• 한국멀티미디어학회논문지
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• 제24권1호
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• pp.40-50
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• 2021

Science museum are dealing with exhibits on field of changing science and technology, and previous research suggested that exhibits replacement should carried out at least every 5 years. In order to efficiently replace exhibits within a limited budget, various studies analyzed visitors' preferences to exhibits. Recently, studies use various technologies to collect the data on visitors' preferences automatically, but almost of studies had a high dependency on their visitors such as visitors needed to carry specific sub-devices in the museums for gathering data. As complementing the limitations of previous research, this study introduces the improved method which is able to automatically collect and quantify visitors' preferences to exhibits using TensorFlow, a deep learning technology. By the proposed analysis method, it was possible to collect 2,520 data of visitors' experience on exhibits in totality. Based on collected data, attraction power and holding power indicating the preference of visitors on exhibits were able to be calculated. The result also confirmed antecedent research conclusion that the attraction power and holding power of the exhibit which consists of 3 dimensional structures work are higher than other exhibits. As a conclusion, the proposed method will provide more convenient data collection method for detecting visitors' preference.

• 한국정보시스템학회지:정보시스템연구
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• 제29권4호
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• pp.101-121
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• 2020

Purpose The purpose of this study is to understand the current status of MIS curriculum and to find ways to improve it. In addition, the results of the research can be used as basic data for improving MIS curriculum. Design/methodology/approach A research framework was designed to derive research results using the keyword network analysis method of this study: 1) Keywords were extracted based on the six units of the big data analysis job competency. 2) And based on the extracted keywords, the relationship between the keywords and MIS curriculum for each university was identified. Findings In the MIS curriculum information of a few universities, education related to big data analysis was conducted. 1) In the MIS curriculum of a few universities, education related to big data analysis was conducted. However, MIS curriculum of the university, which is the subject of analysis, education focused on concepts and theory rather than practical education was conducted. 2) And it was confirmed that there is a difference from the education required by the industry.

• 산경연구논집
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• 제11권12호
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• pp.7-15
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• 2020

Purpose: This study aims to explore the feasibility of expanding complex wards to provide efficient hospital management and high-quality medical services to local residents of Gangneung Medical Center (GMC). Research Design, Data and Methodology: There are four research designs to achieve the research objectives. We analyzed Big Data for 3 months on Social Network Services (SNS). A questionnaire survey conducted on 219 patients visiting the GMC. Surveys of 20 employees of the GMC applied. The feasibility to expand the GMC ward measured through Focus Group Interview by 12 internal and external experts. Data analysis methods derived from various surveys applied with data mining technique, frequency analysis, and Importance-Performance Analysis methods, and IBM SPSS statistical package program applied for data processing. Results: In the result of the big data analysis, the GMC's recognition on SNS is high. 95.9% of the residents and 100.0% of the employees required the need for the complex ward extension. In the analysis of expert opinion, in the future functions of GMC, specialized care (△3.3) and public medicine (△1.4) increased significantly. Conclusion: GMC's complex ward extension is an urgent and indispensable project to provide efficient hospital management and service quality.

• 신재생에너지
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• 제19권4호
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• pp.98-107
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• 2023

A study was conducted to analyze data from 1981 to 2020 for understanding the impact of climate on solar energy generation. A significant increase of 104.6 kWhm^-2 was observed in the annual cumulative solar radiation over this period. Notably, the distribution of solar radiation shifted, with the solar radiation in Busan rising from the seventh place in 1981 to the second place in 2020 in South Korea. This study also examined the correlation between long-term temperature trends and solar radiation. Areas with the highest solar radiation in 2020, such as Busan, Gwangju, Daegu, and Jinju, exhibited strong positive correlations, suggesting that increased solar radiation contributed to higher temperatures. Conversely, regions like Seosan and Mokpo showed lower temperature increases due to factors such as reduced cloud cover. To evaluate the impact on solar energy production, simulations were conducted using climate data from both years. The results revealed that relying solely on historical data for solar energy predictions could lead to overestimations in some areas, including Seosan or Jinju, and underestimations in others such as Busan. Hence, considering long-term climate variability is vital for accurate solar energy forecasting and ensuring the economic feasibility of solar projects.

• 한국인터넷방송통신학회논문지
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• 제24권2호
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• pp.191-196
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• 2024

최근 여러 기업과 연구기관들이 IoT 장비에서 수집되는 다양한 데이터를 분석하고 실제 응용 서비스를 통해 제공하기 위한 지능형 IoT 기술에 관한 연구가 활발히 진행되고 있다. 하지만 IoT 기기에서 수집되는 데이터들을 연구 및 개발에 사용하기 위해 데이터를 송수신하는 과정에서 개인정보유출과 같은 보안상의 이슈가 발생할 수 있다. 그리고 여러 IoT 기기에서 수집되는 데이터가 증가할수록 데이터 관리에 어려움이 존재하며 데이터를 이동하는 데 큰 비용과 시간이 소요된다. 따라서 본 논문에서는 다양한 기기로 이루어진 연합학습 환경에서 보안상의 이슈와 비효율성을 개선하기 위해 신뢰성이 보장된 이더리움 네트워크 기반의 연합학습 시스템을 개발하고자 한다.

