• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.51 no.4
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• pp.535-544
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• 2015

Yield per recruit model is the most popular method for fisheries stock assessment. However, stock assessment using yield per recruit model can lead to recruitment overfishing as this model only considers the maximum yield per recruit without spawning biomass for reproduction. For this reason, spawning biomass per recruit model which reveals variations of spawning stock biomass per fishing mortality (F) and age at first capture ($t_c$) is considered as more proper method for stock assessment. There are mainly two methods for spawning biomass per recruit model known as age specific selectivity method and knife-edged selectivity method. In the knife-edged selectivity method, the spawning biomass per recruit has been often calculated using biomass per recruit value by multiplying the maturity ratio of the recruited age. But the maturity ratio in the previous method was not considered properly in previous studies. Therefore, a new method of the knife-edged selectivity model was suggested in this study using a weighted average of the maturity ratio for ages from the first capture to the lifespan. The optimum fishing mortality in terms of $F_{35%}$ which was obtained from the new method was compared to the old method for small yellow croaker stock in Korea. The value of $F_{35%}$ using the new knife-edged selectivity model was 0.302/year and the value using the old model was 0.349/year. However, the value of $F_{35%}$ using the age specific selectivity model was estimated as 0.320/year which was closer to the value from the new knife-edged selectivity model.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.4
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• pp.436-445
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• 2014

Objectives: Time-activity studies have become an integral part of comprehensive exposure assessment and personal exposure modeling. The aims of this study were to estimate exposure levels to nitrogen dioxide($NO_2$) and volatile organic compounds(VOCs), and to compare estimated exposures by using time-activity patterns and indoor air concentrations. Methods: The major microenvironments for office workers were selected using the Time-Use Survey conducted by the National Statistical Office in Korea in 2009. A total of 9,194 and 6,130 workers were recruited for weekdays and weekends, respectively, from the Time-Use Survey. It appears that workers were spending about 50% of their time in the house and about 30% of their time in other indoor areas during the weekdays. In addition, we analyzed the time-activity patterns of 20 office workers and indoor air concentrations in Daegu using a questionnaire and time-activity diary. Estimated exposures were compared with measured concentrations using the time-weighted average analysis of air pollutants. Conclusions: According to the time-activity pattern for the office workers, time spent in the residence indoors during the summer and winter have been shown as $11.12{\pm}2.20$ hours and $12.48{\pm}1.77$ hours, respectively, which indicates higher hours in the winter. Time spent in the office in the summer has been shown to be 1.5 hours higher than in the winter. The target pollutants demonstrate a positive correlation ($R^2=0.076{\sim}0.553$)in the personal exposure results derived from direct measurement and estimated personal exposure concentrations by applying the time activity pattern, as well as measured concentration of the partial environment to the TWA model. However, these correlations were not statistically significant. This may be explained by the difference being caused by other indoor environments, such as a bar, cafe, or diner.

• Journal of Korea Port Economic Association
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• v.36 no.2
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• pp.109-124
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• 2020

Using panel data on freight rates and ship prices in the dry freighter market from January 2015 to December 2019, this study investigates the characteristics of shipping industry fluctuations. The analysis aims at two aspects of academic contribution. First, this study analyzes the relationship between shipping indicators and ship price based on separate dry-bulk ships, while the previous research considered the overall shipping index and weighted average ship prices. Second, the VAR model for the causality test is extended to a heterogeneous mixed panel model capable of limiting coefficients. There is a peak estimated by removing the cross-correlation problem, which is mainly raised in panel data analysis, using bootstrap estimation and solving the problem of information loss due to differences in non-stationary data. An empirical investigation of the causal relationship between economic fluctuations and ship price shows that the effect on the ship price from the freight is significant at the 1% level. This implies that there is a one-way relationship with demand in the shipping industry rather than a bilateral relationship.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.287-297
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• 2016

This study aims to identify the relationship between climatic factors and stand mean Diameter at Breast Height (DBH) for two major tree species; Pinus densiflora and Quercus mongolica in Andong-dam basin. Forest variables such as age, diameter distribution and number of trees per hectare from the $5^{th}$ and $6^{th}$ National Forest Inventory data were used to develop a DBH estimation model. Climate data were collected from six meteorological observatory station and twelve Automatic Weather System provided by Korea Meteorological Administration to produce interpolated daily average temperature map with Inverse Distance Weighting (IDW) method. Andong-dam basin reflects rugged mountainous terrain, so temperature were adjusted by lapse rate based correction. As a result, predictions of model were consistent with the previous studies; that the rising temperature is negatively related to the growth of Pinus densiflora whereas opposing trend is observed for Quercus mongolica.

