• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.15-31
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• 2023

In this study, we propose a novel approach to analyze big data related to patents in the field of smart factories, utilizing the Latent Dirichlet Allocation (LDA) topic modeling method and the generative artificial intelligence technology, ChatGPT. Our method includes extracting valuable insights from a large data-set of associated patents using LDA to identify latent topics and their corresponding patent documents. Additionally, we validate the suitability of the topics generated using generative AI technology and review the results with domain experts. We also employ the powerful big data analysis tool, KNIME, to preprocess and visualize the patent data, facilitating a better understanding of the global patent landscape and enabling a comparative analysis with the domestic patent environment. In order to explore quantitative and qualitative comparative advantages at this juncture, we have selected six indicators for conducting a quantitative analysis. Consequently, our approach allows us to explore the distinctive characteristics and investment directions of individual countries in the context of research and development and commercialization, based on a global-scale patent analysis in the field of smart factories. We anticipate that our findings, based on the analysis of global patent data in the field of smart factories, will serve as vital guidance for determining individual countries' directions in research and development investment. Furthermore, we propose a novel utilization of GhatGPT as a tool for validating the suitability of selected topics for policy makers who must choose topics across various scientific and technological domains.

• Journal of the Korean Society of Earth Science Education
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• v.3 no.1
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• pp.65-75
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• 2010

The purpose of this study is to present a Freedom Inquiry Method by Revised Science Curriculum in 2007. This study introduced IIM(Independent Inquiry Method), PBL(Problem Based Learning), Small Inquiry Method, Science Notebooks, Project Learning Method about Freedom Inquiry Method. The results of this study are as follows: First, IIM(Independent Inquiry Method) is studying method in the inquiry process center. The inquiry process is composed of total 9 phases, inquiry subject really it is, detailed aim deciding, information searching, it searches, quest result it arranges, aim evaluation, the report making, it announces, it evaluates, it is become accomplished. Second, It is a studying method which it starts with the problem which is Problem Based Learning, study atmosphere creation phase, problematic presentation phase and sleep static problem solving the phase which it attempts, it is become accomplished with autonomous studying phase, coordinated studying and discussion studying phase, discussion resultant announcement studying phase, arrangement and evaluation. Third, Small Inquiry Method, Call it accomplishes the call grade of the students among ourselves 4~8 people degree where only the quest learning capability is similar within class. Also interaction and coordinated function of the members between it leads and the subject which is given in the group it cooperates and it solves with it is a quest method which arrives to aim of commonness. This method divides on a large scale in three parts, it becomes accomplished in programming phase, quest accomplishment and resultant announcement. Fourth, Science Notebooks learns a scientific contents and a scientific quest function and the possibility of decreasing in order to be, from the fact that the help which it understands. This planing, data searching, it searches, becomes accomplished with resultant arrangement, announcement and evaluation. Fifth, The Project Learning Method the studying person oneself studying contents, it establishes a plan and it collects it accomplishes process of etc. it evaluates it leads and a subject and information and with real life it is a method which it studies naturally from the learning environment inside which is similar. This is preliminary phase, project start, project activity and project arrangement.

• Journal of Nutrition and Health
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• v.55 no.6
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• pp.601-616
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• 2022

In the era of the fourth industrial revolution technology, the inclusion of personalized nutrition for healthcare (PNH), when establishing a healthcare platform to prevent chronic diseases such as obesity, diabetes, cerebrovascular and cardiovascular disease, pulmonary disease, and inflammatory diseases, enhances the national competitiveness of global healthcare markets. Furthermore, since the government experienced COVID-19 and the population dead cross in 2020, as well as numerous health problems due to an increasing super-aged Korean society, there is an urgent need to secure, develop, and utilize PNH-related technologies. Three conditions are essential for the development of PNH technologies. These include the establishment of causality between obesity genome (genotype) and prevalence (phenotype) in Koreans, validation of clinical intervention research, and securing PNH-utilization technology (i.e., algorithm development, artificial intelligence-based platform, direct-to-customer [DTC]-based PNH, etc.). Therefore, a national control tower is required to establish appropriate PNH infrastructure (basic and clinical research, cultivation of PNH-related experts, etc.). The post-corona era will be aggressive in sharing data knowledge and developing related technologies, and Korea needs to actively participate in the large-scale global healthcare markets. This review provides the importance of scientific evidence based on a huge dataset, which is the primary prerequisite for the DTC obesity gene-based PNH technologies to be competitive in the healthcare market. Furthermore, based on comparing domestic and internationally approved DTC obese genes and the current status of Korean obesity genome-based PNH research, we intend to provide a direction to PNH planners (individuals and industries) for establishing scientific PNH guidelines for the prevention of obesity.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.115-121
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• 2021

