• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.109.1-109.1
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• 2012

In the past few years, two-planet circumbinary systems (e.g., HW Vir, NN Ser, DP Leo and HU Aqr) have been detected around short-period eclipsing binaries using ground-based telescopes. The existence of these planets has been inferred by interpreting the O-C variations of the mid-eclipse times. We have tested the orbital stability of these systems and propose to use Light Travel Time Effect (LITE) to detect such circumbinary planets from the ground. We generated synthetically the LITE signal of a two-planet circumbinary system with the aim to apply an analytic LITE model to recover the underlying synthetic system. To mimic a degree of realism inherent to ground-based observations, we added to the synthetic LITE data white noise with a Gaussian distribution and sampled the synthetic LITE signal randomly. We successfully recovered the original system demonstrating that two-planet circumbinary systems can be detected using ground-based telescopes, provided the timing measurements of the mid-eclipses are sufficiently accurate and the observing baseline is long enough to ensure a sufficient coverage of all involved periods. We used HU Aqr as a test system and applied our model to its proposed planetary bodies considering near-circular orbits. We present the results of our calculations and discuss the LITE-detectability of a HU Aqr-like system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.10 no.3
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• pp.123-129
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• 1985

This paper describes the determination of threshold value in order to determine the presence of absence of weak signal with SNR of 0 dB in 100kHz bandwidth. As a detection method, it has been used a recent LOC structure fitting for detecting weak signal in stead of a conventional method like Neyman-Peason crtical criterion. The signal for detection is the OOK modulation signal used in data and morse code transmission. The non-Gaussian noise similar to Laplacian type has been chosen in transmission path. As a result of experiment, comparing probability of detection by one critical point with that by two critical points with fixing as arbitrary false alarm probability, we have found that method has been shown to be better than the conventional method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.9
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• pp.430-438
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• 2001

In this study, we introduce the adaptive Polynomial Neuro-Fuzzy Networks(PNFN) architecture generated from the fusion of fuzzy inference system and PNN algorithm. The PNFN dwells on the ideas of fuzzy rule-based computing and neural networks. Fuzzy inference system is applied in the 1st layer of PNFN and PNN algorithm is employed in the 2nd layer or higher. From these the multilayer structure of the PNFN is constructed. In order words, in the Fuzzy Inference System(FIS) used in the nodes of the 1st layer of PNFN, either the simplified or regression polynomial inference method is utilized. And as the premise part of the rules, both triangular and Gaussian like membership function are studied. In the 2nd layer or higher, PNN based on GMDH and regression polynomial is generated in a dynamic way, unlike in the case of the popular multilayer perceptron structure. That is, the PNN is an analytic technique for identifying nonlinear relationships between system's inputs and outputs and is a flexible network structure constructed through the successive generation of layers from nodes represented in partial descriptions of I/O relatio of data. The experiment part of the study involves representative time series such as Box-Jenkins gas furnace data used across various neurofuzzy systems and a comparative analysis is included as well.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2519-2521
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• 2003

In this paper, we propose Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. It is shown that this network exhibits a dynamic structure as the number of its layers as well as the number of nodes in each layer of the SOFPNN are not predetermined (as this is the case in a popular topology of a multilayer perceptron). As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership function are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SOFPNN architectures, that is, the basic and modified one with both the generic and the advanced type. The superiority and effectiveness of the proposed SOFPNN architecture is demonstrated through nonlinear function numerical example.

• Korean Journal of Remote Sensing
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• v.23 no.1
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• pp.33-42
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• 2007

In this paper, an iterative MAP approach using a Bayesian model based on the lognormal distribution for image intensity and a GRF for image texture is proposed for despeckling the SAR images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. MRFs have been used to model spatially correlated and signal-dependent phenomena for SAR speckled images. The MRF is incorporated into digital image analysis by viewing pixel types as slates of molecules in a lattice-like physical system defined on a GRF Because of the MRF-SRF equivalence, the assignment of an energy function to the physical system determines its Gibbs measure, which is used to model molecular interactions. The proposed Point-Jacobian Iterative MAP estimation method was first evaluated using simulation data generated by the Monte Carlo method. The methodology was then applied to data acquired by the ESA's ERS satellite on Nonsan area of Korean Peninsula. In the extensive experiments of this study, The proposed method demonstrated the capability to relax speckle noise and estimate noise-free intensity.

