• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.23-34
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• 2014

Though a dozen of different software life cycle models are suggested, there is no universal model which can satisfy all the characteristics of software. Organizations mix and match different life cycle models to develop a model more tailored for their systems and capabilities. We suggest overlapped-concurrent development life cycle model that is more suitable in various software development environment. Firstly, we divided the development process into abstract and implementation stage. Abstract stage is from software concept phase to detailed design starting time, and implementation stage is from detailed design phase to system testing phase. Next, the abstract stage introduced the overlapped phase concept that begins the next phase when the step is completed 20% by applying pareto's law. In the implementation stage, we introduced the concurrent development which the several phases are performed some time as when one use-case (UC) is completed the next development phase is started immediately. The proposed model has an advantage that it can reduce the inefficiency of development resource greatly. This model can increase the customer satisfaction with a great product at a low cost and on a short schedule. Also, this model can contribute to increase the software development success rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.147-156
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• 2011

Though a dozen of different software life cycle models are suggested, there is no universal model which can satisfy all the characteristics of software. Organizations mix and match different life cycle models to develop a model more tailored for their systems and capabilities. We suggest overlapped-concurrent development life cycle model that is more suitable in various software development environment. Firstly, we divided the development process into abstract and implementation stage. Abstract stage is from software concept phase to detailed design starting time, and implementation stage is from detailed design phase to system testing phase. Next, the abstract stage introduced the overlapped phase concept that begins the next phase when the step is completed 20% by applying pareto's law. In the implementation stage, we introduced the concurrent development which the several phases are performed some time as when one use-case (UC) is completed the next development phase is started immediately. The proposed model has an advantage that it can reduce the inefficiency of development resource greatly. This model can increase the customer satisfaction with a great product at a low cost and on a short schedule. Also, this model can contribute to increase the software development success rate.

• Korea journal of population studies
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• v.25 no.1
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• pp.33-50
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• 2002

This study examines cross-national patterns of asymmetry of marriage tables with respect to educational level and tries to measure the degree of gender inequality across nations. A Primary assumption of the study is that gender inequality inhibits symmetric marriage between men and women. As men and women differ more in status, the rate of symmetric marriage between them declines thus producing asymmetric marriage with respect to social status. More specifically, the main object of the study is to develop statistical models and index with which to assess the patterns and degree of asymmetric marriage. Additionally, it is intended to assess the appropriateness of several theoretical perspectives for explaining these variations identified by the statistical models. Two most important such perspectives are industrialism and theory of politics and culture. To answer these questions, this study relies on twenty-seven marriage tables with respect to educational level, some from published tables, and some extracted from other sources. The main findings of the study are: (1) compared to less industrialized countries, more industrialized countries have lower degrees of asymmetric marriage(gender inequality) with respect to educational level, and (2) other things being equal, differences in politics and culture seem to have the some impact on marriage pattern; for instance, social democracy and state socialism reduce the degree of asymmetric marriage while the high emphasis on gender-based hierarchy in Asian countries seems to increase it In short, these results suggest a weaker or modified version of industrialists That is, while with economic growth most nations show a decline in the degrees of asymmetric marriage with respect to social status, for some nations the degrees of asymmetric marriage are affected by their specific politics or cultures.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.97-105
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• 2020

Recently, Artificial Neural Network receives attention as a data prediction method. Among these, a Long Shot-term Memory (LSTM) model specialized for time-series data prediction was utilized as a prediction method of hydrological time series data. In this study, the LSTM model was constructed utilizing deep running open source library TensorFlow which provided by Google, to predict inflows of multipurpose dams. We predicted the inflow of the Yongdam Multipurpose Dam which is located in the upper stream of the Geumgang. The hourly flow data of Yongdam Dam from 2006 to 2018 provided by WAMIS was used as the analysis data. Predictive analysis was performed under various of variable condition in order to compare and analyze the prediction accuracy according to four learning parameters of the LSTM model. Root mean square error (RMSE), Mean absolute error (MAE) and Volume error (VE) were calculated and evaluated its accuracy through comparing the predicted and observed inflows. We found that all the models had lower accuracy at high inflow rate and hourly precipitation data (2006~2018) of Yongdam Dam utilized as additional input variables to solve this problem. When the data of rainfall and inflow were utilized together, it was found that the accuracy of the prediction for the high flow rate is improved.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.107-115
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• 2011

