• Journal of Korean Society for Geospatial Information Science
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• v.16 no.3
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• pp.105-110
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• 2008

The cross-section data are generally used for hydraulic and hydrologic modeling. However, when the detailed data of river channel are required, it is not available to use because of too wide distance of the offset between cross-sections. Also, the actual form of river channel cannot be reflected with the general interpolation methods which is considering straight line between acquired points. The aim of this paper is to present an algorithm which is to interpolate point using bilinear method and to estimate random cross-section between two surveyed cross-section data. And it is supposed that the proposed algorithm can be able to offer available data for hydraulic and hydrologic modeling.

• Translational and Clinical Pharmacology
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• v.26 no.4
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• pp.155-159
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• 2018

For regulatory approval of a new drug, the most preferred and reliable source of evidence would be randomized controlled trials (RCT). However, a great number of drugs, being developed as well as already marketed and being used, usually lack proper indications for children. It is imperative to develop properly evaluated drugs for children. And expanding the use of already approved drugs for other indications will benefit patients and the society. Nevertheless, to get an approval for expansion of indications, most often with off-label experiences, for drugs that have been approved or for the development of pediatric indications, either during or after completing the main drug development, conducting RCTs may not be the only, if not right, way to take. Extrapolation strategies and modelling & simulation for pediatric drug development are paving the road to the better approval scheme. Making the use of data sources other than RCT such as EHR and claims data in ways that improve the efficiency and validity of the results (e.g., randomized pragmatic trial and randomized registry trial) has been the topic of great interest all around the world. Regulatory authorities should adopt new methodologies for regulatory approval processes to adapt to the changes brought by increasing availability of big and real world data utilizing new tools of technological advancement.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.1
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• pp.75-85
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• 2021

Evaluating the quantitative damage to rocks through acoustic emission (AE) has become a research focus. Most studies mainly used one or two AE parameters to evaluate the degree of damage, but several AE parameters have been rarely used. In this study, several data-driven models were employed to reflect the combined features of AE parameters. Through uniaxial compression tests, we obtained mechanical and AE-signal data for five granite specimens. The maximum amplitude, hits, counts, rise time, absolute energy, and initiation frequency expressed as the cumulative value were selected as input parameters. The result showed that gradient boosting (GB) was the best model among the support vector regression methods. When GB was applied to the testing data, the root-mean-square error and R between the predicted and actual values were 0.96 and 0.077, respectively. A parameter analysis was performed to capture the parameter significance. The result showed that cumulative absolute energy was the main parameter for damage prediction. Thus, AE has practical applicability in predicting rock damage without conducting mechanical tests. Based on the results, this study will be useful for monitoring the near-field rock mass of nuclear waste repository.

• Geomechanics and Engineering
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• v.34 no.5
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• pp.561-575
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• 2023

This study investigates to provide a fast solution to the problem of bearing capacity in layered soils with easily obtainable parameters that does not require the use of any charts or calculations of different parameters. Therefore, a hybrid approach including both the finite element (FE) method and machine learning technique have been applied. Firstly, a FE model has been generated which is validated by the results of in-situ loading tests. Then, a total of 192 three-dimensional FE analyses have been performed. A data set has been created utilizing the soil properties, footing sizes, layered conditions used in the FE analyses and the ultimate bearing capacity values obtained from the FE analyses to be used in multigene genetic programming (MGGP). Problem has been modeled with five input and one output parameter to propose a bearing capacity formula. Ultimate bearing capacity values estimated from the proposed formula using data set consisting of 20 data independent of total data set used in MGGP modelling have been compared to the bearing capacities calculated with semi-empirical methods. It was observed that the MGGP method yielded successful results for the problem considered. The proposed formula provides reasonable predictions and efficient enough to be used in practice.

• Journal of Korea Water Resources Association
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• v.42 no.10
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• pp.773-783
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• 2009

The effects of rainfall input uncertainty on predictions of stream flow are studied based extended GLUE (Generalized Likelihood Uncertainty Estimation) approach. The uncertainty in the rainfall data is implemented by systematic/non-systematic rainfall measurement analysis in Weida catchment, Germany. PDM (Probability Distribution Model) rainfall runoff model is selected for hydrological representation of the catchment. Using general correction procedure and DUE(Data Uncertainty Engine), feasible rainfall time series are generated. These series are applied to PDM in MC(Monte Carlo) and GLUE method; Posterior distributions of the model parameters are examined and behavioural model parameters are selected for simplified GLUE prediction of stream flow. All predictions are combined to develop ensemble prediction and 90 percentile of ensemble prediction, which are used to show the effects of uncertainty sources of input data and model parameters. The results show acceptable performances in all flow regime, except underestimation of the peak flows. These results are not definite proof of the effects of rainfall uncertainty on parameter estimation; however, extended GLUE approach in this study is a potential method which can include major uncertainty in the rainfall-runoff modelling.

