• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.171-176
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• 2019

Drones are highly utilized because they can efficiently acquire long-distance videos. In drone operation, the speed, which is the magnitude of the velocity, can be set, but the moving direction cannot be set, so accurate information about the drone's movement should be estimated. In this paper, we estimate the velocity of the drone moving at a constant speed and direction. In order to estimate the drone's velocity, the displacement of the target frame to minimize the sum of absolute difference (SAD) of the reference frame and the target frame is obtained. The ground truth of the drone's velocity is calculated using the position of a certain matching point over all frames. In the experiments, a video was obtained from the drone moving at a constant speed at a height of 150 meters. The root mean squared error (RMSE) of the estimated velocities in x and y directions and the RMSE of the speed were obtained showing the reliability of the proposed method.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.3
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• pp.103-110
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• 2000

Shaft encoder which encodes the rotational angle of a shaft becomes more important recently due to factory automation and office automation. Although an absolute type encoder is more dsirable due to its convenience an incremental encoder is commonly used because of its cost and technical difficulties Fabricating a high resolution absolute encoder is very diff-cult because the physical size is limited by currently available technology. In order to overcome this difficulty Moire fringe can be used incorporated with gray code. In order to measure the position of fringes which move as the code disk rotates a neural network was developed in this paper. Formerly fringe position is usually measured by a sophisticated software which needs a little long calculation time. However using nerual network method can eliminate such calculation time even though it needs learning job The pro-posed method is verified through several experiments.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.320-325
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• 1999

An absolute type shaft encoder which utilized moire fringe will be presented in this paper. Linear moire fringe is commonly used to measure the displacement of the linear motion. However, an absolute encoder which measure the rotation angle of a shaft is operated usually with a code disk which the gray code pattern is printed on. Such encoder has inherently resolution limit because of the patterning mechanism and sensing mechanism. In order to measure the position of fringes which move as the code disk rotates, neural network was developed in this paper. Formerly fringe position is usually measured by a sophisticated software, which needs a little long calculation time. However, using neural network method can eliminate such calculation time, even though it needs learning job. The proposed method is verified through several experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.694-698
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• 1987

In this paper, the primitive and double combined motion classification of the arm is discussed using pattern recognition of EM signal. The EM signals are detected from Ag-Ag/Cl surface electrodes, and IBM PC, calculated the Likelyhood probability and the decision function on the feature space of integral absolute value. Multiclass decision rule is introduced for higher decision rate. On our experimental results from expert simulator, the decision rate of more than 78% can be obtained.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.3
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• pp.1-6
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• 2016

As the convergence speed of time domain adaptive algorithm on the LMS(Least Mean Square) becomes slow when eigen value distribution width is spread, So variable step size algorithm is used widely. But it needs a lot of calculation load. In this paper we consider new algorithm, which can reduce calculations and improve convergence speed, uses instantaneous absolute value of average noise signal adapting the exponential function. For the performance of proposed algorithm is tested and simulated to system generator. As the result we show the variable step size adaptive algorithm in proportion to instantaneous absolute value is more stable and efficient than others.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.149-159
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• 2011

In this paper, we study the problem of how to design a medical-grade wireless local area network (WLAN) for healthcare facilities. First, unlike the IEEE 802.11e MAC, which categorizes traffic primarily by their delay constraints, we prioritize medical applications according to their medical urgency. Second, we propose a mechanism that can guarantee absolute priority to each traffic category, which is critical for medical-grade quality of service (QoS), while the conventional 802.11e MAC only provides relative priority to each traffic category. Based on absolute priority, we focus on the performance of real-time patient monitoring applications, and derive the optimal contention window size that can significantly improve the throughput performance. Finally, for proper performance evaluation from a medical viewpoint, we introduce the weighted diagnostic distortion (WDD) as a medical QoS metric to effectively measure the medical diagnosability by extracting the main diagnostic features of medical signal. Our simulation result shows that the proposed mechanism, together with medical categorization using absolute priority, can significantly improve the medical-grade QoS performance over the conventional IEEE 802.11e MAC.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.686-692
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• 2016

A Crimp Force Monitor (CFM) is equipment for detecting crimp errors by analyzing crimp signals obtained from force and strain sensors. The analysis is commonly performed by aligning a measured crimp signal with a reference signal and comparing their difference. Current analysis methods often suffer from wrong alignments that result in false negative detections. This paper presents a new crimp signal analysis method in CFM. First, a falling edge alignment is proposed that matches falling edges of the measured and the reference signals by minimizing the absolute difference summation. Second, a signal parameter error is introduced to evaluate the crimp quality difference between the measured signal and the reference. For calculating the signal parameter error, part of a signal is identified and divided into several regions to maximize the signal parameter errors. Experiments showed that the proposed method can improve the signal alignment and accurately detect bad crimps especially with the strain sensor.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2E
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• pp.56-59
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• 2006

A novel audio watermark design using Hilbert transform is proposed, and its superiority of performance over the existing design using the absolute values of the audio signal is demonstrated experimentally with measurements of bit error rate (BER) in correlation based decoder. (Classification No. 3.3)

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.682-685
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• 2004

This paper investigated a method for classifying emotional states by using pulse wave signal. It focused on finding effective features for emotional state classification. The emptional states considered here consisted of interest and neutral. Classification experiments utilized 65 and 60 samples of interest and neutral states respectively. We have investigated 19 features derived from pulse wave signals by using both time domain and frequency domain analysis methods with 2 classifiers of minimum distance (normalized Euclidean distanece) and ${\kappa}$-Nearest Neighbour. The Leave-one-out cross validation was used as an evaluation mehtod. Based on experimental results, the most efficient features were a combination of 4 features consisting of (i) the mean of the first differences of the smoothed pulse rate time series signal, (ii) the mean of absolute values of the second differences of thel normalized interbeat intervals, (iii) the root mean square successive difference, and (iv) the power in high frequency range in normalized unit, which provided 80.8% average accuracy with ${\kappa}$-Nearest Neighbour classifier.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1268-1270
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• 1987

This paper suggests several simple and efficient algorithms for detecting the ECG Signal by Microcomputer's software. The ECG signal detection was performed with the Linear Approximation and the feature extraction. The linear transformation approximates a given waveform by a piecewise-linear function with a preset upper bound on the absolute error between the functional values of the original function and the approximation. And the feature extraction from ECG signal, the features are different wave amplitudes, durations and interwave intervals, used the slope, the amplitude and time-Duration of ECG Sinal.