• 한국지구물리탐사학회:학술대회논문집
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• 2011.10a
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• pp.153-154
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• 2011

• 한국지구물리탐사학회:학술대회논문집
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• 2011.10a
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• pp.151-152
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• 2011

• 한국지구물리탐사학회:학술대회논문집
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• 2011.10a
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• pp.161-162
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• 2011

• 한국지구물리탐사학회:학술대회논문집
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• 2011.10a
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• pp.163-164
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• 2011

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.803-804
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• 2006

This paper presents a multiple branch predictor with perceptrons. We describe our design and evaluate it with the SPEC 2000 benchmarks. Our predictor achieves increased accuracy than the previous multiple branch predictors.

• Proceedings of the Korean Society of Crop Science Conference
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• 1994.10a
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• pp.20-21
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• 1994

• Journal of Biomedical Engineering Research
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• v.36 no.1
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• pp.16-21
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• 2015

Indocyanine green(ICG) and 5-aminolevulinic acid(5-ALA) have been widely used to mark blood vessels or tumors. However, fluorescent dye detection systems were designed to use one type of dyes only. In this study, we proposed a detection system capable of detecting Indocyanine green and 5-aminolevulinic acid. Multiple filters and light sources are integrated into a single system. In this study, we performed analysis of fluorescent dyes and configured a detection system. During the analysis, it was found that Indocyanine green and 5-aminolevulinic acid have the maximum intensity at $40{\mu}M$. We designed light source for fluorescent dyes and conducted compatibility test using a commercial surgical microscope. The fluorescent dye detection system was configured based on the experimental results. The developed system successfully detects Indocyanine green and 5-aminolevulinic acid. Therefore, more efficient surgical operations can be achieved using both fluorescent dyes at the same time. We expect that the developed system can increase the survival rate of patients.

• Nuclear Engineering and Technology
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• v.52 no.6
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• pp.1164-1171
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• 2020

T-junction structures play an important role in nuclear power plant systems. Research on liquid entrainment is mostly based on small-scale branch pipes (d/D ≤ 0.2) and attention paid to large-scale branch pipes (0.33 < d/D < 1) is insufficient. Accordingly, this study implements a series of experiments on the liquid entrainment of T-junction with different angles (32.2°,47.9°,62.3°,90°) through a large-scale branch (d/D = 0.675). The onset liquid entrainment is related to the gas phase Froude number Fr_g, the dimensionless gas chamber height h_b/d and the branch pipe angle 𝜃. As Fr_g increases, h_b/d also rises. With a constant h_b/d, the onset liquid entrainment changes from droplets entrainment by the gas phase to that by the rising liquid film. The steady-state liquid entrainment is related to w_3g, h/d and 𝜃. With constant w_3g and h/d, the branch quality grows as the branch angle increases. With a certain h/d, the branch quality increases, as the w_3g number increases.

• Journal of Internet Computing and Services
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• v.2 no.1
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• pp.23-30
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• 2001

ILP processors require an accurate branch prediction scheme to achieve higher performance. Two-Level branch predictor has been known to achieve high prediction accuracy. But, when a branch accesses a PHT entry that was, previously updated by other branch, Two-level predictor may cause interferences. Negative interferences among all interferences have a negative effect on performance, since they can cause branch mispredictions. Agree predictor achieve high prediction accuracy by converting negative interferences to positive interferences by adding bias bits to BTB, but negative interferences may occur when bias bit is set incorrectly. This paper presents a new dynamic branch predictor which reduces negative interferences. In the proposed predictor, we attach hit bits to entries in BTB to change bias bit dynamically during the execution time, h a result the proposed scheme improve the accuracy of prediction by reducing negative Interferences effectively, To illustrate the effect of the proposed scheme, we evaluate the performance of this scheme using SPEC92int benchmarks, The results show that the proposed scheme can outperform traditional branch predictors.

• Smart Media Journal
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• v.9 no.2
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• pp.9-15
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• 2020

In this paper, we propose a network model composed of Multi path Encoder-Decoder branches that can recognize each instance from the image. The network has two branches, Dot branch and Segmentation branch for finding the center point of each instance and for recognizing area of the instance, respectively. In the experiment, the CVPPP dataset was studied to distinguish leaves from each other, and the center point detection branch(Dot branch) found the center points of each leaf, and the object segmentation branch(Segmentation branch) finally predicted the pixel area of each leaf corresponding to each center point. In the existing segmentation methods, there were problems of finding various sizes and positions of anchor boxes (N > 1k) for checking objects. Also, there were difficulties of estimating the number of undefined instances per image. In the proposed network, an effective method finding instances based on their center points is proposed.