• Korean journal of applied entomology
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• v.62 no.3
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• pp.183-191
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• 2023

After examining the temperature change in berry sawdust during the fermentation period and the developmental characteristics of Protaetia brevitarsis larvae according to the fermentation period, it was found that the fermentation speed was faster than with oak sawdust and a fermentation period of 50-70 days was the most suitable for larval growth. During the fermentation period, the temperature change in sawdust was quickly fermented as berry sawdust passed a high temperature of more than 60℃ in the early stages of fermentation compared to fermentation speed of oak sawdust, and stabilized within 60 days. In individual breeding, the total growth period of P. brevitarsis larvae by fermentation period was the longest at 104.8 days after fermentation for 30 days, which was a significant difference. There was no difference in the growth period between 50 and 90 days of fermentation. Oak sawdust tended to be the same as berry sawdust, but the larval development period was approximately 30 days. The developmental period by larval stage differed greatly between berries and oak sawdust. The weight of larvae according to the fermentation period of sawdust was the lowest after 90 days of fermentation, with a long fermentation period for both berry and oak sawdust, and there was no difference in the 30-70 days fermentation periods. The survival rate was more than 95% in individual breeding, and there was no difference among the fermentation periods. However, in group breeding, the survival rate was lower than that of individual breeding and was lowest at 90 days of fermentation. In group breeding, the ratio of larvae weighing ≥ 2.5 g, which is the standard for shipment, was 85% after 30-70 days of fermentation of berry sawdust, but was lower at 70% after 90 days of fermentation, which was a significant difference. The survival rate with oak sawdust showed the same trend, which was approximately 10% lower than that with sawdust. In addition, the larvae from the berry sawdust weighed > 2.5 g in 40-60 days, while it took 75-105 days with oak-fermented sawdust.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Korean Journal of Environment and Ecology
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• v.38 no.2
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• pp.148-155
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• 2024

This study investigated the ecological characteristics of Odontobutis platycephala at Jaho stream from January to December 2022. The riverbed structure of the species' habitat was rich in cobble and pebble. The water was deep, ranging from 22 to 153 cm, with an average of 64 cm, and the stream velocity was rapid at 0.89 (0.42-1.46) m/sec. The ratio of females to males was 1:1.02, and the total length of collected individuals ranged from 38 to 156 mm. The age according to the total length frequency distribution as of May indicated that the group with a total length of 38-69 mm was one year old, the group with 60-99 mm was two years old, the group with 100-139 mm was three years old, and the group 140-156 mm was four years or older. As a secondary gender characteristic, the genital papilla was cylindrical in females and cone-shaped with a pointed tip in males. Some females with a length ranging from 60 to 69 mm and all females 70 mm or longer were sexually mature. Some males with a length ranging from 70 to 79 mm and all males 80 mm or longer were sexually mature. The spawning season was from May to July, and the water temperature was between 17 ℃ and 28 ℃ during that period. The prosperous spawning season was June (24 ℃). The average number of eggs in the ovaries was 988 (284-2,722) per mature female, and the mature eggs were yellowish and spherical with a mean diameter of 1.46 (1.19-1.71) mm. The correlation between total length and body weight is BW=0.00000006TL^3.12 with the constant a as 0.00000006 and the parameter b as 3.12. The mean condition factor (K) was 1.44 (0.96-2.26), and the slope was negative at -0.0007

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.1-21
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• 2023

