• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.2
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• pp.157-163
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• 2005

In the 21th century as knowledge-based society and according as GIS is embossed, demand of map is increasing rapidly for GIS's basic. Ministry of Construction & Transportation Republic of Korea National Geographic Information Institute who supervise basis geography information run the studies of basis geography information construction, therefore choice of each subject extent and standardization of data model for basis geography information is attained. In this study, framework data has been established in three steps according to Framework data Product Specification in Facility Area. Also the evaluation of usability was implemented as combining Framework data.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.825-832
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• 2021

A simulation-based approach is proposed to study the protective actions taken by residents during nuclear emergencies using cognitive findings. Human perception-based behaviors are not heavily incorporated in the evacuation study for nuclear emergencies despite their known importance. This study proposes a generic framework of perception-based behavior simulation, in accordance with the ecological concept of affordance theory and a formal representation of affordance-based finite state automata. Based on the generic framework, a simulation model is developed to allow an evacuee to perceive available actions and execute one of them according to Newton's laws of motion. The case of a shadow evacuation under nuclear emergency is utilized to demonstrate the applicability of the proposed framework. The illustrated planning algorithm enables residents to compute not only prior knowledge of the environmental map, but also the perception of dynamic surroundings, using widely observed heuristics. The simulation results show that the temporal and spatial dynamics of the evacuation behaviors can be analyzed based on individual perception of circumstances, while utilizing the findings in cognitive science under unavoidable data restriction of nuclear emergencies. The perception-based analysis of the proposed framework is expected to enhance nuclear safety technology by complementing macroscopic analyses for advanced protective measures.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• Molecules and Cells
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• v.44 no.11
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• pp.843-850
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• 2021

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.5
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• pp.132-138
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• 2021

Artificial intelligence technology sheds light on new ways of innovating acupuncture research. As acupoint selection is specific to target diseases, each acupoint is generally believed to have a specific indication. However, the specificity of acupoint selection may be not always same with the specificity of acupoint indication. In this review, we propose that the specificity of acupoint indication can be inferred from clinical data using reverse inference. Using forward inference, the prescribed acupoints for each disease can be quantified for the specificity of acupoint selection. Using reverse inference, targeted diseases for each acupoint can be quantified for the specificity of acupoint indication. It is noteworthy that the selection of an acupoint for a particular disease does not imply the acupoint has specific indications for that disease. Electronic medical record includes various symptoms and chosen acupoint combinations. Data mining approach can be useful to reveal the complex relationships between diseases and acupoints from clinical data. Combining the clinical information and the bodily sensation map, the spatial patterns of acupoint indication can be further estimated. Interoperable medical data should be collected for medical knowledge discovery and clinical decision support system. In the era of artificial intelligence, machine learning can reveal the associations between diseases and prescribed acupoints from large scale clinical data warehouse.

• The Korean Journal of Franchise Management
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• v.13 no.1
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• pp.19-33
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• 2022

Purpose: Based on the resource-based view and the competitive advantage theory, the study views marketing capabilities (product, pricing, delivery/inventory, and promotional support) as sources of competitive advantage (differentiation advantage and low-cost advantage) and examines their impacts on competitive advantage, which in turn, will influence non-business and business performance. Research design, data and methodology: Data were collected from 149 representatives of franchising companies in South Korea and analyzed with SmartPLS 3.3.7. Results: First, promotional support and product have a significant impact on differentiation advantage. Second, pricing and promotional support have a significant impact on low-cost advantage. Third, differentiation advantage has an influence on non-financial and financial business performance. Fourth, low-cost advantage has an impact on non-financial performance but has no significant direct impact on financial performance. Fifth, non-financial performance is related to financial performance. Finally, the result of IPMA shows that importance and performance values of exogeneous variables are different depending on firm size. Conclusions: The findings suggest that franchisors should focus on different marketing capabilities depending on their strategic focus and objectives. Finally, the findings based on an IPMA suggest that small companies perceive low-cost advantage as important, while their counterparts do not. Several theoretical and managerial implications are offered.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.355-365
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• 2023

This study examined the relationship between corporate governance and risk disclosure via a systematic literature review and bibliometric visualization analysis. The study aimed to present evidence of risk disclosure intellectual structure, volume, and development knowledge trends. Data was extracted from Scopus and analyzed with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and RTools. In turn, 64 articles were extracted from the Scopus database. The results demonstrated that the number of corporate governance and risk disclosure publications increased significantly from 2015 to 2019 compared to before 2015. RTools revealed the most prominent journals, authors, and interests in the field. The co-occurrences map was constructed based on 208 keywords from 64 articles, where the keywords were required to appear once in the research. Interestingly, the keyword search yielded new concepts relatively unexplored in the risk disclosure field. The 13 clusters were generated, which contained 1987 total links and 1567 direct citations. Based on the scientific analysis discussion, corporate governance and risk disclosure is an interesting topic that has produced many publications. Applying research keywords arguably aided in producing and publishing papers in top journals. Despite the number of publications decreasing due to the COVID-19 pandemic, the pandemic also presented new opportunities for future research.

• Journal of Information Technology Services
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• v.21 no.6
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• pp.53-72
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• 2022

As a trend of modern service industry, self-service has been widely concerned by all walks of life, but there is a lack of literature on systematic management of the overall research in this field. Recently, people's lifestyle has been forced to change due to the influence of COVID-19, while there have been some changes in the field of self-service research. Based on the Web of Science data source, this study takes the literature of self-service field before and after the outbreak of COVID-19 as the research object, and summarizes the development process, research status and future research trend of self-service field through the Citespace visualization tool. The research shows that firstly, academic circles continue to be enthusiastic about self-service field research, cooperation between countries is becoming more and more diversified. Secondly, the communication between researchers is becoming more and more intensive, and the cooperation between different disciplines gradually becomes the mainstream. Third, related research gradually shifted to the practical application of technology, the research perspective gradually shifted from the initial traditional retail perspective industries such as tourism services. Finally, the role of customer experience and participation behavior in self-service process is gradually emphasized.

• Applied Microscopy
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• v.50
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• pp.23.1-23.9
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• 2020

We propose an effective deep learning model to denoise scanning transmission electron microscopy (STEM) image series, named Noise2Atom, to map images from a source domain 𝓢 to a target domain 𝓒, where 𝓢 is for our noisy experimental dataset, and 𝓒 is for the desired clear atomic images. Noise2Atom uses two external networks to apply additional constraints from the domain knowledge. This model requires no signal prior, no noise model estimation, and no paired training images. The only assumption is that the inputs are acquired with identical experimental configurations. To evaluate the restoration performance of our model, as it is impossible to obtain ground truth for our experimental dataset, we propose consecutive structural similarity (CSS) for image quality assessment, based on the fact that the structures remain much the same as the previous frame(s) within small scan intervals. We demonstrate the superiority of our model by providing evaluation in terms of CSS and visual quality on different experimental datasets.

• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.685-697
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• 2003

The Web based multimedia programmes/courses are becoming widely available in recent years. Most of these courses focus on Behaviorist way of learning, which does not promote deep learning in any way. For Adults this approach further incapacitated, as it does not satisfy Andragogical needs. The search for Constructivist way of learning through the web applied to Indian conditions led to need for developing a curriculum development approach that would promote construction of knowledge through web based collaboration. This paper attempts to reengineer existing curriculum development processes and lays out a framework of‘Problem Based Online Learning (PBOL)’curriculum design. In this context, entire curriculum development life cycle is evolved and explained. This is a part of doctoral work (Ph.D), which is in progress and being undertaken by K.James Mathai, and guided of Dr.D.S.Karaulia.