• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.285-291
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• 2020

The Noah 3.3 Land Surface Model (LSM) was used to estimate the global soil moisture in this study and these soil moisture datasets were assessed against satellite-based and reanalysis soil moisture products. The Noah 3.3 LSM simulated soil moistures in four soil layers and root-zone soil moistures defined as a depth-weighted average in the first three soil layers (i.e., up to 1.0 m deep). The Noah LSM soil moisture products were then compared with a satellite-based soil moisture dataset (European Space Agency Climate Change Initiatives (ESA CCI) SM v04.4) and reanalysis soil moisture datasets (ERA-interim). In addition, the five major basins (Yangtze, Mekong, Mississippi, Murray-Darling, Amazon) were selected for the assesment with the Gravity Recovery and Climate Experiment (GRACE)-based Total Water Storage Anomaly (TWSA) and TWS Change (TWSC). The results revealed that high anomaly correlations were found in most of the Asia-Pacific regions including East Asia, South Asia, Australia, and Noth and South America. While the anomaly correlations in the Murray-Darling basin were somewhat low, relatively higher anomaly correlations in the other basins were found. It is concluded that this study can be useful for the development of soil moisture based drought indices and subsequently can be helpful to reduce damages from drought by timely providing an efficacious strategy.

• Atmosphere
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• v.27 no.1
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• pp.41-54
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• 2017

Organic carbon (OC) and elemental carbon (EC) in $PM_{2.5}$ were measured using Sunset OC/EC Field Analyzer at Seoul Hwangsa Monitoring Center from March to April, 2016. The mean concentrations of OC and EC during the entire period were $4.4{\pm}2.0{\mu}gC\;m^{-3}$ and $1.4{\pm}0.6{\mu}gC\;m^{-3}$, respectively. OC/EC ratio was $3.4{\pm}1.0$. The average concentrations of $PM_{10}$ and $PM_{2.5}$ were $57.4{\pm}25.9$ and $39.7{\pm}19.8{\mu}g\;m^{-3}$, respectively, which were detected by an optical particle counter. The OC and EC peaks were observed in the morning, which were impacted by vehicle emission, however, their diurnal variations were not noticeable. This is determined to be contributed by the long-range transported OC or secondary formation via photochemical reaction by volatile organic compounds at afternoon. A conditional probability function (CPF) model was used to identify the local source of pollution. High concentrations of $PM_{10}$ and $PM_{2.5}$ were observed from the westerly wind, regardless of wind speed. When wind velocity was high, a mixing plume of dust and pollution during long-range transport from China in spring was observed. In contrast, pollution in low wind velocity was from local source, regardless of direction. To know the effect of long-range transport on pollution, a concentration weighted trajectory (CWT) model was analyzed based on a potential source contribution function (PSCF) model in which 75 percentiles high concentration was picked out for CWT analysis. $PM_{10}$, $PM_{2.5}$, OC, and EC were dominantly contributed from China in spring, and EC results were similar in both PSCF and CWT. In conclusion, Seoul air quality in spring was mainly affected by a mixture of local pollution and anthropogenic pollutants originated in China than the Asian dust.

• Journal of Korea Technology Innovation Society
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• v.11 no.2
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• pp.241-263
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• 2008

Within any income approach, a discount rate is used to convert some projected free cash flow to its presented value. In case of valuing companies, the most frequently used discount rate is the weighted average cost of capital(WACC) at the aggregate level. But technology valuation is different to discounting aggregate corporate cash flow since it is concerned about individual Intellectual property. Therefore, blindly applying standard discount rate such as WACC in technology valuation is unlikely to lead to the right result. The primary focus of this paper is to establish the structure of discount rate for technology valuation and to suggest the method of estimation. To determine an appropriate discount rate for technology valuation, the level of technology risk, market risk and competitive risk should be included in the structure of discount rate. This paper suggests the build-up model which consists of three components as a expansion of the CAPM. It includes (1) a risk-free rate of return, (2) general market risk premium and beta and (3) intellectual property risk premium related to technology risk and specific target market risk. However, there is no specific check list for examining the intellectual property risk until now and no specific method for quantifying its risk into risk premium. This paper developed the 10 element to determine the level of the intellectual property risk and applied estimation function such as linear function, natural log function and exponential function to transform the level of risk into risk premium. The limitation of this paper is that the range of intellectual property risk premium is inferred based on the information of foreign and domestic valuation agency. Finally, this paper explored the development of an intellectual property discount rate for technology valuation and presented the method in order to quantify the intellectual property risk premium.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.165-172
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• 2010

In clinical data minig, choosing the optimal subset of features is such important, not only to reduce the computational complexity but also to improve the usefulness of the model constructed from the given data. Moreover the threshold values (i.e., cut-off points) of selected features are used in a clinical decision criteria of experts for differential diagnosis of diseases. In this paper, we propose a fuzzy discretization approach, which is evaluated by measuring the degree of separation of redundant attribute values in overlapping region, based on spatial distribution of data with continuous attributes. The weighted average of the redundant attribute values is then used to determine the threshold value for each feature and rough set theory is utilized to select a subset of relevant features from the overall features. To verify the validity of the proposed method, we compared experimental results, which applied to classification problem using 668 patients with a chief complaint of dyspnea, based on three discretization methods (i.e., equal-width, equal-frequency, and entropy-based) and proposed discretization method. From the experimental results, we confirm that the discretization methods with fuzzy partition give better results in two evaluation measures, average classification accuracy and G-mean, than those with hard partition.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.373-380
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• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.

• 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.