• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.78-85
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• 2007

With the advancement of the digital broadcasting and popularity of the Internet, recently, many studies are making on the digital watermarking for the copyright protection of digital data. This paper proposes the minimum modification method of motion vector to minimize the degradation of video quality, hiding subtitles of many language and information of OST(original sound track), character profiles, etc. as well as the copyright protection. Our proposed algorithm extracts feature vector by comparing motion vector data with watermark data, and minimize the modification of motion vectors by deciding the inversion of bit. Thus the degradation of video quality is minimized comparing to conventional algorithms. This algorithm also can check data integrity, and retrieve embedded hidden data simply and blindly. And our proposed scheme can be useful for conventional MPEG-1, -2 standards without any increment of bit rate in the compressed video domain. The experimental result shows that the proposed scheme obtains better video quality than other previous algorithms by about $0.5{\sim}1.5dB$.

• Journal of Welding and Joining
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• v.13 no.2
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• pp.96-105
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• 1995

A modified method for the analysis of short fatigue crack growth has been presented, and calculations based upon the modified method are compared with experimental results for S45C carbon steel. It is also shown that the modified method is in good agreement with experimental data. The proposed equation for the fatigue crack growth rates includes a material constant which relates the threshold level to the endurance limit, a correction for elastic-plastic behaviour and a means for dealing with the effects of crack closure. In this study one of the modifications is to substitute the Forman' s elastic expression of the stress intensity factor range into the geometrical factor The other is a consideration of the bending effect which is developed from the moment caused by the eccentric cross sectional geometry as the crack grows. Thus, this method is useful for residual life prediction of the mechanical structures as well as the welding structures.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.418-426
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• 2006

This paper proposes a robust and efficient segmentation method for a cardiac ultrasound image taken from a probe inserted into the heart in surgery. The method consists of three steps: initial boundary extraction, whole boundary modification using confidence competition, and local boundary modification using the rolling spoke method. Firstly, the initial boundary is extracted with threshold regions along the global spokes emitted from the center of an ultrasound probe. Secondly, high confidence boundary edges are detected along the global spokes by competing among initial boundary candidate and new candidates achieved by edge and appearance information. finally, the boundary is modified by rolling local spokes along concave regions that are difficult to extract using the global spokes. The proposed method produces promising segmentation results for the ultrasound cardiac images acquired during surgery.

• Polymer(Korea)
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• v.38 no.6
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• pp.767-773
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• 2014

Rheological and electrical properties of polystyrene (PS)/carbon nanotube (CNT) nanocomposites via coagulated precipitation were investigated. Chemical modification and surfactant wrapping of CNT to improve the dispersion of CNTs may reduce the intrinsic properties of pristine CNT. To avoid this problem, PS and CNTs were dissolved and dispersed in dimethylformamide and then PS/CNT nanocomposites were prepared by the coagulated precipitation of CNT-dispersed PS solution in water. The coagulated precipitation method was highly effective enhancing the electrical conductivity of nanocomposites. Furthermore, the effect of adding poly(styrene-co-divinylbenzene) crosslinked particles to PS matrix on the rheological and electrical properties was investigated. With the addition of the crosslinked particles, the electrical percolation threshold of CNT reduced to 0.25 wt% and electrical conductivity increased further. It is speculated that CNTs in the volume occupied by crosslinked particles helped electrical pathway formation.

• Polymer(Korea)
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• v.37 no.4
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• pp.526-532
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• 2013

The effect of CNT dispersion method on rheological and electrical properties of polystyrene/carbon nanotube (PS/CNT) nanocomposites via latex technology was compared. The nanocomposites were prepared through freeze-drying the dispersed suspension comprised of CNTs and PS particles. In this study, physical dispersion method, either sodium dodecylsulfate (SDS) addition or polyvinyl pyrrolidone (PVP) wrapping, was employed to prevent the deterioration of intrinsic properties of CNT caused by chemical modification. The physical method applied to latex technology was very effective in CNT dispersion. With SDS addition, the enhancement of rheological properties was low compared to PVP wrapping because the properties of matrix were deteriorated due to the incorporation of low molecular weight SDS. The electrical percolation threshold of PS/SDS-stabilized CNT and PS/PVP-wrapped CNT nanocomposites was 0.23 and 0.90 wt%, respectively. The enhancement of electrical conductivity was low in the case of PVP wrapping because the non-conducting PVPs wrapped around CNT restricted the electrical connection between CNTs.

• Publications of The Korean Astronomical Society
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• v.27 no.5
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• pp.419-429
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• 2012

In this paper, we propose a new re-quantization method after FFT processing to prevent the distortion of correlation result of VCS (VLBI Correlation Subsystem). The re-quantization is used to rearrange the data bit so as to reduce the data rate processed as 16-bit of FFT result of VCS. Having done this procedure, we found that the distorted spectrum of correlation result occurred in the delay tracking experiments by the re-quantization method introduced for initial design of VCS. In order to solve this, two kinds of re-quantization method, that is, the comparison and selection-type, are proposed. The first is to re-quantize the FFT result as a valid-bit by comparing with the input data after determining the adequate threshold. The second is manually to select the valid-bit of FFT result after finding the valid-field of data according to the bit-distribution of input data. We confirmed that the second is more effective compared with the first through the experimental result, and it will be implemented without so much modification of applied method in the condition of the limited resource of FPGA. The re-quantization is, however, carried out with 4-bit in the proposed second method for FFT result, and then the distortion of correlation result is also appeared. To fix this problem, the bit for re-quantization is extended to 8-bit. The proposed 8-bit selection-type is effectively verified so that the distortion of correlation result disappeared by applying to VCS in consequence of the simulation and correlation experiments.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used