• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.53 no.2
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• pp.169-176
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• 2017

Tuna purse seine fishery in the western and central Pacific Ocean (WCPO) has been rapidly developed since early 1980s due to massive investment of major distant water fishing nations, and catch by purse seine fishery operating in the WCPO accounts for nearly half of the world's tuna total catch. As fishing efficiency is reflected by not only improving of individual vessel's capacity but also increasing number of active vessel, it is essential to understand vessel capacity for reliable assessment result on how fishery affects stock status of target species. In this study, fishing efficiency was analyzed by main factors which are representative of vessel capacity using fishing data and vessel information related to Korean tuna purse seine fishery operating in the western and central Pacific Ocean from 1992 to 2014. It showed that fishing efficiency of vessel tends to be higher when having larger vessel tonnage, higher engine power, lower vessel age and larger length of vessel. As for fishing efficiency by set type, CPUE of associated set with floating objects was generally higher than that of free school set, and CPUE of free school set seemed to have a greater effect on engine power and vessel age compared to other factors.

• Journal of The Geomorphological Association of Korea
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• v.24 no.3
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• pp.105-118
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• 2017

The debris slope landforms which are existent in Korean mountains is generally on the steep slopes and mostly covered by vegetation, it is difficult to investigate the landform. Therefore a scientific method is required to come up with an effective field investigation plan. For this purpose, the use of Remote Sensing and GIS technologies for a spatial analysis is essential. This study has extracted the potential area of debrisslope landform formation using Fuzzy set and Bayesian Predictive Discriminate Model as mathematical data integration methods. The first step was to obtain information about debris locations and their related factors. This information was verified through field investigation and then used to build a database. In the second step, the map that zoning the study area based on the degree of debris formation possibility was generated using two modeling methods, and then cross validation technique was applied. In order to quantitatively analyze the accuracy of two modeling methods, the calculated potential rate of debrisformation within the study area was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). As a result, the prediction accuracy of Fuzzy set model wes 83.1% and Bayesian Predictive Discriminate Model wes 84.9%. It showed that two models are accurate and reliable and can contribute to efficient field investigation and debris landform management.

• Journal of The Geomorphological Association of Korea
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• v.24 no.1
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• pp.77-91
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• 2017

Many debris landforms in the mountains of Korea have formed in the periglacial environment during the last glacial stage when the generation of sediments was active. Because these landforms are generally located on steep slopes and mostly covered by vegetation, however, it is difficult to observe and access them through field investigation. A scientific method is required to reduce the survey range before performing field investigation and to save time and cost. For this purpose, the use of remote sensing and GIS technologies is essential. This study has extracted the potential area of debris landform formation using a fuzzy set model as a mathematical data integration method. The first step was to obtain information about the location of debris landforms and their related factors. This information was verified through field observation and then used to build a database. In the second step, we conducted the fuzzy set modeling to generate a map, which classified the study area based on the possibility of debris formation. We then applied a cross-validation technique in order to evaluate the map. For a quantitative analysis, the calculated potential rate of debris formation was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). The prediction accuracy of the model was found to be 83.1%. We posit that the model is accurate and reliable enough to contribute to efficient field investigation and debris landform management.

• Structural Engineering and Mechanics
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• v.37 no.3
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• pp.331-349
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• 2011

This study investigates the use of a vibration correlation technique (VCT) to identify the buckling load of a rectangular thin plate. It is proposed that the buckling load can be determined experimentally using the natural frequencies of plates under tensile loading. A set of rectangular plates was tested for natural frequencies using an impact test method. Aluminum and stainless steel specimens with CCCC, CCCF and CFCF boundary conditions were included in the experiment. The measured buckling load was determined from the plot of the square of a measured natural frequency versus an in-plane load. The buckling loads from the measured vibration data match the numerical solutions very well. For specimens with well-defined boundary conditions, the average percentage difference between buckling loads from VCT and numerical solutions is -0.18% with a standard deviation of 5.05%. The proposed technique using vibration data in the tensile loading region has proven to be an accurate and reliable method which might be used to identify the buckling load of plates. Unlike other static methods, this correlation approach does not require drawing lines in the pre-buckling and post-buckling regions; thus, bias in data interpretation is avoided.

