• Journal of radiological science and technology
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• v.43 no.6
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• pp.503-509
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• 2020

This study aims to develop a patient-specific radiation exposure dose prediction model based on anthropometric data that can be easily measurable during CT examination, and to be used as basic data for DRL setting and radiation dose management system in the future. In addition, among the machine learning algorithms, the most suitable model for predicting exposure doses is presented. The data used in this study were chest CT scan data, and a data set was constructed based on the data including the patient's anthropometric data. In the pre-processing and sample selection of the data, out of the total number of samples of 250 samples, only chest CT scans were performed without using a contrast agent, and 110 samples including height and weight variables were extracted. Of the 110 samples extracted, 66% was used as a training set, and the remaining 44% were used as a test set for verification. The exposure dose was predicted through random forest, linear regression analysis, and SVM algorithm using Orange version 3.26.0, an open software as a machine learning algorithm. Results Algorithm model prediction accuracy was R^2 0.840 for random forest, R^2 0.969 for linear regression analysis, and R^2 0.189 for SVM. As a result of verifying the prediction rate of the algorithm model, the random forest is the highest with R^2 0.986 of the random forest, R^2 0.973 of the linear regression analysis, and R^2 of 0.204 of the SVM, indicating that the model has the best predictive power.

• Management & Information Systems Review
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• v.31 no.4
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• pp.287-307
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• 2012

We empirically examine the price discovery dynamics among the VKOSPI, the KOSPI200 spot, and the KOSPI200 futures markets. The analysis employs the vector-autoregression, Granger causality, impulse response function, and variance decomposition using both daily data from 2009. 04. 13 to 2011. 12. 30 and 1 minute data from the bull market, bear market, and the flat period. The main results are as follows; First, the lead lag relationships between KOSPI200 spot(futures) yield VKOSPI returns could not be found from the daily data analysis. But KOSPI200 spot(futures) have a predictive power for VKOSPI from 1 minute data. Especially KOSPI200 spot(futures) and VKOSPI show the bi-directional effects to each other during the return rising period Second, We chose the VAR(1) the model in daily data but adopt the VAR(3) model in the one minute data to determine the lead lag time. We know that there is predictability during the very short period Third, Spot returns and futures returns makes no difference in daily data results. According to the one minite data results, VKOSPI returns have a predictive power for KOSPI200 spot return, but have no predictive power for KOSPI200 futures return.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Journal of Korea Technology Innovation Society
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• v.16 no.4
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• pp.937-953
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• 2013

The theoretical discussion of generation process and development directions of economic fluctuations are actively discussed. This study describes the economic changes applied to empirical research on economic volatility in Korea under the economic theory of network theory [Acemoglu, et al. 2012, 2010]. For the three years in 2000, 2005, and 2010, the network analysis were applied to industry input-output tables. The research results show that the network economic structures in Korea is shifted from a high connectivity among sectors to a lower connectivity. Also, the impact of key industries and the mutual connectivity of input and out among industrial sectors are weaken. Implications for industry and technology policy are derived form the study results.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.3
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• pp.141-145
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• 2006

The line current problem(2-dimensional space : point source) is not easy to analyze the magnetic field using the standard finite element method(FEM), such as overhead trolley line or transmission line. To supplement such a defect this paper is proposed the coupling scheme of analytical solution and FEM. In analysis of the magnetic field using the standard FEM. If the current region is a relatively small compared to the whole region. Therefore the current region must be finely divided using a large number of elements. And the large number of elements increase the number of unknown variables and the use of computer memories. In this paper, an analytical solution is suggested to supplement this weak points. When source is line current and the part of interest is far from line current, the analytical solution can be coupling with FEM at the boundary. Analytical solution can be described by the multiplication of two functions. One is power function of radius, the other is a trigonometric function of angle in the cylindrical coordinate system. There are integral constants of two types which can be established by fourier series expansion. Also fourier series is represented as the factor to apply the continuity of the magnetic vector potential and magnetic field intensity with tangential component at the boundary. To verify the proposed algorithm, we chose simplified model existing magnetic material in FE region. The results are compared with standard FE solution. And it is good agreed by increasing harmonic order.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1392-1401
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• 2017

In this study, the design methodology of pattern classification is introduced for avoiding faults through partial discharge occurring in the power facilities and local sites. In order to classify some partial discharge types according to the characteristics of each feature, the model is constructed by using the Radial Basis Function Neural Networks(RBFNNs) and Particle Swarm Optimization(PSO). In the input layer of the RBFNNs, the feature vector is searched and the dimension is reduced through Principal Component Analysis(PCA) and PSO. In the hidden layer, the fuzzy coefficients of the fuzzy clustering method(FCM) are tuned using PSO. Raw datasets for partial discharge are obtained through the Motor Insulation Monitoring System(MIMS) instrument using an Epoxy Mica Coupling(EMC) sensor. The preprocessed datasets for partial discharge are acquired through the Phase Resolved Partial Discharge Analysis(PRPDA) preprocessing algorithm to obtain partial discharge types such as void, corona, surface, and slot discharges. Also, when the amplitude size is considered as two types of both the maximum value and the average value in the process for extracting the preprocessed datasets, two different kinds of feature datasets are produced. In this study, the classification ratio between the proposed RBFNNs model and other classifiers is shown by using the two different kinds of feature datasets, and also we demonstrate the proposed model shows superiority from the viewpoint of classification performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.11
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• pp.183-190
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• 2012

