• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.147-168
• /
• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.123-139
• /
• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Journal of Wetlands Research
• /
• v.18 no.1
• /
• pp.24-31
• /
• 2016

The increase of impervious area in cities caused the unbalanced water cycle system and the accumulated various contaminants, which make troubles as introducing into watershed. In Korea, most of rainfall in a year precipitate in a summer season. This indicate that non-point source pollution control should be more important in summer and careful rainfall reuse strategy is necessary. Accordingly, the aim of this study is to monitor the characteristics of rainfall contaminants harvested in roofs and to develop the rainfall treatment system which are designed to fit well in a typical domestic household including rain garden. The rain garden consists of peatmoss, gravel and san to specially treat the initial rainfall contaminants. For this purpose, lab scale experiments with synthetic rainfall had been conducted to optimize the removal efficiency of TN, TP and CODcr. After lab scale experiments, field scale rainfall treatment system installed as a pilot scale in a field. This system has been monitored during June to July in 2015 in four time rainfall events as investigating the function of time, rainfall, and pollutant concentrations. As results, high loading of pollutants were introduced to the rainfall treatment system and its removal efficiency is increased as increase of pollutant concentrations. Since it is common that the mega-size of rainfall treatment system is not attractive in urban area, small scale rainfall treatment system is promising to treat the non-point source contaminants from cities. In addition, this small scale rainfall treatment system could have a potential to water resue system in islands, which usually suffer the shortage of water.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2004.08a
• /
• pp.104-117
• /
• 2004

Dmitri Donskoi, which went down during the Russo-Japanese War occurred 100 years ago, was found by using geophysical exploration techniques at the 400 m water depth of submarine valley off Jeodong of Ulleung Island. In the submarine area with the rugged seabed topography and volcanic seamounts, in particular, the reliable seabed images were acquired by using the mid-to-shallow Multibeam exploration technique The strength of corrosion (causticity) of the sunken Donskoi, measured by the electrochemical method, decreased to 2/5 compared with the original strength.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2006.05a
• /
• pp.1241-1245
• /
• 2006

절 성토사면에 구조물을 설치할 경우 가장 중요하게 고려되어야 하는 점은 사면의 안정성 여부이다. 특히, 절 성토사면에 설치된 구조물이 붕괴되는 가장 큰 원인은 뒷채움재 내에 존재하는 수압의 영향이라는 것을 우리는 이미 많은 연구와 경험을 통해 알고 있다. 만일 지하수위가 존재하는 상태에서 단시간에 발생되는 집중호우로 인해 수위가 갑자기 상승하였을 경우, 구조물을 통해 전혀 배수되지 않는다면 절 성토사면의 안정성은 급격히 저하될 것이다. 이러한 사면의 배수압을 소산시킬 수 있는 공법은 여러 가지가 있으나, 본 연구에서는 특히 제주도의 지역적 특성을 고려하여 화산석을 채움재로 사용한 Mattress/Filter를 절 성토사면에 설치함으로써 배수압을 소산시킬 수 있는 방법을 연구하였다. Mattress/Filter는 제방 또는 절 성토사면의 파괴와 침식을 방지하기 위해 사면에 설치하는 육각형의 철망구조로서 유연성, 다공성, 배수성 및 식생성과 같은 특징이 있으며, 콘크리트 구조물과 달리 별도의 배수시설을 필요로 하지 않는 장점이 있다. 또한 본 연구에 사용된 Mattress/Filter의 채움재인 화산석은 현재 제주도 지역에 방대하게 분포되어 있다. 특히 현무암은 제주도 암석 전체의 90%이상을 차지하고 있으며, 투수성이 매우 큰 암석이다. 현무암의 공극률은 그 종류에 따라 $0.02{\sim}0.36$의 범위로 나타난다. 특히, 표선리현무암의 경우 평균 공극률이 0.23으로 나타나 모래의 공극률인 $0.3{\sim}0.8$에 비교하여 볼 때, 연구에 사용된 재료는 아주 우수한 투수성을 가진 것으로 판명된다. 또한 현무암의 경우 암석의 겉 표면이 미세한 다공질 조직으로 이루어져 있다. 따라서 암석자체에 물이 정체될 수 있어 구조물을 통해 배수될 때 암석이 머금고 있는 물로 인해 추가적으로 발생하는 중력은 다른 재료가 가지지 못한 화산석의 또 다른 장점이라 할 수 있다.서는 자료변환 및 가공이 필요하다. 즉, 각 상습침수지구에 필요한 지형도는 국립지리원에서 제작된 1:5,000 수치지형도가 있으나 이는 자료가 방대하고 상습침수지구에 필요하지 않은 자료들을 많이 포함하고 있으므로 상습침수지구의 데이터를 인터넷을 통해 서비스하기 위해서는 많은 불필요한 레이어의 삭제, 서비스 속도를 고려한 데이터의 일반화작업, 지도의 축소.확대 등 자료제공 방식에 따른 작업 그리고 가시성을 고려한 심볼 및 색채 디자인 등의 작업이 수반되어야 하며, 이들을 고려한 인터넷용 GIS기본도를 신규 제작한다. 상습침수지구와 관련된 각종 GIS데이타와 각 기관이 보유하고 있는 공공정보 가운데 공간정보와 연계되어야 하는 자료를 인터넷 GIS를 이용하여 효율적으로 관리하기 위해서는 단계별 구축전략이 필요하다. 따라서 본 논문에서는 인터넷 GIS를 이용하여 상습침수구역관련 정보를 검색, 처리 및 분석할 수 있는 상습침수 구역 종합정보화 시스템을 구축토록 하였다.N, 항목에서 보 상류가 높게 나타났으나, 철거되지 않은 검전보나 안양대교보에 비해 그 차이가 크지 않은 것으로 나타났다.의 기상변화가 자발성 기흉 발생에 영향을 미친다고 추론할 수 있었다. 향후 본 연구에서 추론된 기상변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월이하의 중단기가 적합함을 발견하였다. 이상의 행태적 측면과 투자성과측면의 실증결과를 통하여 한국주식시장에 있어서 시장수익률을 평균적으로 초과할 수 있는 거래전략은 존재하므로 이러한 전략을 개발 및 활용할 수 있으며, 특히, 한국주식시장에 적합한 거래전략은 반전거래전략이고, 이 전략의 유용성은 투자자가 설정한 투자기간보다 더욱 긴 분석기간의 주식가격정보에 의하여 최대한 발휘될 수 있음을 확인하였다.(M1), 무역적자의 폭, 산업

