• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.187-190
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• 2003

In this paper, we propose an extrinsic calibration method for a multi-view camera to get an optimal pose in 3D space. Conventional calibration algorithms do not guarantee the calibration accuracy at a mid/long distance because pixel errors increase as the distance between camera and pattern goes far. To compensate for the calibration errors, firstly, we apply the Tsai's algorithm to each lens so that we obtain initial extrinsic parameters Then, we estimate extrinsic parameters by using distance vectors obtained from structural cues of a multi-view camera. After we get the estimated extrinsic parameters of each lens, we carry out a non-linear optimization using the relationship between camera coordinate and world coordinate iteratively. The optimal camera parameters can be used in generating 3D panoramic virtual environment and supporting AR applications.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.111-116
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• 2003

In this paper, object of experiment is to study on the effect of cutting parameters to obtain optimal surface toughness in face turning. Surface roughness is significantly important to be high quality of parts produced by turning process. For this purpose, the optimization of cutting parameters for fan Owning operation is investigated applying the Taguchi method. An orthogonal array, signal-to-noise ratio, and the analysis of variance are employed to evaluate effect of cutting parameters fir face turning. Also confirmation tests were performed to make a comparison between the results predicted from the mentioned correlations and the theoretical results. Cutting experiment is performed without cutting fluid using coated tungsten carbide inserts about workpieces of SM45C.

• Journal of the Semiconductor & Display Technology
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• v.12 no.4
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• pp.9-13
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• 2013

Lapping is a very complicated and random process resulting from the variation of abrasive grains in its sizes and shapes and from the numerous factors having an effect on the process quality. This paper presents a study of a $2^4$ full factorial experimental design and analysis to optimize surface quality in lapping operation. The optimization of the factors to obtain minimum surface roughness was carried out by incorporating effect plots, main effect plots, interaction plots, analysis of variance(ANOVA), surface plots, and contour plots. The statistical design experiments, designed to reduce the total number of experiments required, indicated that, within the selected conditions, all the parameters influenced at a significance level of 5%. In addition, some of the possible interactions between these parameters also influenced the lapping process, especially those that were of third order. A regression model was suggested and fitted the experimental data very well.

• Journal of KSNVE
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• v.7 no.2
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• pp.223-229
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• 1997

Presented is a method to estimate system parameters of a system with polynomial non-linerities from the measured higher order frequency response functions. Higher order FRFs can be measured on some restricted regions by sinusoidally exciting a non-linear system with various input amplitudes and measuring the response component at the excitation frequency. These higher order FRFs can be expressed in terms of system parameter, and the system parameters can be estimated from the measured FRFs. Since the expressions for higher order FRFs are complicated, system parameters can be estimated from them using an optimization technique. The present method has been applied to a simulated single degree of freedom system with non-linear stiffness and damping, and has estimated accurate system parameters.

• Journal of the Korean Society of Safety
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• v.21 no.1 s.73
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• pp.21-27
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• 2006

In this paper, object of experiment is to study on the effect parameters to obtain optimal surface roughness in face turning. Surface roughness is significantly important to be high quality of parts produced by turning process. For this purpose, the optimization of cutting parameters for face turning operation is investigated applying the Taguchi method. An orthogonal array, signal-to-noise, and the analysis of variance are employed to evaluate effect of cutting parameters for face turning. Also confirmation tests were performed to make a comparison between the results predicted from the mentioned correlations and the theoretical results. Cutting experiment is performed without cutting fluid using coated tungsten carbide insert about workpiece of SM45C. And regression analysis technique has been used to study the effects of the cutting parameters.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.8-14
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• 2007

The powder blasting process has become an important machining technique for the cost effective fabrication of micro devices. This process is similar to sand blasting, and effectively removes hard and brittle materials. A large number of investigations on the abrasive jet machining with such output parameters as material removal rate, penetration and surface roughness have been carried out and reported by various authors. To achieve higher surface roughness, to increase material removal rate and to identify the influence of blasting parameters on the output parameters, we use the taguchi method which is one of the design methods of experiments. We can select process parameters to optimize the blasting process of glass. Experimental results indicate that the taguchi method is useful as a robust design methodology for the powder blasting process.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.417-422
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• 2007

A new evolution method termed queen-bee and mutant-bee evolution is based on the previous queen-bee evolution [1]. Even though the queen-bee evolution has shown very good performances, two parameters for strong mutation are added to the genetic algorithms. This makes the application of genetic algorithms with queen-bee evolution difficult because the values of the two parameters are empirically decided by a trial-and-error method without a systematic method. The queen- bee and mutant-bee evolution has no this problem because it does not need additional parameters for strong mutation. Experimental results with typical problems showed that the queen-bee and mutant-bee evolution produced nearly similar results to the best ones of queen-bee evolution even though it didn't need to select proper values of additional parameters.

• Journal of Magnetics
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• v.16 no.3
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• pp.206-210
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• 2011

We optimize a vector network analyzer ferromagnetic resonance (VNA-FMR) measurement system to study spin dynamics and Gilbert damping parameters of thin ferromagnetic films. In order to obtain accurate damping parameters, careful determination of the susceptibility line-width is required. The measured S-parameters are converted into the corresponding susceptibility through a calibration processes. From the line-width measurements, we can successfully extract the saturation magnetizations and Gilbert damping parameters of 5-, 8-, and 10-nm thick $Ni_{81}Fe_{19}$ (Py) films.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2908-2910
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• 1999

In this paper, a fuzzy model is identified and optimized using the hybrid algorithm and HCM clustering method. Here, the hybrid algorithm is carried out as the structure combined with both a genetic algorithm and the improved complex method. The one is utilized for determining the initial parameters of membership function, the other for obtaining the fine parameters of membership function. HCM clustering algorithm is used to determine the confined region of initial parameters and also to avoid overflow phenomenon during auto-tuning of hybrid algorithm. And the standard least square method is used for the identification of optimum consequence parameters of fuzzy model. Two numerical examples are shown to evaluate the performance of the proposed model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.196-201
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• 2008

Statistical learning theory has three analytical tools which are support vector machine, support vector regression, and support vector clustering for classification, regression, and clustering respectively. In general, their performances are good because they are constructed by convex optimization. But, there are some problems in the methods. One of the problems is the subjective determination of the parameters for kernel function and regularization by the arts of researchers. Also, the results of the learning machines are depended on the selected parameters. In this paper, we propose an efficient method for objective determination of the parameters of support vector clustering which is the clustering method of statistical learning theory. Using evolutionary algorithm and bootstrap method, we select the parameters of kernel function and regularization constant objectively. To verify improved performances of proposed research, we compare our method with established learning algorithms using the data sets form ucr machine learning repository and synthetic data.