20633806
Inelastic Simulation of Insect Cuticle Using Artificial Neural Network
Neural networks have been availably applied to the simulations of the mechanical behaviors of many materials. In this work, a neural network material model is built for the simulation of the inelastic behavior of biocomposite insect cuticle. Radial basis function neural network is adopted in the simulation for that the neural network has the characteristic of fast and exactly completing the simulation. In the construction of the neural network, the network is trained based on the experimental data of the load-displacement relationship of a chafer cuticle. A strain-controlled mode and the iterative method of data are adopted in the training process of the neural network. The obtained neural network model is used for the simulation of the inelastic behavior of another kind of insect cuticle. It is shown that the obtained material model of the radial basis function neural network can satisfactorily simulate the inelastic behavior of insect cuticle.
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.
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.
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.
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
15935457
A Design of Data Rebuilding for Decision Support in E-Government System
The E-Government System is composed of three networks and one database. The business network within government offices (the Internal Net), the resource network in central and local administrations and departments (the External Net), the public information network (PIN) relying on the Internet. It is necessary to rebuild data in the three networks of the system in order to construct an effective electronic information database. In data rebuilding, a data exchange platform is set up to enable data sharing between the three networks. The data exchange platform is implemented in a structure that combines the intelligent decision support system (IDSS) and data warehouse (DW).
17507770
An Improved Level Set for Liver Segmentation and Perfusion Analysis in MRIs
Determining liver segmentation accurately from MRIs is the primary and crucial step for any automated liver perfusion analysis, which provides important information about the blood supply to the liver. Although implicit contour extraction methods, such as level set methods (LSMs) and active contours, are often used to segment livers, the results are not always satisfactory due to the presence of artifacts and low-gradient response on the liver boundary. In this paper, we propose a multiple-initialization, multiple-step LSM to overcome the leakage and over-segmentation problems. The multiple-initialization curves are first evolved separately using the fast marching methods and LSMs, which are then combined with a convex hull algorithm to obtain a rough liver contour. Finally, the contour is evolved again using global level set smoothing to determine a precise liver boundary. Experimental results on 12 abdominal MRI series showed that the proposed approach obtained better liver segmentation results, so that a refined liver perfusion curve without respiration affection can be obtained by using a modified chamfer matching algorithm and the perfusion curve is evaluated by radiologists.
3708864
A New Systolic Architecture for Modular Division
A new systolic architecture for modular division is proposed in this paper. The architecture is based on a new hardware algorithm for modular division that is extended from the plus-minus algorithm for the greatest common divisor computation. Both the area complexity and the time complexity of the new architecture are linear with respect to the operand bit length. Compared to the architecture using a redundant number representation proposed by Kaihara and Takagi, the new architecture allows a doubled throughput due primarily to the greatly reduced critical path delay, while the area is only about 20 percent larger. Moreover, it is shown that, with a small addition to the control logic, the architecture can also be used to perform Montgomery modular multiplication, thereby efficiently realizing a unified modular multiplier/divider
38592732
Remote Sensing Image Classification Based on Evidence Theory and Neural Networks
An effective method based on evidence theory and neural networks to classify the remote sensing images is brought forward in the paper. Firstly, with the spatial information in consideration, the original image is smoothed with a modified gradient inverse weighting smoothing method, then the classification of the original and smoothed images is performed separately using a B-P neural network. Finally, result comes out after fusing the two classification results with D-S evidence theory. Experimental results demonstrate that the proposed method is effective and can improve the classification accuracy.
6208997
Network Attack-Defense Simulation Training System Based on HLA
Soldiers possessing of network attack-defense ability are the key factor for future information war. Aiming at the problem of lacking daily training and drilling environment, a method of building Network Attack-Defense Simulation Training System (NADSTS) based on HLA is put forward. With the method, attack and defense training are designed as different federation member. The system is divided to presentation, application and adapter layer clearly. Key technologies involves network attack-defense, network simulation and simulation driving are also presented. Software is developed based on plug-in framework. Its simulation examples show greatest traits on building similar large-scale simulation system.