• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.4
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• pp.346-352
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• 2020

It has been about 30 years since women students entered the Maritime College at Mokpo National Maritime University (MMU) and Korea Maritime & Ocean University to train as maritime seafarers. The women have been choosing a maritime college regardless of the Boarding Service Reserve System. Therefore, it is necessary to continuously study the motivation for admission, preferences for boarding, and desired career paths to guide the distinction and vision of maritime colleges. Accordingly, this study conducted a questionnaire survey on 93 women students attending the Maritime College at MMU. Of the respondents, 35.5 % said that they enrolled to become maritime officials and 30.1 % to become maritime seafarers. In addition to the current training for maritime seafarers, additional courses are required to train maritime experts. The study found that 88.2 % of the respondents thought that women's embarkation was more difficult than usual. It is considered that a systematic education program is needed for the onboard life of women maritime seafarers in schools and shipping companies. It was found that 69.6 % of the respondents preferred to embark as seafarers after graduation. After graduating from university, 32.3 % of the respondents said that they preferred to become navigation officers or engineers. It was also found that 24.7 % preferred to become marine-related civil servants / professionals, and 18.3 % preferred to become marine police. From the total, 83.9 % hoped for careers in marine-related fields. It is, therefore, necessary to organize courses and further education according to the motives for admission and preferred occupations of women students.

• Journal of agricultural medicine and community health
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• v.27 no.1
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• pp.21-31
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• 2002

The aim of this study was to analyse and conduct the comprehensive geriatric assessment for the elderly in rural area. The subjects were 388 older people aged 65 years or older living in the community. Data for comprehensive assessment such as physical, mental, functional, social and environmental conditions were collected from January to February, 2001 through a person-to-person interview. Of the total 388 olders, 169(43.6%) were men and 219(56.4%) were women. Mean ages of men and women were $73.5{\pm}6.4$ and $74.0{\pm}6.2$ years respectively. Three common diseases of the elderly were arthralgia(51.6%), chronic back pain(33.2%) and hypertension(18.6%), and higher in women than in men. Impairment rate of vision, hearing and bowel or bladder control was 59.0%, 20.1%, and 28.4% respectively. But that of lover extremities 3.4%. In terms of cognitive function, short term memory loss was found in 33.7% of males and 44.7% of females. The percentage of fully independent in the six ADL items was 72.2% in men and 58.9% in women. In the social supportive system, 49.5% of the elderly were living with spouse, and 22.9% living alone, 26.3% having care giver. These results will provide basic data for the development of community-based health program, which gives appropriate health service for the elderly living in the community.

• Cartoon and Animation Studies
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• s.45
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• pp.147-175
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• 2016

This thesis aims to research ways for the regional balanced development of cartoon creation & production environment being centered in metropolitan area in Korean cartoon industry which has rapidly changed since 2013. As a cartoon can not only be produced with relative lower production cost comparing to those of other cultural contents industries, but also can be produced only if the minimal requirements for cartoon production is prepared, so the cartoon is a field that the decentralization can be accomplished very easily. Currently, most cartoon-relevant companies and cartoon promotion institutions are located in Seoul an Bucheon, etc. However, cartoon artists live nationwide, and even cartoon artists producing their works abroad are reached to a significant number. In some regions like Daejeon, Busan, Suncheon and Gyeongbuk (Gyeongsangbuk-do), there have been appeared full-scale movement to construct regional cartoon creation & production centers since 2015. This thesis aimed to investigate each region's movement to construct cartoon creation & production center with oversea cases, and to check how such movement could be balanced and harmonized with each region's unique features. First of all, this thesis analyzed the status quo of government's policy nurturing the cartoon industry. Korean government's cartoon-promotion policy around the axis of the Cartoon Industry's Mid.Long-Term Development Plan has been developed around the Korea Creative Content Agency and the Korea Manwha(cartoon) Contents Agency in Bucheon, but as the webtoon industry has rapidly grown up, the necessity for building a cartoon promotion institution in each region has been raised since 2015. With the establishment of 4th Cartoon Industry Mid.Long-Term Development Plan to be executed from 2019, it seems that full-scaled support framework for cartoon regional balanced development should be occupied. For the case of foreign countries, cartoon promotion institutions and relevant events have been developed around regions from early times like San-Diego, USA(Comicon), Angouleme, France(National Image Center), Kyoto (Cartoon Museum), Sakaiminato(Misuki Sigeru Road), Japan gave a lot of implications. In the section of conclusion, this study aimed to suggest the importance of and necessity for establishing a cartoon creation & production center in each region appropriately for the region's identity and characteristics with specific plans. Based on that, this thesis aimed to suggest a vision for cartoon & webtoon industry that regional creation & production system can be settled almost only in the cultural contents industry.

