• Nuclear Engineering and Technology
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• v.52 no.8
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• pp.1626-1637
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• 2020

This work covers an important point of the benchmark released by the expert group on Uncertainty Analysis in Modeling of Light Water Reactors. This ambitious benchmark aims to determine the uncertainty in light water reactors systems and processes in all stages of calculation, with emphasis on multi-physics (coupled) and multi-scale simulations. The Gesellschaft für Anlagen und Reaktorsicherheit methodology is used to propagate the thermal-hydraulic uncertainty of macroscopic parameters through TRACE5.0p3/PARCSv3.0 coupled code. The main innovative points achieved in this work are i) a new thermal-hydraulic model is developed with a highly-accurate 3D core discretization plus an iterative process is presented to adjust the 3D bypass flow, ii) a control rod insertion occurrence -which data is obtained from a real PWR test- is used as a transient simulation, iii) two approaches are used for the propagation process: maximum response where the uncertainty and sensitivity analysis is performed for the maximum absolute response and index dependent where the uncertainty and sensitivity analysis is performed at each time step, and iv) RESTING MATLAB code is developed to automate the model generation process and, then, propagate the thermal-hydraulic uncertainty. The input uncertainty information is found in related literature or, if not found, defined based on expert judgment. This paper, first, presents the Gesellschaft für Anlagen und Reaktorsicherheit methodology to propagate the uncertainty in thermal-hydraulic macroscopic parameters and, then, shows the results when the methodology is applied to a PWR reactor.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.22-28
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• 2023

In this study, we conducted research on the automated trading of U.S. stocks through a volatility strategy using the Fear and Greed index. Volatility in the stock market is a common phenomenon that can lead to fluctuations in stock prices. Investors can capitalize on this volatility by implementing a strategy based on it, involving the buying and selling of stocks based on their expected level of volatility. The goal of this thesis is to investigate the effectiveness of the volatility strategy in generating profits in the stock market.This study employs a quantitative research methodology using secondary data from the stock market. The dataset comprises daily stock prices and daily volatility measures for the S&P 500 index stocks. Over a five-year period spanning from 2016 to 2020, the stocks were listed on the New York Stock Exchange (NYSE). The strategy involves purchasing stocks from the low volatility group and selling stocks from the high volatility group. The results indicate that the volatility strategy yields positive returns, with an average annual return of 9.2%, compared to the benchmark return of 7.5% for the sample period. Furthermore, the findings demonstrate that the strategy outperforms the benchmark return in four out of the five years within the sample period. Particularly noteworthy is the strategy's performance during periods of high market volatility, such as the COVID-19 pandemic in 2020, where it generated a return of 14.6%, as opposed to the benchmark return of 5.5%.

• Journal of Korea Port Economic Association
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• v.33 no.3
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• pp.53-68
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• 2017

The purpose of this study is to develop a new Korean container freight index by applying weights based on the global trade volume. To achieve this, it was decided to determine the conditions such as establishment of routes and regions, weighting of trade volumes which based on prior research and expert advice. Based on this, the individual index and regional index and composite index were calculated, and then reliability and statistical significance of the index was verified through correlation analysis and Granger causality analyses. This study suggest the following findings, through the development of the Korean container freight index. Firstly, Korean freight index reflects the overall market situation and can be used as a benchmark for determining the conditions of each market, consisting of criteria of region and routes. Secondly, it is possible to reflect the market conditions in which actual freight differences exist, since it has developed separate indexes for export and import routes. Finally, The composite index is the only index that reflects not only exports and imports but also 27 individual routes based on Busan, which is the most comprehensive indicator of the korean container freight market.

• Korea Real Estate Review
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• v.27 no.1
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• pp.7-16
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• 2017

In this study, we examine whether there are prospect theory investment patterns for individual investors in the real estate market. We use the maximum potential profit rate and the maximum potential loss rate of individual investors as a research method and additionally analyze it using the Jeong and Park(2015) model. As a result of the analysis, it was found that the investment pattern according to the prospect theory and disposition effect for individual investors. And we find the difference between zoning areas. This difference in investment behavior is believed to be due to the purpose of the real estate and the existence of rent fee, which creates a difference in investment behavior depending on the purpose. The limitations of this study are the analysis measurement of potential profit and potential loss using the land price index like the study of jeong and Park(2015). This implies that a new property price index needs to be developed or a benchmark for real estate assets is needed for deeper study of real estate investment sentiment.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.467-476
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• 2022

