• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.6
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• pp.37-45
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• 2008

It is expected that conditions of water resources will be changed in Korea in accordance with world wide climate change. In order to deal with this problem and find a way of minimizing the effect of future climate change, the usefulness of climate model simulation information is examined in this study. The objective of this study is to assess the applicability of GCM (General Circulation Model) information for Korean water resources management through uncertainty analysis. The methods are based on probabilistic measures of the effectiveness of GCM simulations of an indicator variable for discriminating high versus low regional observations of a target variable. The formulation uses the significance probability of the Kolmogorov-Smirnov test for detecting differences between two variables. An estimator that accounts for climate model simulation and spatial association between the GCM data and observed data is used. Atmospheric general circulation model (AGCM) simulations done by ECMWF (European Centre for Medium-Range Weather Forecasts) with a resolution of $2^{\circ}{\times}2^{\circ}$, and METRI (Meteorological Research Institute, Korea) with resolutions of $2^{\circ}{\times}2^{\circ}$ and $4^{\circ}{\times}5^{\circ}$, were used for indicator variables, while observed mean areal precipitation (MAP) data, discharge data and mean areal temperature data on the seven major river basins in Korea were used for target variables. The results show that GCM simulations are useful in discriminating the high from the low of the observed precipitation, discharge, and temperature values. Temperature especially can be useful regardless of model and season.

• Journal of Biomedical Engineering Research
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• v.32 no.3
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• pp.237-244
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• 2011

In this paper, a feature-based registration technique is proposed for pre-contrast and post-contrast lung CT images. It utilizes three dimensional(3-D) features with their descriptors and estimates feature correspondences by nearest neighborhood matching in the feature space. We design a transformation model between the input image pairs using a free form deformation(FFD) which is based on B-splines. Registration is achieved by minimizing an energy function incorporating the smoothness of FFD and the correspondence information through a non-linear gradient conjugate method. To deal with outliers in feature matching, our energy model integrates a robust estimator which discards outliers effectively by iteratively reducing a radius of confidence in the minimization process. Performance evaluation was carried out in terms of accuracy and efficiency using seven pairs of lung CT images of clinical practice. For a quantitative assessment, a radiologist specialized in thorax manually placed landmarks on each CT image pair. In comparative evaluation to a conventional feature-based registration method, our algorithm showed improved performances in both accuracy and efficiency.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.84-91
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• 2009

This paper presents an online partial 3D modeling methodology that uses a mobile augmented reality system and aerial photographs, and a tracking methodology that compares the 3D model with a video image. Instead of relying on models which are created in advance, the system generates a 3D model for a real building on the fly by combining frontal and aerial views. A user's initial pose is estimated using an aerial photograph, which is retrieved from a database according to the user's GPS coordinates, and an inertial sensor which measures pitch. We detect edges of the rooftop based on Graph cut, and find edges and a corner of the bottom by minimizing the proposed cost function. To track the user's position and orientation in real-time, feature-based tracking is carried out based on salient points on the edges and the sides of a building the user is keeping in view. We implemented camera pose estimators using both a least squares estimator and an unscented Kalman filter (UKF). We evaluated the speed and accuracy of both approaches, and we demonstrated the usefulness of our computations as important building blocks for an Anywhere Augmentation scenario.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.154-160
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• 2006

There wasn't very successful practice cases for the quantity take-off based on the CAD system since the CAD system is used in the construction industry more than 20 years in Korea. It was also not easy to use 3D CAD system in design management and cost management in the construction industry although 3D CAD system is very successful in the manufacturing industry for those areas recently. It is important to construct 3D libraries and to supply those libraries for the designers in time. Architectural work is a kind of creative work. So, Architects like to create their own model. Unlike the manufacturing industry, 3D CAD system can not be survived in the construction industry without new 3D objects supply in the right time. Moreover, the estimation system for 3D must support the schematic design phase, detailed design phase and construction design phase. The product called "Constructor" of Graphisoft consist of modeller, estimator and scheduler based on 3D model. We applied the system to a real project and compared the estimation result and we made a very successful case study.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.423-426
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• 2002

