• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.224-225
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• 2018

Modern people are more interested in the indoor environment as they spend more time indoors than in the past. Among the air pollutants in the indoor air, ladon gas is a colorless, tasteless, odorless, inert gas produced by nuclear decomposition of naturally occurring uranium in rocks and soils. It has been proven that ladon gas is introduced into the room through cracks on the floor of the building or basement wall, and it causes various diseases such as lung cancer when exposed to radon during human breathing. The US Environmental Protection Agency (EPA) specifies 4pCi / L as a necessary measure for radon, and the Korea Environmental Protection Agency has implemented comprehensive indoor radon management measures since 2007. Therefore, in this study, we intend to adsorb and reduce radon in indoor air pollutants.

• Speech Sciences
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• v.13 no.4
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• pp.177-186
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• 2006

In an effort to enhance the quality of feature vector classification and thereby reduce the recognition error rate of the speaker-independent speech recognition, we employ the Mahalanobis distance in the calculation of the similarity measure between feature vectors. It is assumed that the metric matrix of the Mahalanobis distance be diagonal for the sake of cost reduction in memory and time of calculation. We propose that the diagonal elements be given in terms of the variations of the feature vector components. Geometrically, this prescription tends to redistribute the set of data in the shape of a hypersphere in the feature vector space. The idea is applied to the speech recognition by hidden Markov model with fuzzy vector quantization. The result shows that the recognition is improved by an appropriate choice of the relevant adjustable parameter. The Viterbi score difference of the two winners in the recognition test shows that the general behavior is in accord with that of the recognition error rate.

• Structural Engineering and Mechanics
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• v.44 no.3
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• pp.363-378
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• 2012

The stiffness of a suspension system is provided by the bushings and the stiffness of the wheel center controls the suspension's elasto-kinematic (e-k) specification. So the stiffness of the wheel center is very important, but the stiffness of the wheel center is very hard to measure. The paper give a new method that we can use the stiffness of the bushings to calculate the equivalent stiffness of the wheel center, which can quickly and widely be used in all kinds of suspension structure. This method can also be used to optimize and design the suspension system. In the example we use the method to calculate the equivalent stiffness of the wheel center which meets the symmetric and positive conditions of the stiffness matrix.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.9-17
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• 2006

This paper considers a six sigma project for improving productivity of the brace complement center pillar. The project follows a disciplined process of fife phases: define, measure, analyze, improve, and control. A process map is used to identify process input and output variables. Eleven key process input variables are selected by using X&Y matrix and FMEA, and finally eight vital few input variables are selected from analyze phase. The optimum process conditions of the vital few input variables are jointly obtained by maximizing productivity of the brace complement center pillar using DOE and alternative selection method.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2312-2314
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• 1998

To improve the visual servoing performance, several strategies were proposed in the past such as redundant feature points, using a point with different height and weighted selection of image features. The performance of these visual servoing methods depends on the configuration between the camera and object. And redundant feature points require much computation efforts. This paper proposes the visual servoing m based on the disturbance observer, which compe the upper off-diagonal component of image fe Jacobian to be null. The performance indices su sensitivity for a measure of richness, sensitiv the control to noise, and controllability are sho improved when the image feature Jacobian is giv a block diagonal matrix. Computer simulation carried out for a PUMA560 robot and show results to verify the effectiveness of the pro method.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2004.04a
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• pp.250-253
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• 2004

Emphasis has been placed on thin plate fabrication of plain woven carbon fabric reinforced Al matrix composites using liquid pressing process. The composite has potential applications for PDP rear plate. The process is to use the low pressure for infiltration of Al melt into plain woven carbon fabric as the Al melt is pressurized directly. The minimum pressure required for the infiltration was calculated from force balance equation, permeability measurements and compaction behavior of carbon fiber. Also, the melting temperature and the holding time have been optimized. In order to measure coefficient of thermal expansion (CTE) of the composites, the thermal strain measurement using strain gage was performed and the thermal conductivity of the composites was measured using laser flash method. The constituent materials of the composite are PAN type carbon fibers as reinforcements and 6061 Al alloys as matrices.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.294-303
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• 1999

To improve the visual servoing performance, several strategies were proposed in the past such as redundant feature points, using a point with different height and weighted selection of image features. The performance of these visual servoing methods depends on the configuration between the camera and object. And redundant feature points require much computation efforts. This paper proposes the visual servoing method based on the disturbance obsever, which compensates the upper off-diagonal component of image feature jacobian to be the null. The performance indices such as sensitivity for a measure of richness, sensitivity of the control to noise, and comtrollability are shown to be improved when the image feature Jacobian is given as a block diagonal matrix. Computer simulations are carried out for a UUMA560 robot and show some results to verify the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1002-1004
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• 1996

In this paper, presented are the results of observability analysis for the vehicle maneuver during In-Flight Alignment of SDINS. The target system for observability analysis is 10th order one. Three trajectories for IFA are considered. To analyze the observability of the time varying system, correlation coefficient is used and to measure the degree of observability of the given system, simulation is carried out using covariance matrix. The results of simulation show that trajectories which are having continuous changes in attitude and acceleration of system is superior to straight trajectory in correcting navigation errors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1491-1497
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• 2007

This paper considers robust and non-fragile guaranteed cost controller design method for descriptor systems with parameter uncertainties and time delay, and static state feedback controller with gain variations. The existence condition of controller, the design method of controller, the upper bound to minimize guaranteed cost function, and the measure of non-fragility in controller are proposed using linear matrix inequality (LMI) technique, which can be solved efficiently by convex optimization. Therefore, the presented robust and non-fragile guaranteed cost controller guarantees the asymptotic stability and non-fragility of the closed loop systems in spite of parameter uncertainties, time delay, and controller fragility.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.321-331
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• 2015

The K-means clustering algorithm is a popular and widely used method for clustering. For covariance matrices, we consider a geodesic clustering algorithm based on the K-means clustering framework in consideration of symmetric positive definite matrices as a Riemannian (non-Euclidean) manifold. This paper considers a geodesic clustering algorithm for data consisting of symmetric positive definite (SPD) matrices, utilizing the Riemannian geometric structure for SPD matrices and the idea of a K-means clustering algorithm. A K-means clustering algorithm is divided into two main steps for which we need a dissimilarity measure between two matrix data points and a way of computing centroids for observations in clusters. In order to use the Riemannian structure, we adopt the geodesic distance and the intrinsic mean for symmetric positive definite matrices. We demonstrate our proposed method through simulations as well as application to real financial data.