Current Issue : July - September Volume : 2013 Issue Number : 3 Articles : 6 Articles
A new 5-point ternary interpolating scheme with a shape parameter is introduced. The resulting curve is C2 for a certain range of\r\nparameters. The differentiable properties of the proposed scheme to extend its application in the generation of smooth curves are\r\nexplored. Application of the proposed scheme is given to show its visual smoothness. The scheme is also extended to a 5-point\r\ntensor product ternary interpolating scheme, and its numerical examples are also included....
We discuss computation of Gr�¨obner bases using approximate arithmetic for coefficients. We show how certain considerations of\r\ntolerance, corresponding roughly to absolute and relative error from numeric computation, allow us to obtain good approximate\r\nsolutions to problems that are overdetermined.We provide examples of solving overdetermined systems of polynomial equations.\r\nAs a secondary feature we show handling of approximate polynomial GCD computations, using benchmarks from the literature....
Many optimization problems (from academia or industry) require the use of a local search to find a satisfying solution in a\r\nreasonable amount of time, even if the optimality is not guaranteed. Usually, local search algorithms operate in a search space\r\nwhich contains complete solutions (feasible or not) to the problem. In contrast, in Consistent Neighborhood Search (CNS), after\r\neach variable assignment, the conflicting variables are deleted to keep the partial solution feasible, and the search can stop when all\r\nthe variables have a value. In this paper, we formally propose a new heuristic solution method, CNS, which has a search behavior\r\nbetween exhaustive tree search and local search working with complete solutions. We then discuss, with a unified view, the great\r\nsuccess of some existing heuristics, which can however be considered within the CNS framework, in various fields: graph coloring,\r\nfrequency assignment in telecommunication networks, vehicle fleet management with maintenance constraints, and satellite range\r\nscheduling. Moreover, some lessons are given in order to have guidelines for the adaptation of CNS to other problems....
Density-based clustering methods are known to be robust against outliers in data; however, they are sensitive to user-speci??ed\r\nparameters, the selection of which is not trivial. Moreover, relational data clustering is an area that has received considerably less\r\nattention than object data clustering. In this paper, two approaches to robust density-based clustering for relational data using\r\nevolutionary computation are investigated....
Item response theory (IRT) is a popular approach used for addressing statistical problems in psychometrics as well as in other\r\nfields. The fully Bayesian approach for estimating IRT models is computationally expensive. This limits the use of the procedure\r\nin real applications. In an effort to reduce the execution time, a previous study shows that high performance computing provides\r\na solution by achieving a considerable speedup via the use of multiple processors. Given the high data dependencies in a single\r\nMarkov chain for IRT models, it is not possible to avoid communication overhead among processors. This study is to reduce\r\ncommunication overhead via the use of a row-wise decomposition scheme. The results suggest that the proposed approach\r\nincreased the speedup and the efficiency for each implementation while minimizing the cost and the total overhead. This further\r\nsheds light on developing high performance Gibbs samplers for more complicated IRT models....
Many network monitoring applications and performance analysis tools are based on the study of an aggregate measure of network\r\ntraffic, for example, number of packets in transit (NPT). The simulation modeling and analysis of this type of performance\r\nindicator enables a theoretical investigation of the underlying complex system through different combination of network setups\r\nsuch as routing algorithms, network source loads or network topologies. To detect stationary increase of network source load, we\r\npropose a dynamic principal component analysis (PCA) method, first to extract data features and then to detect a stationary load\r\nincrease. The proposed detection schemes are based on either the major or the minor principal components of network traffic\r\ndata. To demonstrate the applications of the proposed method, we first applied them to some synthetic data and then to network\r\ntraffic data simulated from the packet switching network (PSN) model. The proposed detection schemes, based on dynamic PCA,\r\nshow enhanced performance in detecting an increase of network load for the simulated network traffic data. These results show\r\nusefulness of a new feature extraction method based on dynamic PCA that creates additional feature variables for event detection\r\nin a univariate time series....
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