• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.533-540
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• 2010

Dam is very important facility in water supply and flood control. Therefore study needs to analyze reservoir capacity accurately to manage Dam efficiently. This study compared time series reservoir capacity using multi-dimensional spatial information to Chungju Dam reservoir and major conclusions are as follows. First, LiDAR and multi beam echo sounder survey were carried out in land zone and water zone of Dam reservoir area. And calibration process was performed to enhance the accuracy of survey data and it could be constructed that multi dimensional spatial information which was clearly satisfied with the standard of tolerance error by validation with ground control points. Reservoir capacity by water level was calculated using triangle irregular network from detailed topographic data that was constructed by linked with airborne LiDAR and multi beam echo sounder data, and curve equation of reservoir capacity was developed through regression analysis in 2008. In the comparison of the reservoir capacity of 2008 with those of 1986 and 1996, the higher water level goes, total reservoir capacity of 2008 showed decrease because of the increase of sediment in reservoir. Also, erosion and sediment area could be analyzed through calculating the reservoir capacity by the range of water level. Especially the range of water level as 130.0~135.0 which is the upper part of average water level, showed the highest erosion characteristics during 1986~2008 and 1996~2008 and it is considered that the erosion of reservoir slant by heavy rainfall is major reason.

• Steel and Composite Structures
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• v.49 no.1
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• pp.81-89
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• 2023

A simplified calculation method of natural vibration characteristics of high-speed railway multi-span bridge-longitudinal ballastless track system is proposed. The rail, track slab, base slab, main beam, bearing, pier, cap and pile foundation are taken into account, and the multi-span longitudinal ballastless track-beam-bearing-pier-cap-pile foundation integrated model (MBTIM) is established. The energy equation of each component of the MBTIM based on Timoshenko beam theory is constructed. Using the improved Fourier series, and the Rayleigh-Ritz method and Hamilton principle are combined to obtain the extremum of the total energy function. The simplified calculation formula of the natural vibration frequency of the MBTIM under the influence of vertical and longitudinal vibration is derived and verified by numerical methods. The influence law of the natural vibration frequency of the MBTIM is analyzed considering and not considering the participation of each component of the MBTIM, the damage of the track interlayer component and the stiffness change of each layer component. The results show that the error between the calculation results of the formula and the numerical method in this paper is less than 3%, which verifies the correctness of the method in this paper. The high-order frequency of the MBTIM is significantly affected considering the track, bridge pier, pile soil and pile cap, while considering the influence of pile cap on the low-order and high-order frequency of the MBTIM is large. The influence of component damage such as void beneath slab, mortar debonding and fastener failure on each order frequency of the MBTIM is basically the same, and the influence of component damage less than 10m on the first fourteen order frequency of the MBTIM is small. The bending stiffness of track slab and rail has no obvious influence on the natural frequency of the MBTIM, and the bending stiffness of main beam has influence on the natural frequency of the MBTIM. The bending stiffness of pier and base slab only has obvious influence on the high-order frequency of the MBTIM. The natural vibration characteristics of the MBTIM play an important guiding role in the safety analysis of high-speed train running, the damage detection of track-bridge structure and the seismic design of railway bridge.

• Information Systems Review
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• v.25 no.3
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• pp.99-120
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• 2023

Courier services users' experience of violating privacy affects psychology and behavior of protecting personal privacy. Depending on what privacy infringement experience (PIE) of courier services users, learning about perceived privacy infringement incidents is made, recognition is formed, affection is formed, and behavior is appeared. This paradigm of changing in privacy psychologies of courier services users has an important impact on predicting responses of privacy protective action (PPA). In this study, a theoretical research framework are developed to explain the privacy protective action (PPA) of courier services users by applying attitude theory. Based on this framework, the relationships among past privacy infringement experience (PIE), perceived privacy risk (PPR), privacy concerns (i.e., concerns in unlicensed secondary use (CIUSU), concerns in information error (CIE), concerns in improper access (CIA), and concern in information collection (CIC), and privacy protective action (PPA) are analyzed. In this study, the proposed research model was surveyed by people with experience in using courier services and was analyzed for finding relationships among research variables using structured an equation modeling software, SMART-PLS. The empirical results show the causal relationships among PIE, PPR, privacy concerns (CIUSU, CIE, CIA, and CIC), and PPA. The results of this study provide useful theoretical implications for privacy management research in courier services, and practical implications for the development of courier services business model.