• 대한공업교육학회지
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• v.33 no.2
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• pp.259-272
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• 2008

In order to improve the productivity and quality of boat's mold in leisure boat industry, the development of modelling and machining technology of leisure boat's plug is strongly required. The traditional lines drawing approach by hand required the designer to both create fair curves and to make sure that the curves matched up to each other in the three main drawing views: profile, plan, and section. However, one will find when studying lines drawings in books that the curves might look smooth and fair, but the lines do not agree exactly in the three views. Therefore, the 2 dimensional drawing data of leisure boat are transformed using 3 dimensional design s/w and CAM s/w. In addition, the leisure boat is designed with a 3 dimensional s/w. The NC cutting data are generated by the CAM s/w. The surface characteristics of machined surface are investigated at various cutting conditions such as spindle speed, feed speed, and cutting material.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.352-362
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• 2017

This paper proposes an algorithm for automatically converting and displaying rainfall radar data on a 3D GIS platform. The weather information displayed like rainfall radar data is updated frequently and large-scale. Thus, in order to efficiently display the data, an algorithm to convert and output the data automatically, rather than manually, is required. In addition, since rainfall data is extracted from the space, the use of the display image fused with the 3D GIS data representing the space enhances the visibility of the user. To meet these requirements, this study developed the Auto Data Converter application that analyzes the raw data of the rainfall radar and convert them into a universal format. In addition, Unity 3D, which has good development accessibility, was used for dynamic 3D implementation of the converted rainfall radar data. The software applications developed in this study could automatically convert a large volume of rainfall data into a universal format in a short time and perform 3D modeling effectively according to the data conversion on the 3D platform. Furthermore, the rainfall radar data could be merged with other GIS data for effective visualization.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.501-508
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• 2020

The focus of this research was on Bali, Indonesia as an international tourist destination. The survey strategy involved self-administered questionnaires distributed to collect data and information supporting this research. The sampling method was non-probability convenience purposive sampling, which means that only those respondents who had visited Bali as a destination for more than two times for their holiday by the time the research was conducted were eligible to fill in the questionnaires. There were 300 questionnaires distributed, only 254 of which were valid. Interview was also conducted for data collection in this research. The structural equation modelling approach was used to analyze the data obtained from respondents, who had visited Bali at least two times. The outcomes of this research reveal a positive influence of push and pull motivational factors on tourist satisfaction. Moreover, a positive, significant correlation between satisfaction and destination loyalty can be seen in this research. With the aim to sustain and enhance destination competitiveness, the results of this research will be beneficial for stakeholders of Bali as a destination. This study helps stakeholders identify push and pull motivational factors in order to better prepare marketing strategies and utilize indicators of push and pull motivation that affect tourists' experience during their stay.

• Computers and Concrete
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• v.19 no.6
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• pp.717-724
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• 2017

In this paper, four formulas are proposed via gene expression programming (GEP)-based models and regression analysis (RA) to predict the flexural strength ($f_s$) values of mortars containing different mineral admixtures that are ground granulated blast-furnace slag (GGBFS), silica fume (SF) and fly ash (FA) at different ages. Three formulas obtained from the GEP-I, GEP-II and GEP-III models are constituted to predict the $f_s$ values from the age of specimen, water-binder ratio and compressive strength. Besides, one formula obtained from the RA is constituted to predict the $f_s$ values from the compressive strength. To achieve these formulas in the GEP and RA models, 972 data of the experimental studies presented with mortar mixtures were gathered from the literatures. 734 data of the experimental studies are divided without pre-planned for these formulas achieved from the training and testing sets of GEP and RA models. Beside, these formulas are validated with 238 data of experimental studies un-employed in training and testing sets. The $f_s$ results obtained from the training, testing and validation sets of these formulas are compared with the results obtained from the experimental studies and the formulas given in the literature for concrete. These comparisons show that the results of the formulas obtained from the GEP and RA models appear to well compatible with the experimental results and find to be very credible according to the results of other formulas.

• Korean Journal of Construction Engineering and Management
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• v.2 no.2 s.6
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• pp.90-97
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• 2001

The paper considers non-deterministic methods of analysing the risk exposure in a cost estimate. The method(referred to as the 'Monte Carlo simulation' method) interprets cost data indirectly, to generate a probability distribution for total costs from the deficient elemental experience cost distribution. The Monte Carlo method is popular method for incorporating uncertainty relative to parameter values in risk assessment modelling. Non-deterministic methods, they are here presented as possibly effective foundation on which to risk management in cost estimating. The objectives of this research is to develop a computerized algorithms to forecast the probabilistic total construction cost and the elemental work cost at the planning stage.