• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2006.11a
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• pp.257-260
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• 2006

In Video streaming environment, we must consider terminal and network characteristics, such as display resolution, frame rate, computational resource, network bandwidth, etc. The JVT (Joint Video Team) by ISO/IEC MPEG and ITU-TVCEG is currently standardizing Scalable Video Coding (SVC). This can represent video bitstreams in different sealable layers for flexible adaptation to terminal and network characteristics. This characteristic is very useful in video streaming applications. One fully scalable video can be extracted with specific target spatial resolution, temporal frame rate and quality level to match the requirements of terminals and networks. Besides, the extraction process is fast and consumes little computational resource, so it is possible to extract the partial video bitstream online to accommodate with changing network conditions etc. With all the advantages of SVC, we design and implement a network-adaptive SVC streaming system with an SVC extractor and a streamer to extract appropriate amounts of bitstreams to meet the required target bitrates and spatial resolutions. The proposed SVC extraction is designed to allow for flexible switching from layer to layer in SVC bitstreams online to cope with the change in network bandwidth. The extraction is made in every GOP unit. We present the implementation of our SVC streaming system with experimental results.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.83-90
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• 2010

In vocabulary recognition system has reduce recognition rate unrecognized error cause of similar phoneme recognition and due to provided inaccurate vocabulary. Input of inaccurate vocabulary by feature extraction case of recognition by appear result of unrecognized or similar phoneme recognized. Also can't feature extraction properly when phoneme recognition is similar phoneme recognition. In this paper propose vocabulary recognition post-process error correction system using phoneme likelihood based on phoneme feature. Phoneme likelihood is monophone training phoneme data by find out using MFCC and LPC feature extraction method. Similar phoneme is induced able to recognition of accurate phoneme due to inaccurate vocabulary provided unrecognized reduced error rate. Find out error correction using phoneme likelihood and confidence when vocabulary recognition perform error correction for error proved vocabulary. System performance comparison as a result of recognition improve represent MFCC 7.5%, LPC 5.3% by system using error pattern and system using semantic.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.605-611
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• 2019

3D Vision system has many applications recently but popularization have many problems that need to be overcome. Volumetric display may process a amount of visual data and design the high efficient vision system for display. In case of stereo system for volumetric display, disparity vectors from the stereoscopic sequences and residual images with the reference images has been transmitted, and the reconstructed stereoscopic sequences have been displayed at the receiver. So central issue for the design of efficient volumetric vision system lies in selecting an appropriate stereo matching and robust vision system. In this paper, we propose high efficient vision system with the reduction of rectification error which can perform the 3D data extraction efficiently with low computational complexity. In experimental results with proposed vision system, the proposed method can perform the 3D data extraction efficiently with reducing rectification error and low computational complexity.

• Journal of Korea Soil Environment Society
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• v.5 no.1
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• pp.13-23
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• 2000

The effects of operating condition of soil vapor extraction system and the characteristics of site on the remediation of oil contaminated soil were investigated. Thorough investigation showed that the site was contaminated with gasoline leaked from underground storage tank and the maximum concentration of BTEX and TPH were 1,081 ppm and 5,548 ppm respectively. The leaked gasoline were diffused to 6m deep and the area and volume of the polluted soil were assumed to 170$m^2$ and 1,000$\textrm{m}^3$respectively. The site were consisted of three different vertitical layers, the top reclaimed sandy soil between the earth surface and 3~4m deep, middle silty sand between 3~4m and 6m deep, and the bottom bedrock below the 6m deep. The air pemeability of soil was measured to 1.058-1.077$\times$10$^{-6}$ $\textrm{mm}^2$ by vacuum pump tests. The groundwater which level was 3~4m deep was observed in some areas of this site. The soil vapor extraction system which had 7.5 HP vacuum pump and 8 extraction wells was constructed in this site and operated at 8 hrs/day for 100 days. The BTEX was removed with above 90% efficiency where no groundwater and silty sand were observed. On the contrary, the efficiency of BTEX and TPH were dramatically decreased where groundwater and silty sand were observed. The flow rate of soil air induced by soil vapor extraction system was reduced in deeper soil.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.5 no.2
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• pp.123-132
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• 2007

This study was performed to evaluate the co- and mutual separation for Am, Cm and RE elements from the simulated multi-component solution equivalent to real HLW level by a Zr-DEHPA(di-(2-ethylhexyl) phosphoric acid containing Zirconium)/$NDD(n-dodecane)-HNO_3$ extraction system. Zr-DEHPA was self-synthesized and the optimal condition of (15g/L Zr-1M DEHPA)/NDD-1M $HNO_3$ was selected taking into consideration of prevention of the third phase, and effects of concentration of DEHPA, nitric acid and impregnant amount of Zr on the co-extraction of Am, Cm and RE. In that condition, the extraction yields were 81% (Am), 85% (Cm), more than 80% (RE elements), 98% (Mo), 85% (Fe), 98% (U), 73% (Np), and less than 5% (other elements) so that the system developed for the co-extraction of Am-Cm/RE was proved to be available. For that, however, U, Np, Mo and Fe was elucidated to have to be removed in advance, and Zr inducing the third phase formation was found to be practically excluded. The co-extracted Am-Cm/RE were sequentially separated in an order of Am-Cm (stripping agent : 0.05 M DTPA-1M Lactic acid of pH 3.6)${\rightarrow}RE$ (stripping agent : 5M $HNO_3$), and then their separation factors were evaluated. At above conditions, Am of 65.4%, Cm of 63.9%, RE (except for Y) of more than 85% were stripped.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.5 no.2
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• pp.133-143
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• 2007

