• Korean Journal of Breeding Science
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• v.42 no.6
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• pp.721-726
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• 2010

'Nampung', a new soybean cultivar for soy-paste, was developed from the cross between Suwon190 and 'Pokwangkong' by soybean breeding team at the National Institute of Crop Science (NICS) in 2007. A promising line, SS97215-S-S-20, was selected and designated as the name of Milyang162. It was prominent and had good result from regional adaptation yield trials(RYT) for three years from 2005 to 2007 and was released as the name of 'Nampung'. It has a determinate growth habit, white flower, brown pubescence, yellow seed coat, light brown hilum, medium spherical seed (19.9 grams per 100 seeds). 'Nampung' is resistant to soybean mosaic virus and bacterial pustule, the major soybean disease in Korea. It is possible to harvest of 'Nampung' using combine because of it's lodging tolerance, few branches, and high position of pod attachment. The average yield of 'Nampung' is 2.97 ton per hectare in the regional yield trials (RYT) carried out for three years from 2005 to 2007 which is 21 percent higher than the check variety, 'Taekwang'.

• 한국노년학
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• v.41 no.2
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• pp.197-215
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• 2021

This study aims to evaluate Person Centered Care practice and characteristics of care services in Korean long-term care facilities using Dementia Care Mapping as a tool. DCM, systematic observational evaluation tool for measuring dementia patients' QOL, was transformed into self-report rating scale. The process of transforming DCM into a scale of 34 items involves operationalization of DCM concepts and it's adaptation into Korean long-term care practices. Review by research team of Bradford university was added to maintain DCM concept and meaning in this scale. The scale with Cronbach alpha of .88 was surveyed on 343 care workers. Survey result shows PCC value practiced by them is 3.77(of 5 likert scale) and values on each categories of PCC reveal the characteristics of care in Korean facilities; attachment(4.02), comfort(3.95), inclusion(3.89), identity(3.67) and occupation(3.41). Dementia care in Korean facilities focuses on recipients'safety, comfort but lacks individualistic care and the meaningful and fulfilling occupation for patients. Looking at the organizational and individual factors influencing DCM values, the small facilities showed higher PCC values and there are no significant difference in PCC values between public and private facilities. Managers and care workers with career of 1~2 years showed higher PCC values compared to other career ranks and lengthes. This study suggests care practice should be centered on personhood of patients in long-term care facilities, for which introduction of unit care and education of PCC for service providers including support personnel are needed. DCM and Korean DCM scale developed in this study are suggested for the PCC-based assessment on care quality.

• The Korean Journal of Physiology
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• v.1 no.1
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• pp.103-120
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• 1967

As pointed out by many previous investigators, the cardio-pulmonary system of well trained athletes is so adapted that they can perform a given physical exercise more efficiently as compared to non-trained persons. However, the time course of the development of these cardio-pulmonary adaptations has not been extensively studied in the past. Although the development of these training effects is undoubtedly related to the magnitude of an exercise load which is repeatedly given, it would be practical if one could maintain a good physical fitness with a minimal daily exercise. Hence, the present investigation was undertaken to study the time course of the development of cardio-pulmonary adaptations while a group of non-athletes was subjected to a daily 6 to 10 minutes running exercise for a period of 4 weeks. Six healthy male medical students (22 to 24 years old) were randomly selected as experimental subjects, and were equally divided into two groups (A and B). Both groups were subjected to the same daily running exercise (approximately 1,000 kg-m). 6 days a week for 4 weeks, but the rate of exercise was such that the group A ran on treadmill with 8.6% grade for 10 min daily at a speed of 127 m/min while the group B ran for 6 min at a speed of 200 m/min. In order to assess the effects of these physical trainings on the cardio-pulmonary system, the minute volume, the $O_2$ consumption, the $CO_2$ output and the heart rate were determined weekly while the subject was engaged in a given running exercise on treadmill (8.6% grade and 127 m/min) for a period of 5 min. In addition, the arterial blood pressure, the cardiac output, the acid-base state of arterial blood and the gas composition of arterial blood were also determined every other week in 4 subjects (2 from each group) while they were engaged in exercise on a bicycle ergometer at a rate of approximately 900 kg m/min until exhaustion. The maximal work capacity was also determined by asking the subject to engage in exercise on treadmill and ergometer until exhaustion. For the measurement of minute volume, the expired gas was collected in a Douglas bag. The $O_2$ consumption and the $CO_2$ output were subsequently computed by analysing the expired gas with a Scholander micro gas analyzer. The heart rate was calculated from the R-R interval of ECG tracings recorded by an Offner RS Dynograph. A 19 gauge Cournand needle was inserted into a brachial artery, through which arterial blood samples were taken. A Statham $P_{23}AA$ pressure transducer and a PR-7 Research Recorder were used for recording instantaneous arterial pressure. The cardiac output was measured by indicator (Cardiogreen) dilution method. The results may be summarized as follows: (1) The maximal running time on treadmill increased linearly during the 4 week training period at the end of which it increased by 2.8 to 4.6 times. In general, an increase in the maximal running time was greater when the speed was fixed at a level at which the subject was trained. The mammal exercise time on bicycle ergometer also increased linearly during the training period. (2) In carrying out a given running exercise on treadmill (8.6%grade, 127 m/min), the following changes in cardio·pulmonary functions were observed during the training period: (a) The minute volume as well as the $O_2$ consumption during steady state exercise tended to decrease progressively and showed significant reductions after 3 weeks of training. (b) The $CO_2$ production during steady state exercise showed a significant reduction within 1 week of training. (c) The heart rate during steady state exercise tended to decrease progressively and showed a significant reduction after 2 weeks of training. The reduction of heart rate following a given exercise tended to become faster by training and showed a significant change after 3 weeks. Although the resting heart rate also tended to decrease by training, no significant change was observed. (3) In rallying out a given exercise (900 kg-m/min) on a bicycle ergometer, the following change in cardio-vascular functions were observed during the training period: (3) The systolic blood pressure during steady state exercise was not affected while the diastolic blood Pressure was significantly lowered after 4 weeks of training. The resting diastolic pressure was also significantly lowered by the end of 4 weeks. (b) The cardiac output and the stroke volume during steady state exercise increased maximally within 2 weeks of training. However, the resting cardiac output was not altered while the resting stroke volume tended to increase somewhat by training. (c) The total peripheral resistance during steady state exercise was greatly lowered within 2 weeks of training. The mean circulation time during exorcise was also considerably shortened while the left heart work output during exercise increased significantly within 2 weeks. However, these functions_at rest were not altered by training. (d) Although both pH, $P_{co2}\;and\;(HCO_3-)$ of arterial plasma decreased during exercise, the magnitude of reductions became less by training. On the other hand, the $O_2$ content of arterial blood decreased during exercise before training while it tended to increase slightly after training. There was no significant alteration in these values at rest. These results indicate that cardio-pulmonary adaptations to physical training can be acquired by subjecting non-athletes to brief daily exercise routine for certain period of time. Although the time of appearance of various adaptive phenomena is not identical, it may be stated that one has to engage in daily exercise routine for at least 2 weeks for the development of significant adaptive changes.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.