• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.321-325
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• 2004

Linear motor feed drive systems have been broadly used in machine tools or precision automatic feed systems. Recently, modem machine tools require high speed and high precision feed drive system to achieve high productivity. Unfortunately, a feed drive system, even though it was optimum designed, may experience severe transient vibrations during high-speed operation if its feed rate control is unsuitable. A rough feed rate curve having discontinuity in its acceleration profile causes a serious vibration problem in the feed slides system. This paper presents a feed rate optimization of a machine tool feed slide system, which is driven by a linear motor, for its minimum vibrations. Firstly, a 4-degree-of-freedom lumped parameter model is proposed for the vibration analysis of a linear motor driven machine tool feed drive system. Next, a feed rate optimization of the feed slide is carried out for minimum vibrations. The feed rate curve optimization strategy is to find out the most appropriate acceleration profile with jerk continuity. Of course, the optimized feed rate should approximate to the desired one as possible. A genetic algorithm with variable penalty function was used in this feed rate optimization.

• Korean Journal of Optics and Photonics
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• v.8 no.3
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• pp.250-253
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• 1997

The effect of dispersion in optical transmission system changes according to chirp and SPM. Chirp depends on the modulation condition of an external modulator. SPM is proportional to signal power. In this paper, we analyzed the pulse broadening due to SPM and chirp in the system with dispersion by using Split Step Fourier Method and calculated the optimum chirp parameter for 400 km transmission system. Experimental results are presented also.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.962-966
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• 2004

Ball screw feed drive systems have been broadly used in machine tools or precision automatic feed systems. Recently, modern machine tools require high speed and high precision and drive system to achieve high productivity. Unfortunately, a feed drive system, even though it was optimum designed, may experience severe transient vibrations during high-speed operation if its feed rate control is unsuitable. A rough feed rate curve having discontinuity in its acceleration profile causes a serious vibration problem in the feed slide system. This paper presents a feed rate optimization of a machine tool feed slide system, which is driven by a ball screw, for its minimum vibrations. Firstly, a 6-degree-of-freedom lumped parameter model was proposed for the vibration analysis of a ball screw driven machine tool feed drive system. Next, a feed rate optimization of the feed slide was carried out for minimum vibrations. The feed rate curve optimization strategy is to find out the most appropriate acceleration profile having finite jerk. Of course, the optimized feed rate should approximate to the desired one as possible. A genetic algorithm with variable penalty function was used in this feed rate optimization.

• Journal of the Optical Society of Korea
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• v.8 no.2
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• pp.59-64
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• 2004

The response of a single-mode fiber to a sinusoidally modulated input has been studied to see its utility in measuring system performance in the presence of fiber nonlinearities. The sinusoidally modulated signal models an alternating bit sequence of ones and zeros in on-off keying. The sinusoidal response of normally dispersive fiber shows a strong correlation with eye-opening penalty (EOP) over a wide range of the nonlinearity parameter N (0.1 ＜ N$^2$＜ 100). This result implies that the measurement of the sinusoidal response can be an alternate way of measuring EOP without having a long sequence of randomly modulated input bits. But in the anomalous dispersion region, the sinusoidal response has a much more limited range of application to estimate system performance.

• Korean Journal of Computational Design and Engineering
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• v.9 no.3
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• pp.183-191
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• 2004

This paper deals with smooth curve fitting of data corrupt by noise. Most research efforts have been concentrated on employing the smoothness penalty function with the estimation of its optimal parameter in order to avoid the 'overfilling and underfitting' dilemma in noisy data fitting problems. Our approach, called DBSF(Differentiation-Based Smooth Fitting), is different from the above-mentioned method. The main idea is that optimal functions approximately estimating the derivative of noisy curve data are generated first using genetic programming, and then their integral values are evaluated and used to recover the original curve form. To show the effectiveness of this approach, DBSP is demonstrated by presenting two illustrative examples and the application of estimating the principal dimensions of bulk cargo ships in the conceptual design stage.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.3
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• pp.713-723
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• 1995

In intelligent machine tools, a computer based control system, which can adapt the machining parameters in an optimal fashion based on sensor measurements of the machining process, should be incorporated. In this paper, the technology for adaptively optimizing the cutting conditions to maximize the material removal rate in face milling operations is proposed using the exterior penalty function method combined with multilayered neural networks. Two neural networks are introduced ; one for estimating tool were length, the other for mapping input and output relations from experimental data. Then, the optimization of cutting conditions is adaptively implemented using tool were information and predicted process output. The results are demonstrated with respect to each level of machining such as rough, fine and finish cutting.

• Journal of the Korean Society of Safety
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• v.13 no.1
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• pp.40-46
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• 1998

This paper is presented that theoretical optimization design method in order to consider mass reduction for the structural safety In this paper, it described methods for reducing vibration in structural safety by the determination of the optimum sizes and locations of tunning masses through formal mathematical optimization techniques. The optimization procedure which employs the tunning masses and corresponding locations is developed. Design variables are systematically changed to achieve low values of shear without a large mass penalty. Three optimization methods ire developed and tested. The first is based on minimizing the modal shaping parameter which indirectly reduce the modal shear amplitudes corresponding to each harmonic of airload. The second method reduces these amplitudes directly and the third method reduces the shear as a function of time during a revolution of the blade. The first method works well for reducing the shear for one mode responding to a single harmonic of the airload but has been found in some bases to be ineffective for more than one mode.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.573-585
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• 2011

SQP method is one of the most important methods for solving nonlinear programming. But it may fail if the quadratic subproblem is inconsistent. In this paper, we propose a modified nonmonotone filter trust region method in which the QP subproblem is consistent. By means of nonmonotone filter, this method has no demand on the penalty parameter which is difficult to obtain. Moreover, the restoration phase is not needed any more. Under reasonable conditions, we obtain the global convergence of the algorithm. Some numerical results are presented.

• Journal of applied mathematics & informatics
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• v.27 no.5_6
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• pp.981-1000
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• 2009

This paper considers asset liability management problems when their deterministic equivalent formulations are general nonlinear optimization problems. The presented approach uses a nonmonotone trust region strategy for solving a sequence of unconstrained subproblems parameterized by a scalar parameter. The objective function of each unconstrained subproblem is an augmented penalty-barrier function that involves both primal and dual variables. Each subproblem is solved approximately. The algorithm does not restrict a monotonic decrease of the objective function value at each iteration. If a trial step is not accepted, the algorithm performs a non monotone line search to find a new acceptable point instead of resolving the subproblem. We prove that the algorithm globally converges to a point satisfying the second-order necessary optimality conditions.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.453-470
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• 2018

We study how to distinguish the parameters of the sparse autoregressive (AR) process from zero using a non-convex penalized estimation. A class of non-convex penalties are considered that include the smoothly clipped absolute deviation and minimax concave penalties as special examples. We prove that the penalized estimators achieve some standard theoretical properties such as weak and strong oracle properties which have been proved in sparse linear regression framework. The results hold when the maximal order of the AR process increases to infinity and the minimal size of true non-zero parameters decreases toward zero as the sample size increases. Further, we construct a practical method to select tuning parameters using generalized information criterion, of which the minimizer asymptotically recovers the best theoretical non-penalized estimator of the sparse AR process. Simulation studies are given to confirm the theoretical results.