• 한국통신학회논문지
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• 제29권6C호
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• pp.901-906
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• 2004

For a digital image watermarking some autostereograms are used such as random dot images. In there, the extraction efficiency is good and the distortion rate is low. In this paper, we shall select an optimized scaling parameter which derives low distortion rate and high extraction efficiency, when we use a random dot images as like as autostereograms into some images except for extremely biased gray level images.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.99.6-99
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• 2001

The parameter optimization for agile beam radar tracking is addressed to minimize the radar resources that are required to maintain a target under track. The parameters to be optimized include the track-revisit interval and the sequence of pairs of target signal strengths and detection thresholds associated with repeated illumination attempts in each track-revisit. The optimization problem is solved numerically for typical examples.

• 소성∙가공
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• 제8권1호
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• pp.92-100
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• 1999

A procedure is proposed for determining the initial thickness distribution in oder to produce a specified final thickness distribution for the axisymmetrical superplastic blow forming processes. Weighted parameter is introduced to improve the simple ad $d_traction method and the initial blank thickness distribution is obtained by optimizing the weighted parameter. This method is applied to superplastic free bulging process with the uniform thickness distribution of final shape to confirm its validity. The optimum initial blank thickness distributions is obtained from arbitrary axisymmetrical superplastic blow forming processes such as dome, cone and cylindrical cup forming with die contact. It is concluded that the ad $d_traction method with weighted parameter is an effective method for an optimum blank thickness distribution design.esign.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.655-658
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• 1997

In this paper, we intend to develop optimized design algorithm by deciding design parameters which are considered in the first design stage of inkjet printer head. thus, the parameters are such as electric pulse, input voltage of actuator to operate actuator, shape dimension of actuator an so on. in the first design stage, according to such parameters, a lot of time and money to develop inkject printer head are needed. to reduce trial and error and to save development time in the first design stage, optimized design algorithm is required all the more. design algorithm was developed via commercial FE analysis code(ANSYS & COENTOR) for the readability and convenience of algorithm. the reasonability of algorithm was verified by implementing analysis of system stage based on the data of piezoelectric actuator which was designed through algorithm.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2003년도 추계학술발표대회 개요집
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• pp.169-171
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• 2003

In this paper, the prediction for the optimized bead geometry such as bead width, height, penetration and bead area in the Gas Metal Arc (GMA) welding with Taguchi method is presented. An orthogonal array, and the Signal-to-Noise (S/N) ratio employed to investigate the welding quality characteristics together in the selection of process parameters in the GMA welding process, to analyze the effect of each process parameter on the bead geometry and to finally determine the process parameters with the optimal bead geometry. Experimental results fi-om this research show that the Taguchi method provides an effective tool to enhance the accuracy of the optimized bead geometry.

• Journal of Welding and Joining
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• 제17권4호
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• pp.39-45
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• 1999

The procedure of robotic Gas metal Arc (GMA) welding in order to achieve the optimized bead geometry needs the selection of suitable process parameters such as arc current, welding voltage, welding speed. It is required the relationships between process parameters and bead geometry. The objective of this paper is to develop the algorithm that enables the determination of process parameters from the optimized bead geometry for robotic GMA welding. It depends on the inversion of empirical equations derived from multiple regression analysis of the relationships between the process parameters and the bead dimensions using the least square method. The method not only directly determines those parameters which will give the desired set of bead geometry, but also avoids the need to iterate with a succession of guesses employed Finite Element Method(FEM). These results suggest that process parameter from experimental equation for robotic GMA welding may be employed to monitor and control the bead geometry in real time.

• Journal of the Optical Society of Korea
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• 제18권2호
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• pp.118-123
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• 2014

For multi parameter optimization of Raman Fiber Amplifier (RFA), a simple genetic algorithm is presented in the scenario of a 320 channel Dense Wavelength Division Multiplexed (DWDM) system at channel spacing of 25 GHz. The large average gain (> 22 dB) is observed from optimized RFA with the optimized parameters, such as 39.6 km of Raman length with counter-propagating pumps tuned to 205.5 THz and 211.9 THz at pump powers of 234.3 mW, 677.1 mW respectively. The gain flattening filter (GFF) has also been optimized to further reduce the gain ripple across the frequency range from 190 to 197.975 THz for broadband amplification.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.911-917
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• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

• Journal of Electrical Engineering and Technology
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• 제7권1호
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• pp.109-114
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• 2012

In this paper, we utilize training strategy of hidden Markov model (HMM) to use in versatile issues such as classification of time-series sequential data such as electric transient disturbance problem in power system. For this, an automatic means of optimizing HMMs would be highly desirable, but it raises important issues: model interpretation and complexity control. With this in mind, we explore the possibility of using genetic algorithm (GA) and harmony search (HS) algorithm for optimizing the HMM. GA is flexible to allow incorporating other methods, such as Baum-Welch, within their cycle. Furthermore, operators that alter the structure of HMMs can be designed to simple structures. HS algorithm with parameter-setting free technique is proper for optimizing the parameters of HMM. HS algorithm is flexible so as to allow the elimination of requiring tedious parameter assigning efforts. In this paper, a sequential data analysis simulation is illustrated, and the optimized-HMMs are evaluated. The optimized HMM was capable of classifying a sequential data set for testing compared with the normal HMM.

• 한국컴퓨터정보학회논문지
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• 제15권12호
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• pp.125-132
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• 2010

어휘 인식에서는 인식 학습 시 나타나지 않는 미 출현 트라이 폰이 존재하며, 이들 시스템에서는 모델 파라미터들의 초기 추정치를 생성하지 못하고 음소 데이터에 대한 모델을 구성할 수 없는 단점으로 인하여 가우시안 모델의 정확성을 확보하지 못하게 된다. 이를 개선하기 위하여 확률 분포를 이용한 모델 파라미터의 가우시안 모델 최적화 방법을 제안한다. 확률 분포의 가우시안 모델을 최적화하여 가우시안 모델의 정확성을 제공하고, 음소 단위로 데이터의 탐색을 지원하여 신뢰도가 향상되었다. 제안된 방법의 성능 평가를 위하여 실제 다양한 미등록어가 관측될 수 있는 대상으로 실험을 수행하였으며 본 연구에서 제안한 정규화 신뢰도를 이용한 미등록어 거절 알고리즘이 기존의 방법들에 비하여 평균 1.7%의 성능향상을 나타내었다.