• Radiation Oncology Journal
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• v.14 no.1
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• pp.69-76
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• 1996

Purpose : The aim of this study is to investigate the random and systematic errors and tumor movement using electronic portal imaging device in lung cancer patients for the adequate margin in the treatment planning of 3-dimensional conformal therapy. Material and Methods : The electronic portal imaging device is matrix ion chamber type(Portal Vision, Varian). Ten patients of lung cancer treated with chest irradiation were selected for this study. Patients were treated in the supine position without immobilization device. All treatments were delivered by an 10 MV linear accelerator that had the portal imaging system mounted to its ganrty. AP or PA field Portal images were only analyzed. Radiation therapy field included the tumor, mediastinum and supraclavicular lymph nodes. A total of 103 portal images were analyzed for set-up deviation and 10 multiple images were analyzed for tumor movement because of respiration and cardiac motion. Result : The average values of setup displacements in the x, y direction was 1.41 mm, 1 78 mm, respectively. The standard deviation of systematic component was 4.63 mm, 4.11 mm along the x, y axis, respectively while the random component was 4.17 mm in the x direction and 3.31 mm in the y direction. The average displacement from respiratory movement was 12.2 mm with a standard deviation of 4.03 mm. Conclusion : The overall set-up displacement includes both random and systematic component and respiratory movement. About 10 mm, 25 mm margins along x, y axis which considered the set-up displacement and tumor movement were required for initial 3-dimensional conformal treatment planning in the lung cancer patients and portal images should be made and analyzed during first week of treatment, individually.

• Management & Information Systems Review
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• v.31 no.3
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• pp.223-248
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• 2012

This study analyses support policy and structural change of textile industry in Daegu Kyungbuk region, and suggests major issues for textile industry's innovation. In Daegu Kyungbuk, it was 1999 that a policy, so called Milano Project, in order to promote a textile industry was devised. In 2004, the Regional Industrial Promotion Plan was devised. The plan was born from a view point of establishing a regional innovation system and of promoting the innovative clusters under a knowledge based economy. After then, the Regional Industry Promotion Project or Regional Strategic Industry Promotion Project became a core of regional textile industrial policy. Research results indicated that the first stage Milano project (1999-2003) showed both positive and negative effects. There were no long-term development plan, clear vision and strategy. But, core industrial infrastructure for differentiated product development, such as New product Development Support Center and Dyeing Design Practical Application Center, was constructed. The second stage Daegu Textile Industry Promotion Plan (2004-2008) displayed a significant technological performance and new product sales with the assistance of Kyungbuk province. Also, textile industry revealed positive fruits such as financial structure, productivity, and profitability as a result of strong restructuring. In industrial structure, there was a important change from clothe textile material to industry textile material. Most of textile companies did not showed high capability in CEO's technology innovation intention, entrepreneurship, R&D and human resource competency in compare with other industry. We suggested that Daegu Kyungbuk has to select and concentrate on the high-tech textile material and living textile for sustainable development and competitiveness. We also proposed a confidence and cooperation based innovation network and company oriented innovation cluster.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.329-341
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• 2020

As COVID-19 pandemic sweeps across the world, more than 45 million confirmed cases and over 1,000,000 deaths have occurred till now, and this situation is expected to continue for some time. In particular, more than half of the infections in European countries such as Italy and Spain occurred in nursing homes, and it is reported that over 4,000 people died in nursing homes for older adults in the United States. Therefore, the issues that need to be addressed after the COVID-19 crisis include finding a fundamental solution to group care and shifting to family-centered care. More specifically, it is expected that there will be ever more lively discussion on establishing and expanding hyper-technology based community care, that is, family-centered care integrated with ICT and other Industry 4.0 technologies. This poses a challenge of how to combine social security and social welfare with Industry 4.0 in concrete ways that go beyond the abstract suggestions made in the past. A case in point is the proposal involving smart welfare cities. Given this background, the present paper examined the concept, scope, and content of non-face-to-face care in the context of previous literature on the function and scope of the social security platform, and the concept and expandability of the smart welfare city. Implementing a smart city to realize the kind of social security and welfare that our society seeks to provide has significant bearing on the implementation of community care or aging in place. One limitation of this paper, however, is that it does not address concrete measures for implementing non-face-to-face care from the policy and legal/institutional perspectives, and further studies are needed to explore such measures in the future. It is expected that the findings of this paper will provide the future course and vision not only for the smart welfare city but also for the social security and welfare system in administrative, practical, and legislative aspects, and ultimately contribute to improving the quality of human life.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.2
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• pp.118-129
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• 2011