• Applied Chemistry for Engineering
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• v.35 no.1
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• pp.16-22
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• 2024

Polymer electrolyte membrane fuel cells have the advantage of low operating temperatures and fast startup and response characteristics compared to others. Simulation studies are actively researched because their cost and time benefits. In this study, the resistance of water residual in the gas diffusion layer (GDL) of the unit cell was added to the existing equation to compare the actual data with the model data. The experiments were conducted with a 25 cm² unit cell, and the samples were separated into stopping times of 0, 10, and 60 minutes following primary impedance measurement, activation, and polarization curve data acquisition. This gives 0, 10, and 60 minutes for the residual water in the GDL to evaporate. Without the rest period, the magnitude of the performance improvement was not significantly different at the same potential and flow rate, but the rest period did improve the performance of the membrane electrode assembly when measuring impedance. By changing the magnitude of the resistance reduction to an overvoltage, the voltage difference between the fuel cell model with and without residual water was compared, and the error rate in the high current density region, which is dominated by concentration losses, was reduced.

• Journal of Environmental Impact Assessment
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• v.33 no.1
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• pp.9-17
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• 2024

The purpose of this study is to select and predict optimal heatwave indices for describing and predicting heat-related illnesses. Regression analysis was conducted using Heat-related illness surveillance system data for a number of heat-related illnesses and meteorological data from the Korea Meteorological Administration's Automatic Weather Station (AWS) for the period from 2021 to 2023. Daily average temperature, daily maximum temperature, daily average Wet Bulb Globe Temperature (WBGT), and daily maximum WBGT values were calculated and analyzed. The results indicated that among the four indicators, the daily maximum WBGT showed the highest suitability with an R² value of 0.81 and RMSE of 0.98, with a threshold of 29.94 Celsius. During the entire analysis period, there were a total of 91 days exceeding this threshold, resulting in 339 cases of heat-related illnesses. Predictions of heat-related illness cases from 2021 to 2023 using the regression equation for daily maximum WBGT showed an accuracy with less than 10 cases of error annually, demonstrating a high level of precision. Through continuous research and refinement of data and analysis methods, it is anticipated that this approach could contribute to predicting and mitigating the impact of heatwaves.

• Journal of Ecology and Environment
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• v.48 no.2
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• pp.196-206
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• 2024