• Journal of Intelligence and Information Systems
• /
• v.12 no.1
• /
• pp.17-31
• /
• 2006

In this paper, passport recognition and face verification methods which can automatically recognize passport codes and discriminate forgery passports to improve efficiency and systematic control of immigration management are proposed. Adjusting the slant is very important for recognition of characters and face verification since slanted passport images can bring various unwanted effects to the recognition of individual codes and faces. Therefore, after smearing the passport image, the longest extracted string of characters is selected. The angle adjustment can be conducted by using the slant of the straight and horizontal line that connects the center of thickness between left and right parts of the string. Extracting passport codes is done by Sobel operator, horizontal smearing, and 8-neighborhood contour tracking algorithm. The string of codes can be transformed into binary format by applying repeating binary method to the area of the extracted passport code strings. The string codes are restored by applying CDM mask to the binary string area and individual codes are extracted by 8-neighborhood contour tracking algerian. The proposed RBF network is applied to the middle layer of RBF network by using the fuzzy logic connection operator and proposing the enhanced fuzzy ART algorithm that dynamically controls the vigilance parameter. The face is authenticated by measuring the similarity between the feature vector of the facial image from the passport and feature vector of the facial image from the database that is constructed with PCA algorithm. After several tests using a forged passport and the passport with slanted images, the proposed method was proven to be effective in recognizing passport codes and verifying facial images.

• Journal of Intelligence and Information Systems
• /
• v.12 no.1
• /
• pp.1-15
• /
• 2006

In Korea, a resident registration card has various personal information such as a present address, a resident registration number, a face picture and a fingerprint. A plastic-type resident card currently used is easy to forge or alter and tricks of forgery grow to be high-degree as time goes on. So, whether a resident card is forged or not is difficult to judge by only an examination with the naked eye. This paper proposed an automatic recognition method of a resident card which recognizes a resident registration number by using a refined ART2-based RBF network newly proposed and authenticates a face picture by a template image matching method. The proposed method, first, extracts areas including a resident registration number and the date of issue from a resident card image by applying Sobel masking, median filtering and horizontal smearing operations to the image in turn. To improve the extraction of individual codes from extracted areas, the original image is binarized by using a high-frequency passing filter and CDM masking is applied to the binaried image fur making image information of individual codes better. Lastly, individual codes, which are targets of recognition, are extracted by applying 4-directional contour tracking algorithm to extracted areas in the binarized image. And this paper proposed a refined ART2-based RBF network to recognize individual codes, which applies ART2 as the loaming structure of the middle layer and dynamicaly adjusts a teaming rate in the teaming of the middle and the output layers by using a fuzzy control method to improve the performance of teaming. Also, for the precise judgement of forgey of a resident card, the proposed method supports a face authentication by using a face template database and a template image matching method. For performance evaluation of the proposed method, this paper maked metamorphoses of an original image of resident card such as a forgey of face picture, an addition of noise, variations of contrast variations of intensity and image blurring, and applied these images with original images to experiments. The results of experiment showed that the proposed method is excellent in the recognition of individual codes and the face authentication fur the automatic recognition of a resident card.