In this paper, we proposed fast and efficient algorithm for detecting near-duplication based on content based retrieval in large scale video database. For handling large amounts of video easily, we split the video into small segment using scene change detection. In case of video services and copyright related business models, it is need to technology that detect near-duplicates, that longer matched video than to search video containing short part or a frame of original. To detect near-duplicate video, we proposed motion distribution and frame descriptor in a video segment. The motion distribution descriptor is constructed by obtaining motion vector from macro blocks during the video decoding process. When matching between descriptors, we use the motion distribution descriptor as filtering to improving matching speed. However, motion distribution has low discriminability. To improve discrimination, we decide to identification using frame descriptor extracted from selected representative frames within a scene segmentation. The proposed algorithm shows high success rate and low false alarm rate. In addition, the matching speed of this descriptor is very fast, we confirm this algorithm can be useful to practical application.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.113-121
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• 2012

In this paper, we proposed fast and efficient algorithm for detecting near-duplication based on content based retrieval in large scale video database. For handling large amounts of video easily, we split the video into small segment using scene change detection. In case of video services and copyright related business models, it is need to technology that detect near-duplicates, that longer matched video than to search video containing short part or a frame of original. To detect near-duplicate video, we proposed motion distribution and frame descriptor in a video segment. The motion distribution descriptor is constructed by obtaining motion vector from macro blocks during the video decoding process. When matching between descriptors, we use the motion distribution descriptor as filtering to improving matching speed. However, motion distribution has low discriminability. To improve discrimination, we decide to identification using frame descriptor extracted from selected representative frames within a scene segmentation. The proposed algorithm shows high success rate and low false alarm rate. In addition, the matching speed of this descriptor is very fast, we confirm this algorithm can be useful to practical application.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 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 Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 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.

• Korean journal of applied entomology
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• v.55 no.2
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• pp.149-160
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• 2016

Temperature-dependent development and fecundity of apterious Rhopalosiphum padi (L.) (Hemiptera: Aphididae) were examined at six constant temperatures (10, 15, 20, 25, 30 and $35{\pm}1.0^{\circ}C$, RH 50-70%, 16L:8D). Development time of nymphs decreased with increasing temperature and ranged from 42.9 days at $10^{\circ}C$ to 4.7 days at $30^{\circ}C$. The nymphs did not develop until adult at $35^{\circ}C$ because the nymphs died during the 2nd instar. The lower threshold temperature and thermal constant of nymph were estimated as $8.3^{\circ}C$ and 101.6 degree days, respectively. The relationships between development rates of nymph and temperatures were well described by the nonlinear model of Lactin 2. The distribution of development times of each stage was successfully fitted to the Weibull function. The longevity of apterious adults decreased with increasing temperature ranging from 24.0 days at $15^{\circ}C$ to 4.3 days at $30^{\circ}C$, with abnormally short longevity of 11.1 days at $10^{\circ}C$. R. padi showed the highest fecundity at $20^{\circ}C$ (38.2) and the lowest fecundity at $10^{\circ}C$ (3.9). In this study, we provided component sub-models for the oviposition model of R. padi: total fecundity, age-specific cumulative oviposition rate, and age-specific survival rate as well as adult aging rate based on the adult physiological age.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.63-71
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• 2010

Business entrepreneurs reflect their views of domestic and foreign economic activities on their operation for the growth of their business. The decision, forecasting, and planning based on their economic sentiment affect business operation such as production, investment, and hiring and consequently affect condition of national economy. Business survey index(BSI) is compiled to get the information of business entrepreneurs' economic sentiment for the analysis of business condition. BSI has been used as an important variable in the short-term forecasting models for business cycle analysis, especially during the the period of extreme business fluctuations. Recent financial crisis has arised extreme business fluctuations similar to those caused by currency crisis at the end of 1997, and brought back the importance of BSI as a variable for the economic forecasting. In this paper, the meaning of BSI as an economic sentiment index is reviewed and a GUIDE regression tree is constructed to find out the factors which affect on BSI. The result shows that the variables related to the stability of financial market such as kospi index(Korea composite stock price index) and exchange rate as well as manufacturing operation ratio and consumer goods sales are main factors which affect business entrepreneurs' economic sentiment.