• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1287-1300
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• 2017

Based on the observation that ignoring the angle dependency of multigroup resonance cross sections within a fuel pellet would result in nontrivial underestimation of the spatial self-shielding of flux, a parametrized spectral superhomogenization (SPH) factor library (PSSL) method is developed as a practical means of resolving the problem. Region-wise spectral SPH factors are calculated by the normal and transport corrected SPH iterations after ultrafine group slowing down calculations over various light water reactor pin-cell configurations. The parametrization is done with fuel temperature, U-238 number density, fuel radius, moderator source represented by ${\Sigma}_{mod}V_{mod}$, and the number density ratio of resonance nuclides to that of U-238 in a form of resonance interference correction factors. The parametrization is successful in that the root mean square errors of the interpolated SPH factors over the fuel regions of various pin-cells are within 0.1%. The improvement in reactivity error of the PSSL method is shown to be superior to that by the original SPH method in that the reactivity bias of -200 pcm to -300 pcm vanishes almost completely. It is demonstrated that the environment effect takes only about 4% in the reactivity improvement so that the pin-cell based PSSL method is effective in the assembly problems.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.314-333
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• 2014

When analyzing default predictions in real estate companies, the number of non-defaulted cases always greatly exceeds the defaulted ones, which creates the two-class imbalance problem. This lowers the ability of prediction models to distinguish the default sample. In order to avoid this sample selection bias and to improve the prediction model, this paper applies a minority sample generation approach to create new minority samples. The logistic regression, support vector machine (SVM) classification, and neural network (NN) classification use an imbalanced dataset. They were used as benchmarks with a single prediction model that used a balanced dataset corrected by the minority samples generation approach. Instead of using prediction-oriented tests and the overall accuracy, the true positive rate (TPR), the true negative rate (TNR), G-mean, and F-score are used to measure the performance of default prediction models for imbalanced dataset. In this paper, we describe an empirical experiment that used a sampling of 14 default and 315 non-default listed real estate companies in China and report that most results using single prediction models with a balanced dataset generated better results than an imbalanced dataset.

• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.878-888
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• 2020

This paper presents the validation of UNIST in-house Monte Carlo code MCS used for the high-fidelity simulation of commercial pressurized water reactors (PWRs). Its focus is on the accurate, spatially detailed neutronic analyses of startup physics tests for the initial core of the Watts Bar Nuclear 1 reactor, which is a vital step in evaluating core phenomena in an operating nuclear power reactor. The MCS solutions for the Consortium for Advanced Simulation of Light Water Reactors (CASL) Virtual Environment for Reactor Applications (VERA) core physics benchmark progression problems 1 to 5 were verified with KENO-VI and Serpent 2 solutions for geometries ranging from a single-pin cell to a full core. MCS was also validated by comparing with results of reactor zero-power physics tests in a full-core simulation. MCS exhibits an excellent consistency against the measured data with a bias of ±3 pcm at the initial criticality whole-core problem. Furthermore, MCS solutions for rod worth are consistent with measured data, and reasonable agreement is obtained for the isothermal temperature coefficient and soluble boron worth. This favorable comparison with measured parameters exhibited by MCS continues to broaden its validation basis. These results provide confidence in MCS's capability in high-fidelity calculations for practical PWR cores.

• Journal of the Korean earth science society
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• v.42 no.4
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• pp.401-411
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• 2021

This study provides comprehensive assessment results for the most recent high-resolution regional climatology in the East/Japan Sea by comparing with the various existing climatologies. This new high-resolution climatology is generated based on the Optimal Interpolation (OI) method with individual profiles from the World Ocean Database and gridded World Ocean Atlas provided by the National Centers for Environmental Information (NCEI). It was generated from the recent previous study which had a primary focus to solve the abnormal horizontal gradient problem appearing in the other high-resolution climatology version of NCEI. This study showed that this new OI field simulates well the meso-scale features including closed-curve temperature spatial distribution associated with eddy formation. Quantitative spatial variability was compared to the other four different climatologies and significant variability at 160 km was presented through a wavelet spectrum analysis. In addition, the general improvement of the new OI field except for warm bias in the coastal area was confirmed from the comparison with serial observation data provided by the National Fisheries Research and Development Institute's Korean Oceanic Data Center.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.457-467
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• 2018

This paper presents performance comparison of several high-resolution frequency estimation algorithms for pure real tone signal. Algorithms are DFT (Discrete Fourier Transform - for reference purpose), Jacobsen, Candan, reassignment and Cedron. Each algorithm is evaluated under various experimental conditions, e.g., different SNR (Signal to Noise Ratio), window function and window length and performance is compared in the perspective of bias, MSE (Mean Square-Error) and variance. Experimental results indicate that Cedron algorithms works well in the most cases. For actual usage in the engineering problem, each algorithm needs additional analysis and modification.

