• Smart Media Journal
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• v.5 no.3
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• pp.35-41
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• 2016

In this paper, we propose hand-gesture signal recognition based on EPS(Electronic Potential Sensor) using Deep learning model. Extracted signals which from Electronic field based sensor, EPS have much of the noise, so it must remove in pre-processing. After the noise are removed with filter using frequency feature, the signals are reconstructed with dimensional transformation to overcome limit which have just one-dimension feature with voltage value for using convolution operation. Then, the reconstructed signal data is finally classified and recognized using multiple learning layers model based on deep learning. Since the statistical model based on probability is sensitive to initial parameters, the result can change after training in modeling phase. Deep learning model can overcome this problem because of several layers in training phase. In experiment, we used two different deep learning structures, Convolutional neural networks and Recurrent Neural Network and compared with statistical model algorithm with four kinds of gestures. The recognition result of method using convolutional neural network is better than other algorithms in EPS gesture signal recognition.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.04a
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• pp.17-21
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• 2007

Ultrasonic inspection method is more profitable than X-ray radiographic inspection in cost and effect of defect detection such as debond, and it doesn't need special facilities. The method can also be a possible real time inspection with safety. This report explains the experiment and theoretical modeling analysis of the inspection methods of propellant/liner debond using ultrasonic multi-reflection in rocket motor. From the results, it is possible to detect the defect of propellant/liner debond and its signal is distinguishable with normal. And, it is approximately coincide with both experimental signal and modeling.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2196-2197
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• 2011

본 논문에서는 열차이동시 운행선구에 따른 운동에너지를 모델링하기 위한 중력 모델링에 대한 연구를 수행하였다. 중력 모델링은 해당 노선에 투입될 열차의 물리적 특성 및 성능(제동)특성과 선로정보, 신호정보(최고운행속도) 등의 정보를 고려하여 열차의 이동을 물리적으로 모델링해야한다. 차량의 운동을 해석하기 위하여 차량은 하나의 질점으로 모델링하고, 그 질점은 한 방향으로만 직선 운동하는 자유도 모델이다. 차량의 견인력, 제동력, 운행저항력, 선로구배에 의한 중력 공헌력은 그 질점에 작용하는 힘으로 모델링 되어있다. 차량의 이동에 따른 모델링을 통해 산출된 열차 에너지는 고정폐색으로 운용되는 열차제어시스템의 폐색길이를 결정하는데 사용될 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1756-1759
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• 2015

웨어러블 디바이스의 등장과 여러종류의 센서탑재로 행위데이터를 수집하는것이 수월해졌다. 행위패턴 모델링에 앞서 사용자의 행위에따른 신호변화와 신호패턴을 파악하기위해 분석을 실시하였다. Moto360의 3축 가속도 센서를 이용 사용자에 행위에대한 센서신호값을 수집하여 행위에따른 신호값을 수집하였으며, 수집된 신호값과 신호값으로부터 SVM(Signal Vector Magnitude)값을 구해 사용자의 각 행위들에 대해 신호값과 SVM값의 특징을 분석하여 측정 신호값으로부터 행위를 인식할수 있도록 시도하였다.

• Journal of the Korea Computer Industry Society
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• v.6 no.5
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• pp.675-686
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• 2005

This paper proposes a new method of mathematically modeling and computer simulating television ghosts wherein television signals that have undergone multipath fading are generated without using approximations by considering the attenuation, time delay, phase, and timing jitter between consecutive frames. Conventional methods used polynomial interpolation or complex arithmetic to take into account the ghost phase, but our method uses only real arithmetic by employing the Hilbert transform and also reduces the computation time using the FFT (fast Fourier transform) algorithm. Furthermore, it is also possible to observe the transmit waveforms in both RF and IF ranges. Various ghost patterns generated in software provide for essential data required for the development of ghost canceling algorithms, and are deemed to be very useful in analyzing the constituent blocks of the transmitter and receiver chain in television broadcasting. The development of ghost cancelers needs to be preceded by the task of mathematically modeling ghosts and their extensive computer simulations.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.10 s.101
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• pp.1010-1019
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• 2005

In this paper, we used neural network technique to model memory effects of RF power amplifier which is fed by two-tone input signals. The memory effects in power amplifier were identified by observing the unsymmetrical distribution of IMD(Inter-Modulation Distortion) measurements with the change of tone spacings and power levels. Different asymmetries of IMD were also found at different center frequencies. We applied TDNN technique to model LDMOS power amplifier based on two tone IMD data, and the accuracy was very high compared to other modeling methods such as the(memoryless) adaptive modeling method.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1998.06a
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• pp.1109-1112
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• 1998

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1996.10a
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• pp.472-475
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• 1996

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1996.06a
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• pp.140-143
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• 1996

• Proceedings of the Korean Nuclear Society Conference
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• 1999.10a
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• pp.100-100
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• 1999