• Journal of Surface Science and Engineering
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• v.53 no.3
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• pp.116-123
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• 2020

Ion beam irradiation induces self-organization of nanostructure on the surface of polymer film. We show that the incident angle of Ar ions on polyethylene naphthalate(PEN) film changes self-organized nanostructure. PEN film was irradiated by argon ion beams with the ion incident angle of 0°, 30°, 45°, 60°, and 80°. Nanostructure was altered from dimple to ripple structure as the angle increases. The ripple structure changed to pillar structure after 60°due to that the shallow incident angle increased the ion energy transfer per depth up to 50 eV/Å, which value could induce excessive surface heating and oligomer formation reacting as a physical mask for anisotropic etching. And quantitative analysis of the nanostructures was adapted by using ABC model and fractal dimension theory.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.36.2-37
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• 2016

We study the long-term radio variability of 43 radio bright AGNs by exploiting the data base of the University of Michigan Radio Astronomy Observatory (UMRAO) monitoring program. The UMRAO database provides high quality lightcurves spanning 25 - 32 years in time at three observing frequencies, 4.8, 8, and 14.5 GHz. We model the periodograms (temporal power spectra) of the observed lightcurves as simple power-law noise (red noise, spectral power $P(f){\propto}f^{-{\beta}}$ using Monte Carlo simulations, taking into account windowing effects (red-noise leak, aliasing). The power spectra of 39 (out of 43) sources are in good agreement with the models, yielding a range in power spectral index (${\beta}$) from ${\approx}1$ to ${\approx}3$. We find a strong anti-correlation between ${\beta}$ and the fractal dimension of the lightcurves, which provides an independent check of the quality of our modelling of power spectra. We fit a Gaussian function to each flare in a given lightcurve to obtain the flare duration. We discover a correlation between ${\beta}$ and the median duration of the flares. We use the derivative of a lightcurve to obtain a characteristic variability timescale which does not depend on the assumed functional form of the flares, incomplete fitting, and so on. We find that, once the effects of relativistic Doppler boosting on the observed timescales are corrected, the variability timescales of our sources are proportional to the black hole mass to the power of ${\alpha}=1.70{\pm}0.49$. We see an indication for AGNs in different regimes of accretion rate, flat spectrum radio quasars and BL Lac objects, having different scaling relations with ${\alpha}{\approx}1$ and ${\approx}2$, respectively. We find that modelling the periodograms of four of our sources requires the assumption of broken powerlaw spectra. From simulating lightcurves as superpositions of exponential flares we conclude that strong overlap of flares leads to featureless simple power-law periodograms of AGNs at radio wavelengths in most cases (The paper is about to be submitted to ApJ).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.4831-4843
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• 2014

Magazines on specific genres have been operating collaborative, co-working and collective production systems for value maximization using an adaptation strategy on the dynamic, complex and uncertain value network of the magazine industry. The study used a case study method, and data collection was performed by observational research, depth interviews and survey research. The subjects of the study were 'magazine industry', 'magazine firm and community', and 'collaboration system within creative users'. According to the research results, the ecosystem of magazines on a specific genre has been evolving into an innovative value network system, which is combined with the magazine firm, community users and magazine platform. Second, the rapid introduction of smart device environment changes the way of the collaborating system, in which an action and interaction came out within the community, creative users and magazine firms. Third, the production agency shows strong action and interaction, which fits the magazine platform within the ecosystem of a magazine on a specific genre well. This model has a similar fractal structure to the game, publishing, drama, movie, comic, and animation contents industry, converging to an innovative technology-based-creative-industry.

• Journal of The Korean Astronomical Society
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• v.41 no.3
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• pp.65-76
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• 2008

We have investigated the optical properties of the global haze on Titan from spectra recorded between 7100 and $9200{\AA}$, where $CH_4$ absorption bands of various intensities occur. The Titan spectra were obtained on Feb. 23, 2005 (UT), near the times of the Cassini T3 flyby and Huygens probe, using an optical echelle spectrograph (BOES) on the 1.8-m telescope at Bohyunsan Observatory in Korea. In order to derive the optical properties of the haze as a function of altitude, we developed an inversion radiative-transfer program using an atmospheric model of Titan and laboratory $CH_4$ absorption coefficients available from the literature. The derived extinction coefficients of the haze increase toward the surface, and the coefficients at shorter wavelengths are greater than those at longer wavelengths for the 30 - 120 km altitude range, indicating that the Titanian haze becomes optically thin toward the longer wavelength range. Total optical depths of the haze are estimated to be 1.4 and 1.2 for the 7270 - $7360{\AA}$ and 8940 - $9150{\AA}$ ranges, respectively. Based on the Huygens/DISR data set, Tomasko et al. (2005) reported total optical depths of 2.5 - 3.5 at $8290{\AA}$, depending on the assumed fractal aggregate particle model. The total optical depths based on our results are smaller than those of Tomasko et al., but they partially overlap with their results if we consider a large uncertainty from possible variations of the $CH_4$ mixing ratio over Titan's disk. We also derived the single scattering albedo of the haze particles as a function of altitude: it is less than 0.5 at altitudes higher than ${\sim}150\;km$, and approaches 1.0 toward the surface. This behavior suggests that, at altitudes above ${\sim}150\;km$, the average particle radius is smaller than the wavelengths, whereas near the surface, it becomes comparable or greater.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.6
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• pp.507-517
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• 2019

