• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.224-229
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• 2012

In this paper, we propose a correction method for astronomical telescope using recursive least square method. There are two ways to move a telescope : equatorial operation and altazimuth operation. We must align polar axis of a equatorial telescope with the north celestial pole and adjust the horizontal axis of a altazimuth telescope exactly to match the celestial coordinate system with the telescope coordinate system. This process needs time and expertise. We can skip existing process and correct a tracking error easily by deriving the relationship of the celestial coordinate system and the telescope coordinate system using the proposed correction method. We obtain the coordinate of a celestial body in the celestial coordinate system and the telescope coordinate system and derive a transformation matrix through the obtained coordinate. We use recursive least square method to estimate the unknown parameters of a transformation matrix. Finally, we implement a telescope control system using a microprocessor and verify the performance of the correction method. Through an experiment, we show the validity of the proposed correction method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.1
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• pp.65-73
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• 2007

In the inverse perturbation method, enormous computational resource was required to obtain reliable results, because all unspecified DOFs were considered as unknown variables. Thus, in the present study, a reduced system method is used to condense the unspecified DOFs by using the specified DOFs, and to improve the computational efficiency as well as the solution accuracy. In most of the conventional reduction methods, transformation errors occur in the transformation matrix between the unspecified DOFs and the specified DOFs. Thus it is hard to obtain reliable and accurate solution of inverse perturbation problems by reduction methods due to the error in the transformation matrix. This numerical trouble is resolved in the present study by adopting iterative improved reduced system(IIRS) as well as by updating the transformation matrix at every step. In this reduction method, system accuracy is related to the selection of the primary DOFs and Iteration time. And both are dependent to each other So, the two level condensation method (TLCS) is selected as Selection method of primary DOFs for increasing accuracy and reducing iteration time. Finally, numerical verification results of the present iterative inverse perturbation method (IIPM) are presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.9-16
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• 2018

Images with lower illumination from the light source or with dark regions due to shadows, etc., can improve subjective image quality by using retinex-based image enhancement schemes. The retinex theory is a method that recognizes the relative lightness of a scene, rather than recognizing the brightness of the scene. The way the human visual system recognizes a scene in a specific position can be in one of several methods: single-scale retinex, multi-scale retinex, and multi-scale retinex with color restoration (MSRCR). The proposed method is based on the MSRCR method, which includes a color restoration step, which consists of three phases. In the first phase, the existing MSRCR method is applied. In the second phase, the dynamic range of the MSRCR output is adjusted according to its histogram. In the last phase, the proposed method transforms the retinex output value into the display dynamic range using a logarithm transformation function considering human visual system characteristics. Experimental results show that the proposed algorithm effectively increases the subjective image quality, not only in dark images but also in images including both bright and dark areas. Especially in a low lightness image, the proposed algorithm showed higher performance improvement than the conventional approaches.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.179-188
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• 2022

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.33 no.3
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• pp.233-240
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• 2011

Purpose: To apply a computer assisted navigation system to orthognathic surgery, a simple and efficient measuring algorithm calculation based on affine transformation was designed. A method of improving accuracy and reducing errors in orthognathic surgery by use of an optical tracking camera was studied. Methods: A total of 5 points on one surgical splint were measured and tracked by the Polaris $Vicra^{(R)}$ (Northern Digital Inc Co., Ontario, Canada) optical tracking system in two cases. The first case was to apply the transformation matrix at pre- and postoperative situations, and the second case was to apply an affine transformation only after the postoperative situation. In each situation, the predictive measuring value was changed to the final measuring value via an affine transformation algorithm and the expected coordinates calculated from the model were compared with those of the patient in the operation room. Results: The mean measuring error was $1.027{\pm}0.587$ using the affine transformation at pre- and postoperative situations and the average value after the postoperative situation was $0.928{\pm}0.549$. The farther a coordinate region was from the reference coordinates which constitutes the transform matrixes, the bigger the measuring error was found which was calculated from an affine transformation algorithm. Conclusion: Most difference errors were brought from mainly measuring process and lack of reproducibility, the affine transformation algorithm formula from postoperative measuring values by using of optic tracking system between those of model surgery and those of patient surgery can be selected as minimizing the difference error. To reduce coordinate calculation errors, minimum transformation matrices must be used and reference points which determine an affine transformation must be close to the area where coordinates are measured and calculated, as well as the reference points need to be scattered.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.1
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• pp.29-36
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• 2004

