• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.21-30
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• 2013

Korean Volunteer Fire Departments are the representative disaster-related civilian organizations which are based on "Firefighting Framework Act Article 37"and ordinance for complementing the lack of fire-fighting personnel and volunteer and they play a part as community safety keepers. They are operated by the National funding, but cannot be defined as the organization in governmental sources completely or pure volunteer organization in terms of its founding purpose and activities. In these special characteristics, some Volunteer Fire Departments play an important role in Civilian Volunteer Disaster Prevention by being managed effectively, but the others do not. There can be many cause-analyses about this difference. They aren't profit-making organizations and are groups which have no compulsion. So it is important that who the leader is, and what type of leadership he has. By solving this bias by considering these characteristics, in this study we make them perform the center role of community safety network through analyzing the existing status and problems of volunteer fire department education and customized training program reengineering to meet class-specific and regional level.

• Journal of Labour Economics
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• v.26 no.1
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• pp.23-52
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• 2003

This study focus on unemployment and nonparticipation of youth. By dividing youth nonparticipants into 'house work and child care', 'studying and training', 'the others' categories, we estimate the potential wages with selectivity bias model and analyse the factors of choosing unemployment or nonparticipation with multinomial logit model. The differences between the potential market wage and the desired wage of the groups of 'studying and training', 'the others' in the nonparticipants are greater than those of the unemployment group. In the case of the man and lower age, and low schooling the differences of potential and desire wage are larger than woman and higher age, and high schooling. In the choice of unemployment and nonparticipation, man and higher age, and householder, and holder of qualification are not likely to opt nonparticipation. The experience of job lower the rate of probability to choose employment, but raise the rate of probability to choose unemployment and nonparticipation. These results mean that the quality of youth employment is very inferior.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.109-124
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• 2015

A novel approach, hybrid surface rainfall (KNU-HSR) technique developed by Kyungpook Natinal University, was utilized for improving the radar rainfall estimation. The KNU-HSR technique estimates radar rainfall at a 2D hybrid surface consistings of the lowest radar bins that is immune to ground clutter contaminations and significant beam blockage. Two HSR techniques, static and dynamic HSRs, were compared and evaluated in this study. Static HSR technique utilizes beam blockage map and ground clutter map to yield the hybrid surface whereas dynamic HSR technique additionally applies quality index map that are derived from the fuzzy logic algorithm for a quality control in real time. The performances of two HSRs were evaluated by correlation coefficient (CORR), total ratio (RATIO), mean bias (BIAS), normalized standard deviation (NSD), and mean relative error (MRE) for ten rain cases. Dynamic HSR (CORR=0.88, BIAS= $-0.24mm\;hr^{-1}$, NSD=0.41, MRE=37.6%) shows better performances than static HSR without correction of reflectivity calibration bias (CORR=0.87, BIAS= $-2.94mm\;hr^{-1}$, NSD=0.76, MRE=58.4%) for all skill scores. Dynamic HSR technique overestimates surface rainfall at near range whereas it underestimates rainfall at far ranges due to the effects of beam broadening and increasing the radar beam height. In terms of NSD and MRE, dynamic HSR shows the best results regardless of the distance from radar. Static HSR significantly overestimates a surface rainfall at weaker rainfall intensity. However, RATIO of dynamic HSR remains almost 1.0 for all ranges of rainfall intensity. After correcting system bias of reflectivity, NSD and MRE of dynamic HSR are improved by about 20 and 15%, respectively.