Background: Most of the biomass equations were developed using sample trees collected mainly from pan-tropical and tropical regions that may over- or underestimate biomass. Site-specific models would improve the accuracy of the biomass estimates and enhance the country's measurement, reporting, and verification activities. The aim of the study is to develop site-specific biomass estimation models and validate and evaluate the existing generic models developed for pan-tropical forest and newly developed allometric models. Total of 140 trees was harvested from each diameter class biomass model development. Data was analyzed using SAS procedures. All relevant statistical tests (normality, multicollinearity, and heteroscedasticity) were performed. Data was transformed to logarithmic functions and multiple linear regression techniques were used to develop model to estimate aboveground biomass (AGB). The root mean square error (RMSE) was used for measuring model bias, precision, and accuracy. The coefficient of determination (R² and adjusted [adj]-R²), the Akaike Information Criterion (AIC) and the Schwarz Bayesian information Criterion was employed to select most appropriate models. Results: For the general total AGB models, adj-R² ranged from 0.71 to 0.85, and model 9 with diameter at stump height at 10 cm (DSH₁₀), ρ and crown width (CW) as predictor variables, performed best according to RMSE and AIC. For the merchantable stem models, adj-R² varied from 0.73 to 0.82, and model 8) with combination of ρ, diameter at breast height and height (H), CW and DSH₁₀ as predictor variables, was best in terms of RMSE and AIC. The results showed that a best-fit model for above-ground biomass of tree components was developed. AGB_Stem = exp {-1.8296 + 0.4814 natural logarithm (Ln) (ρD²H) + 0.1751 Ln (CW) + 0.4059 Ln (DSH₃₀)} AGB_Branch = exp {-131.6 + 15.0013 Ln (ρD²H) + 13.176 Ln (CW) + 21.8506 Ln (DSH₃₀)} AGB_Foliage = exp {-0.9496 + 0.5282 Ln (DSH₃₀) + 2.3492 Ln (ρ) + 0.4286 Ln (CW)} AGB_Total = exp {-1.8245 + 1.4358 Ln (DSH₃₀) + 1.9921 Ln (ρ) + 0.6154 Ln (CW)} Conclusions: The results demonstrated that the development of local models derived from an appropriate sample of representative species can greatly improve the estimation of total AGB.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.80-90
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• 2014

The objective of this research is to estimate the stand volume of Pinus koraiensis, by using the investigated volume and the information of remote sensing(RS), in the research forest of Kangwon National University. The average volume of the research forest per hectare was $307.7m^3/ha$ and standard deviation was $168.4m^3/ha$. Before and after carrying out 3 by 3 majority filtering on TM image, eleven indices were extracted each time. Independent variables needed for linear regression equation were selected using mean pixel values by indices. The number of indices were eleven: six Bands(except for thermal Band), NDVI, Band Ratio(BR1:Band4/Band3, BR2:Band5/Band4, BR3:Band7/Band4), Tasseled Cap-Greeness. As a result, NDVI and TC G were chosen as the most suitable indices for regression before and after filtering, and R-squared was high: 0.736 before filtering, 0.753 after filtering. As a result of error verification for an exact comparison, RMSE before and after filtering was about $69.1m^3/ha$, $67.5m^3/ha$, respectively, and bias was $-12.8m^3/ha$, $9.7m^3/ha$, respectively. Therefore, the regression conducted with filtering was selected as an appropriate model because of low RMSE and bias. The estimated stand volume applying the regression was $160,758m^3$, and the average volume was $314m^3/ha$. This estimation was 1.2 times higher than the actual stand volume of Pinus koraiensis.

• Korean Journal of Agricultural Science
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• v.8 no.2
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• pp.195-202
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• 1981

The return of water to the atmosphere from water, soil and vegetation surface is one of the most important aspects of hydrological cycle, and the seasonal trend of variation of river basin evaporation is also meaningful in the longterm runoff analysis for the irrigation and water resources planning. This paper has been prepared to show some imformation to estimate the monthly river basin evaporation from pan evaporation, potential evaporation, regional evaporation and temperature through the comparison with river basin evaporation derived from water budget method. The analysis has been carried out with the observation data of Yongdam station in the Geum river basin for five year. The results are summarized as follows and these would be applied to the estimation of river basin evaporation and longterm runoff in ungaged station. 1. The ratio of pan evaporation to river basin evaporation ($E_w/E_{pan}$) shows the most- significant relation at the viewpoint of seasonal trend of variation. River basin evaporation could be estimated from the pan evaporation through either Fig. 9 or Table-7. 2. Local coefficients of cloudness effect and wind function has been determined to apply the Penman's mass and energy transfer equation to the estimation of river basin evaporation. $R_c=R_a(0.13+0.52n/D)$ $E=0.35(e_s-e)(1.8+1.0U)$ 3. It seems that Regional evaporation concept $E_R=(1-a)R_C-E_p$ has kept functional errors due to the inapplicable assumptions. But it is desirable that this kind of function which contains the results of complex physical, chemical and biological processes of river basin evaporation should be developed. 4. Monthly river basin evaporation could be approximately estimated from the monthly average temperature through either the equation of $E_w=1.44{\times}1.08^T$ or Fig. 12 in the stations with poor climatological observation data.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.40 no.2
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• pp.142-149
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• 1995

