• 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.

• The Korean Journal of Air & Space Law and Policy
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• v.18
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• pp.135-183
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• 2003

Aerospace force is a determining factor in a modem war. The combat field is expanding to space. Thus, the legitimacy of establishing aerospace force is no longer an debating issue, but "how should we establish aerospace force" has become an issue to the military. The standard limiting on the military use of space should be non-aggressive use as asserted by the U.S., rather than non-military use as asserted by the former Soviet Union. The former Soviet Union's argument is not even strongly supported by the current Russia government, and realistically is hard to be applied. Thus, the multi-purpose satellite used for military surveillance or a commercial satellite employed for military communication are allowed under the U.S. principle of peaceful use of space. In this regard, Air Force may be free to develop a military surveillance satellite and a communication satellite with civilian research institute. Although MTCR, entered into with the U.S., restricts the development of space-launching vehicle for the export purpose, the development of space-launching vehicle by the Korea Air Force or Korea Aerospace Research Institute is beyond the scope of application of MTCR, and Air Force may just operate a satellite in the orbit for the military purpose. The primary task for multi-purpose satellite is a remote sensing; SAR sensor with high resolution is mainly employed for military use. Therefore, a system that enables Air Force, the Korea Aerospace Research Institute, and Agency for Defense Development to conduct joint-research and development should be instituted. U.S. Air Force has dismantled its own space-launching vehicle step by step, and, instead, has increased using private space launching vehicle. In addition, Military communication has been operated separately from civil communication services or broadcasting services due to the special circumstances unique to the military setting. However, joint-operation of communication facility by the military and civil users is preferred because this reduces financial burden resulting from separate operation of military satellite. During the Gulf War, U.S. armed forces employed commercial satellites for its military communication. Korea's participation in space technology research is a little bit behind in time, considering its economic scale. In terms of budget, Korea is to spend 5 trillion won for 15 years for the space activities. However, Japan has 2 trillion won annul budget for the same activities. Because the development of space industry during initial fostering period does not apply to profit-making business, government supports are inevitable. All space development programs of other foreign countries are entirely supported by each government, and, only recently, private industry started participating in limited area such as a communication satellite and broadcasting satellite, Particularly, Korea's space industry is in an infant stage, which largely demands government supports. Government support should be in the form of investment or financial contribution, rather than in the form of loan or borrowing. Compared to other advanced countries in space industry, Korea needs more budget and professional research staff. Naturally, for the efficient and systemic space development and for the prevention of overlapping and distraction of power, it is necessary to enact space-related statutes, which would provide dear vision for the Korea space development. Furthermore, the fact that a variety of departments are running their own space development program requires a centralized and single space-industry development system. Prior to discussing how to coordinate or integrate space programs between Agency for Defense Development and the Korea Aerospace Research Institute, it is a prerequisite to establish, namely, "Space Operations Center"in the Air Force, which would determine policy and strategy in operating space forces. For the establishment of "Space Operations Center," policy determinations by the Ministry of National Defense and the Joint Chief of Staff are required. Especially, space surveillance system through using a military surveillance satellite and communication satellite, which would lay foundation for independent defense, shall be established with reference to Japan's space force plan. In order to resolve issues related to MTCR, Air Force would use space-launching vehicle of the Korea Aerospace Research Institute. Moreover, defense budge should be appropriated for using multi-purpose satellite and communication satellite. The Ministry of National Defense needs to appropriate 2.5 trillion won budget for space operations, which amounts to Japan's surveillance satellite operating budges.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Korean Journal of Environment and Ecology
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• v.37 no.4
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• pp.302-314
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• 2023

This study aims to explore the direction for Korea's effective response to Target 3 (30by30), which can be said to be the core of the Kunming-Montreal Global Biodiversity Framework (K-M GBF) of the Convention on Biological Diversity (CBD), to find the direction of systematic OECM (Other Effective area-based Conservation Measures) discovery at the national level through a survey of global conceptual review and expert perception of OECM. This study examined ① the use of Korean terms related to OECM, ② derivation of determining criteria reflecting global standards, ③ deriving types of potential OECM candidates in Korea, and ④ considerations for OECM identification and reporting to explore the direction for identifying systematic, national-level OECM that complies with global standards and reflects the Korean context. First, there was consensus for using Korean terminology that reflects the concept of OECM rather than simple translations, and it was determined that "nature coexistence area" was the most preferred term (12 people) and had the same context as CBD 2050 Vision of "a world of living in harmony with nature." This study suggests utilizing four criteria (1. No protected areas, 2. Geographic boundaries, 3. Governance/management, and 4. Biodiversity value) that reflect OECM's core characteristics in the first-stage selection process, carrying out the consensus-building process (stage 2) with the relevant agencies, and adding two criteria (3-1 Effectiveness and sustainability of governance and management and 4-1 Long-term conservation) and performing the in-depth diagnosis in stage 3 (full assessment for reporting). The 28 types examined in this study were generally compatible with OECMs (4.45-6.21/7 points, mean 5.24). In particular, the "Conservation Properties (6.21 points)" and "Conservation Agreements (6.07 points)", which are controlled by National Nature Trust, are shown to be the most in line with the OECM concept. They were followed by "Buffer zone of World Natural Heritage (5.77 points)", "Temple Forest (5.73 points)", "Green-belt (Restricted development zones, 5.63 points)", "DMZ (5.60 points)", and "Buffer zone of biosphere reserve (5.50 point)" to have high potential. In the case of "Uninhabited Islands under Absolute Conservation", the response that they conformed to the protected areas (5.83/7 points) was higher than the OECM compatibility (5.52/7 points), it is determined that in the future, it would be preferable to promote the listing of absolute unprotected islands in the Korea Database on Protected Areas (KDPA) along with their surrounding waters (1 km). Based on the results of a global OECM standard review and expert perception survey, 10 items were suggested as considerations when identifying OECM in the Korean context. In the future, continuous research is needed to identify the potential OECMs through site-level assessment regarding these considerations and establish an effective in-situ conservation system at the national level by linking existing protected area systems and identified OECMs.