• Journal of Digital Contents Society
• /
• v.8 no.4
• /
• pp.441-449
• /
• 2007

In video coding, motion estimation is a process to estimate the pixel of the current frame from the reference frame, which affects directly the predictive quality and the encoding time. This paper is related to AHHS(Adaptive Hierarchical Hexagon Search) using spatio-temporal motion activity for fast motion estimation. The proposed method defines the spatio-temporal motion activity of the current macroblock using the motion vectors of its spatio-temporally adjacent macroblocks, and then conventional AHS(Adaptive Hexagon Search) is performed if the spatio-temporal motion activity is lower, otherwise, hierarchical hexagon search is performed on a multi-layered hierarchical space constructed by multiple sub-images with low frequency in wavelet transform. In the paper, based on computer simulation results for multiple video sequences with different motion characteristics, the performance of the proposed method was analysed and assessed in terms of the predictive quality and the computational time. Experimental results indicate that the proposed method is both suitable for (quasi-) stationary and large motion searches. The proposed method could keep the merit of the adaptive hexagon search capable of fast estimating motion vectors and also adaptively reduce the local minima occurred in the video sequences with higher spatio-temporal motion activity.

• Journal of Broadcast Engineering
• /
• v.8 no.4
• /
• pp.473-480
• /
• 2003

In this paper, we propose a new quadtree coding a1gorithm to improve the performance of the old one. The new algorithm can process any frame of size in standard and reduce encoding and decoding time by decreasing computational load. It also improves the image quality comparing with any old quantizer based on quadtree and zerotree structure. In order for the new algorithm to be applied for real video codec, we analyze the statistical characteristics of coefficients of differential image and add a function that makes It deal with an arbitrary size of image by using new technique while the old one process by block unit. We can also improve the image quality by scaling the coefficient's value from a differential image. By comparing the performance of the new algorithm with quadtree and SPIHT, it Is shown that PSNR is improved, that the computational load is not reduced in encoding and decoding.

• The KIPS Transactions:PartB
• /
• v.18B no.6
• /
• pp.341-348
• /
• 2011

ECG(Electrocardiogram), a field of Bio-signal, is generally experimented with classification algorithms most of which are SVM(Support Vector Machine), MLP(Multilayer Perceptron). But this study modified the Random Forest Algorithm along the basis of signal characteristics and comparatively analyzed the accuracies of modified algorithm with those of SVM and MLP to prove the ability of modified algorithm. The R-R interval extracted from ECG is used in this study and the results of established researches which experimented co-equal data are also comparatively analyzed. As a result, modified RF Classifier showed better consequences than SVM classifier, MLP classifier and other researches' results in accuracy category. The Band-pass filter is used to extract R-R interval in pre-processing stage. However, the Wavelet transform, median filter, and finite impulse response filter in addition to Band-pass filter are often used in experiment of ECG. After this study, selection of the filters efficiently deleting the baseline wandering in pre-processing stage and study of the methods correctly extracting the R-R interval are needed.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.8
• /
• pp.87-96
• /
• 2014

In this paper, we propose a directionally adaptive aliasing and noise removal using dictionary learning based on space-frequency analysis. The proposed aliasing and noise removal algorithm consists of two modules; i) aliasing and noise detection using dictionary learning and analysis of frequency characteristics from the combined wavelet-Fourier transform and ii) aliasing removal with suppressing noise based on the directional shrinkage in the detected regions. The proposed method can preserve the high-frequency details because aliasing and noise region is detected. Experimental results show that the proposed algorithm can efficiently reduce aliasing and noise while minimizing losses of high-frequency details and generation of artifacts comparing with the conventional methods. The proposed algorithm is suitable for various applications such as image resampling, super-resolution image, and robot vision.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.12
• /
• pp.277-284
• /
• 2013

The analysis of phase synchronization characteristics from EEG signals is important for the understanding of information processing functionality in the brain network. In this paper, wavelet transformation(WT), Hilbert tansformation (HT), complex demodulation (CD) methods having time localization characteristics were applied to real evoked potential data and noise added simulation data with center frequencies corresponding to EEG bands for the estimation performance analysis of phase offset, phase changing point, and interband crosstalk. The WT is the best both in ${\delta}$, ${\theta}$, and ${\alpha}$ band signal decomposition, and in analyzing phase synchronization performance. The CD can be efficiently used in changing point detection under tolerant noise condition because of its abrupt performance degradation over noise endurance level. From experimental observations, the WT is the most suitable in phase synchronization application of EEG signal, and the CD can be affordable in restricted application such as changing point detection for higher bands than ${\delta}$. Particularly, WT and CD can be used to detect the changing instant of brain function by indirectly estimating the phase changing point.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.4
• /
• pp.122-128
• /
• 2016

This paper, proposes a new analysis method for the event detection of an OTDR signal. An OTDR signal was passed through the Hamming filter to remove the high frequency noise included in the signal. The signal was then passed consecutively through a differential filter to detect the events of interest. The terminal position was determined using the fact that there is a large gap between the signal and the trailing noise power beyond the terminal. This study examined the local maxima of the signal up to the terminal position and determined the peak regions. The real events were determined from the peak regions using noise information and peak threshold. Finally, the user events were found by inspecting the user peaks beyond the terminal position. The events of the OTDR signal without users are located at less than 17m compared to the optical fiber link setup. The events of the JDSU device are located less than 25m and their users are less than 5m. For the RadianTech device, the events are detected at less than 19m and the users are found in 5m. The results suggest that the proposed method is sufficiently applicable to an optical fiber link monitoring system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.22 no.5
• /
• pp.21-30
• /
• 2008

