44624377
Robust Adaptive Fuzzy Control for Uncertain Nonlinear Systems
Two different fuzzy control approaches are proposed for a class of nonlinear systems with mismatched uncertainties, transformable to the strict-feedback form. A fuzzy logic system (FLS) is used as a universal approximator to approximate unstructured uncertain functions and the bounds of the reconstruction errors are estimated online. By employing special design techniques, the controller singularity problem is completely avoided for the two approaches. Furthermore, all the signals in the closed-loop systems are guaranteed to be semi-globally uniformly ultimately bounded and the outputs of the system are proved to converge to a small neighborhood of the desired trajectory. The control performance can be guaranteed by an appropriate choice of the design parameters. In addition, the proposed fuzzy controllers are highly structural and particularly suitable for parallel processing in the practical applications.
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
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
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.
1721891
Lempel–Ziv Factorization Using Less Time & Space
Abstract.For 30 years the Lempel–Ziv factorization LZx of a string x = x[1..n] has been a fundamental data structure of string processing, especially valuable for string compression and for computing all the repetitions (runs) in x. Traditionally the standard method for computing LZx was based on Θ(n)-time (or, depending on the measure used, O(n log n)-time) processing of the suffix tree STx of x. Recently Abouelhoda et al. proposed an efficient Lempel–Ziv factorization algorithm based on an “enhanced” suffix array – that is, a suffix array SAx together with supporting data structures, principally an “interval tree”. In this paper we introduce a collection of fast space-efficient algorithms for LZ factorization, also based on suffix arrays, that in theory as well as in many practical circumstances are superior to those previously proposed; one family out of this collection achieves true Θ(n)-time alphabet-independent processing in the worst case by avoiding tree structures altogether.
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.
33950497
An Effective Method for Chinese Grammar Rule Learning
A new method of Chinese grammar rules learning is put forward in this paper. The key point of this method is to use part-of-speech (POS), semantic and contextual information together in learning and expressing Chinese grammar rules. In this way, not only Context Free Grammar (CFG) rules can be learnt, but also the ambiguous structures in POS can be identified automatically. Furthermore, non-ambiguous semantic rules and forbidden rules can be produced from ambiguous rules by using semantic and contextual information. Experimental results demonstrate that the complexity of Chinese parsing is greatly reduced using the different rules learnt by our method.
2501005
Coordinating Multiple Agents via Reinforcement Learning
In this paper, we attempt to use reinforcement learning techniques to solve agent coordination problems in task-oriented environments. The Fuzzy Subjective Task Structure model (FSTS) is presented to model the general agent coordination. We show that an agent coordination problem modeled in FSTS is a Decision-Theoretic Planning (DTP) problem, to which reinforcement learning can be applied. Two learning algorithms, ‘‘coarse-grained’’ and ‘‘fine-grained’’, are proposed to address agents coordination behavior at two different levels. The ‘‘coarse-grained’’ algorithm operates at one level and tackle hard system constraints, and the ‘‘fine-grained’’ at another level and for soft constraints. We argue that it is important to explicitly model and explore coordination-specific (particularly system constraints) information, which underpins the two algorithms and attributes to the effectiveness of the algorithms. The algorithms are formally proved to converge and experimentally shown to be effective.
110367358
Unified synthesis techniques for high performance fpga designs
As the device geometry keeps shrinking in today's deep sub-micron technologies, interconnect delay dominates the critical path. In the current FPGA design flow, there are two major drawbacks: the separation of technology-independent optimization and technology mapping in the traditional logic synthesis flow, and the separation of logic synthesis and physical implementation in the traditional implementation flow. Such drawbacks result in sub-optimal performance and a long timing closure cycle. In this thesis we propose the utilization of unified synthesis techniques in the context of FPGAs to address these issues and improve the overall design performance. In Chapter 3 we bridge the gap between technology-independent optimization and technology mapping by combining logic decomposition and mapping. In Chapter 4, we introduce a novel algorithm named SCPlace [17] to perform simultaneous clustering during placement. In Chapter 5 we introduce an algorithm named SPD to perform logic duplication during placement. In Chapter 6 we build the first complete academic CAD flow, which can be easily plugged in to a state-of-the-art commercial tool, Altera's Quartus 4.0 software. Our unified synthesis and placement tool, SPCD, combines both the clustering and duplication techniques. Our experimental results show up to over 30% improvement in performance compared with the current state-of-the-art academic flow.