• Investigative Magnetic Resonance Imaging
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• v.17 no.3
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• pp.181-191
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• 2013

Purpose : To evaluate the usefulness of in vivo magnetic resonance (MR) imaging for tracking intravenously injected superparamagnetic iron oxide (SPIO)-labeled human umbilical vein endothelial cells (HUVECs) in an acute renal failure (ARF) rat model. Materials and Methods: HUVECs were labeled with SPIO and poly-L-lysine (PLL) complex. Relaxation rates at 1.5-T MR, cell viability, and labeling stability were assessed. HUVECs were injected into the tail vein of ARF rats (labeled cells in 10 rats, unlabeled cells in 2 rats). Follow-up serial $T2^*$-weighted gradient-echo MR imaging was performed at 1, 3, 5 and 7 days after injection, and the MR findings were compared with histologic findings. Results: There was an average of $98.4{\pm}2.4%$ Prussian blue stain-positive cells after labeling with SPIOPLL complex. Relaxation rates ($R2^*$) of all cultured HUVECs at day 3 and 5 were not markedly decreased compared with that at day 1. The stability of SPIO in HUVECs was maintained during the proliferation of HUVECs in culture media. In the presence of left unilateral renal artery ischemia, $T2^*$-weighted MR imaging performed 1 day after the intravenous injection of labeled HUVECs revealed a significant signal intensity (SI) loss exclusively in the left renal outer medulla regions, but not in the right kidney. The MR imaging findings at days 3, 5 and 7 after intravenous injection of HUVECs showed a SI loss in the outer medulla regions of the ischemically injured kidney, but the SI progressively recovered with time and the right kidney did not have a significant change in SI in the same period. Upon histologic analysis, the SI loss on MR images was correspondent to the presence of Prussian blue stained cells, primarily in the renal outer medulla. Conclusion: MR imaging appears to be useful for in vivo monitoring of intravenously injected SPIO-labeled HUVECs in an ischemically injured rat kidney.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.133-144
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• 2004

A model and a measure which can evaluate the risk of rear end collision are developed. Most traffic accidents involve multiple causes such as the human factor, the vehicle factor, and the highway element at any given time. Thus, these factors should be considered in analyzing the risk of an accident and in developing safety models. Although most risky situations and accidents on the roads result from the poor response of a driver to various stimuli, many researchers have modeled the risk or accident by analyzing only the stimuli without considering the response of a driver. Hence, the reliabilities of those models turned out to be low. Thus in developing the model behaviors of a driver, such as reaction time and deceleration rate, are considered. In the past, most studies tried to analyze the relationships between a risk and an accident directly but they, due to the difficulty of finding out the directional relationships between these factors, developed a model by considering these factors, developed a model by considering indirect factors such as volume, speed, etc. However, if the relationships between risk and accidents are looked into in detail, it can be seen that they are linked by the behaviors of a driver, and depending on drivers the risk as it is on the road-vehicle system may be ignored or call drivers' attention. Therefore, an accident depends on how a driver handles risk, so that the more related risk to and accident occurrence is not the risk itself but the risk responded by a driver. Thus, in this study, the behaviors of a driver are considered in the model and to reflect these behaviors three concepts related to accidents are introduced. And safe stopping distance and accident occurrence probability were used for better understanding and for more reliable modeling of the risk. The index which can represent the risk is also developed based on measures used in evaluating noise level, and for the risk comparison between various situations, the equivalent risk level, considering the intensity and duration time, is developed by means of the weighted average. Validation is performed with field surveys on the expressway of Seoul, and the test vehicle was made to collect the traffic flow data, such as deceleration rate, speed and spacing. Based on this data, the risk by section, lane and traffic flow conditions are evaluated and compared with the accident data and traffic conditions. The evaluated risk level corresponds closely to the patterns of actual traffic conditions and counts of accident. The model and the method developed in this study can be applied to various fields, such as safety test of traffic flow, establishment of operation & management strategy for reliable traffic flow, and the safety test for the control algorithm in the advanced safety vehicles and many others.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.471-480
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• 2023

