• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.674-679
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• 2017

As the importance of the 4th industrial revolution and ICT technology increased, it became a software centered society. Existing software training was limited to the composition of the learning environment, and a lot of costs were incurred early. In order to solve these problems, a learning method using a web compiler was developed. The web compiler supports various software languages and shows compilation results to the user via the web. However, Web compilers that support artificial intelligence technology are missing. In this paper, we designed and implemented a tensor flow based web compiler, Google's artificial intelligence library. We implemented a system for learning artificial intelligence by building a meteorJS based web server, implementing tensor flow and tensor flow serving, Python Jupyter on a nodeJS based server. It is expected that it can be utilized as a tool for learning artificial intelligence in software centered society.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.189-197
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• 2007

GIS is the tool widely used for the practical solution of spatial problems in every region regardless of the language and ideology. In this paper research trends and tendencies of GIS in North Korea are reviewed compared with those in South Korea. For this purpose, academic publications on GIS in North Korea are surveyed and classified according to main subjects in GIS. Such classification by main subjects of GIS in North Korea are conducted on the basis of the classification of research trends and tendencies of GIS in South Korea. As a result, researches in North Korea are mainly focused upon such fields as geodetic surveying and measurement, map manufacture, atmospheric phenomena, agriculture and disasters, while there are few studies related to such fields as national GIS policy and circulation, GIS education, internet-based GIS, and traffic, to name but a few. Most parts of applied data are based on low and medium resolution image such as meteorological satellite images, Landsat images, and so on. This reflects the low level of the development of GIS DB infrastructure in North Korea.

• Journal of The Korean Association For Science Education
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• v.24 no.5
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• pp.851-868
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• 2004

The main purposes of this study is to develop a web-based STS biology instruction program (WB-STS) for the scientifically gifted students. The specific main research questions were as follows; 1. How can the WB-STS for biology education be developed and what are the primary components involved in it? 2. Is there any proper validity for developed the WB-STS in biology education? To solve the above mentioned problems, several procedures were applied. First, in order to develop WB-STS for the scientifically gifted students, NCISE, Renzulli' s Enrichment Triad Model and the Iowa Chautauqua program's main characteristics were analyzed systematically and the principles and general process for constructing WB-STS were examined. Additionally, the needs of students and the goals of Biology education were identified thoroughly. And then all these ideas were embodied in an agenda for constructing WB-STS. Second, to analyse the validity and utility of developing WB-STS, a questionnaire was developed and submitted to seven specialists and a group of twenty students who would participate in the experiment later. The main results of study are summarized below: First, WB-STS appeared to be successfully constructed based on Renzulli' s Enrichment Triad Model and the Iowa Chautauqua program. Its main features are that it was made emphasizing a learner-centered approach and constructive learning. It is composed of five steps: Scientific theme selection -${\rightarrow}$Exploration ${\rightarrow}$ Concept & Principle Check ${\rightarrow}$ Finding Solution ${\rightarrow}$ Action. Second, seven specialists and a group of students assessed the developed WB-STS's validity and utility with a questionnaire, the results appeared satisfactory. Students showed high interest in WB-STS and gave a positive evaluation of WB-STS.

• Journal of Korean Library and Information Science Society
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• v.29
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• pp.443-464
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• 1998