• The Mathematical Education
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• v.57 no.4
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• pp.413-431
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• 2018

In this study, I developed an activity oriented lesson to support the understanding of probabilistic and quantitative estimating population ratios according to the standard statistical principles and discussed its implications in didactical respects. The developed activity lesson, as an efficient physical simulation activity by sampling with replacement, simulates unknown populations and real problem situations through completely closed 'Closed Box' in which we can not see nor take out the inside balls, and provides teaching and learning devices which highlight the representativeness of sample ratios and the sampling variability. I applied this activity lesson to the gifted students who did not learn estimating population ratios and collected the research data such as the activity sheets and recording and transcribing data of students' presenting, and analyzed them by Qualitative Content Analysis. As a result of an application, this activity lesson was effective in recognizing and reflecting on the representativeness of sample ratios and recognizing the random sampling variability. On the other hand, in order to show the sampling variability clearer, I discussed appropriately increasing the total number of the inside balls put in 'Closed Box' and the active involvement of the teachers to make students pay attention to controlling possible selection bias in sampling processes.

• Journal of Korean Biological Nursing Science
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• v.21 no.1
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• pp.1-11
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• 2019

Purpose: The purpose of this review was to evaluate the effects of emergency nursing simulation program on nursing students and nurses. Methods: This systematic review was performed as per the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and using the R program meta package (version 4.9-2). RISS, KISS, and DBpia Library databases were searched for studies published between June 2000 and August 2018 using the following key words: ($Emerge^*$ OR nursing) AND ($nurs^*$ OR simulation). Selected studies were assessed for methodological quality using Risk of Bias for Non randomized Studies. Results: 7 studies were identified and all of them met the inclusion criteria. The outcome variables were significant clinical performance, self-efficacy except knowledge, and problem-solving ability. Conclusion: This review provides updated evidence of the simulation-based education program in emergency nursing. Further studies are required to increase generalizability using randomized population, research design and controlled trials with sufficient sample size. Moreover, valid measurements are needed to assess the main outcomes.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.1-9
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• 2019

Single image rain removal is a typical inverse problem which decomposes the image into a background scene and a rain streak. Recent works have witnessed a substantial progress on the task due to the development of convolutional neural network (CNN). However, existing CNN-based approaches train the network with synthetically generated training examples. These data tend to make the network bias to the synthetic scenes. In this paper, we present an unsupervised framework for removing rain streaks from real-world rainy images. We focus on the natural phenomena that static rainy scenes capture a common background but different rain streak. From this observation, we train siamese network with the real rain image pairs, which outputs identical backgrounds from the pairs. To train our network, a real rainy dataset is constructed via web-crawling. We show that our unsupervised framework outperforms the recent CNN-based approaches, which are trained by supervised manner. Experimental results demonstrate that the effectiveness of our framework on both synthetic and real-world datasets, showing improved performance over previous approaches.

• Korea Trade Review
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• v.44 no.3
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• pp.227-252
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• 2019

This study explores the effects of exports on the innovation of Korean firms listed on two Korean stock markets, the Korean Stock Exchange and the Korean Securities Dealers Quotations, between 1999 and 2016. By matching exporting firms to non-exporting ones with propensity score matching, this study accounts for a problem from sample selection bias that may arise from differences in firm-characteristics between the two groups. From the study results, first, both export participation and export volume significantly increase subsequent innovation performance, as measured by the number of patent applications. This result seems to support the "learning by exporting" hypothesis for Korean listed firms. Second, both export participation and export volume narrow innovation scope, proxied as the number of unique International Patent Classification (IPC) codes of the patent applied, the degree to which patents are concentrated in a particular class, and the degree of proximity in the patents. The findings of innovation scope suggest a possible explanation that the learning effect appears in familiar technology fields that firms have previously held, rather than in unfamiliar ones. Third, these results are robust using alternative proxies in the innovation scope, Tobit regressions to consider the non-trivial portion of sample firms with patent applications equal to zeros, and generalized method of moments (GMM) to control for the persistence of innovation measures hearing over years. Finally, the two main results are more pronounced in large firms than in small and medium-sized ones. As for Chaebol firms, however, these results do not appear.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.229-235
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• 2020

Investors aim to maximize the return rate for their own investment, utilizing various information as possible as they can access. However those investors, especially individual investors, have limitations of interpretation of the domain-specific information or even the acquisition of the information itself. Thus, individual investors tend to make decision affectively and frequently, which may cause a loss in returns. This study aims to analyze analysts' target price and to suggest the strategy that could maximize individual's return rate. Most previous literature revealed that the optimistic bias exists in the analysts' target price and it is also confirmed in this study. In this context, this study suggests the upper limit of target rate of returns and the optimal value named 'alpha(α)' which performs the adjustment of proposed target rate to maximize excess earning returns eventually. To achieve this goal, this study developed an optimization problem using linear programming. Specifically, when the analysts' proposed target rate exceeds 30%, it could be adjusted to the extent of 59% of its own target rate. As apply this strategy, the investors could achieve 1.2% of excess earning rate on average. The result of this study has significance in that the individual investors could utilize analysts' target price practically.