This paper used big data and artificial intelligence technology to predict the rapidly increasing internet traffic. There have been various studies on traffic prediction in the past, but they have not been able to reflect the increasing factors that induce huge Internet traffic such as smartphones and streaming in recent years. In addition, event-like factors such as the release of large-capacity popular games or the provision of new contents by OTT (Over the Top) operators are more difficult to predict in advance. Due to these characteristics, it was impossible for an ISP (Internet Service Provider) to reflect real-time service quality management or traffic forecasts in the network business environment with the existing method. Therefore, in this study, in order to solve this problem, an Internet traffic collection system was constructed that searches, discriminates and collects traffic data in real time, separate from the existing NMS. Through this, the flexibility and elasticity to automatically register the data of the collection target are secured, and real-time network quality monitoring is possible. In addition, a large amount of traffic data collected from the system was analyzed by machine learning (AI) to predict future traffic of OTT operators. Through this, more scientific and systematic prediction was possible, and in addition, it was possible to optimize the interworking between ISP operators and to secure the quality of large-scale OTT services.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.743-754
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• 2023

The global occurrence of myriad natural disasters and incidents, catalyzed by climate change and extreme meteorological conditions, has engendered substantial human and material losses. International organizations such as the International Charter have established an enduring collaborative framework for real-time coordination to provide high-resolution satellite imagery and geospatial information. These resources are instrumental in the management of large-scale disaster scenarios and the expeditious execution of recovery operations. At the national level, the operational deployment of advanced National Earth Observation Satellites, controlled by National Geographic Information Institute, has not only catalyzed the advancement of geospatial data but has also contributed to the provisioning of damage analysis data for significant domestic and international disaster events. This special edition of the National Disaster Management Research Institute delineates the contemporary landscape of major disaster incidents in the year 2023 and elucidates the strategic blueprint of the government's national disaster safety system reform. Additionally, it encapsulates the most recent research accomplishments in the domains of artificial satellite systems, information and communication technology, and spatial information utilization, which are paramount in the institution's disaster situation management and analysis efforts. Furthermore, the publication encompasses the most recent research findings relevant to data collection, processing, and analysis pertaining to disaster cause and damage extent. These findings are especially pertinent to the institute's on-site investigation initiatives and are informed by cutting-edge technologies, including drone-based mapping and LiDAR observation, as evidenced by a case study involving the 2023 landslide damage resulting from concentrated heavy rainfall.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.6
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• pp.237-247
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• 2008

A virtual screening is the process of reducing an unmanageable number of compounds to a limited number of compounds for the target of interest by means of computational techniques such as molecular docking. And it is one of a large-scale scientific application that requires large computing power and data storage capability. Previous applications or softwares for molecular docking such as AutoDock, FlexX, Glide, DOCK, LigandFit, ViSION were developed to be run on a supercomputer, a workstation, or a cluster-computer. However the virtual screening using a supercomputer has a problem that a supercomputer is very expensive and the virtual screening using a workstation or a cluster-computer requires a long execution time. Thus we propose a service-based virtual screening system using Grid computing technology which supports a large data intensive operation. We constructed 3-dimensional chemical molecular database for virtual screening. And we designed a resource broker and a data broker for supporting efficient molecular docking service and proposed various services for virtual screening. We implemented service based virtual screening system with DOCK 5.0 and Globus 3.2 toolkit. Our system can reduce a timeline and cost of drug or new material design.

• Journal of Korean Library and Information Science Society
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• v.51 no.4
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• pp.83-100
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• 2020

The importance of data publishing in the open-science era is increasing as it can contribute to other scientific discoveries by accelerating the sharing of research data, improving accessibility and citability, and providing standardized technical documentation for research data. In addition, the need for data papers is emerging as a way for data papers to maintain a status equivalent to research papers, and the publication of data journals is on the rise as a new type of scholarly publishing. In particular, the field of Ecology is a field where large-scale research data are produced and managed, thus the data journal publishing in this field is active worldwide. On the other hand, the research on data journal is in its early stages in Korea, and there is no data journal in the field of Ecology. Thus, this study explores and presents strategies for publishing data journals in the ecological field. First, we investigate the publishing status of domestic and international data journals and the publication status of domestic journals. Then, we conducted a focused group interview with experts of scholarly publishing, open access policy and journal publishing in the field of Ecology. Finally, based on the survey and the expert FGI's results, strategies are suggested in terms of publishing data journals in the field of ecology, organizing and publishing journals, organizing journal editors, and receiving manuscripts.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.76-91
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• 2016

