• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.312-316
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• 2004

In this study, a spatial 3-dof haptic mechanism is implemented. The implemented mechanism does not employ the gear transmissions as velocity reducers for all three joints but uses wire-based transmissions, thereby it is able to minimize the frictions significantly. Also, by employing the structure of the four-bar mechanism to drive third joint from close to the base, the mechanism is able to minimize the inertia effect from the third actuator very effectively. Its kinematic analysis such as position and velocity analyses are performed first. Then, its operating software development, hardware implementation, and the related interfaces between a PC and the implemented Haptic device are completed. To evaluate its potential and its performance as a haptic device, a experiment generating a virtual constraint in a operational task space is conducted and preliminary results are discussed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1138-1141
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• 1995

An arc sensor for seam tracking is developed to automate sub-assembly welding in shipbuilding. We utilize a moving average method, which produces an effect of low-pass filter, to generate the position compensation. Therefore the sensor is able to modify the path of the weld seam in real time. By simplifying the compension process, the tunning time is reduced so that operators react quickly. It turns out that this sensor is highly reliable and it is installed and being used in SHI Keoje shipbuilding yard.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5C
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• pp.527-539
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• 2009

In this paper, we propose an asynchonous ICI (Inter-Cell Interference) mitigation techniques for 3GPP LTE MIMO-OFDMA down-link receiver. An increasing in symbol timing misalignments may occur relative to sychronous network as the result of BS (Base Station) timing differences. Such symbol synchronization errors that exceed the guard interval or the cyclic prefix duration may result in MAI (Multiple Access Interference) for other carriers. In particular, at the cell boundary, this MAI becomes a critical factor, leading to degraded channel throughput and severe asynchronous ICI. Hence, many researchers have investigated the interference mitigation method in the presence of asynchronous ICI and it appears that the knowledge of the SCM (Spatial Covariance Matrix) of the asynchronous ICI plus background noise is an important issue. Generally, it is assumed that the SCM estimated by using training symbols. However, it is difficult to measure the interference statistics for a long time and training symbol is also not appropriate for MIMO-OFDMA system such as LTE. Therefore, a noise reduction method is required to improve the estimation accuracy. Although the conventional time-domain low-pass type weighting method can be effective for noise reduction, it causes significant estimation error due to the spectral leakage in practical OFDM system. Therefore, we propose a time-domain sinc type weighing method which can not only reduce the noise effectively minimizing estimation error caused by the spectral leakage but also implement frequency-domain moving average filter easily. By using computer simulation, we show that the proposed method can provide up to 3dB SIR gain compared with the conventional method.

• Journal of IKEEE
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• v.18 no.3
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• pp.376-382
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• 2014

In this paper, we propose a advanced attitude PID controller and sensor fusion method robust to the vibration of the quadcopter unmanned air vehicle using four BLDC motors. When the gyro sensor and acceleration sensor are fused, a complementary filter is designed to ignore the vibrations generated by the motors and to complement the drawbacks. As a result, we obtain accurate results than using each sensor. Also, it is possible to obtain a low delay results in robust to vibration than the low-pass filter or moving average filter, which is generally used for quadcopter. And we improved D controller, which have being used for attitude control of quadcopter, to quadcopter using gyro sensor. it was confirmed that the attitude is stabilized and error is reduced By using gyro sensor output instead of variation of estimated angle in D control.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8A
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• pp.671-678
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• 2005

This paper proposed symbol rate method which does not require a priori knowledge on the symbol rate and simplified modulation identification method to classify BPSK, QPSK, 8PSK signal. In order to estimate the unknown symbol rate, sliding FFT and simple moving average to estimate the spectrum of the signals is utilized, and sliding window and decimation, LPF blcok to estimate the proper symbol rate is used. Although conventional modulation ID method must use SNR value as the test statistics, the receiver cannot estimate the SNR value since the receiver cannot know the modulation type at the start of communication, and bit resolution is high due to using nonlinear function such as log, cosh. Therefore, we proposed the simplified fixed SNR value method. The performance of symbol rate estimation and modulation ID is shown using Monte Carlo computer simulation. This paper show that symbol rate estimation also has good performance in low SNR, and proposed simplified fixed SNR method has almost equivalent performance compared to conventional method.

• Smart Media Journal
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• v.9 no.4
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• pp.102-108
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• 2020

In this paper, we propose a real-time method of detecting hand motions and extracting the signal frame induced by EF(Electric Field) sensors. The signal induced by hand motion includes not only noises caused by various environmental sources as well as sensor's physical placement, but also different initial off-set conditions. Thus, it has been considered as a challenging problem to detect the motion signal and extract the motion frame automatically in real-time. In this study, we remove the PLN(Power Line Noise) using LPF with 10Hz cut-off and successively apply MA(Moving Average) filter to obtain clean and smooth input motion signals. To sense a hand motion, we use two thresholds(positive and negative thresholds) with offset value to detect a starting as well as an ending moment of the motion. Using this approach, we can achieve the correct motion detection rate over 98%. Once the final motion frame is determined, the motion signals are normalized to be used in next process of classification or recognition stage such as LSTN deep neural networks. Our experiment and analysis show that our proposed methods produce better than 98% performance in correct motion detection rate as well as in frame-matching rate.