• Journal of the Korean Geotechnical Society
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• v.32 no.11
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• pp.31-42
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• 2016

Usually the seismic stability analysis of slope uses the pseudostatic analysis considering the inertial force by the earthquake as a static load. Geostructures such as slope include the uncertainty of soil properties. Therefore, it is necessary to consider probabilistic method for stability analysis. In this study, the probabilistic stability analysis of slope considering the uncertainty of soil properties has been performed. The fragility curve that represents the probability of exceeding limit state of slope as a function of the ground motion has been established. The Monte Carlo Simulation (MCS) has been implemented to perform the probabilistic stability analysis of slope with pseudostatic analysis. A procedure to develop the fragility curve by the pseudostatic horizontal acceleration has been presented by calculating the probability of failure based on the Artificial Neural Network (ANN) based response surface technique that reduces the required time of MCS. The results showed that the proposed method can get the fragility curve that is similar to the direct MCS-based fragility curve, and can be efficiently used to reduce the analysis time.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.43-49
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• 1996

This research proposes a system for speaker independent Korean continuous speech recognition with 247 DDD area names using keyword spotting technique. The applied recognition algorithm is the Dynamic Programming Neural Network(DPNN) based on the integration of DP and multi-layer perceptron as model that solves time axis distortion and spectral pattern variation in the speech. To improve performance, we classify word model into keyword model and non-keyword model. We make an experiment on postprocessing procedure for the evaluation of system performance. Experiment results are as follows. The recognition rate of the isolated word is 93.45% in speaker dependent case. The recognition rate of the isolated word is 84.05% in speaker independent case. The recognition rate of simple dialogic sentence in keyword spotting experiment is 77.34% as speaker dependent, and 70.63% as speaker independent.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.19-23
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• 1996

A full automatic interpretation of awake electroencephalogram (EEG) had been developed by the authors and presented at the past KACCs in series. The automatic EEG interpretation consists of four main parts: quantitative EEG interpretation, EEG report making, preprocessing of EEG data and adaptable EEG interpretation. The automatic EEG interpretation reveals essentially the same findings as the electroencephalographer's (EEG's), and then would be applicable in clinical use as an assistant tool for EEGer. The method had been developed through collaboration works between the engineering field (Saga University) and the medical field (Kyoto University). This work can be understood as an artificial realization of human expert skill. The procedure for the artificial realization was summarized in a methodology for artificial realization of human skill which will be applicable in other fields of systems control.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.365-370
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• 1997

In this paper we suggest a development of surface defect inspection algorithms for cold mill strip using tree structure. The defects which exist in a surface of cold mill strip have a scattering or singular distribution. This paper consists of preprocessing, feature extraction and defect classification. By preprocessing, the binarized defect image is achieved. In this procedure, Top-hit transform, adaptive thresholding, thinning and noise rejection are used. Especially, Top-hit transform using local min/max operation diminishes the effect of bad lighting. In feature extraction, geometric, moment, co-occurrence matrix, histogram-ratio features are calculated. The histogram-ratio feature is taken from the gray-level image. For the defect classification, we suggest a tree structure of which nodes are multilayer neural network clasifiers. The proposed algorithm reduced error rate comparing to one stage structure.

• The Journal of the Acoustical Society of Korea
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• v.17 no.3E
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• pp.12-21
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• 1998

In this paper, we describe the one channel five-way, V/U/M/N/S (Voice/Unvoice/Nasal/Silent), classification algorithm for automatically classifying speech. The decision making process is viewed as a pattern viewed as a pattern recognition problem. Two aspects of the algorithm are developed: feature selection and classifier type. The feature selection procedure is studied for identifying a set of features to make V/U/M/N/S classification. The classifiers used are a vector quantization (VQ), a neural network(NN), and a decision tree method. Actual five sentences spoken by six speakers, three male and three female, are tested with proposed classifiers. From a set of measurement tests, the proposed classifiers show fairly good accuracy for V/U/M/N/S decision.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.619-622
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• 2001

In this paper, an efficient approach to classification of transient and harmonic disturbances in power systems is proposed. First, the Stop-and-Go CA CFAR Detector is utilized to detect a disturbance from the power signals which are mixed with other disturbances and noise. Then, (i) Wigner Distribution, SVD(Singular Value Decomposition) and Fisher´s Criterion (ii) DWT and Fisher´s Criterion, are applied to extract an efficient feature vector. For the classification procedure, a combined neural network classifier is proposed to classify each corresponding disturbance class. Finally, the 10 class data simulated by Matlab power system blockset are used to demonstrate the performance of the proposed classification system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.165-168
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• 2002

In most shipyards, the measurement of 3-dimensional relative position of pipes should be connected in the block depends on the manual operation. It results a very tedious and inefficient procedure, thus the proper measurement system is needed to improve productivity and accuracy. This paper describes the development results of pipe measurement system including system concepts, measuring procedures, system calibration, and its accuracy and productivity. And also, the possibility and things to be improved for application in shipyard are discussed in this paper.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.527-529
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• 2006

In this paper, we find a robot's path using a Virtual Reality Modeling Language and overlay vision. For correct robot's path we describe a method for localizing a mobile robot in its working environment using a vision system and VRML. The robt identifies landmarks in the environment, using image processing and neural network pattern matching techniques, and then its performs self-positioning with a vision system based on a well-known localization algorithm. After the self-positioning procedure, the 2-D scene of the vision is overlaid with the VRML scene. This paper describes how to realize the self-positioning, and shows the overlap between the 2-D and VRML scenes. The method successfully defines a robot's path.

• The KSFM Journal of Fluid Machinery
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• v.10 no.3 s.42
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• pp.39-46
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• 2007

This study presents a numerical procedure to optimize the shape of staggered dimple surface to enhance turbulent heat transfer in a rectangular channel. The RBNN method is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis of fluid flow and heat transfer with shear stress transport (SST) turbulence model. The dimple depth-to-dimple print diameter (d/D), channel height-to-dimple print diameter ratio (H/D), and dimple print diameter-to-pitch ratio (D/S) are chosen as design variables. The objective function is defined as a linear combination of heat transfer related term and friction loss related term with a weighting factor. Latin Hypercube Sampling (LHS) is used to determine the training points as a mean of the design of experiment. The optimum shape shows remarkable performance in comparison with a reference shape.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.599-607
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• 2006

The ship radiated noise appear the various characteristic signals due to the mechanic system in the ship, the propeller and the interaction between ship body and sea water. Generally, it is classified two main components: the speed dependent signal and the speed independent signal. It is required that very complex procedure to classify the signal origin from the ship-radiated noise. This paper presents techniques to automatically detect and classify the tonal signals ken the ship-radiated noise, using the Q factor and the neural network.