• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.169-174
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• 2018

In general, the methods of the analysis of variance(ANOVA) for the continuous data and the chi-square test for the discrete data are used for statistical analysis of the effect and the association. In multidimensional data, analysis of hierarchical structure is required and statistical linear model is adopted. The structure of the linear model requires the normality of the data. A multidimensional categorical data analysis methods are used for causal relations, interactions, and correlation analysis. In this paper, Bayesian network model using probability distribution is proposed to reduce analysis procedure and analyze interactions and causal relationships in categorical data analysis.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.3
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• pp.474-485
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• 2018

Smart sensors and connected devices have changed the concept of garments along with IT technology convergent garments that transform the performance of basic functions. Various types of products have been researched and developed due to the increased interest in smart clothing; in addition, studies based on physical and mechanical properties have also been actively studied to improve accuracy and reliability. This study represents a basic study for the development of smart textiles based on motion recognition for the surfing practice of beginners interested in IT convergence type. A physical durability evaluation of conductive yarn according to sewing method was later carried out. This study is a conditional specimen sewn with cotton lower thread and 100mm pattern length based on the results of previous studies. The durability of the conductive yarn according to the sewing method was evaluated according to the sewing method. Durability was evaluated by two kinds of repeated strain and abrasion tests. The specimen with applied cotton in a lower thread zigzag pattern 2mm stitch size 100mm stitch length was shown to have the most suitable durability for smart textile.

• Journal of Biosystems Engineering
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• v.14 no.3
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• pp.158-167
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• 1989

This study was conducted to investigate the applicability of true model theory in a powered lugged wheel-soil system and to examine the possibility of using principles of similitude in investigating the dimensions of soil parameters pertinent to a powered lugged wheel-soil system concerning the sinkage prediction. The following conclusions were derived from the study; 1) The sinkage of prototype wheels proved to be predicted by those of the model wheels for the range of the dynamic weight tested. 2) A conditional equation which can be used for the prediction of sinkage of prototype by model test was derived as $n_f=n{_\ell}{^{-b}}$. The range of the numerical value of b, which is the exponent on the length dimension of the soil property ${\alpha}$, was found to be -1.48~-2.54. 3) Considering a relatively wide variation of b values, it was concluded that there are several soil properties which are pertinent to the powered lugged-wheel soil system concerning the sinkage prediction.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.41-47
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• 2019

In this paper, we propose a application of conditional generative adversarial network (cGAN) for generation of contrast enhanced computed tomography (CT) image. Two types of CT data which were the enhanced and non-enhanced were used and applied by the histogram equalization for adjusting image intensities. In order to validate the generation of contrast enhanced CT data, the structural similarity index measurement (SSIM) was performed. Prepared generated contrast CT data were analyzed the statistical analysis using paired sample t-test. In order to apply the optimized algorithm for the lymph node cancer, they were calculated by short to long axis ratio (S/L) method. In the case of the model trained with CT data and their histogram equalized SSIM were $0.905{\pm}0.048$ and $0.908{\pm}0.047$. The tumor S/L of generated contrast enhanced CT data were validated similar to the ground truth when they were compared to scanned contrast enhanced CT data. It is expected that advantages of Generated contrast enhanced CT data based on deep learning are a cost-effective and less radiation exposure as well as further anatomical information with non-enhanced CT data.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.1-13
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• 2022

This paper empirically explores the predicting ability of the newly proposed smooth transition (ST) time-varying combining forecast methods. The proposed method allows the "weight" of combining forecasts to change gradually over time through its unique feature of transition variables. Stock market returns from 7 countries were applied to Ad Hoc models, the well-known Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models, and the Smooth Transition Exponential Smoothing (STES) models. Of the individual models, GJRGARCH and STES-E&AE emerged as the best models and thereby were chosen for constructing the combined forecast models where a total of nine ST combining methods were developed. The robustness of the ST combining forecasts is also validated by the Diebold-Mariano (DM) test. The post-sample forecasting performance shows that ST combining forecast methods outperformed all the individual models and fixed weight combining models. This study contributes in two ways: 1) the ST combining methods statistically outperformed all the individual forecast methods and the existing traditional combining methods using simple averaging and Bates & Granger method. 2) trading volume as a transition variable in ST methods was superior to other individual models as well as the ST models with single sign or size of past shocks as transition variables.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.9
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• pp.1-9
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• 2021