• Korean Journal of Social Welfare Studies
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• no.37
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• pp.171-197
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• 2008

This study focuses on the following question; self-supporting training program increases participants' income compare to non-participants who have similar characteristics. This question is based on counterfactual assumption. In other words, this study concentrates on what the outcomes would have been if the participants were to be absent. This study adopts a quasi-experimental design. To overcome previous study's methodological weaknesses, especially selection bias, I applied matching procedure based on a propensity-score matching. Matching process was performed by using 'MatchIt' software. The major findings are as follows From Least Squares Regression analysis, I found the poor's income are significantly different according to age, pre-intervention earning, material status, and participation of training. Since the poor have homogeneous education level, education variable was not statistically significant. From the Simulation Quantities of Interest analysis, I also found that treatment group's expected incomes are lower than control's expected incomes. In other words, participation of training has a negative effect on the participants' earnings.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.475-487
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• 2016

Recently, various risks of smartphone hacking are emerging. Smishing crime techniques become more cunning and its damage has been increasing, thereby requiring effective ways of preventing and coping with smishing. Especially, it is emphasized the need for smartphone users' security awareness and training besides technological approach. This study investigates the effective method for providing news messages in order to improve the perception of risk from smishing. This research empirically examines that the degree of optimistic bias on risk perception can vary depending on news frame, topic type, and involvement regarding smishing. Based on the findings, it identifies the factors influencing risk perception and verifies effective ways of promoting individual security awareness on smishing. The results of this study provide implications that assist in educating, campaigning and promoting information security awareness for smart device users.

• Atmosphere
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• v.25 no.1
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• pp.19-30
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• 2015

The Ka-band cloud radar (KCR) has been operated by the National Institute of Meteorological Research (NIMR) of Korea Meteorological Administration (KMA) at Boseong National Center for Intensive Observation of severe weather since 2013. Evaluation of data quality is an essential process to further analyze cloud information. In this study, we estimate the measurement error and the sampling uncertainty to evaluate data quality. By using vertically pointing data, the statistical uncertainty is obtained by calculating the standard deviation of each radar parameter. The statistical uncertainties decrease as functions of sampling number. The statistical uncertainties of horizontal and vertical reflectivities are identical (0.28 dB). On the other hand, the statistical uncertainties of Doppler velocity (spectrum width) are 2.2 times (1.6 times) larger at the vertical channel. The reflectivity calibration of KCR is also performed using X-band vertically pointing radar (VertiX) and 2-dimensional video disdrometer (2DVD). Since the monitoring of calibration values is useful to evaluate radar condition, the variation of calibration is monitored for five rain events. The average of calibration bias is 10.77 dBZ and standard deviation is 3.69 dB. Finally, the statistical characteristics of cloud properties have been investigated during two months in autumn using calibrated reflectivity. The percentage of clouds is about 26% and 16% on September to October. However, further analyses are required to derive general characteristics of autumn cloud in Korea.

• Animal Bioscience
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• v.37 no.4_spc
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• pp.730-741
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• 2024

Pig production is one of the viable enterprises of the livestock sub-sector of agriculture. It contributes significantly to the economy and animal protein supply to enhance food security in Africa and globally. This article explored the present status of pig production in Africa, the challenges, prospects and potentials. The pig population of Africa represents 4.6% of the global pig population. They are widely distributed across Africa except in Northern Africa where pig production is not popular due to religio-cultural reasons. They are mostly reared in rural parts of Africa by smallholder farmers, informing why majority of the pig population in most parts of Africa are indigenous breeds and their crosses. Pig plays important roles in the sustenance of livelihood in the rural communities and have cultural and social significance. The pig production system in Africa is predominantly traditional, but rapidly growing and transforming into the modern system. The annual pork production in Africa has grown from less than a million tonnes in year 2000 to over 2 million tonnes in 2021. Incidence of disease outbreak, especially African swine fever is one of the main constraints affecting pig production in Africa. Others are lack of skills and technical know-how, high ambient temperature, limited access to high-quality breeds, high cost of feed ingredients and veterinary inputs, unfriendly government policies, religious and cultural bias, inadequate processing facilities as well as under-developed value-chain. The projected human population of 2.5 billion in Africa by 2050, increasing urbanization and decreasing farming population are pointers to the need for increased food production. The production systems of pigs in Africa requires developmental research, improvements in housing, feed production and manufacturing, animal health, processing, capacity building and pig friendly policies for improved productivity and facilitation of export.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Advances in Computational Design
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• v.7 no.3
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• pp.253-279
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• 2022

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.