• International Journal of Internet, Broadcasting and Communication
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• 제16권1호
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• pp.287-296
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• 2024

Artificial intelligence (AI), the Internet of Things (IoT), and Big Data are playing important roles in improving or upgrading energy efficiency. Furthermore, their roles in energy efficiency are expected to become more and more essential. This study conducted a bibliometric comparative analysis on the features in the articles on the AI, the IoT, and the Big Data in energy efficiency by using the Web of Science database and compared the features in their trends in article publications, citations, countries, research areas, journals, and funding agencies from 2012 to 2022. This study attempted to make significant contributions by shedding new light on the following features. Among the AI, the IoT, and the Big Data in energy efficiency, the most articles were published and the most article citations were received in the AI in energy efficiency. China was found out to be the most leading country. Engineering and computer science were revealed to be the first research area. IEEE Access and IEEE Internet of Things were ranked with first journal. National Natural Science Foundation of China was the first research funding agency concerning the articles published in the AI, the IoT, and the Big Data in energy efficiency from 2012 to 2022.

• Ocean and Polar Research
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• 제42권2호
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• pp.115-131
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• 2020

The effective range of surface current data observed by high-frequency radar (HFR) operated in the northern coastal area of Jeju Island by Korea Institute of Ocean Science and Technology was estimated and the distribution and variability of the M₂ tidal current of the Jeju Strait was analyzed. To evaluate the HFR data, the M₂ tidal current corrected from 25 hours current data observed by the Korea Hydrographic and Oceanographic Agency (KHOA) was compared with the M₂ tidal current in the Jeju Strait analyzed from the surface currents of HFR. The reliability of HFR data was confirmed by analyzing the characteristics of the tide components of these two data sets, and the effective range of HFR data was estimated through temporal and spatial analysis. The observation periods of HFR used in the analysis were from 2012 to 2014, and it was confirmed that there is a difference in the effective range of HFR data according to the observation time. During the analysis periods, the difference between the M₂ current ellipses from the data of KHOA and the HFR was greater in the eastern than in the western part of the Jeju Strait, and represented a high reliability in the western and central parts of the Jeju Strait. The tidal current of the Jeju Strait analyzed using the HFR data revealed a seasonal variability a relatively weak in summer and a strong in winter, about a 17% fluctuations between the summer and winter based on the length of the semi-major axis of tidal ellipse. Appraisals and results of regarding the characteristics and seasonal variability of the M₂ tidal current in the Jeju Strait using HFR data have not been previously reported, so the results of this study are considered meaningful.

• 한국지리정보학회지
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• 제9권2호
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• pp.67-78
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• 2006

국가GIS구축사업의 일환으로 상 하수도를 비롯한 지하시설물도가 수치지도로 구축되어 왔으며 이렇게 구축된 지하시설물 수치지도를 지자체에서 관련 업무에 효율적으로 활용하기 위해서는 그 정확도를 신뢰할 수 있어야 한다. 본 연구의 목적은 지방자치단체에서 하수시설물에 대한 고품질의 DB를 구축하는데 필요한 효율적인 검수방안을 제시하여 공공측량 성과심사 품질기준에 적합하도록 할 뿐만 아니라 지하시설물도를 지방자치단체의 관련분야에서 활용될 경우 분석 상 오류에 대한 원인을 제거함으로써 부정확한 의사결정을 방지할 수 있도록 하고자 한다. 이를 위해 공공측량 성과심사기관에서 요구하는 지하시설물도의 품질기준과 검수현황을 살펴보았고, 지하시설물에서 발생하는 오류유형을 분석하였다. 또한 이러한 오류유형을 바탕으로 기존 현장 검수 방법의 한계점을 파악하였으며, 하수관거의 속성을 기준으로 정확도를 향상시키기 위한 논리적 일관성, 기하구조의 적합성을 분석하는 관망분석 검수 방안을 제시하였다.

• Nuclear Engineering and Technology
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• 제39권2호
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• pp.129-132
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• 2007

It is generally known that software reliability growth models such as the Jelinski-Moranda model and the Goel-Okumoto's non-homogeneous Poisson process (NHPP) model cannot be applied to safety-critical software due to a lack of software failure data. In this paper, by applying two of the most widely known software reliability growth models to sample software failure data, we demonstrate the possibility of using the software reliability growth models to prove the high reliability of safety-critical software. The high sensitivity of a piece of software's reliability to software failure data, as well as a lack of sufficient software failure data, is also identified as a possible limitation when applying the software reliability growth models to safety-critical software.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2012년도 제43회 하계 정기 학술대회 초록집
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• pp.77-77
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• 2012

As interest has increased in the interaction between low-temperature plasmas and materials, the role of modeling and simulation of processing in plasma has become important in understanding the effects of charged particles and radicals in plasma applications. Thus in this presentation, we present the theoretical and experimental studies of electron impact cross section for plasma processing gas, such as plasma etching and deposition processes. Also, here the work conducted at the Data Center for Plasma Properties (DCPP) over last 7 years on the systematic synthesis and assessment of fundamental knowledge on low-energy electron interactions with plasma processing gases is briefly summarized and discussed.