• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.321-340
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• 2016

The Compressive Video Sensing (CVS) is a useful technology for wireless systems requiring simple encoders but handling more complex decoders, and its rate-distortion performance is highly affected by the quantization of measurements and reconstruction of video frame, which motivates us to presents the Space-Time Quantization (ST-Q) and Motion-Aligned Reconstruction (MA-R) in this paper to both improve the performance of CVS system. The ST-Q removes the space-time redundancy in the measurement vector to reduce the amount of bits required to encode the video frame, and it also guarantees a low quantization error due to the fact that the high frequency of small values close to zero in the predictive residuals limits the intensity of quantizing noise. The MA-R constructs the Multi-Hypothesis (MH) matrix by selecting the temporal neighbors along the motion trajectory of current to-be-reconstructed block to improve the accuracy of prediction, and besides it reduces the computational complexity of motion estimation by the extraction of static area and 3-D Recursive Search (3DRS). Extensive experiments validate that the significant improvements is achieved by ST-Q in the rate-distortion as compared with the existing quantization methods, and the MA-R improves both the objective and the subjective quality of the reconstructed video frame. Combined with ST-Q and MA-R, the CVS system obtains a significant rate-distortion performance gain when compared with the existing CS-based video codecs.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.9
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• pp.1480-1490
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• 1997

A method by which the system parameter matrices can be estimated from measured time data of excitation force and acceleration has been studied. The acceleration data are integrated numerically to obtain the velocities and displacements, and the systm parameters are estimated from these data by solving equations of motion. The characteristics of the method have been investigated through its application to simulated data of 1 DOF and 2 DOF systems and experimental data measured from a simple structure. It was found that the method is very sensitive to measurement noise and the accuracy of the estimated parameters can be improved by averaging the repeatedly measured data and removing the noise. One of the main advantages of the parameter estimation method is that no a priori information about the system under test is required. The method can be easily extended to non-linear parameter estimation.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.770-783
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• 2012

In this paper, we propose a novel structure recovery algorithm in the projective space using image feature points. We use normalized image feature coordinates for the numerical stability. To acquire an initial value of the structure and motion, we decompose the scaled measurement matrix using the singular value decomposition. When recovering structure and motion in projective space, we introduce matrix decomposition constraints. In the reconstruction procedure, a nonlinear iterative optimization technique is used. Experimental results showed that the proposed method provides proper accuracy and the error deviation is small.

• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.240-247
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• 2020

This paper presents a novel knitted data glove system for pattern classification of hand posture. Several experiments were conducted to confirm the performance of the knitted data glove. To find better sensor materials, the knitted data glove was fabricated with stainless-steel yarn and silver-plated yarn as representative conductive yarns, respectively. The result showed that the signal of the knitted data glove made of silver-plated yarn was more stable than that of stainless-steel yarn according as the measurement distance becomes longer. Also, the pattern classification was conducted for the performance verification of the data glove knitted using the silver-plated yarn. The average classification reached at 100% except for the pointing finger posture, and the overall classification accuracy of the knitted data glove was 98.3%. With these results, we expect that the knitted data glove is applied to various robot fields including the human-machine interface.

• Journal of Drive and Control
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• v.14 no.1
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• pp.35-40
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• 2017

Whereas fuel economy of a vehicle is measured using a chassis dynamometer, that of construction machinery such as an excavator shall be presumably measured using simulated work cycle. In order to measure fuel consumption under a simulated work cycle, a measurement methodology, while excavator operates in dynamic(transient) motion, needs to be examined and developed. In this work, three methods (gravimetry, ECU CAN signal and mass flow meter) are studied and compared. This work reveals that when ECU CAN signal is properly calibrated and evaluated, compared to gravimetry or mass flowmeter, it could be used to measure fuel consumption with accuracy and thus for approval of the fuel economy of construction machinery.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.429-435
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• 2011

This paper presents a sensor fusion-based estimation of heading and a Bezier curve-based motion planning for unmanned ground vehicle. For the vehicle to drive itself autonomously and safely, it should estimate its pose with sufficient accuracy in reasonable processing time. The vehicle should also have a path planning algorithm that enables to adapt to various situations on the road, especially at intersections. First, we address a sensor fusion-based estimation of the heading of the vehicle. Based on extended Kalman filter, the algorithm estimates the heading using the GPS, IMU, and wheel encoders considering the reliability of each sensor measurement. Then, we propose a Bezier curve-based path planner that creates several number of path candidates which are described as Bezier curves with adaptive control points, and selects the best path among them that has the maximum probability of passing through waypoints or arriving at target points. Experiments under various outdoor conditions including at intersections, verify the reliability of our algorithm.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.330-335
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• 2017

Cardiac arrest is owing to the failure of the heart that makes the blood circulation stop. Arrested blood circulation prevents the supply of the oxygen and the glucose and it results the loss of consciousness and, finally, brain death. Many public institution installed the AED for emergency treatment, but, it is not efficient when the patient is alone. In this paper, we made multiplexed wearable device for cardiac arrest detection. With this device, we measure the individual's electrocardiography, heart sound and motion. If the cardiac arrest is detected, the device make a warning horn and transmit the signal for defibrillation. We obtain 98.33% of ECG data, 94.5% of PCG data and 98.38% of IMU data accuracy for each evaluation and 93.33% accuracy for integrated evaluation.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.5
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• pp.415-422
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• 2015

Ballscrews are important motion transfer and positioning units of industrial machinery and precision machines. Positioning accuracy of the feed drive system depends upon axial stiffness of ballscrew systems. As the nut stiffness depends upon preload and operating conditions, analytical modeling of the stiffness is performed through the contact and body deformation analysis. For accurate contact analysis, the contact angle variation between balls and grooves is incorporated in the developed model. To verify the developed mathematical stiffness model, experiments are conducted on the test-rig. Through the uncertainty analysis according to GUM (Guide to the expression of Uncertainty in Measurement), it is confirmed that the formulated stiffness model has over 85% estimation accuracy. After constructing the ballscrew DB, a quick turnaround system for the nut stiffness estimation has been developed in this research.

• Journal of Sensor Science and Technology
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• v.25 no.3
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• pp.202-207
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• 2016

Estimation of vertical position is critical in applications of sports science and fall detection and also controls of unmanned aerial vehicles and motor boats. Due to low accuracy of GPS(global positioning system) in the vertical direction, the integration of IMU(inertial measurement unit) with the GPS is not suitable for the vertical position estimation. This paper investigates an IMU-barometer integration for estimation of vertical position (as well as vertical velocity). In particular, a new two-step Kalman/complementary filter is proposed for accurate and efficient estimation using 6-axis IMU and barometer signals. The two-step filter is composed of (i) a Kalman filter that estimates vertical acceleration via tilt orientation of the sensor using the IMU signals and (ii) a complementary filter that estimates vertical position using the barometer signal and the vertical acceleration from the first step. The estimation performance was evaluated against a reference optical motion capture system. In the experimental results, the averaged estimation error of the proposed method was 19.7 cm while that of the raw barometer signal was 43.4 cm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1695-1702
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• 2015

This paper presents the characteristic analysis and experiment of force characteristics in permanent magnet linear synchronous motor for accuracy prediction of linear motion machine tools. In particular, the pitching moment resulting from attraction force ripple has been analysed and tested. Firstly, we analysed the characteristics of detent force, attraction force, and pitching moment in permanent magnet linear synchronous motor according to the design techniques such as auxiliary teeth, chamfering, and permanent magnet skewing. In addition, we suggested the experimental set for measurement of pitching moment. Finally, the results from measurement shows the good agreement with those obtained from finite element analysis results.