Conventional physical activity monitoring systems, which use accelerometers, global positioning system (GPS), heartbeats, or body temperature information, showed limited performances due to their own restrictions on measurement environment and measurable activity types. To overcome these limitations, we developed a portable exercise analysis system that can analyze aerobic exercises as well as isotonic exercises. For bioelectric signal acquisition during exercise, waist belt with two body contact electrodes was used. For exercise analysis, the measured signals were firstly divided into two signal groups with different frequency ranges which can represent respiration related signal and muscular motion related signal, respectively. After then, power values, differential of power values, and median frequency values were selected for feature values. Selected features were used as inputs of support vector machine (SVM) to classify the exercise types. For verification of statistical significance, ANOVA and multiple comparison test were performed. The experimental results showed 100% accuracy for classification of aerobic exercise and isotonic resistance exercise. Also, classification of aerobic exercise, isotonic resistance exercise, and hybrid types of exercise revealed 92.7% of accuracy.

• Journal of Korea Water Resources Association
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• v.48 no.7
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• pp.553-566
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• 2015

Acoustic Doppler Current Profilers(ADCPs) have capability to concurrently capitalize three-dimensional velocity vector and bathymetry with highly efficient and rapid manner, and thereby enabling ADCPs to document the hydrodynamic and morphologic data in very high spatial and temporal resolution better than other contemporary instruments. However, ADCPs are also limited in terms of the inevitable unmeasured regions near bottom, surface, and edges of a given cross-section. The velocity in those unmeasured regions are usually extrapolated or assumed for calculating flow discharge, which definitely affects the accuracy in the discharge assessment. This study aimed at scrutinizing a conventional extrapolation method(i.e., the 1/6 power law) for estimating the unmeasured regions to figure out the accuracy in ADCP discharge measurements. For the comparative analysis, we collected spatially dense velocity data using ADV as well as stationary ADCP in a real-scale straight river channel, and applied the 1/6 power law for testing its applicability in conjunction with the logarithmic law which is another representative velocity law. As results, the logarithmic law fitted better with actual velocity measurement than the 1/6 power law. In particular, the 1/6 power law showed a tendency to underestimate the velocity in the near surface region and overestimate in the near bottom region. This finding indicated that the 1/6 power law could be unsatisfactory to follow actual flow regime, thus that resulted discharge estimates in both unmeasured top and bottom region can give rise to discharge bias. Therefore, the logarithmic law should be considered as an alternative especially for the stationary ADCP discharge measurement. In addition, it was found that ADCP should be operated in at least more than 0.6 m of water depth in the left and right edges for better estimate edge discharges. In the future, similar comparative analysis might be required for the moving boat ADCP discharge measurement method, which has been more widely used in the field.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.3
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• pp.155-162
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• 2014

Using an isogeometric approach, a continuum-based shape design optimization method is developed for steady state power flow problems at high frequencies. In case the isogeometric method is employed to the shape design optimization, the NURBS basis functions used in CAD geometric modeling are directly utilized to embed the exact geometry into the computational framework so that the design parameterization for shape optimization is much easier than that in the finite element method and consequently provides the enhanced smoothness of design perturbations. Thus, exact geometric models can be used in both the response and the shape sensitivity analyses, where normal vector and curvature are continuous over the whole design space so that enhanced shape sensitivity can be expected. Through numerical examples, the developed isogeometric sensitivity is compared with finite difference one to provide excellent agreement. Also, it turns out that the proposed method works very well in the shape optimization problems.

• Journal of Korea Technology Innovation Society
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• v.12 no.2
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• pp.291-311
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• 2009

Intellectual Property(IP) investment and its management are an key driver to create corporate value and intangible asset value through corporate's competitiveness. The purposes of this study are to survey capability of IP management and assess the effects of IP investment and its management on the separation into groups of intangible asset value. In order to attain those purposes of this study, sample companies were taken and categorized into three groups by the level of intangible asset value ratio, and data for IP investment such as R&D expenditure and advertising expenditure were collected from 90 manufacturing companies, and data for IP management capability about patent, design and brand were taken through survey. The final results showed as followed: First, IP management capability were generally not sufficient in the results of survey. Second, mean vector for the four variables were significantly different among three groups in multivariate analysis variance. Third, the order of their contribution to separating the groups were R&D expenditure, advertising expenditure, patent management, management of design and brand in canonical variate analysis. Fourth, R&D and patent management capability were significantly related to the separation of three groups, while advertising expenditure were not significant and management of design and brand were not sure of Significance in multinomial logit discriminant analysis. Fifth, exploratory power of the discriminant model were estimated by 53% in classification analysis. Finally, strategic policy for IP investment and its management should be taken urgently to create intangible asset value and to improve the capability of its management.