This study analyzed workers' residence and home-based work(HBW) trip by utilizing data from mobile carrier base stations of Gwangyang Port and terminal workers. In the past, research on port-related traffic or trip patterns mainly focused on cargo-based movement patterns for estimating cargo volume and port facilities, but this study analyzed trip patterns for workers in Gwangyang Port ports and related industries. As a result of the analysis, the average number of regular workers in the port hinterland Gwangyang Port was 1,295 per month, and the residence of workers was analyzed in Gwangyang City (66.1%)>Suncheon City (26.6%)>Yeosu City (3.1%). The average number of temporary workers in the hinterland was 2,645 per month, and Gwangyang City (45.8%)>Suncheon City (20.1%)>Yeosu City (5.7%). Next, the average number of regular workers at Gwangyang Port terminals was 753 per month, and Gwangyang City (66.1%)>Suncheon City (28.9%)>Yeosu City (3.3%) was analyzed. The average number of temporary workers at Gwangyang Port terminals was 1,893 per month, and Gwangyang City (50.8%)>Suncheon City (19.7%)>Yeosu City (9.8%). This study is expected to calculate the number of workers based on individual traffic using actual mobile carrier data to estimate the actual number of workers if the workplace address and actual work place are different, such as in port-related industries. This study is the first to be conducted on workers at Gwangyang Port. It is expected to be used as basic data for settlement conditions and urban planning, as well as transportation policies for port workers, by identifying the population coming from areas other than Gwangyang, where Gwangyang Port is located.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.3
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• pp.344-352
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• 2012

Soil physical properties determine soil quality in aspect of root growth, infiltration, water and nutrient holding capacity. Although the monitoring of soil physical properties is important for sustainable agricultural production, there were few studies. This study was conducted to investigate the condition of soil physical properties of arable land according to land use across the country. The work was investigated on plastic film house soils, upland soils, orchard soils, and paddy soils from 2008 to 2011, including depth of topsoil, bulk density, hardness, soil texture, and organic matter. The average physical properties were following; In plastic film house soils, the depth of topsoil was 16.2 cm. For the topsoils, hardness was 9.0 mm, bulk density was 1.09 Mg $m^{-3}$, and organic matter content was 29.0 g $kg^{-1}$. For the subsoils, hardness was 19.8 mm, bulk density was 1.32 Mg $m^{-3}$, and organic matter content was 29.5 g $kg^{-1}$; In upland soils, depth of topsoil was 13.3 cm. For the topsoils, hardness was 11.3 mm, bulk density was 1.33 Mg $m^{-3}$, and organic matter content was 20.6 g $kg^{-1}$. For the subsoils, hardness was 18.8 mm, bulk density was 1.52 Mg $m^{-3}$, and organic matter content was 13.0 g $kg^{-1}$. Classified by the types of crop, soil physical properties were high value in a group of deep-rooted vegetables and a group of short-rooted vegetables soil, but low value in a group of leafy vegetables soil; In orchard soils, the depth of topsoil was 15.4 cm. For the topsoils, hardness was 16.1 mm, bulk density was 1.25 Mg $m^{-3}$, and organic matter content was 28.5 g $kg^{-1}$. For the subsoils, hardness was 19.8 mm, bulk density was 1.41 Mg $m^{-3}$, and organic matter content was 15.9 g $kg^{-1}$; In paddy soils, the depth of topsoil was 17.5 cm. For the topsoils, hardness was 15.3 mm, bulk density was 1.22 Mg $m^{-3}$, and organic matter content was 23.5 g $kg^{-1}$. For the subsoils, hardness was 20.3 mm, bulk density was 1.47 Mg $m^{-3}$, and organic matter content was 17.5 g $kg^{-1}$. The average of bulk density was plastic film house soils < paddy soils < orchard soils < upland soils in order, according to land use. The bulk density value of topsoils is mainly distributed in 1.0~1.25 Mg $m^{-3}$. The bulk density value of subsoils is mostly distributed in more than 1.50, 1.35~1.50, and 1.0~1.50 Mg $m^{-3}$ for upland and paddy soils, orchard soils, and plastic film house soils, respectively. Classified by soil textural family, there was lower bulk density in clayey soil, and higher bulk density in fine silty and sandy soil. Soil physical properties and distribution of topography were different classified by the types of land use and growing crops. Therefore, we need to consider the types of land use and crop for appropriate soil management.

