• Korean Journal of Artificial Intelligence
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• v.6 no.2
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• pp.23-27
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• 2018

Korea has a high proportion of self-employment. Many of them start the food business since it does not require high-techs and it is possible to start the business relatively easily compared to many others in business categories. However, the closure rate of the business is also high due to excessive competition and market saturation. Cafés and restaurants are examples of food business where the business analysis is highly important. However, for most of the people who want to start their own business, it is difficult to conduct systematic business analysis such as trade area analysis or to find information for business analysis. Therefore, in this paper, we predicted business status with simple information using Microsoft Azure Machine Learning Studio program. Experimental results showed higher performance than the number of attributes, and it is expected that this artificial intelligence model will be helpful to those who are self-employed because it can easily predict the business status. The results showed that the overall accuracy was over 60 % and the performance was high compared to the number of attributes. If this model is used, those who prepare for self-employment who are not experts in the business analysis will be able to predict the business status of stores in Seoul with simple attributes.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.50-57
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• 2022

Along with the development of the 4^th Industrial Revolution technology, artificial intelligence technology is also being used in the field of systems engineering. This study analyzed the development status of artificial intelligence technology in the areas of systems engineering core processes such as stakeholder needs and requirements definition, system requirement analysis, and system architecture definition, and presented future technology development directions. In the definition of stakeholder needs and requirements, technology development is underway to compensate for the shortcomings of the existing requirement extraction methods. In the field of system requirement analysis, technology for automatically checking errors in individual requirements and technology for analyzing categories of requirements are being developed. In the field of system architecture definition, a technology for automatically generating architectures for each system sector based on requirements is being developed. In this study, these contents were summarized and future development directions were presented.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.104-112
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• 2023

Agriculture plays an industrial and economic role, as well as an environmental and ecological conservation role, group harmony and the inheritance of traditional culture. However, no matter how advanced the industry is, the basic food necessary for human life can only be produced through the photosynthesis of plants with natural resources such as the sun, water, and air. The Food and Agriculture Organization of the United Nations (FAO) predicts that the world's population will increase by another 2 billion people by 2050, and it faces a myriad of complex and diverse factors to consider, including climate change, food security concerns, and global ecosystems and political factors. In particular, in order to solve problems such as increasing productivity and production of agricultural products, improving quality, and saving energy, it is difficult to solve them with traditional farming methods. Recently, with the wind of the 4th industrial revolution, ICT convergence technology and artificial intelligence have been rapidly developing in many fields, but it is also true that the application of new technologies is somewhat delayed due to the unique characteristics of agriculture. However, in recent years, as ICT and artificial intelligence utilization technologies have been developed and applied by many researchers, a revolution is also taking place in agriculture. This paper summarizes the current state of research so far in four categories of agriculture, namely crop cultivation environment management, soil management, pest management, and irrigation management, and smart farm research data that has recently been actively developed around the world.

• Women's Health Nursing
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• v.7 no.4
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• pp.499-507
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• 2001

The purpose of this study was to explore nursing needs during labor pain that had been suffered by women who have given birth. It is essential to identify the nursing needs in order to solve nursing problems and to provide better care for the parturients. The sample consisted of 20 women of primiparas and 17 women of multiparas. They underwent normal labor and delivered a healthy baby at term. The data had been collected through the unstructured interviews conducted 1-2 days after delivery in the admission room from March 1998 to March 1999. On average, the interviews lasted for about 30 minutes. Interviews were taken with the consent of the subjects. The data are categorized according to the similarities of their contents. Seventeen subordinate categories and six superordinate categories have been identified. Six superordinate categories are 1) physical nursing needs 2) nursing needs of medical behavior 3) emotional nursing needs 4) informational and teaching nursing needs 5) nursing needs of pain control 6) nursing needs of respect(personality). Seventeen subordinate categories include: comfortable posture, touch, professional knowledge and techniques, duty execution, support, company and talk, stable surroundings, reassurance, information on delivery, explanation of medical behavior, information on surroundings, instruction on the case of pain, arbitrary adjustment, artificial adjustment, respect, interest and reflection of opinions. The result of this research is the same as that of foreign research and the items of the questionnaire in Korea are the same as the foreign one. Despite the same result, however, this dissertation is significant in that the research identifies the parturients nursing needs and classified the data and thus the basis has been formed to develop the tools to assess the nursing needs of the Korean parturients. The findings can be used as the guide for nursing intervention of parturients.