• Journal of Surface Science and Engineering
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• v.43 no.2
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• pp.64-72
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• 2010

CuSnZn electroplating was investigated as alternative to Ni plating. Evaluation of electrolyte and plating process was performed to control physical characteristics of the film, and to collect practical data for application. Hull-cell test was conducted for basic comparison of two commercialized products and developed product. Based on hull-cell test results, long term test of three electrolytes was performed. Various analysis on long term tested electrolyte and samples have been done. Reliable and practical data was collected using FE-SEM (FEI, Sirion), EDX (ThermoNoran SIX-200E), ICP Spectrometer (GBC Scientifi c, Integra XL), FIB (FEI, Nova600) for anlysis. Physical analysis and reliability test of the long term tested film were also carried out. Through this investigation plating time, plating speed, electrolyte composition, electrolyte metal consumption, hardness and corrosion resistance has been compared. This set of data is used to predict and control the chemical composition of the film and modify the physical characteristics of the CuSnZn alloy.

• Structural Engineering and Mechanics
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• v.70 no.6
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• pp.671-681
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• 2019

This paper examines the applicability of artificial neural network (ANN) and multivariate adaptive regression splines (MARS) to predict the compressive strength of bacteria incorporated geopolymer concrete (GPC). The mix is composed of new bacterial strain, manufactured sand, ground granulated blast furnace slag, silica fume, metakaolin and fly ash. The concentration of sodium hydroxide (NaOH) is maintained at 8 Molar, sodium silicate ($Na_2SiO_3$) to NaOH weight ratio is 2.33 and the alkaline liquid to binder ratio of 0.35 and ambient curing temperature ($28^{\circ}C$) is maintained for all the mixtures. In ANN, back-propagation training technique was employed for updating the weights of each layer based on the error in the network output. Levenberg-Marquardt algorithm was used for feed-forward back-propagation. MARS model was developed by establishing a relationship between a set of predictors and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Six models based on ANN and MARS were developed to predict the compressive strength of bacteria incorporated GPC for 1, 3, 7, 28, 56 and 90 days. About 70% of the total 84 data sets obtained from experiments were used for development of the models and remaining 30% data was utilized for testing. From the study, it is observed that the predicted values from the models are found to be in good agreement with the corresponding experimental values and the developed models are robust and reliable.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.19-27
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• 2014

Software reliability in the software development process is an important issue. Software process improvement helps in finishing with reliable software product. Infinite failure NHPP software reliability models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, proposes the reliability model with log type mean value function (Musa-Okumoto and log power model), which made out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on mean square error (MSE) and coefficient of determination($R^2$), for the sake of efficient model, was employed. Analysis of failure using real data set for the sake of proposing log type mean value function was employed. This analysis of failure data compared with log type mean value function. In order to insurance for the reliability of data, Laplace trend test was employed. In this study, the log type model is also efficient in terms of reliability because it (the coefficient of determination is 70% or more) in the field of the conventional model can be used as an alternative could be confirmed. From this paper, software developers have to consider the growth model by prior knowledge of the software to identify failure modes which can be able to help.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3788-3794
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• 2022

The possibility of denuclearization of the Korean Peninsula has been continuously debated, and the initiative participation of the Republic of Korea has necessitated preemptive measures against neighboring countries. In this study, we present a proposal for formulating a site survey plan when the amount of site information provided is insufficient and the accuracy of the information is not guaranteed. Considering a case wherein "a soil sample analysis is used to determine the presence or absence of nuclear activity" in a radiochemical laboratory, which is a typical key facility for denuclearization, the optimal soil sample collection plan is designed based on international guidelines and public information. In the event of denuclearization, a scenario that is not based on the expertise of the sample collector is set, and the data quality objective (DQO) process is applied to ensure reality. Consequently, the primary sample collection points can be derived in consideration of accessibility, and the sample collection scale can be adjusted according to the cost. By applying the DQO process to ensure sample representativeness and reality, reliable and resource-efficient soil sample collection can be achieved in radiochemical laboratories and other denuclearization facilities.

• Genomics & Informatics
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• v.19 no.1
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• pp.10.1-10.7
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• 2021

Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC₅₀ and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC₅₀ and 0.81 for AUC. We identify common predictor genes for IC₅₀ and AUC, with which the performance was similar to those with genes separately found for IC₅₀ and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC₅₀, 0.7945 for AUC) with 321 predictor genes.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.265-272
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• 2003

In this study, the rock mass classification results from the face mapping and the resistivity inversion data are compared and analyzed for the reliability investigation of the determination of the rock support type based on the surface electrical survey. To get the quantitative correlation, rock engineering indices such as RCR(rock condition rating), N(Rock mass number), Q-system based on RMR(rock mass rating) are calculated. Kriging method as a post processing technique for global optimization is used to improve its resolution. The result of correlation analysis shows that the geological condition estimated from 2D electrical resistivity survey is coincident globally with the trend of rock type except for a few local areas. The correlation between the results of 3D electrical resistivity survey and the rock mass classification turns out to be very high. It can be concluded that 3D electrical resistivity survey is powerful to set up the reliable rock support type.