• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.443-443
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• 2000

A systematic scheme is developed fer the design of new adaptive feedback linearizing controllers for nonlinear systems. The developed adaptation law estimates the uncertain time-varying parameters using the structure of diffeomorphisrn. Our scheme is applicable to a class of nonlinear systems which violates the restrictive parametric-pure-feedback condition [4]-[6].

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.710-713
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• 2005

In this paper, a design method to detect faults in plants with uncertainties is proposed. When a plant has faults, the plant will be corrupted by an unknown fault signal. In addition, the plant also includes uncertainties, such as disturbances and plant parameter deviations. In this case, the proposed method estimates the fault signal by using an adaptive observer. Numerical examples are given to demonstrate the validity of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.275-280
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• 1999

This paper presents a disturbance accommodating sliding mode control for a quarter-car active suspension using an electro-hydraulic actuator. The electro-hydraulic actuator model is nonlinear and uncertain. The hardware constrains on the actuator prevent high gain in a sliding mode control, which deteriorates the force tracking performance. DAC(Disturbance Accommodating Control) is combined with the sliding mode control to improve the tracking performance. DAC observer estimates the pressure due to the actuator uncertainty. The additional control is designed to compensate the estimated pressure. Simulation results show the improved tracking performance with the Proposed control methods.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.445-456
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• 2003

Spatial data are usually obtained at selected locations even though they are potentially available at all locations in a continuous region. Moreover the monitoring locations are clustered in some regions, sparse in other regions. One important goal of spatial data analysis is to predict unknown response values at any location throughout a region of interest. Thus, an appropriate space model should be set up and their estimates and predictions must be accompanied by measures of uncertainty. In this study we see that a space model proposed allows a best interpolation to annual rainfall data in South Korea.

• Journal of Environmental Policy
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• v.9 no.1
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• pp.109-135
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• 2010

This study estimated the social benefits of establishment 01 the Taehwa Field Ecology Park in Ulsan Metropolitan City, using CVM(Contingent Valuation Method) with multiple choices in consideration of respondent's uncertainty. The estimation results 01 lour logit models show that the probability of willingness-to-pay increases significantly with higher income, higher evaluation on the relevancy of establishment of the Park, and male gender, and decreases significantly with the bidding price. Truncated mean household WTP is estimated as 2,409.4 KRW in the MBYES model with the most efficient estimates of WTP among four models. On the basis of the WTP estimates, the present values of total social benefits in Ulsan Metropolitan City are estimated as 236.5 bill ion KRW when applying the 5% discount rate. This result shows that the present values of total social benefits are greater than the total costs in all models, and thus may prove the economic relevancy of the investment for the ecology park establishment.

• Journal of Korea Water Resources Association
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• v.51 no.5
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• pp.439-449
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• 2018

Many potential sources of bias are used in several steps of the radar-rainfall estimation process because the hydrological and meteorological radars measure the rainfall amount indirectly. Previous studies on radar-rainfall uncertainties were performed to reduce the uncertainty of each step by using bias correction methods in the quantitative radar-rainfall estimation process. However, these studies do not provide comprehensive uncertainty for the entire process and the relative ratios of uncertainty between each step. Consequently, in this study, a suitable approach is proposed that can quantify the uncertainties at each step of the quantitative radar-rainfall estimation process and show the uncertainty propagation through the entire process. First, it is proposed that, in the suitable approach, the new concept can present the initial and final uncertainties, variation of the uncertainty as well as the relative ratio of uncertainty at each step. Second, the Maximum Entropy Method (MEM) and Uncertainty Delta Method (UDM) were applied to quantify the uncertainty and analyze the uncertainty propagation for the entire process. Third, for the uncertainty quantification of radar-rainfall estimation at each step, two quality control algorithms, two radar-rainfall estimation relations, and two bias correction methods as post-processing through the radar-rainfall estimation process in 18 rainfall cases in 2012. For the proposed approach, in the MEM results, the final uncertainty (from post-processing bias correction method step: ME = 3.81) was smaller than the initial uncertainty (from quality control step: ME = 4.28) and, in the UDM results, the initial uncertainty (UDM = 5.33) was greater than the final uncertainty (UDM = 4.75). However uncertainty of the radar-rainfall estimation step was greater because of the use of an unsuitable relation. Furthermore, it was also determined in this study that selecting the appropriate method for each stage would gradually reduce the uncertainty at each step. Therefore, the results indicate that this new approach can significantly quantify uncertainty in the radar-rainfall estimation process and contribute to more accurate estimates of radar rainfall.

