• YAKHAK HOEJI
• /
• v.31 no.5
• /
• pp.315-321
• /
• 1987

A high performance liquid chromatographic method was developed for the determination of glycyrrhetinic acid contained in licorice powder. Glycyrrhetinic acid which is hydrolysate of glycyrrhizin extracted from licorice powder, was determined with good result by HPLC using 2-bromoacetyltriphenylene labeling reagent. The glycyrrhetinic acids were labeled with 2-bromoacetyltriphenylene in acetonitrile using 18-crown-6-ether and KOH as a catalyst. Derivatized glycyrrhetinic acids were separated from the extracted licorice powder on a reversed-phase column (chemopak $C_{18}$) using 100% acetonitrile as a mobile phase and monitored by an UV-detector at 268nm. Linearity of calibration curve was obtained between 5 ng and 20 ng, and the lower limit of detection was 2 ng. The recovery of glycyrrhetinic acid to licorice powder was about 99.3%. This method was sensitive, reliable and useful for, determination of glycyrrhetinic acid.

• YAKHAK HOEJI
• /
• v.39 no.5
• /
• pp.516-520
• /
• 1995

The anti-dansyl antibodies were specifically labeled with stable isotope by growing hybridoma cells in serum-free medium. Assignments of the observed carbonyl carbon resonances have been determined by using $^{13}C-{15}N$ double labeling method in order to assign the Leu resonances. However, when the identical dipeptide appears more than twice in the polypeptide sequences, we applied the proteolytic fragments in the fragment-specific method. Carboxypep-tidase B-treated antibody has also been used to assign the Lys-447 in C terminal amino acid. These unambiguously assigned carbonyl carbon resonances in antibodies are thought to be useful in elucidating not only the structure of antibodies but also the structure-function relationship in the antibody by $^{13}C$ neuclear magnetic resonance spectroscopy.

• Proceedings of the Korean Nuclear Society Conference
• /
• 2002.10a
• /
• pp.382.1-382
• /
• 2002

• Food Science and Industry
• /
• v.44 no.1
• /
• pp.49-56
• /
• 2011

• Journal of Biomedical Engineering Research
• /
• v.38 no.2
• /
• pp.82-88
• /
• 2017

Enlargement of the lateral ventricles have been identified as a surrogate marker of neurological disorders. Quantitative measure of the lateral ventricle from MRI would enable earlier and more accurate clinical diagnosis in monitoring disease progression. Even though it requires an automated or semi-automated segmentation method for objective quantification, it is difficult to define lateral ventricles due to insufficient contrast and brightness of structural imaging. In this study, we proposed a fully automated lateral ventricle segmentation method based on a graph cuts algorithm combined with atlas-based segmentation and connected component labeling. Initially, initial seeds for graph cuts were defined by atlas-based segmentation (ATS). They were adjusted by partial volume images in order to provide accurate a priori information on graph cuts. A graph cuts algorithm is to finds a global minimum of energy with minimum cut/maximum flow algorithm function on graph. In addition, connected component labeling used to remove false ventricle regions. The proposed method was validated with the well-known tools using the dice similarity index, recall and precision values. The proposed method was significantly higher dice similarity index ($0.860{\pm}0.036$, p < 0.001) and recall ($0.833{\pm}0.037$, p < 0.001) compared with other tools. Therefore, the proposed method yielded a robust and reliable segmentation result.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.13 no.5
• /
• pp.35-49
• /
• 2014

This paper proposes the on-road object detection and classification algorithm by using a detection system consisting of only laser scanners. Each sensor data acquired by the laser scanner is fused with a grid map and the measurement error and spot spaces are corrected using a labeling method and dilation operation. Fuzzy method which uses the object information (length, width) as input parameters can classify the objects such as a pedestrian, bicycle and vehicle. In this way, the accuracy of the detection system is increased. Through experiments for some scenarios in the real road environment, the performance of the proposed detection and classification system for the actual objects is demonstrated through the comparison with the actual information acquired by GPS-RTK.

