• Journal of Life Science
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• v.16 no.3 s.76
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• pp.409-414
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• 2006

Molecular chaperones and folding enzymes in the endoplasmic reticulum (ER) associate with the newly synthesized proteins to prevent their aggregation and help them fold and assemble correctly. Chaperone function of BiP, which is a Hsp70 homologue in ER, is controlled by the N-terminal ATPase domain. The ATPase activity of the ATPase domain is affected by regulatory factors. BAP was identified as a nucleotide exchange factor of BiP (Grp78), which exchanges ADP with ATP in the ATPase domain of BiP This study presents whether BAP can influence folding of a protein, immunoglobulin heavy chain that is bound to BiP tightly. We first examined which nucleotide of ADP and ATP affects on BAP binding to BiP The data showed that endogenous BAP of HEK293 cells prefers ADP for binding to BiP in vitro, suggesting that BAP first releases ADP from the ATPase domain in order to exchange with ATP. Immunoglobulin heavy chain, an unfolded protein substrate, was released from BiP in the presence of BAP but not in the presence of ERdj3, which is another regulatory factor for BiP accelerating the rate of ATP hydrolysis of BiP The ADP-releasing function of BAP was, therefore, believed to be responsible for immunoglobulin heavy chain release from BiP. Grp170, another Hsp70 homologue in ER, did not co-precipited with BAP from $[^{35}S]$-metabolic labeled HEK293 lysate containing both overexpressed Grp170 and BAP. These data suggested that BAP has no specificity to Grp170 although the ATPase domains of Grp170 and BiP are homologous each other.

• Journal of Korean Home Economics Education Association
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• v.23 no.3
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• pp.121-138
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• 2011

This research examined the amounts and methods change of recommended energy intake(REI) from 1962's recommended dietary intakes for Korean to 2010's dietary reference intakes for Koreans. REI is composed of 3 factors, such basal metabolic rate(or Resting Energy Expenditure, REE), activity energy, and thermogenic effect of foods. The first 1962 calculation formula of REI was weight based formula, that of 95's was the weight based REE multiplied by activity coefficient, and the recent one of 2005's(Estimated Energy Requirement: EER) was age, height. weight, and the activity level applying formula derived from the energy expenditure data by doubly labeled water technique(DLW). During the 50 years or so, REIs were reduced in all age groups, according to the activity(labor) strength and hour were reduced. The individual REI calculation method was introduced in 1995, and individual REI calculation was recommended since to prevent obesity. For the better REI estimation for Koreans, REI calculation formula derived from our peoples' DLW energy expenditure data is required.

• The Korean Journal of Nuclear Medicine
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• v.37 no.3
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• pp.162-170
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• 2003

The probabilistic anatomical maps are used to localize the functional neuro-images and morphological variability. The quantitative indicator is very important to inquire the anatomical position of an activated legion because functional image data has the low-resolution nature and no inherent anatomical information. Although previously developed MNI probabilistic anatomical map was enough to localize the data, it was not suitable for the Korean brains because of the morphological difference between Occidental and Oriental. In this study, we develop a probabilistic anatomical map for Korean normal brain. Normal 75 blains of T1-weighted spoiled gradient echo magnetic resonance images were acquired on a 1.5-T GESIGNA scanner. Then, a standard brain is selected in the group through a clinician searches a brain of the average property in the Talairach coordinate system. With the standard brain, an anatomist delineates 89 regions of interest (ROI) parcellating cortical and subcortical areas. The parcellated ROIs of the standard are warped and overlapped into each brain by maximizing intensity similarity. And every brain is automatically labeledwith the registered ROIs. Each of the same-labeled region is linearly normalize to the standard brain, and the occurrence of each legion is counted. Finally, 89 probabilistic ROI volumes are generated. This paper presents a probabilistic anatomical map for localizing the functional and structural analysis of Korean normal brain. In the future, we'll develop the group specific probabilistic anatomical maps of OCD and schizophrenia disease.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.65-78
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• 2009

The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.153-163
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• 2017

This research is based on the previous research that personally preferred safe distance, rotating angle and speed are differentiated. Thus, we use machine learning model for recognizing maneuvering modes trained per personal or per similar driving pattern groups, and we evaluate automatic driving according to maneuvering modes. By utilizing driving knowledge, we subdivided 8 kinds of longitudinal modes and 4 kinds of lateral modes, and by combining the longitudinal and lateral modes, we build 21 kinds of maneuvering modes. we train the labeled data set per time stamp through RNN, LSTM and Bi-LSTM models by the trips of drivers, which are supervised deep learning models, and evaluate the maneuvering modes of automatic driving for the test data set. The evaluation dataset is aggregated of living trips of 3,000 populations by VTTI in USA for 3 years and we use 1500 trips of 22 people and training, validation and test dataset ratio is 80%, 10% and 10%, respectively. For recognizing longitudinal 8 kinds of maneuvering modes, RNN achieves better accuracy compared to LSTM, Bi-LSTM. However, Bi-LSTM improves the accuracy in recognizing 21 kinds of longitudinal and lateral maneuvering modes in comparison with RNN and LSTM as 1.54% and 0.47%, respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.90-100
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• 2017

