• Journal of the Korean Institute of Intelligent Systems
• /
• v.2 no.1
• /
• pp.17-32
• /
• 1992

The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manupulator.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.5
• /
• pp.641-647
• /
• 2009

In this paper, a new filtering scheme for adaptive cruise control (ACC) system is presented. In the proposed scheme, the identification of the mode of the preceding vehicle is considered as a classification problem and it is done by a neural network classifier. The neural network classifier outputs a posterior probability of the mode of the preceding vehicle and the probability is directly used in the IMM framework. Finally, ten scenarios are made and the proposed NIMM is tested on them to show its validity.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.5
• /
• pp.915-920
• /
• 2007

Using the existing algorithms that estimate the background noise, the detection probability for the week tonals is low and for the even week tonals, there is a limit not detected. Therefore it is required to algorithms which can improve the performance of the tonal extraction. Recently, many researches using artificial neural networks in sonar signal processing are performed. We propose a neural network with double feedback that can remove automatically the background noise and detect the even week tonals buried in background noise, therefore not detected by growing the week tonals lastingly for a certain time. For the real underwater target, experiments for the tonal extraction are performed by using the existing algorithms that estimate the background noise and the proposed neural network. As a result of the experiment, a method using the proposed neural network showed the better performance of the tonal extraction in comparison with the existing algorithms.

• Proceedings of the KIEE Conference
• /
• 2003.07d
• /
• pp.2149-2151
• /
• 2003

본 논문에서는 마이컴(PIC16C74)과 Tabu 탐색법 및 지능기법(퍼지 및 신경회로망)을 이용하여 고정밀 제어 및 강인한 제어 성능을 가지는 초소형 DC 모터용 지능형 제어기를 개발하였다. 이를 위해 마이컴(PIC16C74)를 이용한 지능형 제어 알고리즘을 개발하고, 초소형 DC 모터용 드라이브 회로 설계 및 제작하였다. 개발한 초소형 DC 모터 지능형 제어기는 디지털 자동 용접캐리지에 적용할 예정이며, 다른 응용 분야로써는 자동배수장치, 반도체 분야, 산업용 로봇 분야 및 조립자동화 시스템 분야 등에 사용되는 구동모터에 적용함으로서 정밀도와 외부의 잡음에 대한 영향을 경감시켜 안정성과 효율향상 및 에너지절약이 가능할 것이다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.5
• /
• pp.509-514
• /
• 2015

In this paper, we introduce a new pattern classifier which is based on the learning algorithm of Extreme Learning Machine the sort of artificial neural networks and fuzzy set theory which is well known as being robust to noise. The learning algorithm used in Extreme Learning Machine is faster than the conventional artificial neural networks. The key advantage of Extreme Learning Machine is the generalization ability for regression problem and classification problem. In order to evaluate the classification ability of the proposed pattern classifier, we make experiments with several machine learning data sets.

• Journal of the Korea Concrete Institute
• /
• v.20 no.4
• /
• pp.513-522
• /
• 2008

The fiber dispersion performance in fiber-reinforced cementitious composites is a crucial factor with respect to achieving desired mechanical performance. However, evaluation of the fiber dispersion performance in the composite PVA-ECC (Polyvinyl alcohol-Engineered Cementitious Composite) is extremely challenging because of the low contrast of PVA fibers with the cement-based matrix. In the present work, an enhanced fiber detection technique is developed and demonstrated. Using a fluorescence technique on the PVA-ECC, PVA fibers are observed as green dots in the cross-section of the composite. After capturing the fluorescence image with a Charged Couple Device (CCD) camera through a microscope. The fibers are more accurately detected by employing a series of process based on a categorization, watershed segmentation, and morphological reconstruction.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.38 no.3
• /
• pp.61-67
• /
• 2001

In this paper, a method that apply LP algorithm to image for speech recognition is suggested, using both speech and image information for recogniton of korean numeral speech. The input speech signal is pre-emphasized with parameter value 0.95, analyzed for B th LP coefficients using Hamming window, autocorrelation and Levinson-Durbin algorithm. Also, a gray image signal is analyzed for 2-dimensional LP coefficients using autocorrelation and Levinson-Durbin algorithm like speech. These parameters are used for input parameters of neural network using back-propagation algorithm. The recognition experiment was carried out at each noise level, three numeral speechs, '3','5', and '9' were enhanced. Thus, in case of recognizing speech with 2-dimensional LP parameters, it results in a high recognition rate, a low parameter size, and a simple algorithm with no additional feature extraction algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.10
• /
• pp.1991-2000
• /
• 1994

In this paper, an alagorithm with six types of classification is suggested for the recognition system of hand-written Korean characters. After thinning process and truncating process for noise redection. The input images are used generalized by $64\times64$ size. The six type classification is composed of preliminary and secondary classification process by using the learning algoritm of multi-layer perceptron. Subblock Hough transform is used as local feature and sampling Hough transform is used as global feature. Experiment is conducted for 1800 characters which is written 31 times per each type by 10 persons. The 90% recognition rate is resulted by the preliminary classification of detection the final consonant and by the secondary classification of detecting the vowels.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.18 no.4
• /
• pp.112-117
• /
• 2004

This paper describes the analysis of the UHF signal characteristics due to the partial discharge sources which can exist in a GIS. For the experiment, a model GIS was made and 5 types of discharge source were created as follows; corona discharge, surface discharge, void discharge, discharge due to free particle, discharge from floating electrode. The frequency spectra and the phase characteristics of UHF signals were induced by UHF signal analysis. The results were quantified to systematically adapt to analyze the PD sources in the GIS and utilized as algorithm data based on the neural network for Back-Propagation Algorithm with a multi-layer structure. The perception rate of the constructed algorithm showed approximately 94[%] and 82[%] in learning and testing data, respectively.

• The Journal of the Korea Contents Association
• /
• v.9 no.7
• /
• pp.141-148
• /
• 2009

Recently there have been many research efforts focused on imbalanced data classification problems, since they are pervasive but hard to be solved. Approaches to the imbalanced data problems can be categorized into data level approach using re-sampling, algorithmic level one using cost functions, and ensembles of basic classifiers for performance improvement. As an algorithmic level approach, this paper proposes to use multilayer perceptrons with higher-order error functions. The error functions intensify the training of minority class patterns and weaken the training of majority class patterns. Mammography and thyroid data-sets are used to verify the superiority of the proposed method over the other methods such as mean-squared error, two-phase, and threshold moving methods.