• Radiation Oncology Journal
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• v.18 no.2
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• pp.157-166
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• 2000

Purpose : This investigation was peformed in order to improve the health care of radiation workers, to predict a risk, to minimize the radiation exposure hazard to them and for them to realize radiation exposure danger when they work in radiation area in hospital. Methods and Materials : The documentations checked regularly for personal radiation exposure in four university hospitals in Pusan city in Korea between January 1, 1993 and December 31, 1997 were analyzed. There were 458 persons in this documented but 111 persons who worked less then one year were excluded and only 347 persons were included in this study. Results : The average of yearly radiation exposure of 347 persons was 1.52$\pm$1.35 mSv. Though it was less than 50mSv, the limitaion of radiation in law but 125 (36%) people received higher radiation exposure than non-radiation workers. Radiation workers under 30 year old have received radiation exposure of mean 1.87$\pm$1.01 mSv/year, mean 1.22$\pm$0.69 mSv between 31 and 40 year old and mean 0.97$\pm$0.43 mSv/year over 41year old (p<0.001). Men received mean 1.67$\pm$1.54 mSv/year were higher than women who received mean 1.13$\pm$0.61 mSv/year (p<0.01). Radiation exposure in the department of nuclear modicine department in spite of low energy sources is higher than other departments that use radiations in hospital (p<0.05). And the workers who received mean 3.59$\pm$1.81 msv/year in parts of management of radiation sources and injection of sources to patient receive high radiation exposure in nuclear medicine department (p<0.01). In department of diagnostic radiology high radiation exposure is in barium enema rooms where workers received mean 3.74$\pm$1.74 mSv/year and other parts where they all use fluoroscopy such as angiography room of mean 1.17$\pm$0.35 mSv/year and upper gastrointestinal room of mean 1.74$\pm$1.34 mSv/year represented higher radiation exposure than average radiation exposure in diagnostic radiology (p<0.01). Doctors and radiation technologists received higher radiation exposure of each mean 1.75$\pm$1.17 mSv/year and mean 1.50$\pm$1.39 mSv/year than other people who work in radiation area in hospital (p<0.05). Especially young doctors and technologists have the high opportunity to receive higher radiation exposure. Conclusions : The training and education of radiation workers for radiation exposure risks are important and it is necessary to rotate worker in short period in high risk area. The hospital management has to concern health of radiation workers more and to put an effort to reduce radiation exposure as low as possible in radiation areas in hospital.

• 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.

• Journal of Korean Society of Forest Science
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• v.52 no.1
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• pp.37-49
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• 1981

The purpose of the study is to find out the objective method of valuation on the forest stands through the analysis of logging costs that is positively related to timber production. The two forest (Amgog, Whangryoung), located nereby, but forest type, logging and skidding conditions being slightly different, were slected to carry out the study. The objective timber stumpage value were determined by investigating the appropriate timber production costs and profits of logging operations. The main result obtained in this study are as follows: 1. The rate of logging cost in consisting of timber market price is 13.15% in the area of Amgog logging place and 19.48% in Whangryoung. 2. The rate of the other production cost excluding logging cost is 15.36% in the area of Amgog logging place and 28.85% in Whangryoung. 3. The total rate of timber production cost in consisting of the market price is more than 28.51% in the area of Amgog logging place and 48.33% in Whangryoung, 4. Though the productivity of forest land is affected by the selection of tree species, tending, treatments and effective management of forest land, the more important problem is improvement of logging condition. 5. The rate of production cost in timber price is so high that we should endeavore to improve the productivity of labour and its quality, and minimize the difference of piece work per day in accordance to the various site condition. 6. Although the profit of forest industry is related to the period of recapturing investment, it is more closely related to the working condition, risk of investment and continuous change of social investment interest. 7. If the right variables which are related to the timber market, are objectively obtained, the stumpage value of mature forests can be objectively caculated by applying straight line discounting method or compound discounting method in caculating the stump to market price.

