• Journal of KIISE:Computer Systems and Theory
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• v.35 no.9_10
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• pp.466-475
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• 2008

Owing to the improved wireless communication technologies, it is possible to provide streaming service of multimedia with PDAs and mobile phones in addition to desktop PCs. Since mobile client devices have low computing power and low network bandwidth due to wireless network, the transcoding technology to adapt media for mobile client devices considering their characteristics is necessary. Transcoding servers transcode the source media to the target media within corresponding grades and provide QoS in real-time. In particular, an effective load balancing policy for transcoding servers is inevitable to support QoS for large scale mobile users. In this paper, the transcoding load estimation algorithm is proposed for load balance on the distributed transcoding environments. The proposed algorithm estimates transcoding time from transcoding server information, movie information and target transcoding bit-rate. The estimated transcoding time is proved based on experiments.

• Korean Journal of Environmental Agriculture
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• v.24 no.1
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• pp.17-23
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• 2005

In order to examine effects of sampling frequency during rainfall-runoff process from paddy field on the estimation of pollution load, EMCs of several water sampling frequencies were examined. Water quality samples were conducted by every two hours interval for each event. It was found that difference of load estimation between five times sampling and two hours consecutive sampling during rainfall-runoff showed $15.2{\sim}-15.2%$ for T-N, $20.0{\sim}-26.2%$ for T-P, $28.6{\sim}-35.7%$ for the SS, respectively. In the same way, the effects of number of sampling data on estimation of pollution load using runoff-mass load(L-Q) method were investigated. L-Q equation made of five times sampling data provided 10% differences in estimation of mass loads of T-N, T-P, and SS when compared to those by L-Q equation using entire two hours consecutive sampling data during runoff process.

• Journal of Korean Society on Water Environment
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• v.28 no.2
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• pp.247-254
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• 2012

Soil and Water Assessment Tool (SWAT) model has been widely used in estimation of flow and water quality at various watersheds worldwide, and it has an auto-calibration tool that could calibrate the flow and water quality data automatically from thousands of simulations. However, only continuous measured day flow/water quality data could be used in the current SWAT auto-calibration tool. Therefore, 8-day interval flow and water quality data measured nationwide by Korean Ministry of Environment (MOE) could not be used in SWAT auto-calibration even though long-term flow and water quality data in the Korean Total Maximum Daily Load (TMDL) watersheds available. In this study, current SWAT auto-calibration was modified to calibrate flow and water quality using 8-day interval flow and water quality data. As a result of this study, the Nash and Sutcliffe Efficiency (NSE) values for flow estimation using auto-calibration are 0.77 (calibration period) and 0.68 (validation period), and NSE value for water quality (T-P load) estimation (using the 8-day interval water quality data) is 0.80. The enhanced SWAT auto-calibration could be used in the estimation of continuous flow and water quality data at the outlet of TMDL watersheds and ungaged point of watersheds. In the next study, the enhanced SWAT auto-calibration will be integrated with Web based Load Duration Curve (LDC) system, and it could be suggested as methods of appraisal of TMDL in South Korea.

• Wind and Structures
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• v.6 no.2
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• pp.107-126
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• 2003

This paper describes a simple and practical approach through the application of Linear Stochastic Estimation (LSE) to reconstruct wind-induced pressure time series from the covariance matrix for structural load analyses on a low building roof. The main application of this work would be the reduction of the data storage requirements for the NIST aerodynamic database. The approach is based on the assumption that a random pressure field can be estimated as a linear combination of some other known pressure time series by truncating nonlinear terms of a Taylor series expansion. Covariances between pressure time series to be simulated and reference time series are used to calculate the estimation coefficients. The performance using different LSE schemes with selected reference time series is demonstrated by the reconstruction of structural load time series in a corner bay for three typical wind directions. It is shown that LSE can simulate structural load time series accurately, given a handful of reference pressure taps (or even a single tap). The performance of LSE depends on the choice of the reference time series, which should be determined by considering the balance between the accuracy, data-storage requirements and the complexity of the approach. The approach should only be used for the determination of structural loads, since individual reconstructed pressure time series (for local load analyses) will have larger errors associated with them.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.368-376
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• 2021

Artificial intelligence clouds help to efficiently develop the autonomous things integrating artificial intelligence technologies and control technologies by sharing the learned models and providing the execution environments. The existing autonomous things development technologies only take into account for the accuracy of artificial intelligence models at the cost of the increment of the complexity of the models including the raise up of the number of the hidden layers and the kernels, and they consequently require a large amount of computation. Since resource-constrained computing environments, could not provide sufficient computing resources for the complex models, they make the autonomous things violate time criticality. In this paper, we propose a digital twin software development framework that selects artificial intelligence models optimized for the computing environments. The proposed framework uses a load estimation DNN model to select the optimal model for the specific computing environments by predicting the load of the artificial intelligence models with digital twin data so that the proposed framework develops the control software. The proposed load estimation DNN model shows up to 20% of error rate compared to the formula-based load estimation scheme by means of the representative CNN models based experiments.

