• Journal of Soil and Groundwater Environment
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• v.8 no.3
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• pp.1-7
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• 2003

A modified model was developed for solute transport in porous media that can consider the movement of an LNAPL above the water table. From the results of sensitivity analyses with and without considering LNAPL movement, there are some differences according to the hydraulic gradient, the quantity of oil leakage and dispersivity. The mean deviation between the model in this study and a conventional model without LNAPL movement increases as the hydraulic gradient decreases and the quantity of oil leakage increases. Variation of dispersivity has no influence on the magnitude of the mean deviation. However, the spatial distribution of the deviation between the two models is wider as dispersivity increases. Furthermore, groundwater is at high risk of contamination in the vertical direction in the case that transverse dispersion value is large. A conventional model underestimates the concentration of solute in an aquifer where the movement of an LNAPL cannot be negligible: Based on the study results, it is important to understand how fast the LNAPL moves on the water table for realistic prediction of solute transport in an aquifer with the movable LNAPL on the water table.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.609-620
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• 2016

Recently, there are active studies on a forward collision warning system to prevent the accidents and improve convenience of drivers. For collision evasion, the vehicle detection system is required. In general, existing learning-based vehicle detection methods use the entire appearance of the vehicles from rear-view images, so that each vehicle types should be learned separately since they have distinct rear-view appearance regarding the types. To overcome such shortcoming, we learn Haar-like features from the lower part of the vehicles which contain tail lights to detect vehicles leveraging the fact that the lower part is consistent regardless of vehicle types. As a verification procedure, we detect tail lights to distinguish actual vehicles and non-vehicles. If candidates are too small to detect the tail lights, we use HOG(Histogram Of Gradient) feature and SVM(Support Vector Machine) classifier to reduce false alarms. The proposed forward vehicle detection method shows accuracy of 95% even in the complicated images with many buildings by the road, regardless of vehicle types.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.562-568
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• 2016

In this paper, we focus on recognition of speed-limit signs among a few types of traffic signs because speed-limit sign is closely related to safe driving of drivers. Although histogram of oriented gradient (HOG) and local binary patterns (LBP) are representative features for object recognition, these features have a weakness with respect to rotation, in that it does not consider the rotation of the target object when generating patterns. Therefore, this paper propose the fast rotation-invariant binary patterns (FRIBP) algorithm to generate a binary pattern that is robust against rotation. The proposed FRIBP algorithm deletes an unused layer of the histogram, and eliminates the shift and comparison operations in order to quickly extract the desired feature. The proposed FRIBP algorithm is successfully applied to German Traffic Sign Recognition Benchmark (GTSRB) datasets, and the results show that the recognition capabilities of the proposed method are similar to those of other methods. Moreover, its recognition speed is considerably enhanced than related works as approximately 0.47second for 12,630 test data.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.338-345
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• 2020

Versatile Video Coding (VVC), which has been developing as a next generation video coding standard, has adopted various techniques to achieve more than twice the compression performance of HEVC (High Efficiency Video Coding). The recently released VVC Test Model (VTM) shows 38% Bjontegaard Delta bitrate (BD-rate) improvement and 9x/1.6x encoding/decoding complexity over HEVC. In order to reduce such increased complexity, various fast algorithms have been proposed. In this paper, gradient-based methods of fast intra mode decision and block splitting are presented. Experimental results show that, compared to VTM6.0, the proposed method gives up to 65% encoding time reduction with 3.54% BD-rate loss in All-Intra (AI) configuration.

• Korean journal of applied entomology
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• v.45 no.3 s.144
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• pp.301-307
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• 2006

A ligniolytic bacterial strain was isolated from the digestive tract of a red dragonfly, Sympetrum dopressiusculum. It was identified as a Serratia marcescens HY-5 by 16S rDNA sequence analysis and physiological and biochemical analysis. The isolated strain showed proportional increase of ligninolytic activity to the cell growth in the culture media which include lignin compounds. It showed about 25-45% decomposition of lignin compound by 48 hr incubation especially, showed effective decomposition of monomer lignin compounds, vanillin and guaiacol, and a dimer, dealkaline lignin. PCR amplification of 16S rDNA followed by denaturing gradient gel electrophoresis analysis showed high density of S. marcescens HY-5 in the gut of the S. depressiusculum at both gut samples which collected at different site.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.62-69
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• 2017

Accurate detection of traffic lights is very important for the advanced driver assistance system (ADAS). There have been many research developments in this area. However, conventional of image processing methods are usually sensitive to varying illumination conditions. This paper proposes a traffic light detection algorithm to overcome this situation. The proposed algorithm first detects the candidates of traffic light using the proposed color map and hue-saturation-value (HSV) Traffic lights are then detected using the conventional histogram of oriented gradients (HOG) descriptor and support vector machine (SVM). Finally, the proposed Multilayer HOG descriptor is used to determine the direction information indicated by traffic lights. The proposed algorithm shows a high detection rate in real-time.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.206-217
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• 2018

We develop the signal processing method for adaptive implementing direction of signal and the frequency sensitivity of human visual system(HVS). Existing multiband energy scaling method makes ringing artifact because it does not consider signal direction. To solve this problem, we use block gradient for signal direction in addition to existing method. And we use the fact that frequency component of signal is more sensitive than value of signal over human eyes. we enhance the signal according to contrast sensitivity function(CSF) which is the model of frequency sensitivity of human eye. Compared that the existing analysis models only improve the efficiencies in the existing systems, the developed method can process the image signals to be more desirable and suitable to HVS.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.992-1001
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• 2019

In many applications, images acquired from multiple cameras are stitched to form an image with a wide viewing angle. We propose a method of estimating a seam line using motion information to stitch multiple images without distortion of the moving object. Existing seam estimation techniques usually utilize an energy function based on image gradient information and parallax. In this paper, we propose a seam estimation technique that prevents distortion of moving object by adding temporal motion information, which is calculated from the gradient information of each frame. We also propose a measure to quantify the distortion level of stitched images and to verify the performance differences between the existing and proposed methods.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.870-878
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• 2019

In this paper, we propose an Attention-based BLSTM for predicting the video compression standard of a video. Recently, in NLP, many researches have been studied to predict the next word of sentences, classify and translate sentences by their semantics using the structure of RNN, and they were commercialized as chatbots, AI speakers and translator applications, etc. LSTM is designed to solve the gradient vanishing problem in RNN, and is used in NLP. The proposed algorithm makes video compression standard prediction possible by applying BLSTM and Attention algorithm which focuses on the most important word in a sentence to a bitstream of a video, not an sentence of a natural language.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.633-642
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• 2019

Recently, the convolution neural network (CNN) model at a single image super-resolution (SISR) have been very successful. The residual learning method can improve training stability and network performance in CNN. In this paper, we propose a SISR using recursive residual network architecture by introducing dense skip connections for learning nonlinear mapping from low-resolution input image to high-resolution target image. The proposed SISR method adopts a method of the recursive residual learning to mitigate the difficulty of the deep network training and remove unnecessary modules for easier to optimize in CNN layers because of the concise and compact recursive network via dense skip connection method. The proposed method not only alleviates the vanishing-gradient problem of a very deep network, but also get the outstanding performance with low complexity of neural network, which allows the neural network to perform training, thereby exhibiting improved performance of SISR method.