• Food Science and Biotechnology
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• v.14 no.2
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• pp.301-303
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• 2005

State diagram of highly concentrated branched oligosaccharides (HBOS) was constructed to better understand phase behavior of mixtures with different size of oligosaccharides. It showed dramatic plasticizing effect on glass transition, which was successfully described based on Couchman-Karasz equation model. $T_g$' estimated from state diagram corresponded well with previous empirical data measured by maximum ice formation through isothermal holding (annealing) process. Estimated $T_g$' and $C_g$' values were $-36.3^{\circ}C$ and 79.99%, respectively. $T_g$' value of HBOS was approximately $10^{\circ}C$ higher than that of sucrose, while $C_g$' value was similar to those of general carbohydrate materials, which could be useful for applications in frozen system.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.149.2-149.2
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• 2011

We performed the cross-correlation analysis on energy-dependent light curves of the Z-type source GX 5-1. We observed X-ray delays of a few hundred seconds between hard (16-30 keV) and soft (2-5 keV) X-ray light curves. During these phenomena, the centroid frequency of horizontal branch oscillation (HBO) was found to shift to lower or higher frequency indicating towards the dynamical movement of a Compton cloud or an inner disk front. Both eastern and western approaches were used to unfold the X-ray continuum and systematic changes were observed in soft and hard X-ray spectral components. Simultaneous energy spectral and power density spectral study shows that the production of HBOs is closely related to the Comptonizing region rather than the accretion disk. We discuss the results in the context of re-condensation of coronal material in the inner accretion disk region.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.89-101
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• 2023

In order to prevent damages caused by cyber-attacks on nations, businesses, and other entities, anomaly detection techniques for early detection of attackers have been consistently researched. Real-time reduction and false positive reduction are essential to promptly prevent external or internal intrusion attacks. In this study, we hypothesized that the type and frequency of attack events would influence the improvement of anomaly detection true positive rates and reduction of false positive rates. To validate this hypothesis, we utilized the 2015 login log dataset from the Los Alamos National Laboratory. Applying the preprocessed data to representative anomaly detection algorithms, we confirmed that using characteristics that simultaneously consider the type and frequency of attack events is highly effective in reducing false positives and execution time for anomaly detection.