• Title/Summary/Keyword: 진찬

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Big Data Analysis Using on Based Social Network Service Data (소셜네트워크서비스 기반 데이터를 이용한 빅데이터 분석)

  • Nam, Soo-Tai;Shin, Seong-Yoon;Jin, Chan-Yong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2019.05a
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    • pp.165-166
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    • 2019
  • Big data analysis is the ability to collect, store, manage and analyze data from existing database management tools. Big data refers to large scale data that is generated in a digital environment, is large in size, has a short generation cycle, and includes not only numeric data but also text and image data. Big data is data that is difficult to manage and analyze in the conventional way. It has huge size, various types, fast generation and velocity. Therefore, companies in most industries are making efforts to create value through the application of Big data. In this study, we analyzed the meaning of keyword using Social Matrix, a big data analysis tool of Daum communications. Also, the theoretical implications are presented based on the analysis results.

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A Review on Deep Learning Platform for Artificial Intelligence (인공지능 딥러링 학습 플랫폼에 관한 선행연구 고찰)

  • Jin, Chan-Yong;Shin, Seong-Yoon;Nam, Soo-Tai
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2019.05a
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    • pp.169-170
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    • 2019
  • Lately, as artificial intelligence becomes a source of global competitiveness, the government is strategically fostering artificial intelligence that is the base technology of future new industries such as autonomous vehicles, drones, and robots. Domestic artificial intelligence research and services have been launched mainly in Naver and Kakao, but their size and level are weak compared to overseas. Recently, deep learning has been conducted in recent years while recording innovative performance in various pattern recognition fields including speech recognition and image recognition. In addition, deep running has attracted great interest from industry since its inception, and global information technology companies such as Google, Microsoft, and Samsung have successfully applied deep learning technology to commercial products and are continuing research and development. Therefore, we will look at artificial intelligence which is attracting attention based on previous research.

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A Preference of Smartphone Locking Algorithms Using Delphi and AHP (Aanalytic Hierarchy Process) (델파이와 계층분석기법을 이용한 스마트폰 잠금 알고리즘 선호도 분석)

  • Nam, Soo-Tai;Shin, Seong-Yoon;Jin, Chan-Yong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.10
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    • pp.1228-1233
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    • 2019
  • Recently, a variety of algorithms using encryption technology have been adopted as methods of unlocking smartphone. It is advancing toward the direction to solve the unlocking problem through human biometrics technology, which has already succeeded in commercializing. These include finger print recognition, face recognition, and iris recognition. In this study, the evaluation items are five algorithms, including finger print recognition, face recognition, iris recognition, pattern recognition, and password input method. Based on the algorithms adopted, the AHP (analytic hierarchy process) technique was used to calculate the preferred priorities for smartphone users. Finger print recognition ( .400) was the top priority for smartphone users. Next, pattern recognition ( .237) was placed in the second priority for smartphone users. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

The Effect of Firm's Technology Convergence on Firm Performance (기업의 기술융합 성과수준이 경영성과에 끼치는 영향)

  • Jang, JinChan;Kim, YoungJun
    • Knowledge Management Research
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    • v.22 no.2
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    • pp.77-93
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    • 2021
  • In order to continue to grow in response to the rapidly changing industrial environments, companies must retain technological innovation capabilities and enhance market competitiveness. When competition is intensifying for creating new businesses and developing new products through technology commercialization, creating and utilizing technology convergence performance is an important means to create new competitiveness. However, there has been a lack of effort to systematically understand the level of technology convergence performance of the enterprise and to understand its relationship with management performance. In this paper, we develop a new analytical index by segmenting the technology convergence into patent variety, balance and disparity using patented IPC code information based on the concepts presented in existing diversity studies. In addition, 4,522 patents granted for three years between 2013 and 2015 by 219 KOSDAQ companies belonging to the domestic ICT convergence industry were analyzed to demonstrate that the level of technology convergence performance is positively related to sales growth rate in 2016.

A Reconstruction of Classification for Iris Species Using Euclidean Distance Based on a Machine Learning (머신러닝 기반 유클리드 거리를 이용한 붓꽃 품종 분류 재구성)

  • Nam, Soo-Tai;Shin, Seong-Yoon;Jin, Chan-Yong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.2
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    • pp.225-230
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    • 2020
  • Machine learning is an algorithm which learns a computer based on the data so that the computer can identify the trend of the data and predict the output of new input data. Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a way of learning a machine with given label of data. In other words, a method of inferring a function of the system through a pair of data and a label is used to predict a result using a function inferred about new input data. If the predicted value is continuous, regression analysis is used. If the predicted value is discrete, it is used as a classification. A result of analysis, no. 8 (5, 3.4, setosa), 27 (5, 3.4, setosa), 41 (5, 3.5, setosa), 44 (5, 3.5, setosa) and 40 (5.1, 3.4, setosa) in Table 3 were classified as the most similar Iris flower. Therefore, theoretical practical are suggested.