The foundation for developing a cost estimation system based on historical data has been being prepared in Korea. Historical data is a priori of developing a cost estimation model. Cost Index, one of the historical data, is used to estimate construction cost and to adjust the amount of contract money in the foreign country, whereas it is not used in domestic except for the road construction project in Korea. Construction cost indices can be used by an estimator in tender analysis, pricing, price adjustment, cost planning, and forecasting. In this regards, this paper identified the problems in developing Cost Index evaluation process by comparing the standard of framing Cost Index used in British to the one used in Korea. Then, the scheme for improving a Cost Index required for Site Developing Construction was proposed. Twenty-two cases of engineering estimate data were used to compare the domestic standard to the foreign one in deriving a Cost Index.

• Journal of Advanced Navigation Technology
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• v.4 no.2
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• pp.162-170
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• 2000

Maneuvering targets are difficult for the Kalman filter to track since the target model of tracking filter might not fit the real target trajectory and the statistical characteristics of the target maneuver are unknown in advance. In order to track such a wildly maneuvering target, several schemes had been proposed and improved the tracking performance in some extent. In this paper a Kalman filter-based scheme is proposed for maneuvering target tracking. The proposed scheme estimates the target acceleration input vector directly from the feature of maneuvering target trajectories and updates the simple Kalman tracker by use of the acceleration estimates. Simulation results for various target profiles are analyzed for a comparison of the performances of our proposed scheme with that of conventional trackers.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.130-135
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• 2004

This paper presents a new design methods of the short-term load forecasting system (STLFS) using the data mining. The structure of the proposed STLFS is divided into two parts: the Takagi-Sugeno (T-S) fuzzy model-based classifier and predictor The proposed classifier is composed of the Gaussian fuzzy sets in the premise part and the linearized Bayesian classifier in the consequent part. The related parameters of the classifier are easily obtained from the statistic information of the training set. The proposed predictor takes form of the convex combination of the linear time series predictors for each inputs. The problem of estimating the consequent parameters is formulated by the convex optimization problem, which is to minimize the norm distance between the real load and the output of the linear time series estimator. The problem of estimating the premise parameters is to find the parameter value minimizing the error between the real load and the overall output. Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.6
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• pp.403-410
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• 2008

This paper suggests a standstill estimator to accurately identify induction motor (IM) parameters necessary for the vector control. A mathematical model that faithfully represents the general skin effect is introduced. Then, two exciting signals with a different frequency are sequentially injected to track the parameters based on the skin effect of the rotor bar. Little knowledge of the unknown motor allows the proposed methodology to employ a closed-loop control of an injected current, rather than open-loop voltage injection approaches. Subsequently, this control scheme proactively prevents electrical accidents resulting from an inadequate open-loop voltage injection. We develop a specialized offline commissioning test to compensate the phase delay resulting from the drive, which significantly affects the precision of the IM parameters. The effectiveness of the identification technique is validated by means of experiments performed on the three different IMs.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.1
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• pp.81-90
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• 2002

This paper presents an Implementation of digital high-performance position sensorless control system of Reluctance Synchronous Motor(RSM) drives with Direct Torque Control(DTC). The system consists of stator flux observer, speed and torque estimator, two digital hysteresis controllers, an optimal switching look-up table, Insulated Gate Bipolar Transistor(IGBT) voltage source inverter, and TMS320C31 DSP board. The stator flux observer Is based on the combined voltage and current model with stator flux feedback adaptive control of which inputs are current and voltage sensed on motor terminal for wide speed range. In order to prove the suggested sensorless control algorithm for industrial field application, we have some simulation and actual experiment at low and high speed range. The developed high-performance speed control by fully digital system are shown a good response characteristic of control results and high performance features using 1.0[kW] RSM having 2.57 reluctance ratio of $L_d/L_q$.