• Journal of Korea Water Resources Association
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• v.50 no.8
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• pp.563-577
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• 2017

As the uncertainty of precipitation increases due to climate change, seasonal forecasting and the use of weather forecasts become essential for efficient water resources management. In this study, the categorical probabilistic long-term forecasts implemented by KMA (Korea Meteorological Administration) since June 2014 was evaluated using assessment indicators of Hit Rate, Reliability Diagram, and Relative Operating Curve (ROC) and a technique for obtaining quantitative precipitation estimates based on probabilistic forecasts was proposed. The probabilistic long-term forecasts showed its maximum predictability of 48% and the quantified precipitation estimates were closely matched with actual observations; maximum correlation coefficient (R) in predictability evaluation for 100% accurate and actual weather forecasts were 0.98 and 0.71, respectively. A precipitation quantification approach utilizing probabilistic forecasts proposed in this study is expected to enable water management considering the uncertainty of precipitation. This method is also expected to be a useful tool for supporting decision-making in the long-term planning for water resources management and reservoir operations.

• Journal of the Korea Institute of Building Construction
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• v.10 no.2
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• pp.75-80
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• 2010

With the intensifying of price competition and structural diversifications, the uncertainty of the domestic housing market has been increased. This highlights the importance of the planning stage of construction projects, and the increased need for a higher level of accuracy in approximate estimates. Currently, a number of research and development programs to calculate construction cost at the initial planning stage are being conducted. However, there are few cases in which local characteristics are considered in deriving the results. If local calibration can be conducted during estimates, more accurate cost estimates will be enabled. This could also play a major role in ensuring the success of a project. Therefore, the purpose of this research is to develop a calculation methodology and a model for a local index based on the historical data of public apartment buildings, and to derive a local index that supports accurate construction cost estimates.

• Atmosphere
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• v.30 no.4
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• pp.361-376
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• 2020

This study evaluates multiple Regional Climate Models (RCMs) in simulating temperature and precipitation over the Far East Asia (FEA) and estimates the portions of the total uncertainty originating in the RCMs and the driving Global Climate Models (GCMs) using nine present-day (1981~2000) climate data obtained from combinations of three GCMs and three RCMs in the CORDEX-EA phase2. Downscaling using the RCMs generally improves the present temperature and precipitation simulated in the GCMs. The mean temperature climate in the RCM simulations is similar to that in the GCMs; however, RCMs yield notably better spatial variability than the GCMs. In particular, the RCMs generally yield positive added values to the variability of the summer temperature and the winter precipitation. Evaluating the uncertainties by the GCMs (VARGCM) and the RCMs (VARRCM) on the basis of two-way ANOVA shows that VARRCM is greater than VARGCM in contrast to previous studies which showed VARGCM is larger. In particular, in the winter temperature, the ocean has a very large VARRCM of up to 30%. Precipitation shows that VARRCM is greater than VARGCM in all seasons, but the difference is insignificant. In the following study, we will analyze how the uncertainty of the climate model in the present-day period affects future climate change prospects.

• Journal of Ecology and Environment
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• v.41 no.2
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• pp.34-44
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• 2017

Background: Monitoring terrestrial vegetation cover condition is important to evaluate its current condition and to identify potential vulnerabilities. Due to simplicity and low cost, point intercept method has been widely used in evaluating grassland surface and quantifying cover conditions. Field-based digital photography method is gaining popularity for the purpose of cover estimate, as it can reduce field time and enable additional analysis in the future. However, the caveats and uncertainty among field-based vegetation cover estimation methods is not well known, especially across a wide range of cover conditions. We compared cover estimates from point intercept and digital photography methods with varying sampling intensities (25, 49, and 100 points within an image), across 61 transects in typical steppe, forest steppe, and desert steppe in central Mongolia. We classified three photosynthetic groups of cover important to grassland ecosystem functioning: photosynthetic vegetation, non-photosynthetic vegetation, and bare soil. We also acquired normalized difference vegetation index from satellite image comparison with the field-based cover. Results: Photosynthetic vegetation estimates by point intercept method were correlated with normalized difference vegetation index, with improvement when non-photosynthetic vegetation was combined. For digital photography method, photosynthetic and non-photosynthetic vegetation estimates showed no correlation with normalized difference vegetation index, but combining of both showed moderate and significant correlation, which slightly increased with greater sampling intensity. Conclusions: Results imply that varying greenness is playing an important role in classification accuracy confusion. We suggest adopting measures to reduce observer bias and better distinguishing greenness levels in combination with multispectral indices to improve estimates on dry matter.