• Bulletin of the Korean Chemical Society
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• v.16 no.9
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• pp.873-885
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• 1995

The local structural and thermodynamical properties of blends A-B/H of a diblock copolymer A-B and a homopolymer H are studied using the polymer reference interaction site model (RISM) integral equation theory with the mean-spherical approximation closure. The random phase approximation (RPA)-like static scattering function is derived and the interaction parameter is obtained to investigate the phase transition behaviors in A-B/H blends effectively. The dependences of the microscopic interaction parameter and the macrophase-microphase separation on temperature, molecular weight, block composition and segment size ratio of the diblock copolymer, density, and concentration of the added homopolymer, are investigated numerically within the framework of Gaussian chain statistics. The numerical calculations of site-site interchain pair correlation functions are performed to see the local structures for the model blends. The calculated phase diagrams for A-B/H blends from the polymer RISM theory are compared with results by the RPA model and transmission electron microscopy (TEM). Our extended formal version shows the different feature from RPA in the microscopic phase separation behavior, but shows the consistency with TEM qualitatively. Scaling relationships of scattering peak, interaction parameter, and temperature at the microphase separation are obtained for the molecular weight of diblock copolymer. They are compared with the recent data by small-angle neutron scattering measurements.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.49-60
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• 2019

Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.208-215
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• 2023

Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.71.1-71.1
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• 2021

We perform disk-halo decomposition of the Large Magellanic Cloud (LMC) using a novel HI velocity field extraction method, aimed at better deriving its HI kinematics and thus mass distribution in the galaxy including both baryons and dark matter. We decompose all the line-of-sight velocity profiles of the combined HI data cube of the LMC, taken from the Australia Telescope Compact Array (ATCA) and Parkes radio telescopes with an optimal number of Gaussian components. For this, we use a novel tool, the so-called BAYGAUD which performs profile decomposition based on Bayesian MCMC techniques. From this, we disentangle turbulent non-ordered HI gas motions from the decomposed gas components, and produce an HI bulk velocity field which better follows the global circular rotation of the galaxy. From a 2D tilted-ring analysis of the HI bulk velocity field, we derive the rotation curve of the LMC after correcting for its transverse, nutation and precession motions. The dynamical contributions of baryons like stars and gaseous components which are derived using the Spitzer 3.6 micron image and the HI data are then subtracted from the total kinematics of the LMC. Here, we present the bulk HI rotation curve, the mass models of stars and gaseous components, and the resulting dark matter density profile of the LMC.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.60.3-61
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• 2020

We perform disk-halo decomposition of the Large Magellanic Cloud (LMC) using a novel HI velocity field extraction method, aimed at better deriving its HI kinematics and thus the dark matter density profile. For this, we use two newly developed galaxy kinematic analysis tools, BAYGAUD and 2DBAT which have been used for the kinematic analysis of resolved galaxies from Australian Square Kilometre Array (ASKAP) observations like WALLABY which is an all-sky HI galaxy survey in southern sky. By applying BAYGAUD to the combined HI data cube of the LMC taken with the Australia Telescope Compact Array (ATCA) and Parkes radio telescopes, we decompose all the line-of-sight velocity profiles into an optimal number of Gaussian components based on Bayesian MCMC techniques. From this, we disentangle turbulent non-circular gas motions from the overall rotation of the galaxy. We then derive the rotation curve of the LMC by applying 2DBAT to the separated circular motions. The rotation curve reflecting the total kinematics of the LMC, dark and baryonic matters is then be combined with the mass models of baryons, mainly stellar and gaseous components in order to examine the dark matter distribution. Here, we present the analysis of the extracted HI gas maps, rotation curve, and J, H and K-band surface photometry of the LMC.