• Proceedings of the Korean Society for Language and Information Conference
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• 1995.02a
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• pp.49-56
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• 1995

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.147-147
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• 1994

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.05a
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• pp.62-63
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• 2001

• Journal of electromagnetic engineering and science
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• v.10 no.2
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• pp.61-66
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• 2010

This paper is concerned with an analysis of the back scattering of electromagnetic waves from a target moving along random rough surfaces such as the desert, and sea. First, the discrete ray tracing method(DRTM) is introduced, and then, this method is applied to the back scattering problem in order to investigate the effect of the back scattering from random rough surfaces on the electric field intensities. Finally, numerical examples of various height deviations of the Gaussian type of rough surfaces are shown. It is numerically demonstrated that the back scattering is dominated by the diffractions related to the reflections from the random rough surfaces.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.180-181
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• 2015

In this paper, we present a consistent and efficient edit propagation method that is applied for light field data. Unlike conventional sparse edit propagation, the coherency between light field sub-aperture images is fully considered by utilizing light field consistency in the optimization framework. Instead of directly solving the optimization function on all light field sub-aperture images, the proposed optimization framework performs sparse edit propagation in the extended focus image domain. The extended focus image is the representative image that contains implicit depth information and the well-focused region of all sub-aperture images. The edit results in the extended focus image are then propagated back to each light field sub-aperture image. Experimental results on test images captured by a Lytro off-the-shelf light field camera confirm that the proposed method provides robust and consistent results of edited light field sub-aperture images.

• Advances in aircraft and spacecraft science
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• v.7 no.3
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• pp.187-202
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• 2020

The exhaust nozzle serves back pressure of Pulse detonation combustor, so combustion chamber gets sufficient pressure for propulsion. In this context recent researches are focused on influence of nozzle effect on single cycle detonation wave propagation and propulsion performance of PDE. The effects of various nozzles like convergent-divergent nozzle, convergent nozzle, divergent nozzle and without nozzle at exit section of detonation tubes were computationally investigated to seek the desired propulsion performance. Further the effect of divergent nozzle length and half angle on detonation wave structure was analyzed. The simulations have been done using Ansys 14 Fluent platform. The LES turbulence model was used to simulate the combustion wave reacting flows in combustor with standard wall function. From these numerical simulations among four acquaint nozzles the highest thrust augmentation could be attained in divergent nozzle geometry and detonation wave propagation velocity eventually reaches to 1830 m/s, which is near about C-J velocity. Smaller the divergent nozzle half angle has a significant effect on faster detonation wave propagation.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2001.11a
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• pp.91-95
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• 2001

This paper presents that photonic bandgap structures suppressing the propagation of surface waves can improve the performance of the patch antennas on thick high dielectric constant substrate. The forbidden propagation of surface wave due to the photonic bandgap enhances the radiation efficiency and reduce the back radiation drastically with maintaining the small size and wide bandwidth of the antennas.

• The Journal of the Acoustical Society of Korea
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• v.13 no.6
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• pp.5-14
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• 1994

This paper is about an experiment of speaker-independent automatic Korean spoken words recognition using Multi-Layered Perceptron and Error Back-propagation algorithm. The object words are 50 citynames of D.D.D local numbers. 43 of those are 2 syllables and the rest 7 are 3 syllables. The words were not segmented into syllables or phonemes, and some feature components extracted from the words in equal gap were applied to the neural network. That led independent result on the speech duration, and the PARCOR coefficients calculated from the frames using linear predictive analysis were employed as feature components. This paper tried to find out the optimum conditions through 4 differerent experiments which are comparison between total and pre-classified training, dependency of recognition rate on the number of frames and PAROCR order, recognition change due to the number of neurons in the hidden layer, and the comparison of the output pattern composition method of output neurons. As a result, the recognition rate of $89.6\%$ is obtaimed through the research.

• Journal of KIISE
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• v.44 no.6
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• pp.607-612
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• 2017

This paper presents an attempt to apply Deep Learning technology to solve the problem of forecasting floods in urban areas. We employ Recurrent Neural Networks (RNNs), which are suitable for analyzing time series data, to learn observed data of river water and to predict the water level. To test the model, we use water observation data of a station in the Trinity river, Texas, the U.S., with data from 2013 to 2015 for training and data in 2016 for testing. Input of the neural networks is a 16-record-length sequence of 15-minute-interval time-series data, and output is the predicted value of the water level at the next 30 minutes and 60 minutes. In the experiment, we compare three Deep Learning models including standard RNN, RNN trained with Back Propagation Through Time (RNN-BPTT), and Long Short-Term Memory (LSTM). The prediction quality of LSTM can obtain Nash Efficiency exceeding 0.98, while the standard RNN and RNN-BPTT also provide very high accuracy.

• Journal of Navigation and Port Research
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• v.32 no.8
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• pp.589-596
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• 2008

In this paper, Artificial Neural Network(ANN) is applied to automatic berthing control for a ship. ANN is suitable for a maneuvering such as ship's berthing, because it can describe non-linearity of the system. Multi-layer perceptron which has more than one hidden layer between input layer and output layer is applied to ANN. Using a back-propagation algorithm with teaching data, we trained ANN to get a minimal error between output value and desired one. For the automatic berthing control of a containership, we introduced low speed maneuvering mathematical models. The berthing control with the structure of 8 input layer units in ANN is compared to 6 input layer units. From the simulation results, the berthing conditions are satisfied, even though the berthing paths are different.