• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.61-66
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• 2017

As the fourth industrial revolution becomes more popular and advanced services such as artificial intelligence and Internet of Things technology are widely commercialized, awareness of the importance of software is spreading. Recently, software education has been taught not only in elementary school and college but also in college. Also, there is a growing interest in computational thinking needed to solve problems through computing methodology and model. The purpose of this study is to design an app inventor course for non-majors software education based on computational thinking. As a result of the study, six detailed competencies of computational thinking were derived, and six detailed competencies were mapped to the app inventor learning elements. In addition, based on the computational thinking modeling, I designed an app inventor class for students who participated in IT curriculum of university liberal arts curriculum.

• 한국벤처창업학회:학술대회논문집
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• 2017.04a
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• pp.62-62
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• 2017

최근 인공지능 시대 및 제 4차 산업 혁명 시대의 도래로 제품 간, 제품과 서비스 간, 서비스 간 산업이 다양한 형태로 진화 중이며, 이종기술 산업 간의 융 복합화로 새로운 제3의 신산업이 등장하고 있다. 또한 소위 말하는 O2O(Online to Offline)는 이미 운송, 금융(핀테크), 자동차(카테크), 숙박, 음식, 의료(헬스케어테크) 등 많은 산업 분야에 진출, 기존 전통 산업과 충돌, 인허가에 어려움을 겪고 있으며 이러한 O2O는 IOT(사물인터넷)로 급속도로 가속화 되고 있다. 이렇듯 4차 산업 혁명에 따른 신산업의 탄생은 산업의 경계를 붕괴하고 있음에도 산업별로 구분된 제도, 규제, 지원정책 등은 여전히 신산업 창업의 장애가 되고 있다. 이에, 본 연구에서는 국내 벤처특별법, 창업지원법, 1인 창조기업법 내 투자 지원 업종과 미국을 비롯한 해외 창업 관련법 내 창업 지원 제한 업종과의 비교 분석, 몇 가지 중요한 시사점을 도출했다. 먼저, 우리나라가 선진국에 비해 지나치게 투자 제한 업종이 많음을 확인했다. 또한 선진국은 미풍양속을 해치는 업종, 투기적 사업 등 사회통념상 문제가 되는 사행성 업종을 제외하고는 대다수의 업종이 정책 지원 대상이다. 업종과 더불어 투자 행위로 투자 심사를 해 신산업 투자에 매우 탄력적으로 운영할 수 있게 설계되어 있다. 심지어 미국의 경우, 적극적 창업 투자를 위해 업종 및 투자 행위에 대한 심의를 중소기업청이 직접 수행한다. 특히, 우리나라의 중소기업창업투자회사제도는 미국의 중소기업투자회사(SBIC) 제도와 유사한 반면 투자 제한 업종 뿐 아니라 제도 운영 특히, 창업투자회사에 대한 관리 측면에서 매우 큰 차이가 있음을 보여주고 있다. 우리나라 중기청은 간접관리를 하는 반면, 미국은 중기청 내에서 직접 관리를 하고 있다. 따라서 본 연구에서는 우리나라 창업 벤처 투자 제한을 미국을 비롯한 기타 외국의 사례에서와 같이 사회통념상 불가피한 업종과 더불어 투자 행위로 정의하길 제안한다.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.347-356
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• 2015

Due to the rapid development of IoT(Internet of Things) technology, traditional taxis are connected through dispatchers and location systems. Typically, modern taxis have embedded with GPS(Global Positioning System), which aims for obtaining the route information. By analyzing the frequency of taxi trip events, we can find the frequent route for a given query time. However, a scalability problem would occur when we convert the raw location data of taxi trip events into the analyzed frequency information due to the volume of location data. For this problem, we propose a NoSQL based top-K query system for taxi trip events. First, we analyze raw taxi trip events and extract frequencies of all routes. Then, we store the frequency information into hash-based index structure of MongoDB which is a document-oriented NoSQL database. Efficient top-K query processing for frequent route is done with the top of the MongoDB. We validate the efficiency of our algorithms by using real taxi trip events of New York City.

• KIISE Transactions on Computing Practices
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• v.23 no.12
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• pp.684-690
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• 2017

Researchers have suggested several methods for utilizing various kinds of services in an Internet of Things (IoT) environment. These methods require users to learn some new kinds of tools or expertise in programming. In this paper, we propose a new paradigm, which allows end-users to build IoT-service mashups based on a presentation-authoring scheme. We have also included a framework for this procedure. The design of a mashup model in this framework was based on a task-oriented service framework. Users can build a mashup of this framework in the presentation-file format, convert it into a service flow, and deploy it for running in an IoT environment. In addition, we use a case study to show how PowMash supports end-users' service mashups in an IoT environment.

