• Research in Vestibular Science
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• v.17 no.4
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• pp.152-159
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• 2018

Objectives: The Korean Dizziness Handicap Inventory (KDHI), which includes 25 patient-reported items, has been used to assess self-reported dizziness in Korean patients with Parkinson disease (PD). Nevertheless, few studies have examined the KDHI based on item-response theory within this population. The aim of our study was to address the feasibility and clinimetric properties of the KDHI instrument using polytomous Rasch measurement analysis. Methods: The unidimensionality, scale targeting, separation reliability, item difficulty (severity), and response category utility of the KDHI were statistically assessed based on the Andrich rating scale model. The utilities of the orderedresponse categories of the 3-point Likert scale were analyzed with reference to the probability curves of the response categories. The separation reliability of the KDHI was assessed based on person separation reliability (PSR), which is used to measure the capacity to discriminate among groups of patients with different levels of balance deficits. Results: Principal component analyses of residuals revealed that the KDHI had unidimensionality. The KHDI had satisfactory PSR and there were no disordered thresholds in the 3-point rating scale. However, the KDHI showed several issues for inappropriate scale targeting and misfit items (items 1 and 2) for Rasch model. Conclusions: The KDHI provide unidimensional measures of imbalance symptoms in patients with PD with adequate separation reliability. There was no statistical evidence of disorder in polytomous rating scales. The Rasch analysis results suggest that the KDHI is a reliable scale for measuring the imbalance symptoms in PD patients, and identified parts for possible amendments in order to further improve the linear metric scale.

• Radiation Oncology Journal
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• v.18 no.4
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• pp.277-282
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• 2000

Purpose : Though It has been known that the to tolerance of the liver to external beam irradiation depends on the irradiated volume and dose, few data exist which Quantify this dependence. However, recently, with the development of three dimensional (3-D) treatment planning, have the tools to Quantify the relationships between dose, volume, and normal tissue complications become available. The objective of this study is to investigate the relationships between normal tissue complication probabili쇼 (WCP) and the risk of radiation hepatitis for patients who received variant dose partial liver irradiation. Materials and Methods : From March 1992 to December 1994, 10 patients with hepatoma and 10 patients with bile duct cancer were included in this study. Eighteen patients had normal hepatic function, but 2 patients (prothrombin time 73$\%$, 68$\%$) had mild liver cirrhosis before irradiation. Radiation therapy was delivered with 10MV linear accelerator, 180$\~$200 cGy fraction per day. The total dose ranged from 3,960 cGy to 6,000 cGy (median dose 5,040 cGy). The normal tissue complication probability was calculated by using Lyman's model. Radiation hepatitis was defined as the development of anicteric elevation of alkaline phosphatase of at least two fold and non-malignant ascites in the absence of documented progressive. Results: The calculated NTCP ranged from 0.001 to 0.840 (median 0.05). Three of the 20 patients developed radiation hepatitis. The NTCP of the patients with radiation hepatitis were 0.390, 0.528, 0.844(median : 0.58$\pm$0.23), but that of the patients without radiation hepatitis ranged fro 0.001 to 0.308 (median .0.09$\pm$0.09). When the NTCP was calculated by using the volume factor of 0.32, a radiation hepatitis was observed only in patients with the NTCP value more than 0.39. By contrast, clinical results of evolving radiation hepatitis were not well correlated with NTCP value calculated when the volume factor of 0.69 was applied. On the basis of these observations, the volume factor of 0.32 was more correlated to predict a radiation hepatitis. Conclusion : The risk of radiation hepatitis was increased above the cut-off value. Therefore the NTCP seems to be used for predicting the radiation hepatitis.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.1
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• pp.61-67
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• 2015

Recently, the initiation of outside diameter stress corrosion cracking (ODSCC) at the tube support plate region of domestic steam generators (SG) with Alloy600 HTMA tubes has been increasing. As a result, SGs with Alloy600 HTMA tubes must be replaced early or are scheduled to be replaced prior to their designed lifetime. ODSCC is one of the biggest threats to the integrity of SG tubes. Therefore, the accurate evaluation of tube integrity to determine ODSCC is needed. Eddy current testing (ECT) is conducted periodically, and its results could be input as parameters for evaluating the integrity of SG tubes. The reliability of an ECT inspection system depends on the performance of the inspection technique and abilty of the analyst. The detection probability and ECT sizing error of degradation are considered to be the performance indices of a nondestructive evaluation (NDE) system. This paper introduces an optimized evaluation method for ECT, as well as the sizing error, including the analyst performance. This study was based on the results of a round robin program in which 10 inspection analysts from 5 different companies participated. The analysis of ECT sizing results was performed using a linear regression model relating the true defect size data to the measured ECT size data.