• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1273-1274
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• 2015

본 논문에서는 주파수 무관(frequency independence) 특성을 갖는 자기상보 구조(self-complementary structure)로 된 안테나의 급전선로(feed line)로 사용할 수 있는 초광대역 발룬(ultra-wideband balun)을 제안하였다. 발룬을 설계하기 위해 클로펜스타인 테이퍼(Klopfenstein taper)를 사용하여 임피던스 프로파일(impedance profile)을 구하고 이를 평행 스트립 선로(parallel strip line) 구조로 구현하기 위해 등각사상방법(conformal mapping method)을 이용했다. 설계한 발룬을 직접 제작하여 측정한 결과, 대역폭은 반사손실 -10 dB를 기준으로 할 때 0.45~10.53 GHz까지 10.08 GHz로 나타나, UHF(ultra high frequency) 통신대역에서 UWB(ultra-wideband) 통신대역에 이르기까지 매우 넓은 주파수 범위에서 자기상보형 안테나의 입력 임피던스 $188{\Omega}$을 $50{\Omega}$의 특성 임피던스에 정합시킬 수 있음을 확인하였다.

• Communications of the Korean Mathematical Society
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• v.25 no.4
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• pp.629-645
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• 2010

A metrical common fixed point theorem proved for a pair of self mappings due to Sastry and Murthy ([16]) is extended to symmetric spaces which in turn unifies certain fixed point theorems due to Pant ([13]) and Cho et al. ([4]) besides deriving some related results. Some illustrative examples to highlight the realized improvements are also furnished.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.471-477
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• 2018

As an important key technology using on electronic devices, face recognition has become one of the hottest technology recently. The traditional BP Neural network has a strong ability of self-learning, adaptive and powerful non-linear mapping but it also has disadvantages such as slow convergence speed, easy to be traversed in the training process and easy to fall into local minimum points. So we come up with an algorithm based on BP neural network but also combined with the PCA algorithm and other methods such as the elastic gradient descent method which can improve the original network to try to improve the whole recognition efficiency and has the advantages of both PCA algorithm and BP neural network.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.8
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• pp.752-757
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• 2000

The recursive formulae, which can self-reproduce the state transition graphs, of one-dimensional cellular automata rule 15 with two states (0 and 1) and four different boundary conditions were founded by categorical access. The categorical access makes the evolution process for cellular automata be expressed easily since it enables the mapping of automata between different domains.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.201-206
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• 1998

In this paper, we have considered the method for clustering land cover types over the East Asia from AVHRR data. The feature vectors such that maximum NDVI, amplitude of NDVI, mean NDVI, and NDVI threshold are extracted from the 10-day composite by maximum value composite(MVC) for reducing the effect of cloud contaninations. To find the land cover clusters given by the feature vectors, we are adapted the self-organizing feature map(SOFM) clustering which is the mapping of an input vector space of n-dimensions into a one - or two-dimensional grid of output layer. The approach is to find first the clusters by the first layer SOFM and then merge several clusters of the first layer to a large cluster by the second layer SOFM. In experiments, we were used the 8-km AVHRR data for two years(1992-1993) over the East Asia.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.9
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• pp.985-996
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• 1990

An inverse kinematic solution of a robot manipulator using multilayer perceptrons is proposed. Neural networks allow the solution of some complex nonlinear equations such as the inverse kinematics of a robot manipulator without the need for its model. However, the back-propagation (BP) learning rule for multilayer perceptrons has the major limitation of being too slow in learning to be practical. In this paper, a new algorithm named Dynamically Reconfiguring BP is proposed to improve its learning speed. It uses a modified version of Kohonen's Self-Organizing Feature Map (SOFM) to partition the input space and for each input point, select a subset of the hidden processing elements or neurons. A subset of the original network results from these selected neuron which learns the desired mapping for this small input region. It is this selective property that accelerates convergence as well as enhances resolution. This network was used to learn the parity function and further, to solve the inverse kinematic problem of a robot manipulator. The results demonstrate faster learning than the BP network.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.301-306
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• 2016

This paper presents the results of a study for robust self-localization and indoor slam using external cameras (such as a CCTV) and odometry of mobile robot. First, a position of mobile robot was estimated by using maker and odometry. This data was then fused with camera data and odometry data using an extended kalman filter. Finally, indoor slam was realized by applying the proposed method. This was demonstrated in the actual experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.524-529
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• 2008

Park et.al.[10] defined the intuitionistic fuzzy metric space in which it is a little revised in Park[4], and Park et.a1.[7] proved a fixed point theorem of Banach for the contractive mapping of a complete intuitionistic fuzzy metric space. In this paper, we will establish common fixed point theorem for four self maps in intuitionistic fuzzy metric space. These results have been used to obtain translation and generalization of Grabiec's contraction principle.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.939-942
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• 1996

This paper presents a stable learning algorithm for diagonal recurrent neural network(DRNN). DRNN is applied to a problem of controlling nonlinear dynamical systems. A architecture of DRNN is a modified model of the Recurrent Neural Network(RNN) with one hidden layer, and the hidden layer is comprised of self-recurrent neurons. DRNN has considerably fewer weights than RNN. Since there is no interlinks amongs in the hidden layer. DRNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. To guarantee convergence and for faster learning, an adaptive learning rate is developed by using Lyapunov function. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed algorithm is demonstrated by computer simulation.

• Communications of the Korean Mathematical Society
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• v.18 no.4
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• pp.653-659
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• 2003

Let $1{\;}\leq{\;}p{\;}\infty{\;}and{\;}{\alpha}{\;}>{\;}-1$. If f is a holomorphic self-map of the open unit disc U of C with f(0) = 0, then the quantity $\int_U\;\{\frac{$\mid$f'(z)$\mid$}{1\;-\;$\mid$f(z)$\mid$^2}\}^p\;(1\;-\;$\mid$z$\mid$)^{\alpha+p}dxdy$ is equivalent to the operator norm of the composition operator $C_f{\;}:{\;}B{\;}\rightarrow{\;}A^{p,{\alpha}$ defined by $C_fh{\;}={\;}h{\;}\circ{\;}f{\;}-{\;}h(0)$, where B and $A^{p,{\alpha}$ are the Bloch space and the weighted Bergman space on U respectively.