• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.54
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• pp.155-165
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• 2000

The fault diagnosis is a systematic and unified method to find based on the observing data resulting in noises. This paper presents the fault prediction and diagnosis using fuzzy expert system technique to manipulate the uncertainties efficiently in predictive perspective. We apply a fuzzy event tree analysis to the computer system, and build up the fault prediction and diagnosis using fuzzy expert system that predicts and diagnoses the error of the system in the advance of error.

• Korean Journal of Computational Design and Engineering
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• v.11 no.3
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• pp.223-233
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• 2006

Layered manufacturing(LM) is emerging as a new technology that enables the fabrication of three dimensional heterogeneous objects such as Multi-materials and Functionally Gradient Materials (FGMs). Among various types of heterogeneous objects, more attention has recently paid on the fabrication of FGMs because of their potentials in engineering applications. The necessary steps for LM fabrication of FGMs include representation and process planning of material information inside an FGM. This paper introduces a new process planning algorithm that takes into account the processing of material information. The detailed tasks are discretization (i.e., decomposition-based approximation of volume fraction), orientation (build direction selection), and adaptive slicing of heterogeneous objects. In particular, this paper focuses on the discretization process that converts all of the material information inside an FGM into material features like geometric features. It is thus possible to choose an optimal build direction among various pre-selected ones by approximately estimating build time. This is because total build time depends on the complexity of features. This discretization process also allows adaptive slicing of heterogeneous objects to minimize surface finish and material composition error. In addition, tool path planning can be simplified into fill pattern generation. Specific examples are shown to illustrate the overall procedure.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.465-468
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• 2001

An aerostatic stage has frictionless behavior, so it has a advantage of investigation into positioning characteristics. A one-dimensional aerostatic ceramic stage with ballscrew driven and laser scale feedback system is manufactured, aiming at investigating positioning characteristic of ultra-precision stage. We confirm, this ceramic aerostatic stage has a 10nm micro resolution, and can be reduced mean of position error by compensation of numeric control command. By means of analyzing relationship of position error and change of temperature, we build a on-line compensation algorithm of position error from the measured temperature data. So we can improve repeatability of ultra-precision stage up to 34%($0.095{\mu}$) of the normal condition.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.30-31
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• 2021

This study aims to build a deep learning model that can predict the value of concrete mixing properties according to a given concrete strength value. A model was created for a total of 1,291 concrete data, including 8 characteristics related to concrete mixing elements and environment, and the compressive strength of concrete. As the deep learning model, DNN-3L-256N, which showed the best performance on the prior study, was used. The average value for each characteristic of the data set was used as the initial input value. In results, in the case of 'curing temperature', which had a narrow range of values in the existing data set, showed the lowest error rate with less than 1% error based on MAE. The highest error rate with an error of 12 to 14% for fly and bfs.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.536-538
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• 2006

The most important factor of mobile robot is to build a map for surrounding environment and estimate its localization. This paper proposes a real-time localization and map building method through 3-D reconstruction using scale invariant feature from monocular camera. Mobile robot attached monocular camera looking wall extracts scale invariant features in each image using SIFT(Scale Invariant Feature Transform) as it follows wall. Matching is carried out by the extracted features and matching feature map that is transformed into absolute coordinates using 3-D reconstruction of point and geometrical analysis of surrounding environment build, and store it map database. After finished feature map building, the robot finds some points matched with previous feature map and find its pose by affine parameter in real time. Position error of the proposed method was maximum. 8cm and angle error was within $10^{\circ}$.

• Journal of the Korean Society of Systems Engineering
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• v.12 no.2
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• pp.115-125
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• 2016

An improved RRS (Reactor Regulating System) logic is implemented in this work using systems engineering approach along with GA (Genetic Algorithm) deemed as providing an optimal solution to a given system. The current system works desirably and has been contributed to the safe and stable NPP operation. However, during the ascent and decent section of the reactor power, the RRS output reveals a relatively high steady state error and the output also carries a considerable level of overshoot. In an attempt to consolidate conservatism and minimize the error, this research proposes applying genetic algorithm to RRS and suggests reconfiguring the system. Prior to the use of GA, reverse-engineering is implemented to build a Simulink-based RRS model and re-engineering is followed to apply the GA and to produce a newly-configured RRS generating an output that has a reduced steady state error and diminished overshoot level.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1089-1092
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• 1999

Applications using 3D models are increasing recently. Since 3D polygonal models are structured by a triangular mesh, the coding of polygonal models in strips of triangles is an efficient way of representing the data. These strips may be very long, and may take a long time to render or transmit. If the triangle strips are partitioned, it may be possible to perform more efficient data transmission in an error-prone environment and to display the 3D model progressively. In this paper, we devised the Component Based Data Partitioning (CODAP) which is based on Topological Surgery (TS). In order to support the error resilience and the progressively build-up rendering, we partition the connectivity, geometry, and properties of a 3D polygonal model. Each partitioned component is independently encoded and resynchronization between partitioned components is done.

• Journal of KSNVE
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• v.10 no.1
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• pp.107-116
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• 2000

The gear whine noise caused by tooth profile, elastic deformation, machining error, wear is directly correlated with the transmission error of mating gear. It is very important to build up the synthesized countermeasure by the modeling of the excitation forces and analyzing the vibratory characteristics. The mathematical models on the elements of vehicle transmission which is composed of helical gears, bearings, shafts and cases are developed. The elements are assembled by the substructure synthesis method. The cases of transmission are modeled by ANSYS. The system model of vehicle transmission is also verified by the experiments.

• Journal of computational fluids engineering
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• v.17 no.3
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• pp.75-86
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• 2012

We developed a preliminary CFD analysis methodology to predict a pressure build up due to hydrogen flame acceleration in the APR1400 IRWST on the basis of CFD analysis results for test data of hydrogen flame acceleration in a scaled-down test facility performed by Korea Atomic Energy Research Institute. We found out that ANSYS CFX-13 with a combustion model of the so-called turbulent flame closure and a model constant of A = 5.0, a grid model with a hexahedral cell length of 5.0 mm, and a time step size of $1.0{\times}10^{-5}$ s can be a useful tool to predict the pressure build up due to the hydrogen flame acceleration in the test results. Through the comparison of the simulated results with the test results, we found out that the proposed CFD analysis methodology enables us to predict the peak pressure within an error range of about ${\pm}29%$ for the hydrogen concentration of 19.5%. However, the error ranges of the peak pressure for the hydrogen concentration of 15.4% and 18.6% were about 66% and 51%, respectively. To reduce the error ranges in case of the hydrogen concentration of 15.4% and 18.6%, some uncertainties of the test conditions should be clarified. In addition, an investigation for a possibility of flame extinction in the test results should be performed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.534-539
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• 2010

In genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future observations. There are three inherent steps to build classifiers: a significant gene selection, model selection and prediction assessment. In the paper, with a focus on prediction assessment, we normalize microarray data with quantile-normalization methods that adjust quartile of all slide equally and then design a system comparing several methods to estimate 'true' prediction error of a prediction model in the presence of feature selection and compare and analyze a prediction error of them. LOOCV generally performs very well with small MSE and bias, the split sample method and 2-fold CV perform with small sample size very pooly. For computationally burdensome analyses, 10-fold CV may be preferable to LOOCV.