• 천문학회보
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• 제41권1호
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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).

• 한국산학기술학회논문지
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• 제15권8호
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• pp.4831-4843
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• 2014

전문잡지들은 복잡도가 높고 다이내믹하며 심한 불확실성을 보이는 잡지산업 생태계에 대한 적응 전략으로서, 지식과 아이디어를 기반으로 가치를 극대화하기 위해 유연화되고 개방된 협업적, 동료적, 집합적 생산체계를 작동시키고 있다. 본 논문은 사례연구방법을 적용하였고, 자료수집방법으로서 직접 관찰, 전문가 심층면접, 전문가 설문조사를 실행하였다. 이를 통한 '잡지산업', '전문잡지사 커뮤니티 사용자간의 협업체계', '전문잡지의 생태계 모델'에 대한 분석 결과는 다음과 같다. 첫째, 전문잡지 생태계는 잡지사들의 다양하고 새로운 생산 참여자와 잡지 플랫폼이 결합되는 가치 네트워크의 개방적 혁신의 협업 모델로서 진화/적응하고 있었다. 둘째, 전문잡지사들은 스마트 디바이스의 환경 변화에 힘입어 커뮤니티, 전문가 사회집단/조직, 창조적 사용자의 다양한 행위/상호작용이 핵심적으로 작동되는 협업체계를 구축하고 있었다. 셋째, 전문잡지 생태계 모델은 콘텐츠 공급 영역의 다양한 잡지 생산 주체들이 플랫폼 영역에서 상품군과 플랫폼 유형에 적합한 상호작용을 하며 스마트 디바이스를 통해 사용자/독자와 상호작용하는 체계이다. 전문잡지의 모델은 게임, 출판, 드라마, 영화, 만화, 애니메이션 등의 디지털 콘텐츠 산업과 같은 패턴의 프랙탈 구조를 보이고 있으며, 상호작용성과 사용자 참여의 수준이 가장 높은 창조 친화적 기술 혁신 모델로 수렴되고 있다.

• 천문학회지
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• 제41권3호
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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.

• 한국전자파학회논문지
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• 제30권6호
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• pp.507-517
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• 2019

다기능 레이다(multi-function radar: MFR)는 탐지, 추적, 식별 등 다양한 기능을 동시에 수행하는 레이다 시스템이다. 이러한 MFR은 여러 기능을 실시간 내에 수행해야 하기 때문에, 탐지 모드를 위한 측정 시간이 매우 짧은 특징을 갖고 있다. 또한, 저 레이다 단면적(radar cross section: RCS)을 갖는 해상표적을 탐지하기 위해 개발된 기존의 다양한 탐지기법들이 존재하며, 해당 기법들을 MFR 탐지모드에도 사용할 수 있다. 그러나 기존에 연구된 많은 해상표적 탐지기법은 상대적으로 긴 시간 측정된 해상 신호에 대해 효과적 해상표적 탐지가 가능하도록 개발되었기 때문에, 매우 짧은 측정시간을 갖는 MFR 탐지 모드에는 적합하지 않은 부분이 있다. 본 논문에서는 MFR 탐지 모드의 짧은 측정 시간을 고려한 해상클러터 모델링 방법을 제시하고, 이를 이용하여 해상 클러터 신호를 생성하였다. 또한 해상표적 RCS를 수치해석기법을 이용하여 계산하고, 앞에서 계산된 클러터 신호와 결합하였다. 이렇게 생성된 최종 레이다 수신 신호를 이용하여 기존에 개발된 4가지 서로 다른 해상표적 탐지기법을 적용하고, 탐지성능을 분석하였다.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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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.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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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.

• 한국농공학회논문집
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• 제62권4호
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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.

• 대한지리학회지
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• 제40권3호
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• pp.253-273
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• 2005

최근 한국 사회에는 산지의 공간적 연속성을 파악하고자 하는 사회적인 요구가 높다. 이 연구는 이러한 사회적 요구를 수용하면서 한반도의 산지와 유역분수계의 공간적 특징을 효율적으로 표현할 수 있는 '산줄기 지로'의 개념을 제시하는 것이 목적이다. '산줄기 지도'란 지표면에서 일정한 고도를 가지면서 산으로 인식될 수 있는 지점들을 연결한 선을 표시한 지도이다. 이 연구에서는 먼저 우리 사회에서 전통적인 산지 인식체계로 알려져 있는 백두대간 체계가 한반도의 산지특성과 유역분수계를 얼마나 정확하게 설명하고 있는지 에 대한 검증을 실시하였다 백두대간 체계는 유역분수계의 특성을 파악하거나 산지의 연속성을 명확하게 제시하는 목적에 사용되기에는, 1)유역분수계 구분의 대표성 결여, 2)유역분수계 표현의 부정확성, 3) 산지 표현의 대표성 결여, 그리고 4) 지정학적 측면에서의 문제점 등을 안고 있다. 이러한 문제점들을 극복하기 위해 한반도의 산지와 유역분수계의 공간적 분포 특성을 정량적으로 분석하였다. 그 결과를 토대로 한반도의 산지 분포를 유역분수계의 관점 에서 계층화하여 산줄기 지도를 제시하였다. 제시된 산줄기 지도에서는 한반도에서 유역 면적이 $5,000km^2$ 이상 되는 유역분지의 분수계 중에서, 고도가 100m 이상이 되는 지점들을 연결한 선을 1차 산줄기로 규정하였다. 그 다음 차수의 산줄기들은 기준 유역면적을 매 차수마다 반분하여 산줄기를 그릴 수 있도록 설계하였다. 이 과정에서 각 차수의 산줄기 가 표현할 수 있는 각종 지형학적 특성을 제시하는 경험공식들을 개발하였다. 이러한 과정을 통해 한반도 전체 산줄기 의 분포와 특성을 필요한 목적과 표현하려는 지도의 축적에 따라 계층적으로 표현할 수 있는 토대를 마련하였다. 이 지도는 유역분수계에 근거했다는 점에서 산경표의 산줄기 체계와 유사성을 지니고 있으나, 근대 지형학의 관점에서 산지의 규모와 연속성을 보다 체계적으로 해석한 것이다. 제시된 산줄기 체계는 산지의 형성작용과 그 과정을 설명하는 교육적인 모형인 산맥체계와 뚜렷이 구별된다는 점을 유념할 필요가 있다.