The purpose of this study is to detect and label the vehicles using the drone images as a way to overcome the limitation of the existing point and section detection system and vehicle gap estimation on Arterial road. In order to select the appropriate time zone, position, and altitude for the acquisition of the drone image data, the final image data was acquired by shooting under various conditions. The vehicle was detected by applying mixed Gaussian, image binarization and morphology among various image analysis techniques, and the vehicle was labeled by applying Kalman filter. As a result of the labeling rate analysis, it was confirmed that the vehicle labeling rate is 65% by detecting 185 out of 285 vehicles. The gap was calculated by pixel unitization, and the results were verified through comparison and analysis with Daum maps. As a result, the gap error was less than 5m and the mean error was 1.67m with the preceding vehicle and 1.1m with the following vehicle. The gaps estimated in this study can be used as the density of the urban roads and the criteria for judging the service level.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.3 no.2
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• pp.153-169
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• 1996

This study was designed to identify, describe and classify orientations of cancer patient's family members to death and to identify factors related to their attitudes on death. Death to the male is understood as a comprehensive system and believed to be highly subjective experience. Therefore attitude on death is affected by personalities. As an attempt to measure the subjective meaning toward death, the unstructured Q-methodology was used. Korean Death Orientation Questonaire prepared by Kim was used. Item-reliability and Sorting-reliability were tested. Forty five cancer patients' family members hospitalized in one university medical center in Seoul were sampled. Sorting the 65 Q-itmes according to the level of personal agreement ; A forced normal distribution into the 11 levels, were carried out by the 45 P-samples. The demographic data and information related to death orientation of the P-sample was collected through face to face in depth interviews. Data was gathered from August 30 till September 22, 1995. The Z-scores of the Q-items were computed and principal component factor analysis was carried out by PC-QUANL Program. Three unique types of the death orientation were identified and labeled. Type I consists of twenty P-samples. Life and death was accepted as people's destiny, They firmly believed the existence of life after life. They kept aloof from death and their concern was facing the and of the life with dignity, They were in favor of organ donation. Type II consists of Nine P-Samples. They considered that death was the end of everything and did not believed the life after life. They were very concerned about the present life. Type III consists of Sixteen P-samples. They regarded the death as a natural phenomena. And they considered that the man is just a traveller and is bound to head for the next life which is believed to be free of agony, pain or darkness. They neither feared death nor its process. Their conserns were on the activities to prepare themselves for the eternal-life after death. Thus, it was concluded that there were three distinctiven type of attitudes on death among cancer patient family members, and their death attitudes were affected by demographic and socio-cultural factors such as sex, education, and religion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.135-144
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• 2017

The purpose of this study was to revalidate the 'Hospital Violence Attitude Scale-18 (HVAS-18) in clinical nurses'. 150 clinical nurses from three general hospitals in two cities participated in this study. Data were collected from March to April in 2017. The collected data were analyzed using factor analysis, Pearson correlation coefficients, and Cronbach's alpha. The final HVAS-14 consisted of fourteen items and four factors emerged, which explained 74.1% of the total variance. These four factors were labeled: Factor 1 (3 items) 'awareness' which explained 20.3%; Factor 2 (4 items) 'response' which explained 20.2%; Factor 3 (3 items) 'reaction' which explained 15.5%; and Factor 4 (4 items) 'result' which explained 15.4%. The internal consistency and intraclass correlation coefficient (ICC), as measured by Cronbach's alpha, were both .87, and the reliability of the subscales ranged from .78 to .86. The results of this study indicate that HVAS-14 is a useful, reliable and valid instrument to measure the hospital violence attitude of clinical nurses.

• Korean Journal of Remote Sensing
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• v.22 no.4
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• pp.265-274
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• 2006

Extracting ground points is the kernel of DTM generation being considered as one of the most popular LIDAR applications. The previous extraction approaches can be mostly characterized as a point based approach, which sequentially examines every individual point to determine whether it is measured from ground surfaces. The number of examinations to be performed is then equivalent to the number of points. Particularly in a large set, the heavy computational requirement associated with the examinations is obviously an obstacle to employing more sophisticated criteria for the examination. To reduce the number of entities to be examined and produce more robust results, we developed an approach based on features rather than points, where a feature indicates an entity constructed by grouping some points. In the proposed approach, we first generate a set of features by organizing points into surface patches and grouping the patches into surface clusters. Among these features, we then attempt to identify the ground features with the criteria based on the attributes of the features. The points grouped into these identified features are labeled ground points, being used for DTM generation afterward. The Proposed approach was applied to many real airborne LIDAR data sets. The analysis on the results strongly supports the prominent performance of the proposed approach in terms of not only the computational requirement but also the quality of the DTM.

• Journal of the Korea Society for Simulation
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• v.30 no.2
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• pp.41-48
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• 2021

As the rural population continues to decline and aging, the improvement of agricultural productivity is becoming more important. Early prediction of crop quality can play an important role in improving agricultural productivity and profitability. Although many researches have been conducted recently to classify diseases and predict crop yield using CNN based deep learning and transfer learning technology, there are few studies which predict postharvest crop quality early in the planting stage. In this study, a early quality prediction model is proposed for sprout ginseng, which is drawing attention as a healthy functional foods. For this end, we took pictures of ginseng seedlings in the planting stage and cultivated them through hydroponic cultivation. After harvest, quality data were labeled by classifying the quality of ginseng sprout. With this data, we build early quality prediction models using several pre-trained CNN models through transfer learning technology. And we compare the prediction performance such as learning period and accuracy between each model. The results show more than 80% prediction accuracy in all proposed models, especially ResNet152V2 based model shows the highest accuracy. Through this study, it is expected that it will be able to contribute to production and profitability by automating the existing seedling screening works, which primarily rely on manpower.