• Journal of Forest and Environmental Science
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• v.37 no.2
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• pp.128-140
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• 2021

Tropical dry forests are one of the most threatened, widely distributed ecosystems in tropics and estimation of forest biomass is a crucial component of global carbon emission estimation. Therefore, the present study was aimed to quantify the biomass and carbon storage in trees on large scale (10, 1 ha plots) in the dry mixed evergreen forest of Javadhu forest of Eastern Ghats. Biomass of adult (≥10 cm DBH) trees was estimated by non-harvest methods. The total biomass of trees in this tropical dry mixed evergreen forest was ranged from 160.02 to 250.8 Mg/ha, with a mean of 202.04±24.64 Mg/ha. Among the 62 tree species enumerated, Memecylon umbellatum accumulated greater biomass and carbon stocks (24.29%) more than the other species in the 10 ha study plots. ANOVA revealed that there existed a significant variation in the total biomass and carbon stock among the three plant types (Evergreen, brevi-deciduous and deciduous (F (2, 17)=15.343, p<0.001). Basal area and density was significant positively correlated with aboveground biomass (R² 0.980; 0.680) while species richness exhibited negative correlation with above ground biomass (R² 0.167). Finding of present study may be interpreted as most of the trees in this forest are yet to be matured and there is a net addition to standing biomass leading to carbon storage.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

• Parasites, Hosts and Diseases
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• v.33 no.1
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• pp.19-26
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• 1995

Ecological studies of Aedes togoi, the vector of malayan filariasis, were ci,wind out at Tolsando, Yosu and Sokcho area in 1991. The adult population of Aedes togoi was continuously appeared from the first week of April to the end of November showing the highest density in .truly. The larvae of Aedes togoi were found at rock pools from March to December in Sokcho area and the density was highest in July and August, whereas in the southern coastal area (Yosu), the larvae were found throughout the year and the density was the highest in Tune. The rate of larvae inhabited below 0.5% salinity was 45.7% in Sokcho and 51.7% in Yosu. The feeding activity of Aedes togoi was nocturnal, with the peak period of 01 :00-03:00 hours. Indoor feeding activities were slightly higher than outdoors showing the biting ratio of 1 :0.8 (indoor: outdoor). The average number of Aedes togoi attracted to CO2 gas was 8.5 whereas 117 Anophelei sinensis was attracted. The result indicates that CO2 is not an effective attractant for host seeking of Aedes togoi compared to Anopheles sineteis. The most common place was bedroom with 54.5% of total collections and next to stock place (18.2%), floor(9.1%) and kitchen (9.1%).

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.441-445
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• 2005

Researches on estimating national-scaled forest biomass are being carried out to quantify the carbon stock of forests with the Kyoto Protocol. In general, estimates of national-scaled forest biomass are based on forest inventory data which provides estimates of forest area, stem volume, and growth of stem by age classes. Estimates of forest biomass are, however, obtained by converting stem volumes to dry weight with stem density and thereafter to whole tree biomass with biomass expansion factors (ratios of whole tree dry weight to stem dry weight). Pinus densiflora is widely distributed and one of the most economically important timber species in Korea. The species are largely grouped into two ecotypes of Geumgang and Jungbu. Stems of Geumgang type trees are straight and high compared to those of Jungbu type trees. The objective of this study was to determine and compare stem density and biomass expansion factors fore two ecotypes of Pinus densiflora according to stand age. Stem density of both ecotypes of Pinus densora increased and biomass expansion factors of them decreased with increasing tree age. In he same age class, stem density and biomass expansion factor of Geungang type Pinus densiflora were lower than those of Jungbu type Pinus densiflora. There were statistically significant differences in stem density and biomass expansion factors between Geumgang type and Jungbu type Pinus densiflora in 0-20-year-old stands and 40-60-year-old stands. Our results suggested that the reliability of the national forest biomass inventory could be improved by applying the ecotype- and age-dependent stem density and biomass expansion factors.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.30 no.4
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• pp.620-626
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• 1997

