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High-Speed IP Routing Lookups with Fast Updates
Kim, Byung-Yeob,Kim, Kyung-Duk,Choi, Yoon-Hwa Research Institute for Science & Technology, Hong- 2001 Hongik Journal of Science and Technology Vol.5 No.-
Most of the existing IP address lookup schemes emphasize the table lookups with little attention to table updates. Updating the routing table often requires a considerable time overhead, especially for gigabit backbone routers. This paper presents a high-speed lookup scheme with a small bounded update time for IP forwarding engines in IP routers. It performs one IP address lookup per memory access by employing memory access pipelining and updates the table in the time bounded by the IP packet interarrival time on a gigabit link, regardless of the size of the database. The resulting design requires manageably small memories to be implemented using current SRAM technology.
Two Unrecorded Species of the Snapper (Perciformes: Lutjanidae) Collected from Jeju Island, Korea
Kim, Maeng Jin,Kim, Byung Yeob,Kim, Joon Sang,Song, Choon Bok The Korean Society of Fisheries and Aquatic Scienc 2012 Fisheries and Aquatic Sciences Vol.15 No.4
Two unrecorded species of the snapper, Lutjanus malabaricus (296.0 mm standard length [SL]) and L. stellatus (350.0 mm SL) belonging to the family Lutjanidae, were first collected from the western coastal waters of Jeju Island, Korea. L. malabaricus is characterized by having a dark marking on the upper half of the caudal peduncle, a band of vomerine teeth that does not protrude posteriorly at the middle, and nine anal soft rays. Compared to its Korean relative, L. malabaricus is distinguishable by having rows of scales that run obliquely in the dorsal-posterior direction above the lateral line (vs. parallel to the lateral line in L. argentimaculatus). L. stellatus can be identified by the absence of wavy blue lines on the head (vs. many blue lines in L. rivulatus) and presence of a white spot above the lateral line (vs. a black blotch on the lateral line in L. rivulatus). We propose new Korean names, "Jin-hong-tung-dom" and "Huin-jeom-tung-dom," for L. malabaricus and L. stellatus, respectively.
김병조(Byung-Jo Kim),김주엽(Ju-Yeob Kim),이미영(Mi-Young Lee),김진규(Jin-Kyu Kim),이주현(Joo-Hyun Lee) 대한전자공학회 2018 대한전자공학회 학술대회 Vol.2018 No.11
In this paper, we present the design and implementation results of memory controller in VIC(Visual Intelligent Chip) with general object recognition function. Designed memory controller can support scalable memory bandwidth depending on application of VIC and can also support read timing alignment function to predict delay time in connection with external memory on system PCB
Seung Yeob Song,Sul Hye Hur,Byung Whan Min,In-Jung Kim,Suk Weon Kim 한국육종학회 2012 한국육종학회 심포지엄 Vol.2012 No.07
In this study we established the high throughput screening system of high functional soybean cultivars using PLS modeling from FT-IR spectral data of soybean(Glycine max L) seeds. Crude extract of 20% methanol from soybean seed powders (153 lines) were used for FT-IR spectroscopy. Total fatty acid, carotenoids, flavonoids and phenolic compounds contents from soybean seed powders were analyzed using UV-spectrum and GC analysis respectively. PCA analysis showed that 153 soybean lines formed a single clusters with a few outlier. PC score 1 and 2 represented 39.5, 16.4% of total variation, respectively. And than showed change patten from the middle to outside for PCA plot. We conducted PLS regression analysis between FT-IR spectral data and fatty acids data. Palmitic acid showed the highest regression coefficient (R=0.78). This result implied that the content of palmitic acid could be predicted from FT-IR spectral data from soybean seed powders with relatively high fidelity. PLS modeling of total carotenoids also showed regression coefficient of 0.69. Regression coefficient of total flavnoids and phenolic compounds were 0.44, 0.39, respectively. At present, we are trying to confirm the accuracy of PLS prediction modeling using targeted metabolite analysis (GC-MS, LC-MS) from predicted soybean lines. To increase the accuracy of PLS modeling, we also trying to standardization of spectroscopy and spectral data processing. Furthermore we are going to develop PLS modeling from GC-MS, LC-MS data. The PLS prediction modeling established in this study could be applied for high throughput screening of other leguminous plant.