• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.5 no.2
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• pp.12-23
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• 1987

This paper deals with the analysis and adjustment computation for the Korean 1st order levelling data obtained by the National Geography Institute from 1974 to 1986. An evaluation of the accuracy of level lines using forward/back discrepancies and adjustment residuals has been carried out. A general assessment of the network in the light of current standards with other countries is discussed. The results of this study show that systematic errors are relatively high but the accuracy of the primary levelling is good enough within the limitation of precision levelling according to International Association of Geodesy(IAG) Resolution, and therefore it is concluded that this levelling will satisfy the present mapping and engineering survey.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2006.05a
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• pp.163-166
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• 2006

The purpose of the research is to assess the accuracy of steel bar detector among other nondestructive testing equipment. The result of previous research shows that the average errors of rebar detector are 14.7% for the cover depth, 2.3% for the rebar spacing, and 11% for the rebar diameter. But this experiment was performed at the laboratory and the mortar was used for covering the steel bars instead of concrete. In situ condition can be different from the laboratory's so the outcomes do not correspond with those of laboratory. This research was performed at the buildings to be reconstructed. Nondestructive and destructive testing can be performed side by side since the building if to be destroyed. Steel bar detector was operated on the beam and the column and concrete cover of those members was removed for the actual measurement of rebar depth, spacing, and diameter finally, presumed value can be directly compared with actual data.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.179-192
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• 2023

The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.467-485
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• 2024

Policymakers and educational researchers have grown increasingly interested in the extent to which study results generalize across different groups of students. Current generalization research in education has largely focused on the compositional similarity among students based on a set of observable characteristics. However, generalization is defined differently across various disciplines. While the concept of compositional similarity is prominent in causal research, generalization among the statistical learning community refers to the extent to which a model produces accurate predictions across samples and populations. The purpose of this study is to assess the extent to which concepts related to contextual generalization (based on compositional similarity) are associated with the ideas related to model generalization (based on accuracy of prediction). We use observational data from the Programme for International Student Assessment (PISA) 2015 wave as a case study to examine the conditions under which contextual and model generalization are aligned. We assess the correlations between statistical measures that quantify compositional similarity and prediction accuracy and discuss the implications for generalization research.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.387-397
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• 2013

An assessment of two-equation turbulence models, the low Reynolds k-${\varepsilon}$ and k-${\omega}$ SST models, with the compressibility corrections proposed by Sarkar and Wilcox, has been performed. The compressibility models are evaluated by investigating transonic or supersonic flows, including the arc-bump, transonic diffuser, supersonic jet impingement, and unsteady supersonic diffuser. A unified implicit finite volume scheme, consisting of mass, momentum, and energy conservation equations, is used, and the results are compared with experimental data. The model accuracy is found to depend strongly on the flow separation behavior. An MPI (Message Passing Interface) parallel computing scheme is implemented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.169-178
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• 2011

In this study, a forecasting engine from the user perspective is studied and developed. Characteristics of forecasting engine can be divided into a few categories, an algorithms for predicting variety of situations and the depth of algorithms based on the number and the types of data. Then applying a variety of algorithms that most closely match the predicted values for the actual value that deduce criteria for selecting an appropriate forecasting algorithm is to organize. Through the forecast quality assessment, the suggested distribution algorithm compared to the existing demand forecast algorithms is good indicators for its accuracy.

• Genomics & Informatics
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• v.16 no.4
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• pp.24.1-24.4
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• 2018

Taxonomic identification is fundamental to all microbiology studies. Particularly in metagenomics, which identifies the composition of microorganisms using thousands of sequences, its importance is even greater. Identification is inevitably affected by the choice of database. This study was conducted to evaluate the accuracy of three widely used 16S databases-Greengenes, Silva, and EzBioCloud-and to suggest basic guidelines for selecting reference databases. Using public mock community data, each database was used to assign taxonomy and to test its accuracy. We show that EzBioCloud performs well compared with other existing databases.

• Journal of Environmental Impact Assessment
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• v.24 no.4
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• pp.367-374
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• 2015

To examine the effect of thermal discharge from nuclear power plants, Sea Surface Temperature (SST) is one of the most important variables measured by satellite remote sensing. However, the study was not much comparison of field data and satellite SST from operational Landsat 8 Thermal Infrared Sensor(TIRS) and Landsat 7 ETM+. The Landsat 8 TIRS have 2 spilt Thermal Infrared channels but ETM+ uses one channel for extracting of SST. In spite of that this research carried out that Landsat 7 ETM+ have more profitable for correction of SST than Landsat 8 TIRS. The used 15 Landsat 7 and 8 Thermal Infrared data of path/row 114-36 were processed by SST algorithm of ENVI and IDL. The in-situ SST data from KHOA(Korea Hydrographic and Oceanographic Administration) compared with satellite SST and the accuracy of extracted SST were assessed by each field sites in-situ point data with time series satellite SST.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.114-121
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• 1996

This paper presents an useful technique for assessing the volumetric errors in multi-axis machine tools using the kinematic double ball bar. This system has been developed based on the volumetric error map which describes the 3 dimensional errors of machine tools. The developed system inputs the measured radial data of 3 different planes, respectively XY,YZ,ZX, analysing the volumetric errors such as positional. straightness, angle, and squareness errors, etc. The developed system has been tested in a practical machine tool, and showed high potential for the error assessment of multi-axis machine tools.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.269-274
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• 2024

High-quality precipitation data are crucial for various industries, including disaster prevention. In South Korea, long-term high-quality data are collected through numerous ground observation stations. However, data between these stations are reprocessed into a grid format using interpolation methods, which may not perfectly match actual precipitation. A prime example of real-time observational grid data globally is the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) from National Aeronautics and Space Administration (NASA), while in South Korea, ground radar data are more commonly used. GPM and ground radar data exhibit distinct differences due to their respective processing methods. This study aims to analyze the characteristics of GPM and Constant Altitude Plan Position Indicator(CAPPI),representative real-time grid data, by comparing them with ground-observed precipitation data. The study period spans from 2021 to 2022, focusing on hourly data from Automated Synoptic Observing System (ASOS) sites in South Korea. The GPM data tend to underestimate precipitation compared to ASOS data, while CAPPI shows errors in estimating low precipitation amounts. Through this comparative analysis, the study anticipates identifying key considerations for utilizing these data in various applied fields, such as recalculating design rainfall, thereby aiding researchers in improving prediction accuracy by using appropriate data.