• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.781-788
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• 1999

In this paper, an expert system is proposed, which can be effectively applied to the large-scale systems with the diversity time constraints, the objectives and the unfixed system structure. The inference scheme of the expert system have the integrated structure composed of the intuitive inference module and logical inference module in order to support effectively the operating constraints of system. The intuitive inference module is designed using the pattern matching or pattern recognition method in order to search a same or similar pattern under the fixed system structure. On the other hand, the logical inference module is designed as the structure with the multiple inference mode based on the heuristic search method in order to determine the optimal or near optimal control strategies satisfing the time constraints for system events under the unfixed system structure, and in order to use as knowledge generator. Here, inference mode consists of the best-first, the local-minimum tree, the breadth-iterative, the limited search width/time method. Finally, the application results for large-scale distribution SCADA system proves that the inference scheme of the expert system is very effective for the large-scale system. The expert system is implemented in C language for the dynamic mamory allocation method, database interface, compatability.

• Fire Science and Engineering
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• v.24 no.4
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• pp.47-54
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• 2010

This study is analyze the damage of warehouse fire accident be made through the fire characteristic database of combustibles and real scale fire test of warehouse mock-up. Combustibles fire tests are carried out for database using RCT (Room Corner Tester) to predict fire growth the goods. A mockup ($3m{\times}3m{\times}2.4m$) of clothes warehouse was built and real scale fire test by LSC (Large Scale Calorimeter) base on the fire characteristic DB. The mock-up of clothes warehouse is made of two type sandwich panels (Glass wool, EPS foam sandwich panel). As a mock-up test result, test 1 (Glass wool sandwich panel) and test 2 (EPS foam sandwich panel) indicating fire growth such as 5 MW, 11 MW of maximum HRR (Heat Release Rate).

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.134-135
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• 2003

Abstract in case of the small scale power system separated from large scale power system N-2 contingency, the Over excitation, Under Frequency and Power swing Tripping Relay operate depending on unbalance Of Generation and Load. and then we studied and measured the angle and frequency deviation between generators. moreover, we compared real fault example with simulation results.

• Smart Structures and Systems
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• v.14 no.6
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• pp.1131-1150
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• 2014

One of the issues in extending the range of applicable problems of real-time hybrid simulation is the computation speed of the simulator when large-scale computational models with a large number of DOF are used. In this study, functionality of real-time dynamic simulation of MDOF systems is achieved by creating a logic circuit that performs the step-by-step numerical time integration of the equations of motion of the system. The designed logic circuit can be implemented to an FPGA-based system; FPGA (Field Programmable Gate Array) allows large-scale parallel computing by implementing a number of arithmetic operators within the device. The operator splitting method is used as the numerical time integration scheme. The logic circuit consists of blocks of circuits that perform numerical arithmetic operations that appear in the integration scheme, including addition and multiplication of floating-point numbers, registers to store the intermediate data, and data busses connecting these elements to transmit various information including the floating-point numerical data among them. Case study on several types of linear and nonlinear MDOF system models shows that use of resource sharing in logic synthesis is crucial for effective application of FPGA to real-time dynamic simulation of structural response with time step interval of 1 ms.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.25 no.11
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• pp.1616-1623
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• 2001

A basic experimental study has been carried out to find out the design parameters of fuel 2-staging atomizers in order to reduce nitrogen oxides(NOx) rate emitted from the steam boilers used the liquid fuel. The heavy fuel oil(B-Coil) and fuel 2-staging Y-jet twin-fluid atomizers were adopted in this study. The results of this paper were obtained from the real as well as the model scale atomizers. In the case of model atomizers test, NOx reduction rate was strongly dependent on the staged fuel rate, but it was weakly dependent on the injection hole arrangement and air swirl conditions. The real scale atomizers was designed and manufactured on the base of these test results, and those was mounted and operated in the real boiler generates 185 ton steam per an hour. The reduction rate of the model and real plant was reached 10∼30% of base NOx by atomizers. but dust was sharply increased in the low O$_2$combustion region of the real plant.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• Journal of Korea Multimedia Society
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• v.17 no.6
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• pp.733-740
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• 2014

With the development and spread of 3D display, users can easily experience an augmented reality with 3D features. Therefore, the demand for content of an augmented reality is exponentially growing in various fields. A traditional augmented reality environment was generally created by CG(Computer Graphics) modelling production tools. However, this method takes too much time and efforts to create an augmented environment. To create an augmented environment similar to the real world, everything in the real world should be measured, gone through modeling, and located in an augmented environment. But the time and efforts spent in the creation don't produce the same environment as the real world, making it hard for users to feel the sense of reality. In this study, multi-scale 3D panorama content augmented system is suggested by using a depth-map. By finding matching features from images to add 3D features to an augmented environment, a depth-map is derived and embodied as panorama, producing high-quality augmented content system with a sense of reality. With this study, limits of 2D panorama technologies will be overcome and a sense of reality and immersion will be provided to users with a natural navigation.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.3
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• pp.3-10
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• 2007

In general method to estimate the water supplies in the large-scale basin, indirect estimation method such as unit loading factor method has been used. However, the estimated water supplies are much different to the real water supplies used in the any basin because these general methods estimate them considering water supply demands only. Especially, water supplies for irrigation are big different to the real water supplies in which the water supplies for irrigation are depend on the weather conditions such as evaporation, basin conditions such as infiltration, the reservoir operation rule for irrigation water, and distribution methods. Thus, a new estimation method is developed to estimate the real water demands which is essential factors for the effective water resources operation in the basin. This method is for estimating the water supplies and return rates based on the survey of the irrigation reservoirs and the analysis of effects to the stream flows, return flows, and water supplies for irrigation which water supplies and return rates are used in the basin water management model. The water supply usages in each subbasin are validated by comparisons between the simulated discharges from the basin water management model and the discharges measured in the control points.

• Smart Structures and Systems
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• v.23 no.4
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• Structural Engineering and Mechanics
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• v.77 no.3
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• pp.305-314
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• 2021

In order to reduce enormous cost of real-scale underwater explosion experiments on ships, the mechanical response of the ships have been analyzed by combining scaled-down experiments and Hopkinson's scaling law. However, the Hopkinson's scaling law is applicable only if all variables vary in an identical ratio; for example, thickness of ship, size of explosive, and distance between the explosive and the ship should vary with same ratio. Unfortunately, it is infeasible to meet such uniform scaling requirement because of environmental conditions and limitations in manufacturing scaled model systems. For the facile application of the scaling analysis, we propose a generalized scaling law that is applicable for non-uniform scaling cases in which different parts of the experiments are scaled in different ratios compared to the real-scale experiments. In order to establish such a generalized scaling law, we conducted a parametric study based on numerical simulations, and validated it with experiments and simulations. This study confirms that the initial peak value of response variables in a real-scale experiment can be predicted even when we perform a scaled experiment composed of different scaling ratios for each experimental variable.