• Korean Journal of Soil Science and Fertilizer
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• v.5 no.2
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• pp.1-38
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• 1972

This study, designed to establish a classification system of paddy soils and suitability groups on productivity and management of paddy land based on soil characteristics, has been made for the paddy soils on the Gimje-Mangyeong plains. The morphological, physical and chemical properties of the 15 paddy soil series found on these plains are briefly as follows: Ten soil series (Baeggu, Bongnam, Buyong, Gimje, Gongdeog, Honam, Jeonbug, Jisan, Mangyeong and Suam) have a B horizon (cambic B), two soil series (Geugrag and Hwadong) have a Bt horizon (argillic B), and three soil series (Gwanghwal, Hwagye and Sindab) have no B or Bt horizons. Uniquely, both the Bongnam and Gongdeog series contain a muck layer in the lower part of subsoil. Four soil series (Baeggu, Gongdeog, Gwanghwal and Sindab) generally are bluish gray and dark gray, and eight soil series (Bongnam, Buyong, Gimje, Honam, Jeonbug, Jisan, Mangyeong and Suam) are either gray or grayish brown. Three soil series (Geugrag, Hwadong and Hwagye), however, are partially gleyed in the surface and subsurface, but have a yellowish brown to brown subsoil or substrata. Seven soil series (Bongnam, Buyong, Geugrag, Gimje, Gongdeog, Honam and Hwadong) are of fine clayey texture, three soil series (Baeggu, Jeonbug and Jisan) belong to fine loamy and fine silty, three soil series (Gwanghwal, Mangyeong and Suam) to coarse loamy and coarse silty, and two soil series (Hwagye and Sindab) to sandy and sandy skeletal texture classes. The carbon content of the surface soil ranges from 0.29 to 2.18 percent, mostly 1.0 to 2.0 percent. The total nitrogen content of the surface soil ranges from 0.03 to 0.25 percent, showing a tendency to decrease irregularly with depth. The C/N ratio in the surface soil ranges from 4.6 to 15.5, dominantly from 8 to 10. The C/N ratio in the subsoil and substrata, however, has a wide range from 3.0 to 20.25. The soil reaction ranges from 4.5 to 8.0. All soil series except the Gwanghwal and Mangyeong series belong to the acid reaction class. The cation exchange cpacity in the surface soil ranges from 5 to 13 milliequivalents per 100 grams of soil, and in all the subsoil and substrata except those of a sandy texture, from 10 to 20 milliequivalents per 100 grams of soil. The base saturation of the soil series except Baeggu and Gongdeog is more than 60 percent. The active iron content of the surface soil ranges from 0.45 to 1.81 ppm, easily-reduceable manganese from 15 to 148 ppm, and available silica from 36 to 366 ppm. The iron and manganese are generally accumulated in a similar position (10 to 70cm. depth), and silica occurs in the same horizon with that of iron and manganese, or in the deeper horizons in the soil profile. The properties of each soil series extending from the sea shore towards the continental plains change with distance and they are related with distance (x) as follows: y(surface soil, clay content) = $$-0.2491x^2+6.0388x-1.1251$$ y(subsoil or subsurface soil, clay content) = $$-0.31646x^2+7.84818x-2.50008$$ y(surface soil, organic carbon content) = $$-0.0089x^2+0.2192x+0.1366$$ y(subsoil or subsurface soil, pH) = $$-0.0178x^2-0.04534x+8.3531$$ Soil profile development, soil color, depositional and organic layers, soil texture and soil reaction etc. are thought to be the major items that should be considered in a paddy soil classification. It was found that most of the soils belonging to the moderately well, somewhat poorly and poorly drained fine and medium textured soils and moderately deep fine textured soils over coarse materials, produce higher paddy yields in excess of 3,750 kg/ha. and most of the soils belonging to the coarse textured soils, well drained fine textured soils, moderately deep medium textured soils over coarse materials and saline soils, produce yields less than 3,750kg/ha. Soil texture of the profile, available soil depth, salinity and gleying of the surface and subsurface soils etc. seem to be the major factors determining rice yields, and these factors are considered when establishing suitability groups for paddy land. The great group, group, subgroup, family and series are proposed for the classification categories of paddy soils. The soil series is the basic category of the classification. The argillic horizon (Bt horizon) and cambic horizon (B horizon) are proposed as two diagnostic horizons of great group level for the determination of the morphological properties of soils in the classification. The specific soil characteristics considered in the group and subgroup levels are soil color of the profile (bluish gray, gray or yellowish brown), salinity (salic), depositonal (fluvic) and muck layers (mucky), and gleying of surface and subsurface soils (gleyic). The family levels are classified on the basis of soil reaction, soil texture and gravel content of the profile. The definitions are given on each classification category, diagnostic horizons and specific soil characteristics respectively. The soils on these plains are classified in eight subgroups and examined under the existing classification system. Further, the suitability group, can be divided into two major categories, suitability class and subclass. The soils within a suitability class are similar in potential productivity and limitation on use and management. Class 1 through 4 are distinguished from each other by combination of soil characteristics. Subclasses are divided from classes that have the same kind of dominant limitations such as slope(e), wettness(w), sandy(s), gravels(g), salinity(t) and non-gleying of the surface and subsurface soils(n). The above suitability classes and subclasses are examined, and the definitions are given. Seven subclasses are found on these plains for paddy soils. The classification and suitability group of 15 paddy soil series on the Gimje-Mangyeong plains may now be tabulated as follows.

