• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.9
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• pp.113-119
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• 2005

The methods that are used for receiving data from attached devices under real-time preemptive multi-task operating system (OS) by general processors can be categorized as polling and interrupt driven. The technical approach to these methods may be different due to the application specific scheduling policy of the OS and the programming architecture of the flight software. It is one of the most important requirements on the development of the flight software to process the data received from satellite subsystems or components with the exact timeliness and accuracy. This paper presents the analysis of the I/O method of device related scheduling mechanism and the reliable data I/O methods between processor and devices.

• Nuclear Engineering and Technology
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• v.52 no.6
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• pp.1148-1155
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• 2020

This work presents two different methods for quantifying and propagating the uncertainty associated with fuel composition at end of life for cask criticality calculations. The first approach, the computational approach uses parametric uncertainty including those associated with nuclear data, fuel geometry, material composition, and plant operation to perform forward depletion on Monte-Carlo sampled inputs. These uncertainties are based on experimental and prior experience in criticality safety. The second approach, the data-driven approach relies on using radiochemcial assay data to derive code bias information. The code bias data is used to perturb the isotopic inventory in the data-driven approach. For both approaches, the uncertainty in k_eff for the cask is propagated by performing forward criticality calculations on sampled inputs using the distributions obtained from each approach. It is found that the data driven approach yielded a higher uncertainty than the computational approach by about 500 pcm. An exploration is also done to see if considering correlation between isotopes at end of life affects k_eff uncertainty, and the results demonstrate an effect of about 100 pcm.

• Coupled systems mechanics
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• v.11 no.1
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• pp.43-54
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• 2022

In order to analyse the thermal performance of battery systems in electric vehicles complex simulation models with high computational cost are necessary. Using reduced order methods, real-time applicable model can be developed and used for on-board monitoring. In this work a data driven model of the cooling plate as part of the battery system is built and derived from a computational fluid dynamics (CFD) model. The aim of this paper is to create a meta model of the cooling plate that estimates the temperature at the boundary for different heat flow rates, mass flows and inlet temperatures of the cooling fluid. In order to do so, the cooling plate is simulated in a CFD software (ANSYS Fluent ®). A data driven model is built using the design of experiment (DOE) and various approximation methods in Optimus ®. The model can later be combined with a reduced model of the thermal battery system. The assumption and simplification introduced in this paper enable an accurate representation of the cooling plate with a real-time applicable model.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.811-816
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• 2000

The purpose of this paper is to study of data-driven smoothing goodness of it test, when the hypothesis is complete. The smoothing goodness of fit test statistic by nonparametric function estimation techniques is proposed in this paper. The results of simulation studies for he powers of show that the proposed test statistic compared well to other.

• Journal of the Korean Statistical Society
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• v.20 no.1
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• pp.1-28
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• 1991

Considerable progress on the problem of data-driven bandwidth selection in kernel density estimation has been made recently. The goal of this paper is to provide an introduction to the methods currently available, with discussion at both a practical and a nontechnical theoretical level. The main setting considered here is global bandwidth kernel estimation, but some recent results on variable bandwidth kernel estimation are also included.

• The KIPS Transactions:PartD
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• v.15D no.5
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• pp.681-690
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• 2008

In proteomics research, the identification of differentially expressed proteins observed under specific conditions is one of key issues. There are several ways to detect the change of a specific protein's expression level such as statistical analysis and graphical visualization. However, it is quiet difficult to handle the spot information of an individual protein manually by these methods, because there are a considerable number of proteins in a tissue sample. In this paper, using database and data mining techniques, the application plan of OLAP data cube and Discovery-driven exploration is proposed. By using data cubes, it is possible to analyze the relationship between proteins and relevant clinical information as well as analyzing the differentially expressed proteins by disease. We propose the measure and exception indicators which are suitable to analyzing protein expression level changes are proposed. In addition, we proposed the reducing method of calculating InExp in Discovery-driven exploration. We also evaluate the utility and effectiveness of the data cube and Discovery-driven exploration in the lung cancer 2-DE gel image.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.44-51
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• 2016

Recently, artificial intelligence has reached the level of top information technologies that will have significant influence over many aspects of our future lifestyles. In particular, in the fields of machine learning technologies for classification and decision-making, there have been a lot of research efforts for solving estimation and control problems that appear in the various kinds of portfolio management problems via data-driven approaches. Note that these modern data-driven approaches, which try to find solutions to the problems based on relevant empirical data rather than mathematical analyses, are useful particularly in practical application domains. In this paper, we consider some applications of modern data-driven machine learning methods for portfolio management problems. More precisely, we apply a simplified version of the sparse Gaussian process (GP) classification method for classifying users' sensitivity with respect to financial risk, and then present two portfolio management issues in which the GP application results can be useful. Experimental results show that the GP applications work well in handling simulated data sets.

• Asia Pacific Journal of Corpus Research
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• v.2 no.2
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• pp.43-48
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• 2021

The aim of this study is to analyze the trends of corpus driven research in Korean education. For this purpose, a total of 14 papers was searched online with the keywords including Korean corpus and Korean education. The data was categorized into three: vocabulary education, grammar education and corpus data construction methods. The analysis results suggest that the number of corpus studies in the field of Korean education is not large enough but continues to increase, especially in the research on data construction tools. This suggests there is a significant demand in corpus driven studies in Korean education field.

• ETRI Journal
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• v.45 no.6
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• pp.929-938
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• 2023

The detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model-based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data-driven methods perform slightly worse than model-based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning-based method by improving a state-of-the-art data-driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error-propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model-based methods in perfect CSI environments and the best performance in imperfect CSI environments.

• Geomechanics and Engineering
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• v.1 no.1
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• pp.17-34
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• 2009

There has been growing agreement that geotechnical reliability-based design (RBD) is necessary for establishing more advanced and integrated design system. In this study, resistance factors for LRFD pile design using CPT results were investigated for axially loaded driven piles. In order to address variability in design methodology, different CPT-based methods and load-settlement criteria, popular in practice, were selected and used for evaluation of resistance factors. A total of 32 data sets from 13 test sites were collected from the literature. In order to maintain the statistical consistency of the data sets, the characteristic pile load capacity was introduced in reliability analysis and evaluation of resistance factors. It was found that values of resistance factors considerably differ for different design methods, load-settlement criteria, and load capacity components. For the total resistance, resistance factors for LCPC method were higher than others, while those for Aoki-Velloso's and Philipponnat's methods were in similar ranges. In respect to load-settlement criteria, 0.1B and Chin's criteria produced higher resistance factors than DeBeer's and Davisson's criteria. Resistance factors for the base and shaft resistances were also presented and analyzed.