This study was conducted to investigate the relationships between yearly variation of climatic components and yearly variations of productivity in monoculture cotton. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components from the four varieties(Kinggus, Yongdang local. 113-4, 380) were collected from 1978 to 1992 in Mokpo area. The meteorological data gathered at the Mokpo Weather Station for the same period were used to find out the relationships between climatic components and productivity. Yearly variation of the amount of precipitation and number of stormy days in July are large with coefficients of the variations(C.V)84.89 and 97.05%, respectively, while yearly variation, of the average temperature, maximum temperature, minimum temperature from May to Sep. are relatively small. Seed cotton yield before frost in Sep. and Oct. very greatly with C.V. of 68.77, 78.52%, respectively. Number of boll bearing branches and lint percentage show more or less small in C.V. with 11.77 and 19.13%, respectively and flowering date and boll opening date show still less variation. Correlation coefficients between precipitation in May and number of boll bearing branches, duration of sunshine in July and number of bolls per plant, maximum temperature in July and total seed cotton before the frost in Sep., Oct., and Nov. evaporation in Aug. are positively sig-nificant at the 1% level. There are highly significantly positive correlated relationships among yield(total seed cotton) and yield components. Total seed cotton yield(Y) can be predicted by multiple regression equation with independent variables of climatic factors in July such as monthly averages of average temperature($X_1$), maximum temperature($X_2$) and minimum temperature($X_3$), monthly amount of precipitation ($X_4$), evaporation($X_5$), monthly average of relative humidity($X_6$), monthly hours with sunshine($X_7$) and number of rainy days($X_8$). The equation is estimatedas Y =-1080.8515 + 144.7133$X_1$+15.8722$X_2$ + 164.9367$X_3$ + 0.0802$X_4$ + 0.5932$X_5$ + 11.3373$X_6$ + 3.4683$X_7$- 9.0846$X_8$. Also, total seed cotton yield(Y) can be predicted by the same method with climatic components in Aug., Y =2835.2497 + 57.9134$X_1$ - 46.9055$X_2$ - 41.5886X$_3$ + 1.2559$X_5$ - 21.9687$X_6$ - 3.3763$X_7$- 4.1080$X_8$- 17.5586$X_9$. And the error between observed and theoretical yield were less with approached linear regression.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.275-283
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• 2010

This experiment was conducted to construct process models to estimate grain weight (GW) and grain nitrogen content (GN) in rice. A model was developed to describe the dynamic pattern of GW and GN during grain-filling period considering their relationships with temperature, solar radiation and growth traits such as LAI, shoot dry-weight, shoot nitrogen content, grain number during grain filling. Firstly, maximum grain weight (GWmax) and maximum grain nitrogen content (GNmax) equation was formulated in relation to Accumulated effective temperature (AET) ${\times}$ Accumulated radiation (AR) using boundary line analysis. Secondly, GW and GN equation were created by relating the difference between GW and GWmax and the difference between GN and GNmax, respectively, with growth traits. Considering the statistics such as coefficient of determination and relative root mean square of error and number of predictor variables, appropriate models for GW and GN were selected. Model for GW includes GWmax determined by AET ${\times}$ AR, shoot dry weight and grain number per unit land area as predictor variables while model for GN includes GNmax determined by AET ${\times}$ AR, shoot N content and grain number per unit land area. These models could explain the variations of GW and GN caused not only by variations of temperature and solar radiation but also by variations of growth traits due to different sowing date, nitrogen fertilization amount and row spacing with relatively high accuracy.