• Korean Journal of Environmental Biology
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• v.17 no.3
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• pp.233-247
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• 1999

A survey of syntaxa of vegetation of evergreen broad-leaved forests in Korea, class Camellietea japonicae is presented. 399 releve's were arranged two phytosociological tables, each representing an alliance. A synoptic table comprising all alliances is presented. The vegetation of evergreen broad-leaved forests is divided into three alliances including twelve new associations: (1) Querco - Castanopsion all. nov., split into four associations, Castanopsietum sieboldii, Quercetum acutae, Quercetum myrsinaefoliae and Litseetum japonicae; (2) Machilo-Camellion all. nov., separate into ten associations, Machiletum thunbergii, Pittosporetum tobirae, Aucubetum japonicae, Neolitsetum sericeae, Euryetum emarginatae, Elaeagnetum macrophyllae, Camellietum japonicae, Theo-Camellietom japonicae, Raphiolepietum umbellatae and Daphniphylletum macropodae; (3) Dendropanaco-Castanopsion sieboldii including one association, Hosto minoris-Castanopsietum sieboldii. The alliances are floristically and ecologically characterized and their distribution in Korea shown on the map.

• Nuclear Engineering and Technology
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• v.41 no.9
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• pp.1143-1156
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• 2009

The SPACE code that is based on a multi-dimensional two-fluid, three-field model is under development for licensing purposes of pressurized water reactors in Korea. Among the participating research and industrial organizations, KAERI is in charge of developing the physical models and correlation packages for the constitutive equations. This paper introduces a developed wall-to-fluid heat transfer package for the SPACE code. The wall-to-fluid heat transfer package consists of twelve heat transfer subregions. For each sub-region, the models in the existing safety analysis codes and the leading models in literature have been peer reviewed in order to determine the best models which can easily be applicable to the SPACE code. Hence a wall-to-fluid heat transfer region selection map has been developed according to the non-condensable gas quality, void fraction, degree of subcooling, and wall temperature. Furthermore, a partitioning methodology which can take into account the split heat flux to the continuous liquid, entrained droplet, and vapor fields is proposed to comply fully with the three-field formulation of the SPACE code. The developed wall-to-fluid heat transfer package has been pre-tested by varying the independent parameters within the application range of the selected correlations. The smoothness between two adjacent heat transfer regimes has also been investigated. More detailed verification work on the developed wall-to-fluid heat transfer package will be carried out when the coupling of a hydraulic solver with the constitutive equations is brought to completion.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.11
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• pp.5011-5017
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• 2016

Background: Cervical cancer is a major public health problem worldwide. There have been several studies indicating that risk is associated with geographic location and that the incidence of cervical cancer has changed over time. In Thailand, incidence rates have also been found to be different in each region. Methods: Participants were women living or having lived in upper Northern Thailand and subjected to cervical screening at Maharaj Nakorn Chiang Mai Hospital between January 2010 and December 2014. Generalized additive models with Loess smooth curve fitting were applied to estimate the risk of cervical cancer. For the spatial analysis, Google Maps were employed to find the geographical locations of the participants' addresses. The Quantum Geographic Information System was used to make a map of cervical cancer risk. Two univariate smooths: x equal to the residency duration was used in the temporal analysis of residency duration, and x equal to the calendar year that participants moved to upper Northern Thailand or birth year for participants already living there, were used in the temporal analysis of the earliest year. The spatial-temporal analysis was conducted in the same way as the spatial analysis except that the data were split into overlapping calendar years. Results: In the spatial analysis, the risk of cervical cancer was shown to be highest in the Eastern sector of upper Northern Thailand (p-value <0.001). In the temporal analysis of residency duration, the risk was shown to be steadily increasing (p-value =0.008), and in the temporal analysis of the earliest year, the risk was observed to be steadily decreasing (p-value=0.016). In the spatial-temporal analysis, the risk was stably higher in Chiang Rai and Nan provinces compared to Chiang Mai province. According to the display movement over time, the odds of developing cervical cancer declined in all provinces. Conclusions: The risk of cervical cancer has decreased over time but, in some areas, there is a higher risk than in the major province of Chiang Mai. Therefore, we should promote cervical cancer screening coverage in all areas, especially where access is difficult and/or to women of lower socioeconomic status.

• Journal of The Geomorphological Association of Korea
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• v.27 no.1
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• pp.61-89
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• 2020

The objective of this study is to characterize landslide susceptibility depending on various geo-environmental variables as well as to compare the Frequency Ratio (FR) and Evidential Belief Function (EBF) methods for landslide susceptibility analysis of rainfall-induced landslides. In 2013, a total of 259 landslides occurred in Chuncheon, Gangwon Province, South Korea, due to heavy rainfall events with a total cumulative rainfall of 296~721mm in 106~231 hours duration. Landslides data were mapped with better accuracy using the geographic information system (ArcGIS 10.6 version) based on the historic landslide records in Chuncheon from the National Disaster Management System (NDMS), the 2013 landslide investigation report, orthographic images, and aerial photographs. Then the landslides were randomly split into a testing dataset (70%; 181 landslides) and validation dataset (30%; 78 landslides). First, geo-environmental variables were analyzed by using FR and EBF functions for the full data. The most significant factors related to landslides were altitude (100~200m), slope (15~25°), concave plan curvature, high SPI, young timber age, loose timber density, small timber diameter, artificial forests, coniferous forests, soil depth (50~100cm), very well-drained area, sandy loam soil and so on. Second, the landslide susceptibility index was calculated by using selected geo-environmental variables. The model fit and prediction performance were evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under Curve (AUC) methods. The AUC values of both model fit and prediction performance were 80.5% and 76.3% for FR and 76.6% and 74.9% for EBF respectively. However, the landslide susceptibility index, with classes of 'very high' and 'high', was detected by 73.1% of landslides in the EBF model rather than the FR model (66.7%). Therefore, the EBF can be a promising method for spatial prediction of landslide occurrence, while the FR is still a powerful method for the landslide susceptibility mapping.