Multi-function radar(MFR) is a system that uses various functions such as detection, tracking, and classification. To operate the functions in real-time, the detection stage in MFR usually uses radar signals for short measurement time. We can utilize several conventional detectors in the MFR system to detect low radar cross section maritime targets in the sea-clutter; however, the detectors, which have been developed to be effective for radar signals measured for a longer time, may be inappropriate for MFR. In this study, we proposed a modelling technique of sea-clutter short measurement time. We combined the modeled sea-clutter signal with the maritime-target signal, which was obtained by the numerical analysis method. Using this combined model, we exploited four independent detectors and analyzed the detection performances.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.4
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• pp.33-43
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• 2020

In order to reduce damage from farmland inundation caused by recent climate change, it is necessary to predict the risk of farmland inundation accurately. Inundation modeling should be performed by considering multiple time distributions of possible rainfalls, as digital forecasts of Korea Meteorological Administration is provided on a six-hour basis. As building multiple inputs and creating inundation models take a lot of time, it is necessary to shorten the forecast time by building a data base (DB) of farmland inundation probability. Therefore, the objective of this study is to establish a DB of farmland inundation probability in accordance with forecasted rainfalls. In this study, historical data of the digital forecasts was collected and used for time division. Inundation modeling was performed 100 times for each rainfall event. Time disaggregation of forecasted rainfall was performed by applying the Multiplicative Random Cascade (MRC) model, which uses consistency of fractal characteristics to six-hour rainfall data. To analyze the inundation of farmland, the river level was simulated using the Hydrologic Engineering Center - River Analysis System (HEC-RAS). The level of farmland was calculated by applying a simulation technique based on the water balance equation. The inundation probability was calculated by extracting the number of inundation occurrences out of the total number of simulations, and the results were stored in the DB of farmland inundation probability. The results of this study can be used to quickly predict the risk of farmland inundation, and to prepare measures to reduce damage from inundation.

• Journal of the Korean Geographical Society
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• v.40 no.3 s.108
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• pp.253-273
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• 2005

In recent years, there are strong social demands to characterize the spatial distribution of mountains in Korea. This study aims to develop a 'Sanjulgi-Jido(mountain ridge map)' that might be used not only to satisfy these social demands but also to effectively present the spatial distribution of mountains and drainage basins in the Korean Peninsular. The 'Sanjulgi-Jido' developed in this study is a map that presents the continuity of mountains based on the drainage divides that are delineated by a pre-defined drainage basin size and elevation. This study first validated the Bakdudaegan system through the analyses of a digital elevation model. The Bakdudaegan system has long been recognized as the Koreans traditional conceptual framework to characterize the spatial distribution of mountains. The analyses showed that the Bakdudaegan system has several problems to represent the mountain systems in Korea, which includes 1) the lack of the representativeness of drainage basins, 2) inaccuracy to depict the boundary of drainage basins, 3) the lack of representativeness of mountains, and 4) geo-polical issue that confines the spatial extent of mountain systems within the Korean Peninsular. In order to represent the mountains system in a more quantitative manner, we applied several terrain analysis techniques to understand the spatial distribution of mountains and drainage basins. Based on these analyses, we developed an hierarchical system to classify the continuity (If mountains, which are presented as the spatial distribution of drainage divides with a certain elevation. The first-order Sanjulgi is the drainage divides whose drainage basin are bigger than $5,000km^2$ and the point elevation is above 100m. The next order Sanjulgi is delineated as the size of drainage basin is successively divided by two. This kind of design is able to provide a logical framework to present the mountain systems at different details, depending on the purpose and scale of maps. We also provide several empirical functions to calculate various geomorphological indices for each order of Sanjulgi. The 'Sanjulgi Jido' is similar with the Bakdudaegan system, since it characterizes the continuity of mountains based on the spatial distribution of the drainage divide. It, however, has more scientific criteria to define the scale and continuity of mountains. It should be also noted that the 'Sanjulgi Jido' proposed has different logical and methodological background, compared with the mountain range map that explains the genesis of mountain systems in addition to the continuity of mountains.