• Journal of Korea Water Resources Association
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• v.49 no.7
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• pp.589-597
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• 2016

In this study, we investigated the use of satellite-derived flow (SDF) signals and a data-based model for the estimation of outflow for the river reach where in situ measurements are either completely unavailable or are difficult to access for hydraulic and hydrology analysis such as the upper basin of Imjin River. It has been demonstrated by many studies that the SDF signals can be used as the river width estimates and the correlation between SDF signals and river width is related to the shape of cross sections. To extract the nonlinear relationship between SDF signals and river outflow, Artificial Neural Network (ANN) model with SDF signals as its inputs were applied for the computation of flow discharge at Imjin Bridge located in Imjin River. 15 pixels were considered to extract SDF signals and Partial Mutual Information (PMI) algorithm was applied to identify the most relevant input variables among 150 candidate SDF signals (including 0~10 day lagged observations). The estimated discharges by ANN model were compared with the measured ones at Imjin Bridge gauging station and correlation coefficients of the training and validation were 0.86 and 0.72, respectively. It was found that if the 1 day previous discharge at Imjin bridge is considered as an input variable for ANN model, the correlation coefficients were improved to 0.90 and 0.83, respectively. Based on the results in this study, SDF signals along with some local measured data can play an useful role in river flow estimation and especially in flood forecasting for data-scarce regions as it can simulate the peak discharge and peak time of flood events with satisfactory accuracy.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Progress in Medical Physics
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• v.22 no.3
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• pp.107-116
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• 2011

Respiratory gated radiation therapy and stereotactic body radiation therapy require identical tumor motions during each treatment with the motion detected in treatment planning CT. Therefore, this study developed a tumor motion monitoring and analysis system during the treatments employing RPM data, gated setup OBI images and a data analysis software. A respiratory training and guiding program which improves the regularity of breathing was used to patients. The breathing signal was obtained by RPM and the recorded data in the 4D console was read after treatment. The setup OBI images obtained gated at 0% and 50% of breathing phases were used to detect the tumor motion range in crenio-caudal direction. By matching the RPM data recorded at the OBI imaging time, a factor which converts the RPM motion to the tumor motion was computed. RPM data was entered to the institute developed data analysis software and the maximum, minimum, average of the breathing motion as well as the standard deviation of motion amplitude and period was computed. The computed result is exported in an excel file. The conversion factor was applied to the analyzed data to estimate the tumor motion. The accuracy of the developed method was tested by using a moving phantom, and the efficacy was evaluated for 10 stereotactic body radiation therapy patients. For the sine wave motion of the phantom with 4 sec of period and 2 cm of peak-to-peak amplitude, the measurement was slightly larger (4.052 sec) and the amplitude was smaller (1.952 cm). For patient treatment, one patient was evaluated not to qualified to SBRT due to the usability of the breathing, and in one patient case, the treatment was changed to respiratory gated treatment due the larger motion range of the tumor than treatment planed motion. The developed method and data analysis program was useful to estimate the tumor motion during treatment.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• The Journal of Korean Society for Radiation Therapy
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• v.24 no.2
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• pp.123-135
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• 2012

Purpose: It is essential to minimize the movement of tumor due to respiratory movement at the time of respiration controlled radiotherapy of non-small cell lung cancer patient. Accordingly, this Study aims to evaluate the usefulness of restricted respiratory period by comparing and analyzing the treatment plans that apply free and restricted respiration period respectively. Materials and Methods: After having conducted training on 9 non-small cell lung cancer patients (tumor n=10) from April to December 2011 by using 'signal monitored-breathing (guided- breathing)' method for the 'free respiratory period' measured on the basis of the regular respiratory period of the patents and 'restricted respiratory period' that was intentionally reduced, total of 10 CT images for each of the respiration phases were acquired by carrying out 4D CT for treatment planning purpose by using RPM and 4-dimensional computed tomography simulator. Visual gross tumor volume (GTV) and internal target volume (ITV) that each of the observer 1 and observer 2 has set were measured and compared on the CT image of each respiratory interval. Moreover, the amplitude of movement of tumor was measured by measuring the center of mass (COM) at the phase of 0% which is the end-inspiration (EI) and at the phase of 50% which is the end-exhalation (EE). In addition, both observers established treatment plan that applied the 2 respiratory periods, and mean dose to normal lung (MDTNL) was compared and analyzed through dose-volume histogram (DVH). Moreover, normal tissue complication probability (NTCP) of the normal lung volume was compared by using dose-volume histogram analysis program (DVH analyzer v.1) and statistical analysis was performed in order to carry out quantitative evaluation of the measured data. Results: As the result of the analysis of the treatment plan that applied the 'restricted respiratory period' of the observer 1 and observer 2, there was reduction rate of 38.75% in the 3-dimensional direction movement of the tumor in comparison to the 'free respiratory period' in the case of the observer 1, while there reduction rate was 41.10% in the case of the observer 2. The results of measurement and comparison of the volumes, GTV and ITV, there was reduction rate of $14.96{\pm}9.44%$ for observer 1 and $19.86{\pm}10.62%$ for observer 2 in the case of GTV, while there was reduction rate of $8.91{\pm}5.91%$ for observer 1 and $15.52{\pm}9.01%$ for observer 2 in the case of ITV. The results of analysis and comparison of MDTNL and NTCP illustrated the reduction rate of MDTNL $3.98{\pm}5.62%$ for observer 1 and $7.62{\pm}10.29%$ for observer 2 in the case of MDTNL, while there was reduction rate of $21.70{\pm}28.27%$ for observer 1 and $37.83{\pm}49.93%$ for observer 2 in the case of NTCP. In addition, the results of analysis of correlation between the resultant values of the 2 observers, while there was significant difference between the observers for the 'free respiratory period', there was no significantly different reduction rates between the observers for 'restricted respiratory period. Conclusion: It was possible to verify the usefulness and appropriateness of 'restricted respiratory period' at the time of respiration controlled radiotherapy on non-small cell lung cancer patient as the treatment plan that applied 'restricted respiratory period' illustrated relative reduction in the evaluation factors in comparison to the 'free respiratory period.

• 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.