersonic and nozzle flows. A root mean square (rms) error is employed as an indicator of the level of the statistical fluctuations. The effects of number of particles per cell and sample size are analyzed and quantified. It is found that in order to adequately model the physics of interest, the number of particles in the simulation must be greater than a certain minimum. An equation is developed to model and analyze the rms errors. A range is provided of the appropriate number of particles to be employed in the simulation in order to achieve the smallest statistical error at a fixed computational cost. Values are also recommended for the maximum number of sampling time steps to be used for efficient computation on memory limited computers. The effects of collision model and of cloning particles on the statistical scatter are analyzed.
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.