• Journal of Animal Science and Technology
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• v.48 no.1
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• pp.29-38
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• 2006

The objective of this study was to estimate genetic variances of growth curve parameters in Hanwoo cows. The data used in this study were records from 1,083 Hanwoo cows raised at Hanwoo Experiment Station, National Livestock Research Institute(NLRI). First evaluation model(Model I) fit year-season of birth and age of dam as fixed effects and second model(Model II) added age at the final weight as a linear covariate to Model I. Heritability estimates of A, b and k from Gompertz model were 0.22, 0.11 and 0.07 using modelⅠ and 0.28, 0.11 and 0.12 using modelⅡ. Those from Von Bertalanffy model were 0.22, 0.11 and 0.07 using modelⅠ, 0.28, 0.11 and 0.12 using modelⅡ. Heritability estimates of A, b and k from Logistic model were 0.14, 0.07 and 0.05 using modelⅠ, 0.18, 0.07 and 0.12 using modelⅡ. Heritability estimates of A from Gompertz model were higher than those from Von Bertalanffy model or Logistic model in both model Ⅰand model Ⅱ. Heritability estimates of b from Logistic model were higher than those from Gompertz model or Von Bertalanffy model in both modelⅠand model Ⅱ. Heritability estimates of birth weight, weaning weight, 3 month weight, 6 month weight, 9 month weight, 12 month weight, 18 month weight, 24 month weight, 36 month weight were after linear age adjustment 0.27, 0.11, 0.19, 0.14, 0.16, 0.23, 0.52 and 0.32, respectively. Heritability estimates of birth weight, weaning weight, 3 month weight, 6 month weight, 9 month weight and 24 month weight fit by Gompertz model were larger than those estimated from linearly adjusted data. Heritability estimates of 12 month weight, 18 month weight and 36 month weight fit by Von Bertalanffy model were larger than those estimated from linearly adjusted data. In the multitrait analyses for parameters from Gompertz model, genetic and phenotypic correlations between A and k parameters were －0.47 and －0.67 using modelⅠand －0.56 and －0.63 using model Ⅱ. Those between the A and b parameters were 0.69 and 0.34 using modelⅠand 0.72 and 0.37 using model Ⅱ. Those between the b and k parameters were －0.26 and 0.01 using modelⅠand －0.30 and 0.01 using model Ⅱ. In the multitrait analyses for parameters from Von Bertalanffy model, genetic and phenotypic correlations between A and k parameters were －0.49 and －0.67 suing model Ⅰ and －0.57 and －0.70 using modelⅡ. Those between the A and b parameters were 0.61 and 0.33 using modelⅠ and 0.60 and 0.30 using model Ⅱ. Those between the b and k parameters were －0.20 and 0.02 using modelⅠ and 0.16 and 0.00 using modelⅡ. In the multitrait analyses for parameters from Logistic model, genetic and phenotypic correlations between A and k parameters were －0.43 and －0.67 using model Ⅰ and －0.50 and －0.63 using modelⅡ. Those between the A and b parameters were 0.47 and 0.22 using modelⅠ and 0.38 and 0.24 using modelⅡ. Those between the b and k parameters were －0.09 and 0.02 using model Ⅰ and －0.02 and 0.13 using model Ⅱ.