This study aimed to investigate the existence of the Feldstein-Horioka (1980) puzzle in international macroeconomics by applying the conditional Autoregressive Distributed Lag (ARDL) model to examine the long-run relationship between national savings and investments in Thailand and China. The input of this study relied on annual national savings and investments as a fraction of GDP during 1980-2019 which was collected from China National Bureau of Statistics (NBS) and Thailand National Economic and Social Development Council (NESDC). Hypothetically, Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests were applied to test the stationary properties and to investigate the integration level of selected time series. The empirical results, confirmed by cumulative sum (CUSUM) and cumulative sum square (CUSUMSQ), maintained no serial correlation and structural break problems. The finding of this study suggested that the Feldstein-Horioka puzzle in Thailand did not exist significantly. Thailand's national savings and investments nexus was independent, following the classic economic idea that financial liberalization, or perfect capital mobility, allowed national savings and investments to flow freely to countries with better interest rates. Whereas, a strong significant correlation was found in the case of China during the fixed exchange rate regime switching in 1994 and post WTO participation after 2001-2019.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.617-620
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• 2010

최근 고성능 모바일 단말기와 다양한 컨텐츠가 등장하면서 유비쿼터스 환경에서의 사용자 인터페이스에 대한 관심이 높아지고 있다. 특히 모바일 프로젝터는 장소의 제한을 받지 않고 큰 화면을 다른 사람과 공유할 수 있는 장점이 있는 반면에 단말기를 직접 제어해야 하는 불편함이 있다. 본 논문에서는 모바일 환경에서 카메라를 통해 입력된 영상 정보만으로 사용자가 스크린에 비추는 레이저 포인터를 실시간으로 검출하는 알고리즘을 제안한다. 제안하는 알고리즘은 색상 감지와 움직임 감지로 나뉜다. 단일 프레임에서 영상 성분의 평균을 이용한 조건을 검사하여 레이저 포인터 색상 영역을 검출하고, 인접한 프레임과 현재 프레임과의 차를 구하며 그 차이가 임계값보다 큰 영역을 움직임 영역으로 검출한다. 마지막으로 색상 검출 영역과 움직임 검출 영역을 동시에 만족하는 영역을 최종적으로 레이저 포인터 영역으로 인식한다. 본 기법은 영상 정보만 사용하기 때문에 센서나 불필요한 장비를 착용할 필요가 없고 영상 성분 평균을 이용하므로 프로젝터 성능에 따른 조도의 변화에 강건하여 효과적인 레이저 포인터 검출이 가능하다. 실험결과는 주변 조명의 밝기에 따라 차이가 있지만 대부분 80% 이상의 검출률과 16% 미만의 오검출률의 성능으로 나타났고, 이 같은 결과는 사용자의 주관적인 만족을 보장하였다.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.42.3-42.3
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• 2019

We translate Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA) ultraviolet (UV) multi-channel images into another UV single-channel image using a deep learning algorithm based on conditional generative adversarial networks (cGANs). The base input channel, which has the highest correlation coefficient (CC) between UV channels of AIA, is 193 Å. To complement this channel, we choose two channels, 1600 and 304 Å, which represent upper photosphere and chromosphere, respectively. Input channels for three models are single (193 Å), dual (193+1600 Å), and triple (193+1600+304 Å), respectively. Quantitative comparisons are made for test data sets. Main results from this study are as follows. First, the single model successfully produce other coronal channel images but less successful for chromospheric channel (304 Å) and much less successful for two photospheric channels (1600 and 1700 Å). Second, the dual model shows a noticeable improvement of the CC between the model outputs and Ground truths for 1700 Å. Third, the triple model can generate all other channel images with relatively high CCs larger than 0.89. Our results show a possibility that if three channels from photosphere, chromosphere, and corona are selected, other multi-channel images could be generated by deep learning. We expect that this investigation will be a complementary tool to choose a few UV channels for future solar small and/or deep space missions.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.43.1-43.1
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• 2019

In this study, we apply a deep learning model to denoising solar magnetograms. For this, we design a model based on conditional generative adversarial network, which is one of the deep learning algorithms, for the image-to-image translation from a single magnetogram to a denoised magnetogram. For the single magnetogram, we use SDO/HMI line-of-sight magnetograms at the center of solar disk. For the denoised magnetogram, we make 21-frame-stacked magnetograms at the center of solar disk considering solar rotation. We train a model using 7004 paris of the single and denoised magnetograms from 2013 January to 2013 October and test the model using 1432 pairs from 2013 November to 2013 December. Our results from this study are as follows. First, our model successfully denoise SDO/HMI magnetograms and the denoised magnetograms from our model are similar to the stacked magnetograms. Second, the average pixel-to-pixel correlation coefficient value between denoised magnetograms from our model and stacked magnetogrmas is larger than 0.93. Third, the average noise level of denoised magnetograms from our model is greatly reduced from 10.29 G to 3.89 G, and it is consistent with or smaller than that of stacked magnetograms 4.11 G. Our results can be applied to many scientific field in which the integration of many frames are used to improve the signal-to-noise ratio.