The purpose of this study is to analyze the effects of spatial characteristics, such as land cover and topography, on heat wave days from the city of Milyang, which has recently drawn attention for its heat wave problems. The number of heat wave days was calculated utilizing RCP-based South Korea climate data from 2000 to 2010. Land cover types were reclassified into urban area, agricultural area, forest area, water, and grassland using 2000, 2005, and 2010 land cover data constructed by the Ministry of Environment. Topographical features were analyzed by topographic position index (TPI) using a digital elevation model (DEM) with 30 m spatial resolution. The results show that the number of heat wave days was 31.4 days in 2000, which was the highest, followed by 26.9 days in 2008, 24.2 days in 2001, and 24.0 days in 2010. The heat wave distribution was relatively higher in agricultural areas, valleys, and rural areas. The topography of Milyang contains more mountainous slope (51.6%) than flat (19.7%), while large-scale valleys (12.2%) are distributed across some of the western region. Correlation analysis between heat wave and spatial characteristics showed that the correlation between forest area land cover and number of heat wave days was negative (-0.109), indicating that heat wave can be mitigated. Topographically, flat areas and heat wave showed a positive correlation (0.305). These results provide important insights for urban planning and environmental management for understanding the impact of land development and topographic change on heat wave.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.77-86
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• 2018

For the past few years, KISTI has been servicing an online simulation execution platform, called EDISON, allowing users to conduct simulations on various scientific applications supplied by diverse computational science and engineering disciplines. Typically, these simulations accompany large-scale computation and accordingly produce a huge volume of output data. One critical issue arising when conducting those simulations on an online platform stems from the fact that a number of users simultaneously submit to the platform their simulation requests (or jobs) with the same (or almost unchanging) input parameters or files, resulting in charging a significant burden on the platform. In other words, the same computing jobs lead to duplicate consumption computing and storage resources at an undesirably fast pace. To overcome excessive resource usage by such identical simulation requests, in this paper we introduce a novel framework, called IceSheet, to efficiently manage simulation data based on execution metadata, that is, provenance. The IceSheet framework captures and stores each provenance associated with a conducted simulation. The collected provenance records are utilized for not only inspecting duplicate simulation requests but also performing search on existing simulation results via an open-source search engine, ElasticSearch. In particular, this paper elaborates on the core components in the IceSheet framework to support the search and reuse on the stored simulation results. We implemented as prototype the proposed framework using the engine in conjunction with the online simulation execution platform. Our evaluation of the framework was performed on the real simulation execution-provenance records collected on the platform. Once the prototyped IceSheet framework fully functions with the platform, users can quickly search for past parameter values entered into desired simulation software and receive existing results on the same input parameter values on the software if any. Therefore, we expect that the proposed framework contributes to eliminating duplicate resource consumption and significantly reducing execution time on the same requests as previously-executed simulations.

• Atmosphere
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• v.28 no.1
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• pp.25-35
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• 2018

This study presents future potential sea level change over the seas surrounding Korea using Climate Model Intercomparison Project Phase 5 9 model ensemble result from Representative Concentration Pathways (RCPs), downloaded from icdc.zmaw.de. At the end of 21st century, regional sea level changes are projected to rise 37.8, 48.1, 47.7, 65.0 cm under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenario, respectively with the large uncertainty from about 40 to 60 cm. The results exhibit similar tendency with the global mean sea level rise (SLR) with small differences less than about 3 cm. For the East Sea, the Yellow Sea, and the southern sea of Korea, projected SLR in the Yellow Sea is smaller and SLR in the southern sea is larger than the other coastal seas. Differences among the seas are small within the range of 4 cm. Meanwhile, Commonwealth Scientific and Industrial Research Organization (CSIRO) data in 23 years shows that the mean rate of sea level changes around the Yellow Sea is high relative to the other coastal seas. For sea level change, contribution of ice and ocean related components are important, at local scale, Glacial Isostatic Adujstment also needs to be considered.