This paper proposes a new algorithm,named as the improved bottom-up algorithm,to approximate time series in a linear *** algorithm starts by the finest possible approximation of time series,so that n-1 segments are us...
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This paper proposes a new algorithm,named as the improved bottom-up algorithm,to approximate time series in a linear *** algorithm starts by the finest possible approximation of time series,so that n-1 segments are used to approximate the n-length time *** fitting errors of merging each pair of adjacent segments are *** algorithm iteratively merges the minimal fitting error pair until the number of segments meets a given value *** main novelty of the proposed algorithm is to devise a decision function to determine an optimal choice of *** decision function is composed of two parts,namely,the weighted fitness and trend similarity between the time series and its corresponding *** effectiveness of the proposed algorithm is illustrated via numerical and industrial examples.
We analyze two bottom-up reduction algorithms over binary trees that represent replaceable data within a certain system, assuming the binary search tree (BST) probabilistic model. These reductions are based on idempot...
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We analyze two bottom-up reduction algorithms over binary trees that represent replaceable data within a certain system, assuming the binary search tree (BST) probabilistic model. These reductions are based on idempotent and nilpotent operators, respectively. In both cases, the average size of the reduced tree, as well as the cost to obtain it, is asymptotically linear with respect to the size of the original tree. Additionally, the limiting distributions of the size of the trees obtained by means of these reductions satisfy a central limit law of Gaussian type. (c) 2006 Elsevier B.V. All rights reserved.
Decomposition of structural domains is and essential task in classifying protein structures, predicting protein function, and many other proteomics problems. As the number of known protein structures in PDB grows expo...
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Decomposition of structural domains is and essential task in classifying protein structures, predicting protein function, and many other proteomics problems. As the number of known protein structures in PDB grows exponentially, the need for accurate automatic domain decomposition methods becomes more essential. In this article, we introduce a bottom-up algorithm for assigning protein domains using a graph theoretical approach. This algorithm is based on a center-based clustering approach. For constructing initial clusters, members of an independent dominating set for the graph representation of a protein are considered as the centers. A distance matrix is then defined for these clusters. To obtain final domains, these clusters are merged using the compactness principle of domains and a method similar to the neighbor-joining algorithm considering some thresholds. the thresholds are computed using a training set consisting of 50 protein chains. The algorithm is implemented using C++ language and is named ProDomAs. To assess the performance of ProDomAs, its results are compared with seven automatic methods, against five publicly available benchmarks. The results show that ProDomAs outperforms other methods applied on the mentioned benchmarks. The performance of ProDomAs is also evaluated against 6342 chains obtained from ASTRAL SCOP 1.71 ProDomAs is freely available at http://***/software/prodomas.
Great improvements have been made in methodologies on reengineering of legacy systems to enhance the migration process. Unfortunately, taking into account the large scale of these systems, the duration of reengineerin...
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ISBN:
(纸本)9781424427932
Great improvements have been made in methodologies on reengineering of legacy systems to enhance the migration process. Unfortunately, taking into account the large scale of these systems, the duration of reengineering process can still be quite long which will make an impact on the business the systems supported. In this paper we propose a parallel iterative reengineering model to reduce the reengineering process duration. The model uses the method of Formal Concept Analysis to process the complex access relationship between legacy components and shared data. An algorithm named "bottom-up", which consists of two parts, is introduced to build the parallel schedule for the reengineering process. This model shortens the reengineering duration and has been proven to be successful in improving the transition system's performance in a comparative experiment.
Finding an optimal execution order of join operations is a crucial task in every cost-based query optimizer. Since there are many possible join trees for a given query, the overhead of the join (tree) enumeration algo...
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ISBN:
(纸本)9781424489589
Finding an optimal execution order of join operations is a crucial task in every cost-based query optimizer. Since there are many possible join trees for a given query, the overhead of the join (tree) enumeration algorithm per valid join tree should be minimal. In the case of a clique-shaped query graph, the best known top-down algorithm has a complexity of Theta(n(2)) per join tree, where n is the number of relations. In this paper, we present an algorithm that has an according O(1) complexity in this case. We show experimentally that this more theoretical result has indeed a high impact on the performance in other non-clique settings. This is especially true for cyclic query graphs. Further, we evaluate the performance of our new algorithm and compare it with the best top-down and bottom-up algorithms described in the literature.
As an open source library for large-scale terrain rendering in a continuous LOD high field, libMini takes a top-down method for static terrain rendering and achieves good perfounance. However, it uses the same top-dow...
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ISBN:
(纸本)9782952474788
As an open source library for large-scale terrain rendering in a continuous LOD high field, libMini takes a top-down method for static terrain rendering and achieves good perfounance. However, it uses the same top-down method to render elevation and texture for dynamic terrain. The method consumes a few seconds to update dynamic terrain and thus does not meet real-time requirements for rendering and leads to possible holes and gaps between adjacent terrain tiles. A millisecond-level real-time bottom-up libMini-based algorithm is proposed to render dynamic terrain while a method is presented to blend holes and gaps produced during the process of rendering dynamic tiled terrain.
Automatic generation control systems are designed to adjust electric power outputs of multiple generators simultaneously in accordance with the current load. However, the control instruction and the main steam pressur...
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Automatic generation control systems are designed to adjust electric power outputs of multiple generators simultaneously in accordance with the current load. However, the control instruction and the main steam pressure have significant impacts on the resulting active power generation of a conventional thermal generator, and the impacts may be associated with nonlinear characteristics. As a result, the control instruction requires an accurate modeling of the relationship between these three variables for a satisfactory control performance. This paper proposes a method to build a piecewise linear model for the nonlinear relationship from steady-state data hidden in historical data samples. The proposed method is composed by two main steps of steady-state interval detection and steady-state data segmentation. Historical data samples are grouped using the k-means clustering algorithm, and the time domains of each cluster are merged in a specific way to obtain the steady-state intervals. The steady-state data are taken as the samples means of data in the steady-state intervals. A bottom-up algorithm is utilized to partition the steady-state data into numbers of sets iteratively, and the parameters of the piecewise linear model for each data set are estimated by the least squares algorithm. The effectiveness of the proposed method is illustrated via industrial applications to two thermal power generation units.
k-Anonymity is a famous and widely used privacy principle for protecting private information. It requires that each tuple of a public released data table must be indistinguishable from at least other k -1 tuples. Give...
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k-Anonymity is a famous and widely used privacy principle for protecting private information. It requires that each tuple of a public released data table must be indistinguishable from at least other k -1 tuples. Given a table, finding an optimal k-anonymous version is NP-hard in most previous recoding "model". Thus, designing an efficient algorithm to find high-quality k-anonymous version is still challenge, though k-anonymity is well-researched. In recent years, hierarchical partition is proposed and widely accepted. Viewing the given table as a multidimensional space, each hierarchical partition of the space is a multidimensional recoding under some special constraints. Previous works need huge computation to find optimal hierarchical partition, and efficient algorithms just find a reasonable hierarchical partition. In this paper, we show that optimal hierarchical partition for k-anonymity can be obtained within polynomial time when a fixed quasi-identifier is given. We then design a bottom-up algorithm using dynamic approach. Through theoretical analysis and experiments, we show that our algorithm finds better results than related works, and our algorithm runs significantly fast comparing with other optimal algorithms for hierarchical partition.
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