The fuel cell generation convert fuel source, and gas directly to electricity in an electro-chemical process. Unlike traditional and conventional turbine engines, the process of fuel cell generation do not burn the fuel and run pistons or shafts, and it has not revolutionary machine, so have fewer efficiency losses, low emissions and no noisy moving parts. A high power density allows fuel cells to be relatively compact source of electric power, beneficial in application with space constraints. In this system, the fuel cell itself is nearly small-sized by other components of the system such as the fuel reformer and power inverter. So, the fuel cell energy's stationary fuel cells produce reliable electrical power for commercial and industrial companies as well as utilities. In this paper, a fuel cell system has been modeled using PSCAD/EMTDC to analyze its electric signals and characteristics. Also the power quality of the fuel cell system has been evaluated and the problems which can be occurred during its operation have been studied by modeling it more detailed. Particularly, we have placed great importance on its power quality and signal characteristics when it is connected with a power grid.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.28 no.1
• /
• pp.82-89
• /
• 2024

Black ice is very difficult to recognize and reduces the friction of the road surface, causing automobile accidents. Since black ice is difficult to detect, there is a need for a system that identifies black ice in real time and warns the driver. Various studies have been conducted to prevent black ice on road surfaces, but there is a lack of research on systems that identify black ice in real time and warn drivers. In this paper, an real-time image-based analysis system was developed to identify the condition of asphalt road surface, which is widely used in Korea. For this purpose, a dataset was built for each asphalt road surface image, and then the road surface condition was identified as dry, wet, black ice, and snow using deep learning. In addition, temperature and humidity data measured on the actual road surface were used to finalize the road surface condition. When the road surface was determined to be black ice, the salt spray equipment installed on the road was automatically activated. The surface condition recognition system for the asphalt concrete pavement and black ice automatic prevention system developed in this study are expected to ensure safe driving and reduce the incidence of traffic accidents.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.9
• /
• pp.24-33
• /
• 2017

Multitemporal MODIS vegetation index (VI) data are widely used in vegetation monitoring research into environmental and climate change, since they provide a profile of vegetation activity. However, MODIS data inevitably contain disturbances caused by the presence of clouds, atmospheric variability, and instrument problems, which impede the analysis of the NDVI time series data and limit its application utility. For this reason, preprocessing to reduce the noise and reconstruct high-quality temporal data streams is required for VI analysis. In this study, a data reconstruction method for MODIS NDVI is proposed to restore bad or missing data based on the statistical properties of the oscillations in the NDVI temporal dynamics. The first derivatives enable us to examine the monotonic properties of a function in the data stream and to detect anomalous changes, such as sudden spikes and drops. In this approach, only noisy data are corrected, while the other data are left intact to preserve the detailed temporal dynamics for further VI analysis. The proposed method was successfully tested and evaluated with simulated data and NDVI time series data covering Baekdu Mountain, located in the northern part of North Korea, over the period of interest from 2006 to 2012. The results show that it can be effectively employed as a preprocessing method for data reconstruction in MODIS NDVI analysis.

• 한국신재생에너지학회:학술대회논문집
• /
• 2005.06a
• /
• pp.643-646
• /
• 2005

The gas hydrate exploration using seismic reflection data, the detection of BSR(Bottom Simulating Reflector) on the seismic section is the most important work flow because the BSR have been interpreted as being formed at the base of a gas hydrate zone. Usually, BSR has some dominant qualitative characteristics on seismic section i.e. Wavelet phase reversal compare to sea bottom signal, Parallel layer with sea bottom, Strong amplitude, Masking phenomenon above the BSR, Cross bedding with other geological layer. Even though a BSR can be selected on seismic section with these guidance, it is not enough to conform as being true BSR. Some other available methods for verifying the BSR with reliable analysis quantitatively i.e. Interval velocity analysis, AVO(Amplitude Variation with Offset)analysis etc. Usually, AVO analysis can be divided by three main parts. The first part is AVO analysis, the second is AVO modeling and the last is AVO inversion. AVO analysis is unique method for detecting the free gas zone on seismic section directly. Therefore it can be a kind of useful analysis method for discriminating true BSR, which might arise from an Possion ratio contrast between high velocity layer, partially hydrated sediment and low velocity layer, water saturated gas sediment. During the AVO interpretation, as the AVO response can be changed depend upon the water saturation ratio, it is confused to discriminate the AVO response of gas layer from dry layer. In that case, the AVO modeling is necessary to generate synthetic seismogram comparing with real data. It can be available to make conclusions from correspondence or lack of correspondence between the two seismograms. AVO inversion process is the method for driving a geological model by iterative operation that the result ing synthetic seismogram matches to real data seismogram wi thin some tolerance level. AVO inversion is a topic of current research and for now there is no general consensus on how the process should be done or even whether is valid for standard seismic data. Unfortunately, there are no well log data acquired from gas hydrate exploration area in Korea. Instead of that data, well log data and seismic data acquired from gas sand area located nearby the gas hydrate exploration area is used to AVO analysis, As the results of AVO modeling, type III AVO anomaly confirmed on the gas sand layer. The Castagna's equation constant value for estimating the S-wave velocity are evaluated as A=0.86190, B=-3845.14431 respectively and water saturation ratio is $50\%$. To calculate the reflection coefficient of synthetic seismogram, the Zoeppritz equation is used. For AVO inversion process, the dataset provided by Hampson-Rushell CO. is used.