Annual biomasses of the hairtail, Trichiurus lepturus, were estimated from the biomass-based cohort analysis (Zhang, 1987), using data of annual catch in weight at age during $1970\~1988$ in Korean waters. Annual biomass of the hairtail was peaked at about 240,000 mt in 1975, and thereafter declined with a slight fluctuation. Adult biomass showed a peak in 1978 with about 55,000 mt. However, it has continuously decreased untill 1980 to the level of 9,000 mt and remained at this level till 1988. Age compositions of the hairtail in the 1980s differed greatly from those in the 1970s. The proportions of older hairtail (>4 years) were very low in the 1980s and even the biomasses of young hairtail $(1\~3\;years)$ were at a low evel in the 1980s compared with the level in 1970s. The 1973 and 1974 year classes appeared to be relatively dominant. The mean value of instantaneous rate of fishing mortality (F) in the 1980s was significantly different from that of the 1910s (P<0.05). Recruitment of the hairtail exhibited a similar trend with stock biomass until 1974, indicating the density-dependent Ricker curve.

• Korean Journal of Ecology and Environment
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• v.56 no.4
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• pp.330-338
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• 2023

To investigate the impact of forestry projects on the carbon stocks of forests, we estimated the carbon stock change of above-ground and soil before and after forestry projects using forest type maps, forestry project information, and soil information. First, we selected six map sheet with large areas and declining age class based on forest type map information. Then, we collected data such as forest type maps, growth coefficients, soil organic matter content, and soil bulk density of the estimated areas to calculate forest carbon storage. As a result, forest carbon stocks decreased by about 34.1~70.0% after forestry projects at all sites. In addition, compared to reference studies, domestic forest soils store less carbon than the above-ground, so it is judged that domestic forest soils have great potential to store more carbon and strategies to increase carbon storage are needed. It was estimated that the amount of carbon stored before forestry projects is about 1.5 times more than after forestry projects. The study estimated that it takes about 27 years for forests to recover to their pre-thinning carbon stocks following forestry projects. Since it takes a long time for forests to recover to their original carbon stocks once their carbon stocks are reduced by physical damage, it is necessary to plan to preserve them as much as possible, especially for highly conservative forests, so that they can maintain their carbon storage function.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.1025-1032
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• 2017

In this paper, the level of service (LOS) is defined for high-speed railway links and rolling stocks. Based on this LOS, how to manage high-speed railway facility is also suggested. The LOS is divided into 6 levels from A to F. The measurement of effectiveness (MOE) for railway links is derived from the relationship between a total delay time and a railway link utilization ratio. Another MOE, volume over capacity (V/C), is also proposed. On the other hand, the LOS for high-speed railway rolling stocks is based on the density of people in a rolling stock. Above all, LOS D is defined to the total number of seats. Then, LOS A is 50% of the LOS D, LOS B is 70% of the LOS D, LOS C is 90% of the LOS D and LOS D~F is defined as the maximum seats and standing people at the level of each. Finally, a method to manage high-speed railway links and rolling stocks is proposed in order to keep the level of service at the target by the government.

• Journal of Radiation Protection and Research
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• v.12 no.2
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• pp.19-27
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• 1987

In order to determine the $^{56}Mn\;{\gamma}$-detection efficiency of a $MnSO_4$ bath system, it is essential to do the absolute activity measurement of $^{56}Mn$ solution. For the fabrication of $^{56}Mn$ samples, a 13.718 mg of $^{56}Mn$ metal flake with 99.99% purity was irradiated for 12 minutes at the thermal neutron field of about $10^{13}n/cm^2s$ of flux density. The neutron activated $^{56}Mn$ metal sample was dissolved in 50 ml of 0.1 N-HCl solution. The $^{56}Mn$ samples were fabricated by using the dissolved stock solution and the activity of each of them was measured by the $4{\pi}{\beta}-{\gamma}$ coincidence counting technique. The obtained result was 408.070 kBq/mg with total uncertainty of 0.366% at reference date, 0 h on October 15, 1987.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.55-66
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• 2012

Option pricing models using L$\acute{e}$evy processes are suggested as an alternative to the Black-Scholes model since empirical studies showed that the Black-Sholes model could not reflect the movement of underlying assets. In this paper, we investigate whether the Variance Gamma model can reflect the movement of underlying assets in the Korean stock market better than the Black-Scholes model. For this purpose, we estimate parameters and perform likelihood ratio tests using KOSPI 200 data based on the density for the log return and the option pricing formula proposed in Madan et al. (1998). We also calculate some statistics to compare the models and examine if the volatility smile is corrected through regression analysis. The results show that the option price estimated under the Variance Gamma process is closer to the market price than the Black-Scholes price; however, the Variance Gamma model still cannot solve the volatility smile phenomenon.