• Journal of KIISE
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• v.44 no.1
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• pp.21-26
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• 2017

In recent years, new technologies such as Internet of Things, Cloud Computing and Big Data are being widely used. And the type and amount of data is dramatically increasing. This makes security an important issue. In terms of leakage of sensitive personal information. In order to protect confidential information, a method called anonymization is used to remove personal identification elements or to substitute the data to some symbols before distributing and sharing the data. However, the existing method performs anonymization by generalizing the level of quasi-identifier hierarchical. It requires a higher level of generalization in case where k-anonymity is not satisfied since records in data table are either added or removed. Loss of information is inevitable from the process, which is one of the factors hindering the utility of data. In this paper, we propose a novel anonymization technique using decision tree based machine learning to improve the utility of data by minimizing the loss of information.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.117-126
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• 2017

This Study Focuses on the Customer Experience Story of Bandi&Luni's Centum City branch which Offers the Different Service Concept on the Competitive Book Selling Market. Bandi&Luni's Stands as one of the Largest Bookstore Chains that has 14 Offline Bookstores and an Online Bookstore Shop. The Purpose of this Study takes Lessons from the Unique Practices and Efforts of Bandi&Lunis's Online-to-Offline(O2O) Service and Service Design. In Order to Achieve this Propose, we Investigated the History, Service Concept, Value, and Performance of Bandi&Luni's from a Variety of sources and Reviewed the Previous Literatures of O2O Service and Service Design. In order to Offer Better Customer Experience the Service should be Designed from Customer's View and be Delivered through the Lifecycle of Customers. In a era of Big Data, IOT(internet of things), AI(artificial intelligence), The Service Design Innovation using Information and Communication Technology will be needed in order to Break down the Boundaries Between Online and Offline.

• Journal of Advanced Navigation Technology
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• v.20 no.3
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• pp.232-237
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• 2016

As a possible standardization of wireless local area network (WLAN), IEEE 802.11 LRLP is under discussion in order to support long range and low power (LRLP) communication for internet of things (IoT) including drones and many other IoT devices. In this paper, an efficient adaptive resource unit allocation scheme for uplink multiuser transmission in IEEE 802.11 LRLP networks is proposed. In the proposed scheme, which adopts OFDMA random access based transmission scheme of IEEE 802.11ax, in order to enhance the efficiency of the slotted OFDMA random access, access point (AP) traces the history of the sizes of successfully transmitted uplink data, and adjusts the sizes of resource units for the next uplink multiuser transmission adaptively. Our simulation results corroborate that the proposed scheme significantly improves the system throughput.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.87-91
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• 2017

Recently, several kinds of researches using IoT wearable devices are active in the field of sports, design, emotional sciences and so on. The human bio data such as blood pulse, ECG, SKT signal, and GSR Signal producing from IoT wearable devices such as Watch, Smart-band, Grass can adapt to the meaningful future applications. Using the human's emotional data and a physical status with variation and so on, we can individually get the personal status. Due to knowing the personal emotion or physical status is related and connected to the valuable wallet of customers, the approach is more important in nowadays. Therefore, the personal information can effectively adapt to the marketing of the culture industry, which deals with emotions of customers. The research shows implementation steps for explaining overall architecture of the convergence research between Art and Technologies.

• Korea Journal of Hospital Management
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• v.23 no.2
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• pp.54-66
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• 2018

Purpose: This study was to figure out relationships of perceived Technology Readiness Index(TRI), usefulness, acceptance intension, and the recognition of substitute employment of medical personnel on the artificial intelligence (AI) and internet of things (IoT) among main technologies. Methodology: To achieve the purpose, this study utilized structured survey tools to conduct a questionnaire survey of nursing, administrative and medical technology professionals at six university hospitals in Korea metropolitan area. A PLS(Partial Least Square) Path analysis was utilized To analyze the material. Findings: In the relation with the technology readiness and perceived usefulness, it had a positive influence to the perceived usefulness when the optimism and innovativeness were higher and the discomfort was lower. In the relation with the technology readiness and acceptance intension, it showed a positive influence when the innovativeness was higher and the discomfort was lower. In the relation with the perceived usefulness and acceptance intension, it had a positive influence to the acceptance intension when the perceived usefulness was higher. In the relation with the acceptance intension and the recognition of substitute employment, it showed a positive influence to the recognition of substitute employment when the acceptance intension was higher. Practical Implications: Judging based on the above study results and reference reviews, it confirmed that it is necessary to prepare in the level of hospital organization in the $4^{th}$ Industrial Revolution. They should increase the efficiency of human resources through the technological factors or changes of employment types for the additional demands of human resources to handle increasing medical demands or induce to secure necessary abilities which are changing at the right time by performing the $4^{th}$ Industrial Revolution related re-training continuously to develop the value of existing human resources.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.19-24
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• 2020

Recently, due to the rapid increase of pets, there is a need for an integrated pet-related personalized product recommendation service such as feed recommendation using a health status check of pets and various collected data. This paper implements a product recommendation system that can perform various personalized services such as collection, pre-processing, analysis, and management of pet-related data using big data. First, the sensor information worn by pets, customer purchase patterns, and SNS information are collected and stored in a database, and a platform capable of customized personalized recommendation services such as feed production and pet health management is implemented using statistical analysis. The platform can provide information to customers by outputting similarity product information about the product to be analyzed and information, and finally outputting the result of recommendation analysis.