• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.93-99
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• 2004

Neural oscillator can be applied to oscillatory systems such as analyses of image information, voice recognition and etc. Conventional EBPA (Error back Propagation Algorithm) is not proper for oscillatory systems with the complicate input`s patterns because of its tedious training procedures and sluggish convergence problems. However, these problems can be easily solved by using a synchrony characteristic of neural oscillator with PLL(Phase Locked Loop) function and by using a simple Hebbian learning rule. Therefore, in this paper, a technique for Recognition of the Korean Alphabet using Phase Synchronized Neural Oscillator was introduced.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.45-48
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• 2000

A typical map image is consisted of number, symbols and lines that represent contour lines, geographical bounaries, roads, rivers and etc. This paper describe the separation. The separation algorithm is based on structural relation. The separation of proposed method is better than method using only a size of connected component. This proposed method will be used in GIS input system efficiently.

• International Journal of Safety
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• v.10 no.1
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• pp.22-31
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• 2011

Seismic response control method of the bridge structures with semi-active control device, i.e., magneto-rheological (MR) damper, is studied in this paper. Design of various kinds of clipped optimal controller and fuzzy controller are suggested as a semi-active control algorithm. For determining the control force of MR damper, clipped optimal control method adopts bi-state approach, but the fuzzy control method continuously quantifies input currents through fuzzy inference mechanism to finely modulate the damper force. To investigate the performances of the suggested control techniques, numerical simulations of a multi-span continuous bridge system subjected to various earthquakes are performed, and their performances are compared with each other. From the comparison of results, it is shown that the fuzzy control system can provide well-balanced control force between girder and pier in the view point of structural safety and stability and be quite effective in reducing both girder and pier displacements over the existing control method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.125-129
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• 2012

In this paper, we propose an intelligent method to surveil moving objects and to judge an unusual-behavior by using network cameras. To surveil moving objects, the Scale Invariant Feature Transform (SIFT) algorithm is used to characterize the feature information of objects. To judge unusual-behaviors, the virtual human skeleton is used to extract the feature points of a human in input images. In this procedure, the Principal Component Analysis (PCA) improves the accuracy of the feature vector and the fuzzy classifier provides the judgement principle of unusual-behaviors. Finally, the experiment results show the effectiveness and the feasibility of the proposed method.

• Journal of Power Electronics
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• v.19 no.6
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• pp.1477-1485
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• 2019

This paper proposes an effective model predictive current control (MPCC) that involves using 10 virtual voltage vectors to reduce the current harmonics and common-mode voltage (CMV) for a two-level five-phase voltage source inverter (VSI). In the proposed scheme, 10 virtual voltage vectors are included to reduce the CMV and low-order current harmonics. These virtual voltage vectors are employed as the input control set for the MPCC. Among the 10 virtual voltage vectors, two are applied throughout the whole sampling period to reduce current ripples. The two selected virtual voltage vectors are based on location information of the reference voltage vector, and their duration times are calculated using a simple algorithm. This significantly reduces the computational burden. Simulation and experimental results are provided to verify the effectiveness of the proposed scheme.

• Geomechanics and Engineering
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• v.1 no.4
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• pp.307-321
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• 2009

The paper employs a feed forward neural network with back-propagation algorithm for modeling time dependent swell in clays containing carbonate in the presence of sulfuric acid. The oedometer swell percent is estimated at a nominal surcharge pressure of 6.25 kPa to develop 612 data sets for modeling. The input parameters used in the network include time, sulfuric acid concentration, carbonate percentage, and liquid limit. Among the total data sets, 280 (46%) were assigned to training, 175 (29%) for testing and the remaining 157 data sets (25%) were relegated to cross validation. The network was programmed to process this information and predict the percent swell at any time, knowing the variable involved. The study demonstrates that it is possible to develop a general BPNN model that can predict time dependent swell with relatively high accuracy with observed data ($R^2$=0.9986). The obtained results are also compared with generated non-linear regression model.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.31-34
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• 2021

The classical HMM is defined by a parameter triple �� = (��, A, B), where each parameter represents a collection of probability distributions: initial state, state transition and output distributions in order. This paper proposes a new stationary parameter e = (e1, e2, …, eN) where N is the number of states and et = P(|xt = i, y) for describing how an input pattern y ends in state xt = i at time t followed by nothing. It is often said that all is well that ends well. We argue here that all should end well. The paper sets the framework for the theory and presents an efficient inference and training algorithms based on dynamic programming and expectation-maximization. The proposed model is applicable to analyzing any sequential data with two or more finite segmental patterns are concatenated, each forming a context to its neighbors. Experiments on online Hangul handwriting characters have proven the effect of the proposed augmentation in terms of highly intuitive segmentation as well as recognition performance and 13.2% error rate reduction.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.6
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• pp.895-901
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• 1986

This paper presents direct adaptive controllers for single-input single-output nonminimum phase systems based on PID structures. Also, characteristics of these schemes are compared, and convergence properties are considered. In these schemes, controller parameters are estimated from the least-square algorithm and some additional auxiliary parameters are obtained from the proposed polynomial identity which is derived from the pole placement equation and the Bezout identity. The effectiveness of these schemes is demonstrated by computer simulation that has been carried out for a very difficult example.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.5
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• pp.631-636
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• 1986

The self-tuning control theory which has so far been studied has the type of a moving average noise mode. In this paper we propose a self-tuning munimum varinace control of the plant with an autoregressive noise model. New identities are introduced to find a munimum variance control input, and the stability and convergence properties in a closed loop system are studied using the BIBO concepts and ODE method. Also the proposed algorithm is compared withe that of the original self-tuning control by computer simulation.