• Journal of KIISE
• /
• v.43 no.12
• /
• pp.1376-1384
• /
• 2016

Computers require analytic and processing capability for all possibilities of human expression in order to process sentences like human beings. Linguistic information processing thus forms the initial basis. When analyzing a sentence syntactically, it is necessary to divide the sentence into components, find obligatory arguments focusing on predicates, identify the sentence core, and understand semantic relations between the arguments and predicates. In this study, the method applied a case frame dictionary based on The Korean Standard Dictionary of The National Institute of the Korean Language; in addition, we used a CRF Model that constructed subcategorization of predicates as featured in Korean Lexical Semantic Network (UWordMap) for semantic role labeling. Automatically tagged semantic roles based on the CRF model, which established the information of words, predicates, the case-frame dictionary and hypernyms of words as features, were used. This method demonstrated higher performance in comparison with the existing method, with accuracy rate of 83.13% as compared to 81.2%, respectively.

• Journal of Positioning, Navigation, and Timing
• /
• v.12 no.2
• /
• pp.91-100
• /
• 2023

The existing indoor localization method using Wi-Fi fingerprinting has a high collection cost and relatively low accuracy, thus requiring integrated correction of convergence with other technologies. This paper proposes a new method that significantly reduces collection costs compared to existing methods using Wi-Fi fingerprinting. Furthermore, it does not require labeling of data at collection and can estimate pedestrian travel paths even in large indoor spaces. The proposed pedestrian movement path estimation process is as follows. Data collection is accomplished by setting up a feature area near an indoor space intersection, moving through the set feature areas, and then collecting data without labels. The collected data are processed using Kernel Linear Discriminant Analysis (KLDA) and the valley point of the Euclidean distance value between two data is obtained within the feature space of the data. We build learning data by labeling data corresponding to valley points and some nearby data by feature area numbers, and labeling data between valley points and other valley points as path data between each corresponding feature area. Finally, for testing, data are collected randomly through indoor space, KLDA is applied as previous data to build test data, the K-Nearest Neighbor (K-NN) algorithm is applied, and the path of movement of test data is estimated by applying a correction algorithm to estimate only routes that can be reached from the most recently estimated location. The estimation results verified the accuracy by comparing the true paths in indoor space with those estimated by the proposed method and achieved approximately 90.8% and 81.4% accuracy in two experimental spaces, respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.42 no.1
• /
• pp.27-34
• /
• 2005

In this paper, we propose the Q-edge labeling method related to the graph embedding problem in binomial trees. This result is able to design a new reliable interconnection networks with maximum connectivity using Q-edge labels as jump sequence of circulant graph. The circulant graph is a generalization of Harary graph which is a solution of the optimal problem to design a maximum connectivity graph consists of n vertices End e edgies. And this topology has optimal broadcasting because of having binomial trees as spanning tree.

• IMMUNE NETWORK
• /
• v.12 no.6
• /
• pp.223-229
• /
• 2012

Clinical and preclinical in vivo immune cell imaging approaches have been used to study immune cell proliferation, apoptosis and interaction at the microscopic (intra-vital imaging) and macroscopic (whole-body imaging) level by use of ex vivo or in vivo labeling method. A series of imaging techniques ranging from non-radiation based techniques such as optical imaging, MRI, and ultrasound to radiation based CT/nuclear imaging can be used for in vivo immune cell tracking. These imaging modalities highlight the intrinsic behavior of different immune cell populations in physiological context. Fluorescent, radioactive or paramagnetic probes can be used in direct labeling protocols to monitor the specific cell population. Reporter genes can also be used for genetic, indirect labeling protocols to track the fate of a given cell subpopulation in vivo. In this review, we summarized several methods dealing with dendritic cell, macrophage, and T lymphocyte specifically labeled for different macroscopic whole-body imaging techniques both for the study of their physiological function and in the context of immunotherapy to exploit imaging-derived information and immune-based treatments.