• Journal of Climate Change Research
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• v.5 no.2
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• pp.165-178
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• 2014

National and local governmental adaptation plan for climate change will become mandatory in 2015. In order to establish the plan, assessment of vulnerability to climate change needs to be preceded. LCCGIS, a toolkit for vulnerability assessment, has been widely used by many local governments. However, assessment results by LCCGIS are not yet reliable because most of the vulnerability indices applied to LCCGIS　have the same value for almost all administrative units in Korea. In this study, proxy variables for hard-collectable indices were introduced, and the results were compared with those without any proxy variables. Vulnerability assessment could be conducted subjectively due to uncertainty. Thus, determination of objective indices, understanding the available data, and changes of indices in local conditions were organized. Results from this study are expected to make vulnerability assessment reliable and contribute to assessing vulnerability to climate change reflecting on local governmental characteristics.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.396-400
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• 2007

Various ways of automated guideway transit construction are being planned recently owing to the policies of the national government and local municipalities as well as increasing investment from the private sector. Particularly, the increase in the private investment is increasing greatly in SOC (Social Overhead Cost). This trend of promoting private sector investment must be conducted on the basis of a thorough analysis of the economic feasibility of the project from the government and construction companies in the private sector. In other words, an accurate cost analysis of initial investment cost (Construction cost), maintenance/repair cost, profit making through the operation of the concerned facilities, cost of dissolution, etc. in terms of the life cycle is very much in need. Nevertheless, the analysis of uncertainty factors and its probabilistic theory are in need of development so that they can be used in the analysis of the economic feasibility of a construction project. First of all, the actual studies on maintenance/repair cost of automated guideway transit are scarce as of yet, prohibiting an accurate computation of the cost and its economic analysis. Accordingly, this study focused on the uncertainty analysis of the economic feasibility for civil engineering structures among automated guideway transit construction projects based on the rapidly increasing investment on such structures from the private sector. For this research purpose, a cost classification system for the automated guideway transit is proposed, first of all, and the data On the cost cycle of the civil structure facilities and their unit cost are collected and analyzed. Then, the uncertainty in the cost is analyzed from the perspective of LCC. In consideration of the current status with almost no. studies on maintenance/repair of such facilities, it is expected that the cost classification system and the uncertainty analysis technique proposed in this study will greatly enhance LCC analysis and economic feasibility studies for automated guideway transit projects in the future.

• Journal of the Korean earth science society
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• v.36 no.6
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• pp.567-579
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• 2015

The accurate simulation of micro-scale weather phenomena such as fog using the mesoscale meteorological models is a very complex task. Especially, the uncertainty arisen from initial input data of the numerical models has a decisive effect on the accuracy of numerical models. The data assimilation is required to reduce the uncertainty of initial input data. In this study, the limitation of the mesoscale meteorological model was verified by WRF (Weather Research and Forecasting) model for a summer fog event around the Nakdong river in Korea. The sensitivity analyses of simulation accuracy from the numerical model were conducted using two different initial and boundary conditions: KLAPS (Korea Local Analysis and Prediction System) and LDAPS (Local Data Assimilation and Prediction System) data. In addition, the improvement of numerical model performance by FDDA (Four-Dimensional Data Assimilation) using the observational data from AWS (Automatic Weather System) was investigated. The result of sensitivity analysis showed that the accuracy of simulated air temperature, dew point temperature, and relative humidity with LDAPS data was higher than those of KLAPS, but the accuracy of the wind speed of LDAPS was lower than that of KLAPS. Significant difference was found in case of relative humidity where RMSE (Root Mean Square Error) for LDAPS and KLAPS was 15.7 and 35.6%, respectively. The RMSE for air temperature, wind speed, and relative humidity was improved by approximately $0.3^{\circ}C$, $0.2m\;s^{-1}$, and 2.2%, respectively after incorporating the FDDA.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1572-1581
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• 2011

Since 'The Act on Private Investment of The Infrastructure' was established in 1994, private investment as well as government's investment has been active on transport infrastructure. But investment of transport infrastructure has more risks than others due to overforecast of transport demand for ensuring project validity, and cost uncertainty arising from financial crisis, commodity prices and so on. In the case of Incheon international airport express, after 2 years and 6 months, Incheon international airport express is opened, Korail take over equity stake in private investor due to the problems of MRG(Minimum Revenue Guarantee) be contracted with private investor. Not only that, in other case of Yong-in light rail, it is ongoing for legal disputes between Yong-in local government and private investor on account of opening delaying. On current Investment Assessment System of Transport Infrastructure, Risk Management system on investment of transport infrastructure is inadequate because Sensitivity Analysis in economic efficiency have been performed on the simple method which only changes benefits, expense and social discount rate. For this reason, this study analyze risks for investment of transport infrastructure demand forecast, and rise to the management practice for every particular item.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.102-108
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• 2008