This study was performed to evaluate the co- and sequential separation of Tc, Np and U from the simulated multi-component HLW solution by a TBP (tributyl phosphate)-TOA (tri- octyl amine)/NDD $(n-dodecane)-HNO_3$ extraction system. An optimal condition of (30% TBP-0.5% TOA)/NDD-1 M $HNO_3$ was selected by taking account of a prevention of the 3rd phase and effects of concentration of TBP, TOA and nitric acid on the co-extraction of Tc, Np and U. In that condition, the extraction yields were 81% (Tc), 85% (Np), less than 9% (Am and RE elements), about 8% (Pd), and less than 5% (other elements) so that the system developed for the co-extraction of Tc, Np and U was proved to be available. For that, however, more than 99% of Zr was found to be pre-removed. The co-extracted Tc, Np and U were sequentially separated in order of Tc(stripping agent : 5 M $HNO_3$)${\rightarrow}Np$ by reductive stripping (reductive-stripping agent : 0.1 M AHA)${\rightarrow}U$ (stripping agent : 0.01 M $HNO_3$), and then their separation factors were evaluated. At these conditions, 95% of Tc, 98% of Np and 99% of U could be recovered in each step.

• Journal of KIISE:Software and Applications
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• v.34 no.9
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• pp.869-879
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• 2007

This paper proposes a neural network based approach for automatic human emotion recognition in textile images. To investigate the correlation between the emotion and the pattern, the survey is conducted on 20 peoples, which shows that a emotion is deeply affected by a pattern. Accordingly, a neural network based classifier is used for recognizing the pattern included in textiles. In our system, two schemes are used for describing the pattern; raw-pixel data extraction scheme using auto-regressive method (RDES) and wavelet transformed data extraction scheme (WTDES). To assess the validity of the proposed method, it was applied to recognize the human emotions in 100 textiles, and the results shows that using WTDES guarantees better performance than using RDES. The former produced the accuracy of 71%, while the latter produced the accuracy of 90%. Although there are some differences according to the data extraction scheme, the proposed method shows the accuracy of 80% on average. This result confirmed that our system has the potential to be applied for various application such as textile industry and e-business.

• Economic and Environmental Geology
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• v.40 no.5
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• pp.551-561
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• 2007

This case study is about the sampling and interpretation of soil pore water in order to understand and to predict the diffusion and behavior of soil pollution. For the measurement of polycyclic aromatic hydrocarbons(PAHs) in two representative hydrocarbon-contaminated sites, the extraction system of the soil pore water was set up with respect to soil depths and the behavior of contaminants was interpreted. The soil solution extraction system consisted of peristaltic pump, and extraction and sampling compartment, and can measure simultaneously the soil water pressure. The concentration of PAHs with respect to extraction pressure and time decreased due to dilution through soil pore water. Particularly, the concentration of PAHs was more reduced under the unsaturated oxic condition than saturated anoxic condition. Therefore, the soil solution extraction with respect to soil water pressure can interpret the extent of equilibrium between porewater and soil surface.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.198-207
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• 1996

A PSRI (position, scale, and rotation invariant ) feature extraction and automatic target recognition system using a cooperative network and an MLP is proposed. We can extract position invarient features by obtaining the target center using the projection and the moment in preprocessing stage. The scale and rotation invariant features are extracted from the contour projection of the number of edge pixels on each of the concentric circles, which is input to the cooperative network. By extracting the representative PSRI features form the features and their differentiations using max-net and min-net, we can rdduce the number of input neurons of the MLP, and make the resulted automatic target recognition system less sensitive to input variances. Experiments are conduted on various complex images which are shifted, rotated, or scaled, and the results show that the proposed system is very efficient for PSRI feature extractions and automatic target recognitions.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1172-1179
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• 2015

A wireless license plate region extracting module is proposed for LPR system controlling multiple gates. This module is cheaply implemented using Raspberry Pi which is open source and high performance. First, as the upper 1/3 of the captured image is discarded as it has no useful information on license plate. Using the OpenCV libraries the edge image is got by Canny algorithm after applying Gaussian filtering to gray image, and the labeling is conducted for 4 consecutive numbers in license plate. These numbers are located using various decision equations, and expanding the numbers region the final license plate region can be extracted. The result image is transferred to Server using wifi direct. Using the proposed module it becomes easy to set up and maintain the LPR system. The experimental results showed that the successful extracting rate was 98.4% using 500 car images with 640 × 480 resolution.