• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1095-1098
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• 1996

In this work, attitude determination with Inertial Reference Unit as attitude sensor is considered. Usually, the attitude error from IRU increases because of gyro rate bias and noise. Therefore, other attitude sensors(sun sensor, horizon sensor, star tracker) are needed to compensate for error from IRU. In this paper, we use the extended Kalman filter for attitude estimation of spacecraft with IRU and star tracker.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1721-1728
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• 2009

Future computer interface was interchanged with person's hands or eyes from traditional mouse. In this research, I developed glove type radio mouse using gyro sensor that is replaced mouse cursor movement with persons's hand movement. Introducing a mobile sensor network technology based on bluetooth, radio mouse connected to computer system wirelessly for overcoming wire mouse's activity restrictions. Also, using USB port interface made be easy to install and to use. Developed glove type radio mouse was confirmed in the possibility of using as well as an input pointing device for presentations or games that needed many kinds of user interaction.

• Aerospace Engineering and Technology
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• v.5 no.2
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• pp.90-101
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• 2006

In this paper, the calibration parameters of the gyros and star hackers are estimated by using an on-orbit AOCS sensor calibration algorithm. The calibration algorithm was implemented by Kalman filter. In order to estimate gyro calibration parameters, the calibration algorithm requires calibration maneuver and it was analyzed whether the star trackers are protected by Sun, Moon and Earth or not. Also the star tracker calibration algorithm used the camera image information. This kinds of camera image information simulated ground control point and orbit information. The estimated accuracy of star tracker calibration parameters depends on camera image information.

• Journal of Ocean Engineering and Technology
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• v.31 no.2
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• pp.164-169
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• 2017

Studies were performed on an ARS using SDINS's RLG and the geomatic sensor. To develop the ARS, experiments were performed to determine the characteristics of the RLG and geomatic sensor. Based on the results, to reduce the angular position errors of the RLG, which accumulate from the angular velocity data, an algorithm was studied that uses the Extended Kalman filter (EKF) to compensate the RLG data and geomatic sensor data. To verify the performance of the developed algorithm for reducing the cumulative angular errors, experiments that included the developed EKF were performed. Through these, it was shown that a drastic reduction in the angular errors of the RLG were achieved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.973-975
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• 2016

In this paper, we propose a selective extended Kalman filter based accurate pose estimation of the rigid body using a sensor fusion method. The pose of a rigid body can be estimated roughly by the Gauss-Newton method using the acceleration data and geomagnetic data, which can be refined with vision information and the gyro sensor information. However strong external interference noise makes the rough pose estimation difficult. In this paper, according to the measurement level of the external interference noise, the extended Kalman filter selectively uses mostly vision and gyro sensor information to increase the estimation credibility under strong interference noise environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.469-475
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• 2015

This paper describes about the method for designing an extended Kalman filter to accurately measure the position of the spatial-phase system using a semiconductor sensor. Spatial position is expressed by the correlation of the rotated coordinate system attached to the body from the inertia coordinate system (a fixed coordinate system). To express the attitude, quaternion was adapted as a state variable, Then, the state changes were estimated from the input value which was measured in the gyro sensor. The observed data is the value obtained from the acceleration sensor. By matching between the measured value in the acceleration sensor and the predicted calculation value, the best variable was obtained. To increase the accuracy of estimation, designation of the extended Kalman filter was performed, which showed excellent ability to adjust the estimation period relative to the sensor property. As a result, when a three-axis gyro sensor and a three-axis acceleration sensor were adapted in the estimator, the RMS(Root Mean Square) estimation error in simulation was retained less than 1.7[$^{\circ}$], and the estimator displayed good property on the prediction of the state in 100 ms measurement period.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.355-361
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• 2020

The squat is an exercise that can effectively improve the muscle strength of the lower body, which can be performed in a variety of places without restrictions on places including homes. However, injuries due to incorrect motion or excessive angles are frequently occurring. In this study, we developed a single sensor-based squat angle measurement system that can inform the squat angle according to the correct motion during the squat exercise. The sensor module, including the acceleration sensor and the gyro sensor, is attached to the user's thigh. The squat angle was calculated using the complementary filter complementing the pros and cons of acceleration and gyro sensor. It was found that the calculated squat angle showed the proper correlation compared to the squat angle measured by a goniometer, and the influence of the coefficient of the complementary filter on the accuracy was evaluated.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.65-74
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• 2010

At the aim of solving the problems appearing in traditional optical motion capturing systems such as the interference among multiple patches and the complexity of sensor and patch allocations, this paper proposes a new motion capturing system which is composed of a single camera and multiple motion sensors. A motion sensor is consisted of an acceleration sensor and a gyro sensor to detect the motion of a patched body and the orientation (roll, pitch, and yaw) of the motion, respectively. Although Image information provides the positions of the patches in 2D, the orientation information of the patch motions acquired by the motion sensors can generate 3D pose of the patches using simple equations. Since the proposed system uses the minimum number of sensors to detect the relative pose of a patch, it is easy to install on a moving body and can be economically used for various applications. The performance and the advantages of the proposed system have been proved by the experiments.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.54-62
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• 2017

Currently, falling to industrial field workers is causing serious injuries. Therefore, many researchers are actively studying the fall by using acceleration sensor, gyro sensor, pressure sensor and image information.Also, as the spread of smartphones becomes common, techniques for determining the fall by using an acceleration sensor built in a smartphone are being studied. The proposed method has complexity due to fusion of various sensor data and it is still insufficient to develop practical application. Therefore, in this paper, we use acceleration sensor module built in smartphone to collect acceleration data, propose a simple falling algorithm based on accelerometer sensor data after normalization and preprocessing, and implement an Android based app.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.10
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• pp.83-90
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• 2007

The vehicle dynamic control system needs to detect the yaw rate of vehicle and a yaw rate sensor is required as a central component. Therefore, A sensor on the basic of the "tuning fork method" for automotive controls is being developed. The sensor was fabricated by the surface micro machining process to miniaturize its size. The sensor output offset is ${\pm}0.37^{\circ}/sec$ in the room temperature. The resonance frequency of the fabricated yaw rate sensor is measured to 5.29kHz for the drive mode. Tests of the sensor demonstrate that its performance is equivalent to that required for implementation of a yaw control system. Vehicle handling and safety are substantially improved using the sensor to implement yaw control.