• Knowledge Management Research
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• v.1 no.1
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• pp.91-107
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• 2000

This paper proposes the Organizational Knowledge Discovery Process Model (OK-DPM) as an initiative for developing a knowledge management methodology. OK-DPM is a model designed to effectively discover knowledge useful to the organization. It explains the knowledge discovery process from the conceptual level to the application level. It decomposes the organizational knowledge discovery process into 3 sub-processes; Creation, Suggestion and Validation. For each sub-process, design components are identified and possible methods for supporting each one are suggested. Also, the relationship patterns between the knowledge discovery process and knowledge type are explored. By applying OK-DPM to two real cases where the knowledge management projects are ongoing, the model was validated and revised. Even though we need to investigate with more cases to refine the OK-DPM, we found that it could provide some insights in developing the effective knowledge discovery process.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1452-1461
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• 2020

A triple bubbler sensor was tested in LiCl-KCl molten salt from 450 to 525 ℃ in a transparent furnace to validate thermal-expansion corrections and provide additional molten salt data sets for calibration and validation of the sensor. In addition to these tests, a model was identified and further developed to accurately determine the density, surface tension, and depth from the measured bubble pressures. A unique feature of the model is that calibration constants can be estimated using independent depth measurements, which allow calibration and validation of the sensor in an electrorefiner where the salt density and surface tension are largely unknown. This model and approach were tested using the current and previous triple bubbler data sets, and results indicate that accuracies are as high as 0.03%, 4.6%, and 0.15% for density, surface tension, and depth, respectively.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.209-216
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• 2015

Quantile regression models with covariate measurement errors have received a great deal of attention in both the theoretical and the applied statistical literature. A lot of effort has been devoted to develop effective estimation methods for such quantile regression models. In this paper we propose the partially linear support vector orthogonal quantile regression model in the presence of covariate measurement errors. We also provide a generalized approximate cross-validation method for choosing the hyperparameters and the ratios of the error variances which affect the performance of the proposed model. The proposed model is evaluated through simulations.

• Membrane and Water Treatment
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• v.1 no.3
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• pp.181-192
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• 2010

A linear quantitative structure-property relationship (QSPR) model is presented for the prediction of rejection in permeation through membrane. The model was produced by using the multiple linear regression (MLR) technique on the database consisting of retention data of 25 pesticides in 4 different membrane separation experiments. Among the 3224 different physicochemical, topological and structural descriptors that were considered as inputs to the model only 50 were selected using several criteria of elimination. The physical meaning of chosen descriptor is discussed in detail. The accuracy of the proposed MLR models is illustrated using the following evaluation techniques: leave-one-out cross validation procedure, leave-many-out cross validation procedure and Y-randomization.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.551-560
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• 1998

This paper presents a neural network model representing complex hydro-thermo-dynamic characteristics of a steam generator in nuclear power plants. The key modeling processes include training data gathering process, analysis of system dynamics and determining of the neural network structure, training process, and the final process for validation of the trained model. In this paper, we suggest a training data gathering method from an unstable steam generator so that the data sufficiently represent the dynamic characteristics of the plant over a wide operating range. In addition, we define the inputs and outputs of neural network model by analyzing the system dimension, relative degree, and inputs/outputs of the plant. Several types of neural networks are applied to the modeling and training process. The trained networks are verified by using a class of test data, and their performances are discussed.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1309-1313
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• 2008

This paper suggests an example of modification of the durability test specifications of electronic control unit for an automotive system in phase of design validation. The basic concept to redefine the specifications of durability test is based on the Arrhenius relationship for accelerated temperature test and the modified Coffin-Manson model for temperature cycle test. The ambient temperature of the powered-event durability test is increased to reduce the required test time of the current specification. Furthermore, the holding time between the events to cool down the temperature of the components is shortened and the resultant temperature rise affects the durability of the components. Thus, the acceleration factor due to the increased temperature range of temperature cycle is also estimated by the modified Coffin-Manson model.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.383-387
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• 2012

Development of automated document management system specified for scanned invoice images suffers from rigorous accuracy requirements for extraction of monetary data, which necessiate automatic validation on the extracted values for a generative invoice table model. Use of certain internal constraints such as "amount = unit price times quantity" is typical implementation. In this paper, we propose a noble invoice information extraction model with improved auto-validation method by utilizing table header detection and column classification.

• Structural Engineering and Mechanics
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• v.54 no.3
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• pp.475-489
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• 2015

This study presents the validation of a numerical model developed for dynamic analysis of buildings with roller seismic isolation bearings. Experimental methods allowed validation of the motion equations of a physical model of a building with and without roller bearings under base excitation. The results are presented in terms of modal parameters, frequency response functions (FRFs) and acceleration response. The agreement between numerical and experimental results proves the accuracy of the developed numerical model. Finally, the performance of the constructed seismic protection system is assessed through a parametric study.

• Journal of Integrative Natural Science
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• v.10 no.3
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• pp.119-124
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• 2017

Glucagon-like peptide 2 receptor, a GPCR, binds with the glucagon-like peptide, GLP-2 and regulates various metabolic functions in the gastrointestinal tract. It plays an important role in the nutrient homeostasis related to nutrient assimilation by regulating mucosal epithelium. GLP-2 receptor affects the cellular response to external injury, by controlling the intestinal crypt cell proliferation. As they are therapeutically attractive towards diseases related with the gastrointestinal tract, it becomes essential to analyse their structural features to study the pathophysiology of the diseases. As the three dimensional structure of the protein is not available, in this study, we have performed the homology modelling of the receptor based on single- and multiple template modeling. The models were subjected to model validation and a reliable model based on the validation statistics was identified. The predicted model could be useful in studying the structural features of GLP-2 receptor and their role in various diseases related to them.

• Journal of the Korean Society for Heat Treatment
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• v.34 no.5
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• pp.211-217
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• 2021

To predict mechanical properties of secondary hardening martensitic steels, a machine learning ensemble model was established. Based on ANN(Artificial Neural Network) architecture, some kinds of methods was considered to optimize the model. In particular, interaction features, which can reflect interactions between chemical compositions and processing conditions of real alloy system, was considered by means of feature engineering, and then K-Fold cross validation coupled with bagging ensemble were investigated to reduce R2_score and a factor indicating average learning errors owing to biased experimental database.