• Smart Media Journal
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• v.9 no.3
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• pp.41-45
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• 2020

Recently, artificial intelligence (AI) and the Internet of things (IoT), which are represented by machine learning and deep learning among IT technologies related to the Fourth Industrial Revolution, are applied to our real life in various fields through various researches. In this paper, IoT and AI using object recognition technology are applied to classify clothing. For this purpose, the image dataset was taken using webcam and raspberry pi, and GoogLeNet, a convolutional neural network artificial intelligence network, was applied to transfer the photographed image data. The clothing image dataset was classified into two categories (shirtwaist, trousers): 900 clean images, 900 loss images, and total 1800 images. The classification measurement results showed that the accuracy of the clean clothing image was about 97.78%. In conclusion, the study confirmed the applicability of other objects using artificial intelligence networks on the Internet of Things based platform through the measurement results and the supplementation of more image data in the future.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.379-389
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• 2020

This is a qualitative study about Google 'Nest Hub Max' that displays proactive intelligent personal assistant. Following the step of grounded theory, an in-depth interview for 6 users who had used this device for a month was taken. 186 concepts were discovered, categorized as 11 top-categories and 24 sub-categories. Paradigm diagram, considering axis-coding, was made and it have been narrowed down to 'Usage patterns' of proactive IPA, considering selective coding aspects. 'Usage patterns' were divided to passive and active user. Thus, neither passive user nor active user was satisfied about device and proactive IPA. This study is meaningful that it constructed basic data about the user experience of proactive IPA on this device. It will support the device or service that consists proactive IPA in the future.

• Korean Institute of Interior Design Journal
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• v.21 no.3
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• pp.22-30
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• 2012

Nowadays the issue of environmental pollution and ecological destruction is not a simple issue but an important issue to be continuously considered. It is deemed that a study for recycled new materials is immediately required and this study is to analyze features and processing methods of new materials which can be used to interior space. We found the recycled new materials used for space through researching various web sits. And then we analyzed what the base materials are and classified that base materials are whether natural or artificial of the recycled materials. We classified processing methods of the recycled new materials after researching general processing methods. The result of this study would be an important material to the research and development of new finishing materials with consideration of environment and to the research for a guideline of applicable new materials. The results of this study are as follows : First, we could classify widely 2 categories into natural material and artificial material and then 10 subcategories into metal, glass, wood, rubber, stone, plastic, leather or fabric, ceramic, concrete and so on, and analyzed that which material is mostly used and whether it is single material or multiple material. In order to analyze the feature of processing method. Second, we could classify into 4 categories such as junction, surface process, molding, and insert, and found out which processing method is applied based on objects of research. Third, as an analysis result of the recycled new material feature, in order to develop various new materials, it is required to study on combination and application of 2 materials or more rather than single material. Four, as a analysis result of the processing method feature, I would like to suggest that development and application of various processing methods are required. Especially, it is necessary to grope for a way to develop new functional materials for interior space through a systemic research and analysis of processing method of other fields. Furthermore, a way to reuse recycled new materials should be considered in a stage of selection and application of processing method.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.116-124
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• 2018

This study aimed to interpreting the view of current media by artificial intelligence(AI) with the case of a TV show Person of Interest, which was broadcasted until 2016 in US. In this study, we first attempted to find how AI reflects the three laws of robotics, which often appear in Science Fictions. In addition, we paid attention to how the society looks according to the change of the manipulator of the AI, and as a result we derived the distinguishes of the show from other content dealing with robots or AI in terms of narrative. Through this study, we classified the messages in accordance of AI into three categories- importance of data sovereignty, potential of intelligence explosion, and blind faith in high technology. This study suggested that the show emphasizes the consciousness and attitude that should be possessed as a citizen who meets the era of AI prior to raising issue of 'what artificial intelligence is should be developed for our convenience?'

• Archives of Plastic Surgery
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• v.35 no.2
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• pp.127-133
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• 2008

Purpose: The purpose of this study is to evaluate the remodeling process of the various skin substitutes in 4th and 6th weeks following the transplantation when transplanted onto nude mice. Methods: Three types of artificial skin substitutes, such as PLGA scaffold with keratinocyte sheets(group 1), acellular human dermis($Surederm^{(TM)}$) and keratinocyte sheet(group 2), bioengineered skin($Neoderm^{(TM)}$)(group 3), were applied to the wound on nude mice. All mice were killed in 2, 4 weeks and/or 6 weeks after grafting and tissue samples were harvested from the back of mice. The changes in wound size, degree of angiogenesis, formation of basement membrane and epidermis, density of collagen fibers and neural restoration were examined. Results: There was no significant changes in wound size among the three groups. However, the size of wound decreased in the non-substituted group due to contracture. Degree of angiogenesis and systhesis of collagen or neurofilaments were mostly increased in bioengineered skin($Neoderm^{(TM)}$)(group 3), followed by acellular human dermis($Surederm^{(TM)}$) and keratinocyte sheet(group 2), PLGA scaffold with keratinocyte sheets (group 1). However, group 3 and group 2 showed similar thickness of basement membrane and epidermis. Conclusion: We found that degree of angiogenesis, formation of basement membrane and skin appendages, density of collagen fibers and neurofilaments can be the categories to evaluate the success of artificial skin substitution in early stages.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.