Residue Characteristics and Risk Assessment of Pesticides (Boscalid and Pyraclostrobin) in Hylomecon vernalis (피나물 중 boscalid 및 pyraclostrobin의 토양 처리시 잔류특성 및 안전성 평가)

  • Yu, Ji-Woo;Song, Min-Ho;Kim, Jinchan;Lee, Kwanghun;Ko, Rakdo;Keum, Young-Soo;Lee, Jiho
    • Korean Journal of Environmental Agriculture
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    • v.41 no.2
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    • pp.95-100
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    • 2022
  • BACKGROUND: This study was aimed to determine characteristics of residues of the soil-treated boscalid and pyraclostrobin within Hylomecon vernalis and to evaluate the risks from intake of the residual pesticides in the crop. METHODS AND RESULTS: The pesticides were treated to soils at two different concentrations, and the plant samples were collected 57 days after seeding. The samples were extracted using the QuEChERS extraction kit (MgSO4 4 g, NaCl 1 g). The quantitative methods for boscalid and pyraclostrobin were validated using linearity, recovery, and CV (coefficient of variation). Risk assessment of the pesticides was performed using Korea national nutrition statistics 2019. CONCLUSION(S): The residual levels of boscalid were 0.02-0.05 mg/kg (for the treatment at 6 Kg/10a) and 0.05-0.08 mg/kg (for the treatment at 12 Kg/10a), respectively. The residual concentrations of pyraclostrobin were below the LOQ. The amounts of pesticides were less than Maximum Residue Limits specified by the Korean Ministry of Food and Drug Safety. The maximum hazard indices of boscalid in chwinamul and amaranth for consumers were 0.0075% and 0.1525%, respectively, and it indicates that the risk of the pesticides from the crop is considered to be low.

Traditional Style of Flower Arrangement According to Diagram of Royal Protocol and Folding Screen in the Late Joseon Dynasty (조선시대 후기 궁중 행사도의 의궤(儀軌) 도식(圖式)과 도병(圖屛)에서 찾아 본 전통 꽃꽂이 양식)

  • Han, Sang Sook;Yi, Bu Young
    • Journal of the Korean Society of Floral Art and Design
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    • no.41
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    • pp.61-92
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    • 2019
  • We attempted to find the style of flower arrangement from the drawings of Uigwe and paintings of folding screens for the royal ceremonies of the late Joseon dynasty. In the pictures of the Uigwe and folding screens, we could see the linear, circular, and oval types Junhwa used to decorate the left and right sides of the throne placed in the center of main parish at the national banquet. There were also identified the Sanghwa which was used to decorate food on it, Jamhwa which was used to decorate head to be worn on the caps or hats, and Hwaga which was used to decorate the style supporting the large awnings at the national banquet. Hwaga was found, in the Musin Jinchan Dobyeong. In 1795, it was found that decorations on the floor, which are quite similar to the table decorations and modern space decorations, and flower shoot presented by king and flower decorations which were bound to the stick which was presented by king to country old men from Wonhaeng Eulmyo Jeongri Uigwe and Hwaseong Reunghaengdobyeong

Analysis of Energy Preference in the 4th Industrial Revolution Based on Decision Making Methodology (의사결정 방법론 기반 4차 산업혁명 시대 에너지 선호도 분석)

  • Nam, Soo-Tai;Shin, Seong-Yoon;Jin, Chan-Yong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.05a
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    • pp.328-329
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    • 2021
  • Newly, the fourth industrial revolution is a way of describing the blurring of boundaries between the physical, digital, and biological worlds. It's a fusion of advances in AI (artificial intelligence), robotics, the IoT (Internet of Things), 3d printing, genetic engineering, quantum computing, and other technologies. At the world economic forum in Davos, switzerland, in january 2016, chairman professor klaus schwab proposed the fourth industrial revolution for the first time. In order to apply the AHP (analytic hierarchy process) analysis method, the first stage factors were designed as Natural, Water, Earth and Atom energy. In addition, the second stage factors were organized into 9 detailed energies presented in the conceptual model. Thus, we present the theoretical and practical implications of these results.

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Visualizing Unstructured Data using a Big Data Analytical Tool R Language (빅데이터 분석 도구 R 언어를 이용한 비정형 데이터 시각화)

  • Nam, Soo-Tai;Chen, Jinhui;Shin, Seong-Yoon;Jin, Chan-Yong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.05a
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    • pp.151-154
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    • 2021
  • Big data analysis is the process of discovering meaningful new correlations, patterns, and trends in large volumes of data stored in data stores and creating new value. Thus, most big data analysis technology methods include data mining, machine learning, natural language processing, and pattern recognition used in existing statistical computer science. Also, using the R language, a big data tool, we can express analysis results through various visualization functions using pre-processing text data. The data used in this study was analyzed for 21 papers in the March 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was "Data", which ranked first 305 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

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Visualizing Article Material using a Big Data Analytical Tool R Language (빅데이터 분석 도구 R 언어를 이용한 논문 데이터 시각화)

  • Nam, Soo-Tai;Shin, Seong-Yoon;Jin, Chan-Yong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.05a
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    • pp.326-327
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    • 2021
  • Newly, big data utilization has been widely interested in a wide variety of industrial fields. Big data analysis is the process of discovering meaningful new correlations, patterns, and trends in large volumes of data stored in data stores and creating new value. Thus, most big data analysis technology methods include data mining, machine learning, natural language processing, and pattern recognition used in existing statistical computer science. Also, using the R language, a big data tool, we can express analysis results through various visualization functions using pre-processing text data. The data used in this study were analyzed for 29 papers in a specific journal. In the final analysis results, the most frequently mentioned keyword was "Research", which ranked first 743 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

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