• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.6
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• pp.101-108
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• 1998

An efficient approach to recognize occluded objects is to detect a number of essential features on the boundary of the unknown shape. The salient points including corner points, tangential points and inflection points are detected by the relation of neighboring pixels of each pixel on the boundaries. Corner points are usually detected in the curvature function and tangential points and inflection points are detected by median filtering the curvature function to avoid the effect of quantization noise as corner points is not sufficient to represent an object with lines and arcs. Then, these salient points are used as features for object matching. Discrete Hopfield Neural Network is used for object matching. Experimental results show that the matching result using salient points is better than those of using corner points only when an object consists of lines and arcs.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2003.05a
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• pp.87-91
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• 2003

It has well known that MUSIC and ESPRIT algorithms estimate angle of arrival(AOA) with high resolution by eigenvalue decomposition of the covariance matrix which were obtained from the array antennas. However, the disadvantage of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. The other problem of MUSIC and ESRPIT is to require calibrated antennas with uniform features, and are sensitive to the manufacturing facult and other physical uncertainties. To overcome these disadvantages, several method using neural model have been study. For multiple signals, those require huge training data prior to AOA estimation. This paper proposes the algorithm for AOA estimation by interconnected hopfield neural model. Computer simulations show the validity of the proposed algorithm. The proposed method does not require huge training procedure and only assigns interconnected coefficients to the neural network prior to AOA estimation.

• Journal of Navigation and Port Research
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• v.27 no.4
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• pp.465-469
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• 2003

It has well known that MUSIC and ESPRIT algorithms estimate angle of arrival(AOA) with high resolution by eigenvalue decomposition of the covariance matrix which were obtained from the array antennas, However, the disadvantage of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. the other problem of MUSIC and ESPRIT is to require calibrated antennas with uniform features, and are sensitive ti the manufacturing fault and other physical uncertainties. To overcome these disadvantages, several method using neural model have been study. For multiple signals, those methods require huge training data prior to AOA estimation. This paper proposes the algorithm for AOA estimation by interconnected Hopfield neural model. Computer simulations show the validity of the proposed algorithm. It follows that the proposed method yields better AOA estimates than MUSIC. Moreover, out method does not require huge training procedure and only assigns interconnected coefficients to the neural network prior to AOA estimation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.3
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• pp.283-291
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• 2002

Since GPS's SA cancellation, the interest is converged in correction of errors such as atmospheric delay and multipath that weight had been small relatively, which can improve the accuracy of positioning through modelling research. The aim of this study have an extensive comparison of the various tropospheric delay models (Goad&Goodman, A&K, Hopfield and Sasstamoinen) and mapping functions(Niell, Chao, and Marini). Expecially, the tropospheric delay amounts by change of the GPS satellite elevations, and the delay by various combination between zenith delay models and mapping functions, compared and examined. For this, programmed the total delay models and the combined models which can be described as a product of the delay at the zenith and a mapping function. The result of study, especially, as the minimum elevation of included data is reduced under $10^{\circ}$, it was considered to be reasonable that the prediction of tropospheric delay considering combination and mapping character of functions about the transition of the zenith delay to a delay with arbitrary zenith angle.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.587-591
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• 2003

This study develops a neural network for solving optimization problems. Hopfield network has been used for such problems, but it frequently gives abnormal solutions or non-optimal solutions. Moreover, it takes much time for solving a solution. To overcome such disadvantages, this study adopts a neural network whose output nodes change with a small value at every evolution, and the proposed neural network is applied to solve ALB (Assembly Line Balancing) problems . Given a precedence diagram and a required number of workstations, an ALB problem is solved while achieving even distribution of workload among workstations. Here, the workload variance is used as the index of workload deviation, and is reflected to an energy function. The simulation results show that the proposed neural network yields good results for solving ALB problems with high success rate and fast execution time.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.254-257
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• 2002

In previous study about combinatorial optimization problem solver by using neural network, since Hopfield method, to converge into the optimum solution sooner and certainer is regarded as important. Namely, only static states are considered as the information. However, from a biological point of view, the dynamical system has lately attracted attention. Then we propose the "dynamical" combinatorial optimization problem solver using hysteresis neural network. In this article, the proposal system is evaluated by the N-Queen problem.

• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.201-205
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• 1993

A decision-theoretic concept is introduced to investigate whether targets of interest in array sensor systems are present at some steering direction or not. The solutions to this problem are described as a set of simple numbers 0 or 1 corresponding to the direction under consideration. This coded number representation is transplanted in the optimisation technique based on the Hopfield neural network, which may provide a new aspect of determining the direction of arrival (DOA) of sources. To cast the perspectives of the proposed approach and illustrate its effectiveness in source direction finding in array sensor systems, simulation results and related discussions are presented in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.325-330
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• 2001

It has been realized that the results of circuit level simulation of neural networks, used for optimization problems, arc much different from those of algorism level simulation. In other words, the outputs converges asymptotically as time elapes, however, the input convergence depends on the value of parasitic conductance connected between input node and ground. Also, this conductance affects system performance. This paper discusses the influence of input conductance on the convergece of the continuous Hopfield neural networks. The convergence has been analyzed for the input and output nodes of neurons. Also, the characteristics of equilibrium points has been analyzed depending on different values of the input conductance.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.333-336
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• 1991

In this paper, Hopfield & Tank model-like artificial neural network structure is proposed, which can be used for the optimal path planning problems such as the unit commitment problems or the maintenance scheduling problems which have been solved by the dynamic programming method or the branch and bound method. To construct the structure of the neural network, an energy function is defined, of which the global minimum means the optimal path of the problem. To avoid falling into one of the local minima during the optimization process, the simulated annealing method is applied via making the slope of the sigmoid transfer functions steeper gradually while the process progresses. As a result, computer(IBM 386-AT 34MHz) simulations can finish the optimal unit commitment problem with 10 power units and 24 hour periods (1 hour factor) in 5 minites. Furthermore, if the full parallel neural network hardware is contructed, the optimization time will be reduced remarkably.

• Proceedings of the Materials Research Society of Korea Conference
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• 2003.11a
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• pp.157-157
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• 2003

수평 전기로에서 CdIn2Te4 다결정을 용융법으로 합성하고 Bridgman법으로 tetragonal structure의 c축에 평행한 CdIn2Te4 단결정을 성장시켰다. c축에 평행한 시료의 광흡수와 광전류 spectra를 293K에서 10K까지 측정하였다. 광흡수 spectra에 의해 band gap Eg(T)는 varshni공식에 따라 계산한 결과 1.4753eV-(7.78$\times$$10^{-3}$eV/K)T$^2$/(T+2155K)임을 확인하였다. Hall 효과는 van der Pauw 방법에 의해 측정되었으며, 온도에 의존하는 운반자 농도와 이동도는 293K에서 각각 9.01$\times$$10^{16}$ /㎤, 219 $\textrm{cm}^2$/V.S였다. 광전류 스펙트럼으로부터 Hamilton matrix(Hopfield quasicubic mode)법으로 계산한 결과 crystal field splitting $\Delta$cr값이 0.2704 eV이며 spin-orbit $\Delta$so 값은 0,1465 eV임을 확인하였다. 10K일 때 광전류 봉우리들은 n=1일때 Al-, Bl-와 Cl-exciton 봉우리임을 알았다.