• The Journal of Korean Society for Radiation Therapy
• /
• v.24 no.1
• /
• pp.31-37
• /
• 2012

Purpose: This study evaluated the usefulness of Helmet bolus device using Bolx-II, paraffin wax, solid thermoplastic material in total scalp irradiation. Materials and Methods: Using Rando phantom, we applied Bolx-II (Action Products, USA), paraffin wax (Densply, USA), solid thermoplastic material (Med-Tec, USA) on the whole scalp to make helmet bolus device. Computed tomography (GE, Ultra Light Speed16) images were acquired at 5 mm thickness. Then, we set up the optimum treatment plan and analyzed the variation in density of each bolus (Philips, Pinnacle). To evaluate the dose distribution, Dose-homogeneity index (DHI, $D_{90}/D_{10}$) and Conformity index (CI, $V_{95}/TV$) of Clinical Target Volume (CTV) using Dose-Volume Histogram (DVH) and $V_{20}$, $V_{30}$ of normal brain tissues. we assessed the efficiency of production process by measuring total time taken to produce. Thermoluminescent dosimeters (TLD) were used to verify the accuracy. Results: Density variation value of Bolx-II, paraffin wax, solid thermoplastic material turned out to be $0.952{\pm}0.13g/cm^3$, $0.842{\pm}0.17g/cm^3$, $0.908{\pm}0.24g/cm^3$, respectively. The DHI and CI of each helmet bolus device which used Bolx-II, paraffin wax, solid thermoplastic material were 0.89, 0.85, 0.77 and 0.86, 0.78, 0.74, respectively. The result of Bolx-II was the best. $V_{20}$ and $V_{30}$ of brain tissues were 11.50%, 10.80%, 10.07% and 7.62%, 7.40%, 7.31%, respectively. It took 30, 120, 90 minutes to produce. The measured TLD results were within ${\pm}7%$ of the planned values. Conclusion: The application of helmet bolus which used Bolx-II during total scalp irradiation not only improves homogeneity and conformity of Clinical Target Volume but also takes short time and the production method is simple. Thus, the helmet bolus which used Bolx-II is considered to be useful for the clinical trials.

• Korean Journal of Food Science and Technology
• /
• v.21 no.1
• /
• pp.154-163
• /
• 1989

This studies were designed to improve the productivity of L-lysine by protoplast fusion and immobilized system of fusants using strains of Brevibacterium flavum ATCC 21528, Brevibacterium lactofermentum ATCC 21086 and Corynebacterium glutamicum 820. Mutants were isolated with concentration method of $300{\mu}g/ml$ penicillin-G after treatment of $250{\mu}g/ml$ N-methyl-N-nitro-N-nitrosoguanidine. B. flavum $37-2(Hos^-,\;Kan^r,\;AEC^r)$, B. lactofermentum $6-2(Ile^-,\;Val^-,\;Str^r,\;AEC^r)$ and C. glutamicum 57-5$(Met^-,\;Thr^-,\;Rif^r,\;AEC^r)$ were isolated from mutants. Protoplasts were induced by being incubated with $500{\mu}g/ml$ lysozyme of lysis solution for 6 hr and the ratio of protoplast formation and regeneration were ranging from 97-99% and 33-37%, respectively. Fusion frequencies of fusants of BBFL 21, BCFG 37 and BCLG 59 were shown in the range from $1.25{\times}10^{-6}\;to\;5.83{\times}10^{-7}$ under the optimum conditions. The fusant BBFL 21 showed the highest productivity of $411.1\;ng/ml{\cdot}hr$ L-lysine in the lysine productivity broth at $30^{\circ}C$ for 72hr. In the immobilization systems, fusant BBFL 21 was employed in various polymer matrices such as sodium alginate, polyacrylamide, agar and ${\alpha}-carrageena$. The immobilization of sodium alginate showed the highest productivity of $413\;ng/ml{\cdot}hr$ L-lysine in the batch system. Continuous fermentation of immobilization system by using tube fermentor was produced the highest productivity $416.7\;ng/ml{\cdot}hr $ L-lysine under optimum condition.

• Journal of Life Science
• /
• v.27 no.4
• /
• pp.456-463
• /
• 2017

The purpose of this study was to investigate the effect of 3 types of medicinal herbs (Glycyrrhizae radix, Astragali radix and Dioscorea rhizoma) extracts on estrogen-like activities, proliferation and differentiation in osteoblast. Human breast cancer cell line MCF7 was transfected using an estrogen responsive luciferase reporter plasmid for measure the estrogen-like activity. Estrogen-like activities of extracts were in the range of 1.11~5.73 fold to that of negative control. The extract of G. radix showed the strongest estrogen-like activities. The estrogen-like activities of 50 and $500{\mu}g/ml$ extracts of G. radix were similar to that of $10^{-8}$ and $10^{-7}$ M standard solution ($17{\beta}-estradiol$), respectively. G. radix extract showed no cytotoxicity against osteoblast MC3T3-E1 cells at $1{\sim}1,000{\mu}g/ml$. The extract of A. radix showed no significant proliferation of osteoblast. However, the extract of G. radix and D. rhizome showed maximum 148% and 133% proliferation effects. The extract of G. radix also increased alkaline phosphatase activity and the maximum was 122% at $100{\mu}g/ml$ compared to that of control. The nodule formation by the method of the Alizarin red S staining increased compared to control. These results suggest that G. radix is able to perform the bone formation and prevent osteoporosis.