This study is about the governor general's official residence and its garden in Yongsan that were constructed during the Japanese occupational time. The garden design drawing was also made while planning such Neo-Baroque style building, and it contains particular information of the garden unlike the other existing landscape drawings. The content of garden translated and landscape historical value drawn out by analysis of garden drawings, press articles and literatures are as follows; First, such governor general's official residence garden in Yongsan is likely to be the Korean first western style landscape form. For, from the point that it was completely constructed together with such official residential building in 1909, its construction time should be before that of the garden of Seokjojeon, Deoksu Palace, which was constructed in 1911. Second, it shows the garden style and garden planting factors introduced together with the modern architecture then. Such garden planting factors are placed from the center axis of the garden that is connected to the center of the building and monument as well. Such style and factors cover and show the flower bed appearing in Baroque style gardens, the monument that forms Vista playing the center of audience's vision, water space that is placed symmetrically against the axis, planting pattern that emphasizes the plants' space, flower bed shape and axis, and what kinds of plants were introduced then. Third, it shows the using pattern of western style gardens. Western style garden parties used to take in place in this garden while official dinner and reception were held in the evening in the official residence. Fourth, it shows the historical value as a modern landscape drawing, which is the Korean first landscape drawing that shows the plants' names and planting techniques marking the current height and planned height for change of topography and water system as a water landscape factor. That is, this drawing has the value that it was upgraded from the other existing ones that expressed only simple plants' symbols or flower bed shapes. I, therefore, hope that the studies on the modern landscape would be getting wider by excavation of new historical records in the future.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.143-174
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• 2023

With the development of deep learning technologies, Artificial Intelligence powered Optical Character Recognition (AI-OCR) has evolved to read multiple languages from various forms of images accurately. For the financial industry, where a large number of diverse documents are processed through manpower, the potential for using AI-OCR is great. In this study, we present a configuration and a design of an AI-OCR modality for use in the financial industry and discuss the platform construction with application cases. Since the use of financial domain data is prohibited under the Personal Information Protection Act, we developed a deep learning-based data generation approach and used it to train the AI-OCR models. The AI-OCR models are trained for image preprocessing, text recognition, and language processing and are configured as a microservice architected platform to process a broad variety of documents. We have demonstrated the AI-OCR platform by applying it to financial domain tasks of document sorting, document verification, and typing assistance The demonstrations confirm the increasing work efficiency and conveniences.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.171-187
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• 2023

When a person's face is recognized through a recording device such as a low-pixel surveillance camera, it is difficult to capture the face due to low image quality. In situations where it is difficult to recognize a person's face, problems such as not being able to identify a criminal suspect or a missing person may occur. Existing studies on face recognition used refined datasets, so the performance could not be measured in various environments. Therefore, to solve the problem of poor face recognition performance in low-quality images, this paper proposes a method to generate high-quality images by performing image quality improvement on low-quality facial images considering various environments, and then improve the performance of facial feature point detection. To confirm the practical applicability of the proposed architecture, an experiment was conducted by selecting a data set in which people appear relatively small in the entire image. In addition, by choosing a facial image dataset considering the mask-wearing situation, the possibility of expanding to real problems was explored. As a result of measuring the performance of the feature point detection model by improving the image quality of the face image, it was confirmed that the face detection after improvement was enhanced by an average of 3.47 times in the case of images without a mask and 9.92 times in the case of wearing a mask. It was confirmed that the RMSE for facial feature points decreased by an average of 8.49 times when wearing a mask and by an average of 2.02 times when not wearing a mask. Therefore, it was possible to verify the applicability of the proposed method by increasing the recognition rate for facial images captured in low quality through image quality improvement.