21세기를 맞이하여 대학도서관은 정보기술의 도입, 업무내용의 변화, 이용자의 요구변화등 급격하게 변화하는 새로운 환경에 직면해 있다. 본 연구는 한국의 대학도서관 조직구조의 현황에 대한 분석과 함께 다양한 조직이론들과 정보환경의 변화에 기초해서 도서관조직을 활성화시키기위한 개념적인 조직모델을 제시하고자 한다. 한국의 대학도서관은 거의 10년동안 법적인 제약과 조직내외의 환경적인 한계 등으로 인해 전산화시스템의 도입, 도서관부관장의 임명, 그리고 도서관과 컴퓨터 센터와의 통합시도와 같은 약간의 변화외에는 거의 변화가 없었다. 전형적인 한국의 대학도서관은 수서, 기술서비스, 열람과 참고봉사 부문으로 조직되었다. 여기서 수서 기능을 기술서비스의 부문으로 간주한다면, 본 연구의 대상인 대학도서관 114개관 중 95개관(82.5%)이 전통적인 도서관조직의 형태인 기술서비스와 공공서비스 부문으로 조직된 것으로 나타났다. 본 연구에서는 전통적인 도서관조직의 문제점들을 급복할 수 있는 21세기의 개념적인 대학도서관 조직모델로서, 네가지 부문 - 서비스 부문, 서비스지원 부문, 기술지원 부문, 그리고 통합·조정부문-을 대학도서관의 개념적인 기본 구성요소로써 제안하였다. 그러나 모든 도서관의 서비스나 업무과정에 대해 적합한 잉상적인 조직구조는 없으며, 조직의 재조직과정은 도서관의 형태와 목적, 업무과정에 따라 매우 다양하다. 따라서 도서관의 재조직화는 환경의 변화에 따라 끊임없는 과정이 될 것이며, 도서관조직의 성공은 이러한 변화에 적응할 수 있는 개인과 조직의 역량에 달려있다고 하겠다.대한 순서에 있어서 차이가 있다. 4) 도서관에 대한 태도에 있어서 두 집단은 상이한 입장을 보이고 있다. 학자들의 과반수는 중요 정보원으로서 자신의 개인장서를 활용하며, 도서관의 장서 및 그 조직방법에 대해서도 별로 만족하지를 못하고 있다. 반면에, 실무가들은 도서관에 대하여 비교적 만족하며 따라서 도서관에 대한 이용도도 높다. 5) 두 집단 모두 보조인을 적극적으로 활용하지 않으며 사서의 도움을 받는 경우도 극소수에 불과하다. 이러한 조사결과를 기초로 하여 볼 때 법률전문직을 둘러싼 정보환경을 개선하기 위하여는, 인쇄된 일차적 정보자료의 검색방법등을 개선하고, 나아가서는 법령과 판례정보를 위한 효율적인 시스템을 구축하며, 뿐만 아니라 이용자의 요구에 충분히 대처할 수 잇는 도서관으로 변화되는 것이다. 이와 함께 가장 중요한 것은 법과대학과 사법연수원에서 법학 연구방법에 관한 강좌를 개설하여 각종 법률정보원의 활용 내지 도서관 이용방법에 관하여 교육하는 것이다.글을 연구하고, 그 결과에 의존하여서 우리의 실제의 생활에 사용하는 $\boxDr$한국어사전$\boxUl$등을 만드는 과정에서, 어떤 의미에서 실험되었다고 말할 수가 있는 언어과학의 연구의 결과에 의존하여서 수행되는 철학적인 작업이다. 여기에서는 하나의 철학적인 연구의 시작으로 받아들여지는 이 의미분석의 문제를 반성하여 본다. 것이 필요하다고 사료된다.크기에 의존하며, 또한 이러한 영향은 $(Ti_{1-x}AI_{x})N$ 피막에 존재하는 AI의 함량이 높고, 초기에

• Journal of the Korean Society of Earth Science Education
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• v.17 no.2
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• pp.86-101
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• 2024

The purpose of this study is to provide basic data on the importance of accurate scientific communication and the current status of popular astronomy based on science articles that occurred during the so-called Super Blue Moon astronomical phenomenon in August 2023. To this end, the subjects were divided into non-experts, quasi-experts, and experts based on the degree of knowledge of the astronomical universe to investigate the data interpretation ability of astronomical science information and to analyze the causes of errors in the interpretation process through in-depth interviews. We also investigated the favorability and reliability of research institutes that strive to provide scientific information and the media that strive to spread it and also investigated the changes in existing favorability and reliability when incorrect scientific information spreads, as in this case. Although there were differences in the interpretation of scientific information about the astronomical universe depending on the cognitive aspect, the influence of linguistic elements or literacy, which could be called communication, could not be ignored. In particular, it was confirmed that misconceptions inherent in the existing research subjects could be expressed, leading to errors in accurate information interpretation. In addition, after recognizing that errors were included in the spread of scientific information, the subjects' favorability toward research institutes and the media fell 12.30% and 17.58%, respectively, while reliability fell 19.40% for research institutes and 24.49% for media outlets. Regardless of the cause of the error, the importance of providing accurate scientific information is further emphasized, considering that the overall favorability and reliability of both research institutes and the media decline. In order for research institutes and media outlets to spread accurate scientific information about the astronomical universe based on the public's trust, it is necessary to establish a system that can accurately deliver error-free information generated by research institutes related to astronomical space to media or science communicators and to develop a system that quickly retrieves and corrects incorrect scientific information through continuous monitoring.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11B
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• pp.980-990
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• 2004

In this paper, we propose the dynamic bandwidth allocation algorithm for the IEEE802.3ah Ethernet Passive Optical Network(EPON) system to provide the fairness among terminals, and evaluate the delay-throughput performance by simulation. For the conventional EPON systems, an Optical Line Termination (OLT) schedules the upstream bandwidth for each Optical Network Unit (ONU), based on its buffer state. This scheme can provide a fair bandwidth allocation for each ONU. However, it has a critical problem that it does not guarantee the fair bandwidth among terminals which are connected to ONUs. For an example, we assume that the traffic from a greedy terminal increases at a time. Then, the buffer state of its ONU is instantly reported to the OLT, and finally the OW can get more bandwidth. As a result, the less bandwidth is allocated to the other ONUs, and thus the transfer delay of terminals connected to the ONUs gets inevitably increased. Noting that this unfairness problem exists in the conventional EPON systems, we propose a fair bandwidth allocation scheme by OLT with considering the buffer state of ONU as welt as the number of terminals connected it. For the performance evaluation, we develop the EPON simulation model with SIMULA simulation language. From the result of the throughput-delay performance and the dynamics of buffer state along time for each terminal and ONU, respectively, one can see that the proposed scheme can provide the fairness among not ONUs but terminals. Finally, it is worthwhile to note that the proposed scheme for the public EPON systems might be an attractive solution for providing the fairness among subscriber terminals.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.