• Journal of Microbiology and Biotechnology
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• v.26 no.3
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• pp.558-566
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• 2016

Biotic agents such as fumarate-reducing bacteria can be used for controlling methane (CH₄) production in the rumen. Fumarate-reducing bacteria convert fumarate to succinate by fumarate reductase, ultimately leading to the production of propionate. Fumarate-reducing bacteria in the genus Enterococcus were isolated from rumen fluid samples from slaughtered Korean native goats. The enterococci were identified as Enterococcus faecalis SROD5 and E. faecium SROD by phylogenetic analyses of 16S rRNA gene sequences. The fumarate reductase activities of the SROD5 and SROD strains were 42.13 and 37.05 mM NADH oxidized/min/mg of cellular nitrogen (N), respectively. Supplementation of rumen fermentation in vitro with the SROD5 and SROD strains produced significantly higher propionate, butyrate, and total volatile fatty acid (VFA) concentrations than controls at 12 h; VFA concentrations tended to increase after 24 h of incubation. The generated CH₄ concentration was significantly lower in the SROD5 and SROD treatment groups after 24 h of incubation. These findings indicate that E. faecium SROD has potential as a direct-fed microbial additive for increasing total VFAs while decreasing CH₄ production in rumen fermentation in vitro.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.547-551
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• 2009

In recent years, a film restoration has gained increasing attention by many researchers, to emergence of variety multimedia and to importance of video preservation. Blotch is the most frequent degradation in old film. This paper presents a blotch detection method using color and shape feature. The proposed method is two major modules: a SROD detector using impulsive feature and NN-based detector using shape feature. To assess the validity of the proposed method, the experiments have been performed on several old films.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.120-124
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• 2010

This paper presents a method that can automatically detect variety of degradations (i.e., scratches and blotches) in old film archive. The proposed method consists of candidate detection and verification. Degradations are first identified by finding the local extreme of a frame in spatiotemporal domains, thereby using edge detector and SROD detector. Then, to remove some false alarms occurred in the first stages, the verification is performed using the texture and shape properties of scratches and blotches. The textural properties of scratches and blotches are learned using neural networks (NNs) and their shapes are represented using morphological filters. The experiments were performed on several old films, then the results demonstrated the effectiveness of the proposed method, where it has a precision of 81% and a recall of 79%.