• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.2
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• pp.444-450
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• 1998

In the present investigation, it is attempted to derive a yield function and associated flow rules of loose metal powders to predict plastic deformation and density change during compaction. The loose metal powders yield by shear stress as well as hydrostatic stress and the yield strength is much smaller in tension than compression. Therefore, a yield function for the powders is expressed as a shifted ellipse toward the negative direction in the hydrostatic stress axis in the space defined by the two stresses. Each of parameters A, B and .delta. used in the yield function is expressed as a function of relative density and it is determined by uniaxial strain and hydrostatic compressions using Cu powder. Flow rules obtained by imposing the normality rule to the yield function are applied to the analyses of unidirectional, bidirectional and hydrostatic compressions, resulting in an excellent agreement with experiments. The yield function is further examined by checking volume changes in plane stain, uniaxial strain and shear deformations.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.6
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• pp.82-86
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• 2005

PID controller is widely used as automatic equipment for industry. However when a system has various characters of intermittence or continuance, a new parameter decision for accurate control is a bud task. As a method of solving this problem, in this paper, a teaming gain regulator as PID controller functions is presented. A propriety teaming gain of system is decided by a rule of Delta learning. The function of proposed loaming gain regulator is verified by simulation results of DC motor.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.84-91
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• 2004

A CCD camera with a laser stripe was applied to realize the automatic weld seam tracking in GMAW. It takes relatively long time to process image on-line control using the basic Hough transformation, but it has a tendency of robustness over the noises such as spatter and arc tight. For this reason, it was complemented with adaptive Hough transformation to have an on-line processing ability for scanning specific weld points. The adaptive Hough transformation was used to extract laser stripes and to obtain specific weld points. The 3-dimensional information obtained from the vision system made it possible to generate the weld torch path and to obtain the information such as width and depth of weld line. In this study, a neural network based on the generalized delta rule algorithm was adapted for the process control of GMA, such as welding speed, arc voltage and wire feeding speed.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1991.10a
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• pp.29-32
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• 1991

Non-volatile SNOSFET memory devices using CMOS 1Mbit design rule(1.2$\mu\textrm{m}$), whose channel width and length are 15${\times}$1.5$\mu\textrm{m}$, 15${\times}$1.5$\mu\textrm{m}$, 2.0${\times}$15$\mu\textrm{m}$ and length are 15${\times}$1.7$\mu\textrm{m}$, respectivley, were fabricated. And the transfer, Id-Vd and switching characteristics of the devices were investigated. As a result, the 15${\times}$1.5$\mu\textrm{m}$ device was good in the transfer characteristics and the switching characteristics were favourable, which had $\Delta$V$\sub$TH/=6.3V by appling pulse voltage of V$\sub$w/=+34V, Tw=50msec.

• The Korean Journal of Financial Management
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• v.19 no.2
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• pp.211-231
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• 2002

본 연구의 목적은 VECM(Vector Error Correction Model)과 인공지능모형(Artificial Neural Networks)을 이용하여 우리나라 증권시장과 거시경제 변수들과의 장기적 관계에 대한 설명력을 비교해보고자 함에 있다. VECM이 APT(Arbitrage Pricing Theory)에 기초를 둔 선형동학모형이라고 한다면, 인공지능모형은 비모수적 비선형모형이라는 점에서, 두 방법론의 분석결과를 직접 비판하는 것은 의미있는 연구라고 할 수 있다. 인공지능모형을 주로 활용하는 선행연구들에 의하면, 증권시장은 시장의 특이패턴들로 인해 계량경제학적 접근인 선형 모형보다는 인공지능모형을 통해 증권시장의 움직임을 설명하고 예측하는 것이 더 바람직할 수도 있다는 것이다. 따라서, 본 연구에서는 VECM분석에서 자료의 안정성을 검증하고, 공적분 백터를 발견한 이후, 장기적 균형관계의 실증적 분석을 하였다. 그리고, 인공지능모형에서는 delta rule과 Sigmoid 함수를 이용한 GRNN(General Regression Neural Net)과 Back-Propagation등의 방법들을 활용하였다. 이러한 분석결과, Back-Propagation 모형이 다른 모든 모형들보다도 더 우수한 설명력을 보여주고 있었다. 이러한 결과들은 인공지능모형이 동태적인 선형 모형보다도 더 우수한 설명력을 제공할 수 있는 가능성을 보여주고 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.653-662
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• 1989

Cerebellar Model Arithmetic Controller (CMAC) has been introduced as an adaptive control function generator. CMAC computes control functions referring to a distributed memory table storing functional values rather than by solving equations analytically or numerically. CMAC has a unique mapping structure as a coarse coding and supervisory delta-rule learning property. In this paper, learning aspects and a convergence of the CMAC were investigated. The efficient training algorithms were developed to overcome the limitations caused by the conventional maximum error correction training and to eliminate the accumulated learning error caused by a sequential node training. A nonlinear function generator and a motion generator for a two d.o.f. manipulator were simulated. The efficiency of the various learning algorithms was demonstrated through the cpu time used and the convergence of the rms and maximum errors accumulated during a learning process. A generalization property and a learning effect due to the various gains were simulated. A uniform quantizing method was applied to cope with various ranges of input variables efficiently.

• Structural Engineering and Mechanics
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• v.60 no.1
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• pp.71-90
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• 2016

In this paper, the normalized variable V=(log N-B)(log ${\Delta}{\sigma}-C$-C), as derived from the probabilistic S-N field of Castillo and Canteli, is taken as a reference for calculation of damage accumulation and probability of failure using the Miner number in scenarios of variable amplitude loading. Alternative damage measures, such as the classical Miner and logarithmic Miner, are also considered for comparison between theoretical lifetime prediction and experimental data. The suitability of this approach is confirmed for it provides safe lifetime prediction when applied to fatigue data obtained for riveted joints made of a puddle iron original from the Fao bridge, as well as for data from experimental programs published elsewhere carried out for different materials (aluminium and concrete specimens) under distinct variable loading histories.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.236-242
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• 1992

Multi-layered neural networks are applied to the identification and control of nonlinear dynamical system. Traditional adaptive control techniques can only deal with linear systems or some special nonlinear systems. A scheme for combining multi-layered neural networks with model reference network techniques has the capability to learn the nonlinearity and shows the great potential for adaptive control. In many interesting cases the system can be described by a nonlinear model in which the control input appears linearly. In this paper the identification of linear and nonlinear part are performed simultaneously. The projection algorithm and the new estimation method which uses the delta rule of neural network are compared throughout the simulation. The simulation results show that the identification and adaptive control schemes suggested are practically feasible and effective.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.384-387
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• 1998

This paper presents a newly developed speed sensorless drive using RLS(Recursive Least Squares) based on Neural Network Training Algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known back-propagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.

• Korean Management Science Review
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• v.18 no.2
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• pp.25-38
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• 2001

The well-known back-propagation algorithm for multi-layered neural network has successfully been applied to pattern c1assification problems with remarkable flexibility. Recently. the multi-layered neural network is used as a powerful data mining tool. Nevertheless, in many cases with complex boundary of classification, the successful learning is not guaranteed and the problems of long learning time and local minimum attraction restrict the field application. In this paper, an Improved learning procedure of multi-layered neural network is proposed. The procedure is based on the generalized delta rule but it is particular in the point that the architecture of network is not fixed but enlarged during learning. That is, the number of hidden nodes or hidden layers are increased to help finding the classification boundary and such procedure is controlled by entropy evaluation. The learning speed and the pattern classification performance are analyzed and compared with the back-propagation algorithm.