• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.3012-3023
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• 1996

In this paper, we analyze the compression of optical soliton which is obtained by proceeding the optical pulse in FSDD(Fiber with Slowly Decreasing Dispersion) using both NSE(Nonlinear Schrodinger Equation) and GNSE(General Nonlinear Schrodinger Equation) and compare the results. We replace the FSDD with a sequence of fibers having different dispersion values and pompre the results with those obtained in FSDD. It is found that the same results in peak value and FWHM(Full width Half Maximum) can be obtained by replacing FSDD with a sequence of fibers having proper length. We vary the shape of initial pulse which is the input of FSDD and suggest the condition to obtain higher compression rate.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.247-253
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• 2017

Feature selection or variable selection is a data mining scheme for selecting highly relevant features with target concept from high dimensional data. It decreases dimensionality of data, and makes it easy to analyze clusters or classification. A feature selection scheme requires an evaluation function. Most of current evaluation functions are based on statistics or information theory, and they can evaluate only for single feature (one-dimensional data). However, features have interactions between them, and require evaluation function for multi-dimensional data for efficient feature selection. In this study, we propose modification of FSDD evaluation function for utilizing evaluation of multiple features using extended distance function. Original FSDD is just possible for single feature evaluation. Proposed approach may be expected to be applied on other single feature evaluation method.