This study describes the development of coordinate transformation tool for transforming the digital map using newly derived transformation parameters which are determined from the data referred to the local geodetic datum and the geocentric datum (ITRF2000) and the distortion modelling derived from collocation method. We prepared 190 common points and used 107 points to calculate 7 transformation parameters. In order to evaluate an accuracy of coordinate transformation, 83 common points were tested. In this study, we used Molodensky-Badekas model to derive the 7 transformation Parameters. An accuracy of 0.22m was obtained applying 7 Parameters transformation and the distortion modelling together. It shows that the accuracy of coordinate transformation is improved 72% against the result of 7 parameters transformation only. We developed the transformation tool, GDKtrans, which can be transformed the digital map of scales 1/50,000, 1/25,000 and 1/5,000. We also analyzed the digital map of l/5,000 at six urban areas by GPS observations. The result shows less RMSE of about 1.9 m and large disagreement at position and features. Consequently, we suggests that l/5,000 digital map is necessary of whole revision.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.583-592
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• 2000

This paper proposes feature parameter transformation method using independent component analysis (ICA) for speaker identification. The proposed method assumes that the cepstral vectors from various channel-conditioned speech are constructed by a linear combination of some characteristic functions with random channel noise added, and transforms them into new vectors using ICA. The resultant vector space can give emphasis to the repetitive speaker information and suppress the random channel distortions. Experimental results show that the transformation method is effective for the improvement of speaker identification system.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.49-56
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• 2004

Model updating is a very active research field, in which significant efforts has been invested in recent years. Model updating methodologies are invariably successful when used on noise-free simulated data, but tend to be unpredictable when presented with real experimental data that are-unavoidably-corrupted with uncorrected noise content. In this paper, Reanalysis using frequency response functions for correlating and updating dynamic systems is presented. A transformation matrix is obtained from the relationship between the complex and the normal frequency response functions of a structure. The transformation matrix is employed to calculate the modified damping matrix of the system. The modified mass and stiffness matrices are identified from the normal frequency response functions by using the least squares method. One simulated system is employed to illustrate the applicability of the proposed method. The result indicate that the damping matrix of correlated finite element model can be identified accurately by the proposed method. In addition, the robustness of the new approach uniformly distributed measurement noise Is also addressed.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.683-688
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• 2017

The unscented Kalman filter features a direct transforming process involving unscented transformation for removing the linearization process error that may occur in the extended Kalman filter. This paper proposes a reduced-order unscented Kalman filter for the sensorless control of a permanent magnet synchronous motor. The proposed method can reduce the computational load without degrading the accuracy compared to the conventional Kalman filters. Moreover, the proposed method can directly estimate the electrical rotor position and speed without a back-electromotive force. The proposed Kalman filter for the sensorless control of a permanent magnet synchronous motor is verified through the simulation and experimentation. The performance of the proposed method is evaluated over a wide range of operations, such as forward and reverse rotations in low and high speeds including the detuning parameters.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.124-130
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• 1996

A compensation method of color error resulting from dynamic range transformation of color image is propsoed. The color error is measured using the CIE L a b uniform color space, and the color image is compensated to minimize it. The color error is significant in the dark region of image, which is caused by the lunimance scaling factor is relatively large in that case. In the proposed method, we weight the luminance scaling factor corresponding to luminnce to minimize the error. Because the weighting factor depends on luminace distribution of image, a decisio method of weighting factor using histogram is also proposed.