• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.3
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• pp.639-646
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• 1995

The objective of this study is to minimize the weight of a damped anisotropic roto-bearing system considering whirl natural frequency and stability. The system is modeled as an assemblage of rigid disks, flexible shafts and discrete bearings. The system design variables are the crosssectional areas of shaft elements and the properties of bearings. To analyze the system, the polynomial method which is derived by rearranging the calculations performed by a transfer matrix method is adopted. For the optimization, the optimization software IDOL (Integrated Design Optimization Library) which is based on the Augmented Lagrange Multiplier (ALM) method is employed. Also, an analytical design sensitivity analysis of the system is used for high accuracy and efficiency. To demonstrate the usefulness of the proposed optimal design program incorporating analysis, design sensitivity analysis, and optimization modules, a damped anisotropic rotor-bearing system is optimized to obtain 34$ weight reduction.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.3
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• pp.226-231
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• 2008

In this paper, we propose novel eigenstructure assignment method in full-state feedback for linear time-invariant MIMO system such that directions of some left eigenvectors are exactly assigned to the desired directions. It is required to consider the direction of left eigenvector in designing eigenstructure of closed-loop system, because the direction of left eigenvector has influence over excitation by associated input variables in time-domain response. Exact direction of a left eigenvector can be achieved by assigning proper right eigenvector set satisfying the conditions of the presented theorem based on Moore's theorem and the orthogonality of left and right eigenvector. The right eigenvector should reside in the subspace given by the desired eigenvalue, which restrict a number of designable left eigenvector. For the two cases in which desired eigenvalues are all real and contain complex number, design freedom of designable left eigenvector are given.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.609-616
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• 2017

In this paper, we propose an algorithm to obtain more stable and high recognition rate by using left and right rotation information of input image in order to obtain a stable recognition rate in face recognition. The proposed algorithm uses the facial image as the input information in the web camera environment to reduce the size of the image and normalize the information about the brightness and color to obtain the improved recognition rate. We apply Principal Component Analysis (PCA) to the detected candidate regions to obtain feature vectors and classify faces. Also, In order to reduce the error rate range of the recognition rate, a set of data with the left and right $45^{\circ}$ rotation information is constructed considering the directionality of the input face image, and each feature vector is obtained with PCA. In order to obtain a stable recognition rate with the obtained feature vector, it is after scattered in the eigenspace and the final face is recognized by comparing euclidean distant distances to each feature. The PCA-based feature vector is low-dimensional data, but there is no problem in expressing the face, and the recognition speed can be fast because of the small amount of calculation. The method proposed in this paper can improve the safety and accuracy of recognition and recognition rate faster than other algorithms, and can be used for real-time recognition system.