This paper describes a mapping and localization based on wave algorithm[11] and Kalman filter for effective SLAM. Each robot in a multi robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robot scans actively seek to verify their relative locations. For simultaneous localization the algorithm which is well known as Kalman Filter (KF) is used. For modelling the robot position we wish to know three parameters (x, y coordinates and its orientation) which can be combined into a vector called a state variable vector. The Kalman Filter is a smart way to integrate measurement data into an estimate by recognizing that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate. In addition to an estimate of the state variable vector, the algorithm provides an estimate of the state variable vector uncertainty i.e. how confident the estimate is, given the value for the amount of error in it.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.219-230
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• 2015

It is difficult to make plans about the production schedule and volume of seasonal products due to the huge uncertainty in the prediction of their demands, which is why the amounts of carryover seasonal products increase after the peak season. Traditional models fail to meet the important requirements of production and stock plans related to the enhanced efficiency of logistics system due to the reduced value of carryover products by the disposal based on large discounts and deterioration, which poses considerable difficulties with actual problem solving. This study examined the stages of product storage from the specialized factory warehouses during a low season through the stores and the warehouses of local distribution centers during a high season to stock disposal and carryover product warehouses after a high season. The study developed a model for logistics rationalization plans to minimize carryover products by advance selling new products by subscription during a low season in anticipation of high season demands, increasing the accuracy of demands prediction, and making stable production plans, as well as demonstrated its excellence through numerical analysis.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.195.2-195.2
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• 2011

The borehole heat exchanger of Geothermal Heat Pump (GHP) system should be sustainable and cost effective for long term operation. To guaranty the performance of the system thermal Response Tests (TRTs) with simple recommended procedures have been applied in many countries. Korea government developed a standard TRT procedure in order to control the quality on GHP projects. In the TRT procedure interpretation method has a rule that data set has to be interpreted by the line source model(LSM). The LSM employes some assumptions that surrounding medium is homogeneous and the line source is infinite and constant heat flux, however real ground condition is unisotropic and heterogeneous, and showing regional or local ground water flows in many cases. We need to develope improved evaluation models to estimate accurate ground thermal conductivity with respect to geological and influence of ground water because current TRT standard test procedure has limitations to be applied for every locations and system. This study surveyed the uncertainty of the thermal parameters from the interpretation method considering different evaluation period. The interpretation of 208 TRT data sets represents limitations of LSM application that some obtained ground thermal conductivities are statistically unstable and convergence time of ground thermal conductivity over test period shows trends responding the length of test period. This evaluation study will be helpful to provide some effective procedure for the thermal parameter estimation and to complement current TRT standard procedure.

• Structural Engineering and Mechanics
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• v.20 no.3
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• pp.293-312
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• 2005

In this paper, free vibration of rotating beam with random properties is studied. The cross-sectional area, elasticity modulus, moment of inertia, shear modulus and density are modeled as random fields and the rotational speed as a random variable. To study uncertainty, stochastic finite element method based on second order perturbation method is applied. To discretize random fields, the three methods of midpoint, interpolation and local average are applied and compared. The effects of rotational speed, setting angle, random property variances, discretization scheme, number of elements, correlation of random fields, correlation function form and correlation length on "Coefficient of Variation" (C.O.V.) of first mode eigenvalue are investigated completely. To determine the significant random properties on the variation of first mode eigenvalue the sensitivity analysis is performed. The results are studied for both Timoshenko and Bernoulli-Euler rotating beam. It is shown that the C.O.V. of first mode eigenvalue of Timoshenko and Bernoulli-Euler rotating beams are approximately identical. Also, compared to uncorrelated random fields, the correlated case has larger C.O.V. value. Another important result is, where correlation length is small, the convergence rate is lower and more number of elements are necessary for convergence of final response.

• International Journal of Concrete Structures and Materials
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• v.19 no.1E
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• pp.11-16
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• 2007

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.4
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• pp.203-209
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• 2001

This paper develops a control method for some generic formation tasks of multiple mobile robots with inaccurate sensor information. Inaccurate sensor information means that all the robots have only local sensors that cannot accurately measure absolute distances and directions of objects. That is, all the sensors have limitation on the range, and uncertainty in the values. Therefore, more robust and reliable control logic is proposed and implemented. The logic is developed considering generic situations and increasing the number of robots participating in the formation. Petri nets are used for modeling and design of the control logic, which can visualize the control models and make it easy to check the states of each robot. Physically homogeneous mobile robots are designed and built to evaluate the developed logic. Each robot is equipped with eighteen infrared sensors and a UHF transceiver module. The experiment results are analyzed quantitatively by using the data of the relative distances and angles between the robots. And the trajectories of the robots during the formation are also evaluated. The developed control approach is demonstrated with experiments to be successful and efficient for the formation of autonomous mobile robots.