• Tunnel and Underground Space
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• v.28 no.1
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• pp.72-96
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• 2018

Accurate estimation of the uniaxial compressive strength of rock is very crucial for the safety of construction activities occurring in the rock mass. However, the uniaxial compressive strength test is expensive and time consuming. Moreover, the uniaxial compressive strength test cannot be performed in the field. In order to solve this kind of problem, many foreign researchers investigated the use of the point load strength test for the estimation of uniaxial compressive strength of rock. However, the result of research obtained for rocks from other countries may not be directly applicable for rocks in Korea. The correlation between the point load strength index and the uniaxial compressive strength for rocks in Korea is suggested in the form of table by using the results of the extensive literature reviews and laboratory tests. The suggested result is expected to be used for the simple and quick estimation of uniaxial compressive strength of rocks in Korea.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.37-42
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• 2015

In order to provide fundamental data for the creation of environmental design criteria for horticultural facilities, we developed a method to easily calculate the seasonal heating load applying heating degree-hour while taking into account heating load reductions due to solar radiation in the daytime, and reviewed through greenhouse heating experiments. Heating experiments and measuring meteorological environments were carried out in three greenhouses located at Buyeo, Cheonan, and Buan, and we derived reduction factors of seasonal heating load according to hours of sunshine. Daily mean hours of sunshine during the experiment period in each of the greenhouse was 4.0 to 8.3 hours, and the reduction factor of seasonal heating load was 0.64 to 0.85, has been shown to decrease linearly with the increase in hours of sunshine. A method to estimate the seasonal heating load for greenhouses was developed using the reduction factor of seasonal heating load derived from the greenhouse heating experiment, including the adjustment factor of seasonal heating load according to hours of sunshine. The developed method was validated through heating experiments in a greenhouse located at Cheonan. Greenhouse seasonal heating loads calculated by the method developed in this study were analyzed to show the estimate error of 1.2 to 5.0%. It showed that the accuracy increased 2.3 times more than when using the heating load reduction factor of 0.75 applied uniformly in previous studies. Thus, the calculation method of seasonal heating load for greenhouses considering hours of sunshine developed in this study could be utilized for energy estimation, management planning, and economic evaluation in greenhouse design.

• Journal of Korean Society on Water Environment
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• v.32 no.4
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• pp.375-383
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• 2016

Total Pollution Load Control (TPLC) calculates and manages the allowable pollutant load that is discharged from the watershed, which can meet the water quality target. Delivery Ratio (DR) is generally used for predicting the variation of pollutant mass balance between the pollutants discharged from the watershed and a certain point in the stream, and it is very important for estimation of accurate allowable pollutant load. The concept of DR in TPLC is different from prevalent DR, because DR in TPLC includes both the discharge of pollutants from the watershed and the delivery mechanism. Therefore, DR in TPLC should be estimated by using a proper and unified methodology. The appropriate method and equation for estimation of DR in TPLC was developed through the review of various methodologies, and the applicability of the equation was evaluated in a study area (Geumho A). Determination coefficients (R²) of regression were shown to be relatively high (BOD 0.71~0.87, T-N 0.86~0.90, T-P 0.62~0.69). Applicability of the developed methodology and equations was evaluated as appropriate for TPLC, and it is suggested.

• Journal of Wetlands Research
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• v.18 no.3
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• pp.226-231
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• 2016

Land Generated Waste Load Unit(LGWLU) estimation based on land use data is required to understand the impact of land use on water quality. The method of estimating LGWLU based on the monitoring data requires a lot of time and manpower. In this study, we propose a method of land pollution unit load estimation based on land use data with LP optimization. Optimization is the process to obtain the best possible optimal solution in a given condition. This study carried out optimization by using excel solver in Microsoft Excel. This study derived LGWLU of BOD, T-N, T-P in Gongju-Si and Seocheon-Gun by using the 2012 land use map made by ministry of environment based on 2010 satellite image. This study about LGWLU estimation is expected to be able to determine more clearly the water pollution caused by land use changes.

• JOURNAL OF ELECTRICAL WORLD
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• no.2 s.74
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• pp.25-28
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• 1983