The Korean construction order volume in South Korea grew significantly from 91.3 trillion won in public orders in 2013 to a total of 212 trillion won in 2021, particularly in the private sector. As the size of the domestic and overseas markets grew, the scale and complexity of EPC (Engineering, Procurement, Construction) projects increased, and risk management of project management and ITB (Invitation to Bid) documents became a critical issue. The time granted to actual construction companies in the bidding process following the EPC project award is not only limited, but also extremely challenging to review all the risk terms in the ITB document due to manpower and cost issues. Previous research attempted to categorize the risk terms in EPC contract documents and detect them based on AI, but there were limitations to practical use due to problems related to data, such as the limit of labeled data utilization and class imbalance. Therefore, this study aims to develop an AI model that can categorize the contract terms based on the FIDIC Yellow 2017(Federation Internationale Des Ingenieurs-Conseils Contract terms) standard in detail, rather than defining and classifying risk terms like previous research. A multi-text classification function is necessary because the contract terms that need to be reviewed in detail may vary depending on the scale and type of the project. To enhance the performance of the multi-text classification model, we developed the ELECTRA PLM (Pre-trained Language Model) capable of efficiently learning the context of text data from the pre-training stage, and conducted a four-step experiment to validate the performance of the model. As a result, the ensemble version of the self-developed ITB-ELECTRA model and Legal-BERT achieved the best performance with a weighted average F1-Score of 76% in the classification of 57 contract terms.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.69-84
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• 1997

We consider a linear calibration problem, $y_i = $$\alpha + \beta (x_i - x_0) + \epsilon_i$, $i=1, 2, {\cdot}{\cdot},n$ $y_f = \alpha + \beta (x_f - x_0) + \epsilon, $ where we observe $(x_i, y_i)$'s for the controlled calibration experiments and later we make inference about $x_f$ from a new observation $y_f$. The objective of the calibration design problem is to find the optimal design $x = (x_i, \cdots, x_n$ that gives the best estimates for $x_f$. We compare Kim(1989)'s Bayesian design which minimizes the expected value of the posterior variance of $x_f$ and some optimal designs from literature. Kim suggested the Bayesian optimal design based on the analysis of the characteristics of the expected loss function and numerical must be equal to the prior mean and that the sum of squares be as large as possible. The designs to be compared are (1) Buonaccorsi(1986)'s AV optimal design that minimizes the average asymptotic variance of the classical estimators, (2) D-optimal and A-optimal design for the linear regression model that optimize some functions of $M(x) = \sum x_i x_i'$, and (3) Hunter & Lamboy (1981)'s reference design from their paper. In order to compare the designs which are optimal in some sense, we consider two criteria. First, we compare them by the expected posterior variance criterion and secondly, we perform the Monte Carlo simulation to obtain the HPD intervals and compare the lengths of them. If the prior mean of $x_f$ is at the center of the finite design interval, then the Bayesian, AV optimal, D-optimal and A-optimal designs are indentical and they are equally weighted end-point design. However if the prior mean is not at the center, then they are not expected to be identical.In this case, we demonstrate that the almost Bayesian-optimal design was slightly better than the approximate AV optimal design. We also investigate the effects of the prior variance of the parameters and solution for the case when the number of experiments is odd.

• Journal of environmental and Sanitary engineering
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• v.15 no.3
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• pp.1-7
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• 2000

There is increasing evidence suggestion that passive smoking increases the risk of lung cancer and other disease, though the potential health effects of exposure to environmental tobacco smoke (ETS) is a controversial subject. Since smoking in restaurant is prevalent in Korea, the concern on passive smoking exposure of non-smoking service-workers has been requested. ETS exposure of non-smoking service-workers at restaurant was assessed because they hare spent their times in restaurant indoors. The purpose of this study was feasibility of nitrogen dioxide($NO_2$) as exposure marker of ETS. The results of the study were as follows; 1. Average $NO_2$ concentrations in indoor and outdoor t restaurants were 57.1ppb(${\pm}12.4$) and 54.29ppb(${\pm}9.54$), respectively. Comparing office-workers, service-workers at restaurants were exposured highly. 2. The personal $NO_2$ measurement as exposure marker of ETS could cause the exposure error because $NO_2$ can be generated by combustion appliances in indoor. 3. Service-workers spent their most time(86.6%) in indoor. Mean time spent at restaurant indoors and at home was 9.4 hours and 10.9 hours, respectively. 4. Personal $NO_2$ levels correlated with indoor $NO_2$ concentrations of restaurant (r=0.70) and of their home (r=0.52) rather than of outdoor $NO_2$ concentration of restaurant (r=0.35). The cause of personal $NO_2$ exposure of non-smoking service-workers were considered as smoking of guests and combustion appliance indoors. 5. personal $NO_2$ exposures were estimated using Monte-Carlo simulation and time-weighted model. Estimated personal $NO_2$ level was 47.25ppb(${\pm}8.3$).