The Bank of Korea raised the benchmark interest rate by a quarter percentage point to 1.75 percent per year, and analysts predict that South Korea's policy rate will reach 2.00 percent by the end of calendar year 2022. Furthermore, because market volatility has been significantly increased by a variety of factors, including rising rates, inflation, and market volatility, many investors have struggled to meet their financial objectives or deliver returns. Banks and financial institutions are attempting to provide Robo-Advisors to manage client portfolios without human intervention in this situation. In this regard, determining the best hyper-parameter combination is becoming increasingly important. This study compares some activation functions of the Deep Deterministic Policy Gradient(DDPG) and Twin-delayed Deep Deterministic Policy Gradient (TD3) Algorithms to choose a sequence of actions that maximizes long-term reward. The DDPG and TD3 outperformed its benchmark index, according to the results. One reason for this is that we need to understand the action probabilities in order to choose an action and receive a reward, which we then compare to the state value to determine an advantage. As interest in machine learning has grown and research into deep reinforcement learning has become more active, finding an optimal hyper-parameter combination for DDPG and TD3 has become increasingly important.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1109-1116
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• 2023

Object tracking is used to track a goal in a video sequence by using coordinate information provided as annotation in the first frame of the video. In this paper, we propose a tracking algorithm that combines deep features and region inference modules to improve object tracking accuracy. In order to obtain sufficient object information, a convolution neural network was designed with a Siamese network structure. For object region inference, the region proposal network and overlapping confidence module were applied and used for tracking. The performance of the proposed tracking algorithm was evaluated using the Object Tracking Benchmark dataset, and it achieved 69.1% in the Success index and 89.3% in the Precision Metrics.

• Journal of The Korean Association For Science Education
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• v.24 no.6
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• pp.1235-1244
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• 2004

The purpose of the study was to produce resources to help develop instructional methods and programs for school science to reduce gender differences in science achievement by analyzing TIMSS-R results according to item type, benchmark, and content category. Korean male students performed higher than Korean female students and gender differences of Korean students were higher than international means in all item types including multiple-choice, short answer, and extended response type. GDI(Gender Difference Index) of Korean students was lower than international mean in top 10% benchmark but higher than international means in other benchmarks. Korean male students also exhibited higher performance than Korean female students in all content categories except scientific inquiry and the nature of science category. Regarding items that GDIs were higher than 10, the number of items belonged to life science and earth science category was relatively larger and a high percentage of them was not included in school science curriculum. In addition, the items were equally distributed in each performance category. In sum, the study showed various gender differences according to item type, benchmark, and content category. The results could be used to find appropriate instructional methods to reduce gender differences in science achievement.

• Journal of The Korean Association For Science Education
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• v.26 no.4
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• pp.492-501
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• 2006

Gender differences in TIMSS 2003 science achievement by item type, benchmark, and content area were examined by producing a Gender Differences Index (GDI) in this study. International trends identified that male students performed better than female students in TIMSS 2003 science achievement in all types of items. The overall achievement of Korean male students was better than Korean female students, especially in multiple-choice type items. Male students outperformed females in three benchmarks, including advanced, high, and intermediate international benchmark, but they did not outperform females in the low international benchmark when gender differences of the international average as well as the Korean average were taken into account. The results of the analysis of the international average and the Korean average by content area showed that gender differences were the greatest in earth science and smallest in chemistry. In life science, female students excelled when considering the international average while male students excelled when considering the average of Korean students' performance. In addition, the number of items in which male students outperformed females was larger in both factual knowledge and the conceptual understanding domain. Implications for reducing gender differences in science achievement in Korea based on the results were provided.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.448-463
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• 2017

Multimedia is a ubiquitous and indispensable part of our daily life and learning such as audio, image, and video. Objective and subjective quality evaluations play an important role in various multimedia applications. Blind image quality assessment (BIQA) is used to indicate the perceptual quality of a distorted image, while its reference image is not considered and used. Blur is one of the common image distortions. In this paper, we propose a novel BIQA index for Gaussian blur distortion based on the fact that images with different blur degree will have different changes through the same blur. We describe this discrimination from three aspects: color, edge, and structure. For color, we adopt color histogram; for edge, we use edge intensity map, and saliency map is used as the weighting function to be consistent with human visual system (HVS); for structure, we use structure tensor and structural similarity (SSIM) index. Numerous experiments based on four benchmark databases show that our proposed index is highly consistent with the subjective quality assessment.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.112-127
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• 2002

A useful pattern is a pattern that contributes much to learning. For a classification problem those patterns near the class boundary surfaces carry more information to the classifier. For a regression problem the ones near the estimated surface carry more information. In both cases, the usefulness is defined only for those patterns either without error or with negligible error. Using only the useful patterns gives several benefits. First, computational complexity in memory and time for learning is decreased. Second, overfitting is avoided even when the learner is over-sized. Third, learning results in more stable learners. In this paper, we propose a pattern 'utility index' that measures the utility of an individual pattern. The utility index is based on the bias and variance of a pattern trained by a network ensemble. In classification, the pattern with a low bias and a high variance gets a high score. In regression, on the other hand, the one with a low bias and a low variance gets a high score. Based on the distribution of the utility index, the original training set is divided into a high-score group and a low-score group. Only the high-score group is then used for training. The proposed method is tested on synthetic and real-world benchmark datasets. The proposed approach gives a better or at least similar performance.