• 제어로봇시스템학회논문지
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• 제6권12호
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• pp.1126-1132
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• 2000

In this paper we propose an angular rate and acceleration matching method for initial transfer alignment in aircraft. The conventional angular rate and acceleration matching method performs compensation for the lever arm effects between the master and slave INS before initial alignment. However, the conventional method does not take the flexure angular acceleration into account and thus is not effective when the flexure angular acceleration is large. We propose a new angular rate and acceleration matching method to cope with the flexure acceleration between the master and slave INS and compare the results with those of the conventional method by simulation. The simulation results show that the proposed matching method is better than the conventional matching method in case of large flexure acceleration.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1860-1863
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• 1997

Transfer alignement is the process of initializing attitude of slave INS using the data of master INS. This paper presents the performance analysis of transfer alignment at sea using convariance analysis method. Velocity & angle matching and angular rate & acceleration matching are used for analysis, and the performance of two matching methods are compared. We propose a new method for angular rate & acceleration matching. Under the assumption of accurate modeling of ship flexure, the performance of transfer alignment time and accuray is improved very much for the new method.

• 융합신호처리학회논문지
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• 제14권1호
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• pp.21-26
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• 2013

The objective of this study was to develop a system for handwriting recognition in three dimensions (3D) to authenticate users. While previous studies have used a stylus pen for two-dimensional input on a tablet, this study uses the Wii Remote controller because it can capture 3D human motion and could therefore be more effective means of recognition. The information obtained from a Wii Remote controller included x and y coordinates, acceleration (x, y, z), angular velocity (pitch, yaw, roll), twelve input buttons, and time. The proposed system calculates distances using six features extracted after preprocessing the data. In an experiment where 15 subjects wrote "AIZU" 10 times, we obtained a 94.8% identification rate using a combination of writing velocity, the peak value of pitch, and the peak value of yaw. This suggests that this system holds promise for handwriting-based authentication in the future.