• Journal of Digital Convergence
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• v.17 no.5
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• pp.145-155
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• 2019

The Healthcare supply chain management has recently attracted attention as a critical tool to improve service quality and reduce healthcare operational cost. Improving large hospital supply chain performance has become increasingly important as healthcare organizations strive to improve the service quality, while reducing the ever-increasing healthcare cost. This paper explores the strategic areas where the traditional supply chain management may enhance the overall performance of the large hospitals. Based on the literature review and relevant case analysis, this paper argues that the visibility, information sharing and standardization are the critical factors for deploying the supply chain principles, and also proposes the supply chain framework for efficient planning and execution, the use of RFID-enabled system for the end-to-end traceability of medical products, and cross-docking system for minimizing the inventory level in the hospital supply chain. Implications of the study findings are discussed.

• Convergence Security Journal
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• v.20 no.4
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• pp.113-123
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• 2020

The fourth industrial revolution can be called the hyper-connected-based intelligent revolution triggered by advanced information technology and intelligent technology, and the basis for implementing these technologies is 'data'. This study proposes a way to strategically use data in order to lead this intelligent revolution in the defense area. First of all, implications through analysis of domestic and international trends and prior research and current status of defense data management were analyzed, and four directions for development were presented. If the government composes conditions for building, releasing, sharing, distribution, and convergence of defense data considering the environment of national defense in the future, it is expected that it will serve as a foundation and a shortcut to be a digitalized strong military through smart defense innovation in the era of the fourth industrial revolution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.223-229
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• 2022

During the winter season, when the weather gets colder every year, electricity consumption increases rapidly. The occurrence of fires is increasing due to a short circuit in electrical facilities of buildings such as markets, bathrooms, and apartments with high population density while using a lot of electricity. The cause of these short circuit fires is mostly due to the aging of the wires, the usage increases, and the excessive load cannot be endured, and the wire sheath is melted and caused by nearby ignition materials. In this paper, the load and overheat generated in the electric wire are measured through a complex sensor composed of an overload sensor, a VoC sensor, and an overheat sensor. Based on this, big data analysis is carried out to develop a platform capable of predicting, alerting, and blocking electric fires in real time, and a simulator capable of simulated fire experiments.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.37-48
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• 2022

With development of Digital Technology, social issues are communicated through digital-based platform such as SNS and form public opinion. This study attempted to analyze big data from Twitter, a world-renowned social network service, and find out the public opinion. After collecting Twitter data based on 14 keywords for 1 year in 2021, analyzed the term-frequency and relationship among keyword documents with pearson correlation coefficient using Data-mining Technology. Furthermore, the 6 main topics that on the center of information security field in 2021 were derived through topic modeling using the LDA(Latent Dirichlet Allocation) technique. These results are expected to be used as basic data especially finding key agenda when establishing strategies for the next step related industries or establishing government policies.

• Ocean and Polar Research
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• v.45 no.2
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• pp.43-55
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• 2023

This study was conducted to understand the seasonal patterns and variation of the copepod community in the Anma Islands of Yeonggwang, Yellow Sea, with a focus on seasonal surveys to assess the factors affecting their occurrence. Throughout the survey period, Acartia hongi, Paracalanus parvus s. l., and Ditrichocorycaeus affinis were dominant species, while Acartia ohtsukai, Acartia pacifica, Bestiolina coreana, Centropages abdominalis, Labidocera rotunda, Paracalanus sp., Tortanus derjugini, Tortanus forcipatus occurred differently by season and station. As a results of cluster analysis, the copepod communities were distinguished into three distinct groups: spring-winter, summer, and autumn. The results of this study showed that the occurrence patterns of copepod species can vary depending on environmental conditions (topographic, distance from the inshore, etc.), and their spatial occurrence patterns between seasons were controlled by water temperature and prey conditions. One of the physical mechanisms that can affect the distribution of zooplankton in the Yellow Sea is the behavior of the Yellow Sea Bottom Cold Water (YSBCW), which shows remarkable seasonal fluctuations. More detailed further studies are needed for clear grounds for mainly why to many Calanus sinicus in the central region of the Yellow Sea are seasonally moving to the inshore, what strategies to seasonally maintain the population, and support the possibilities of complex factors.

• Parasites, Hosts and Diseases
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• v.61 no.1
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• pp.24-32
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• 2023

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), encoded by the polymorphic var multigene family, is a highly polymorphic antigen that plays a crucial role in the pathology of malaria. The contribution of the genetic diversity of var toward the immune escape of P. falciparum has not yet been fully elucidated. This study aimed to characterize the diversity of var repertoires by screening P. falciparum Duffy-binding-like α domain (PfDBLα) among field isolates from central Myanmar. Genetic analysis revealed that the D-H segments of var in Myanmar populations have an extensive polymorphic repertoire, with high numbers of unique sequence types in each individual. However, var genes from the global population, including Myanmar, shared close genetic lineages regardless of their geographic origins, indicating that they have not undergone rapid evolutionary changes.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.1
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• pp.139-166
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• 2022

Ethical Issues increase when people engage in smart technological systems such as 5G, IoT, Cloud computing services and AI applications. Range of this research is comparison of various literacy concepts and its ethical issues in considering of 5G features and UX. 86 research papers and reports which have been published within the recent 5 years (2017-2022), relating the research subject, are investigated and analyzed. Two results show that various literacies can be grouped into four areas and that some of common issues among those areas as well as unique issues of each area are identified. Based on the literature analysis, an Operational Definition of Ethics-Literacy is presented and the model of ethics-literacy curriculum supporting ethical behavior of 5G information professionals is developed and suggested.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.168-168
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• 2020

This study is to evaluate the drought-related bigdata characteristics published from South Korean by developing crawler. The 5 years (2013 ~ 2017) drought-related posted articles were collected from Korean internet search engine 'NAVER' which contains 13 main and 81 local daily newspapers. During the 5 years period, total 40,219 news articles including 'drought' word were found using crawler. To filter the homonyms liken drought to soccer goal drought in sports, money drought economics, and policy drought in politics often used in South Korea, the quality control was processed and 47.8 % articles were filtered. After, the 20,999 (52.2 %) drought news articles of this study were classified into four categories of water deficit (WD), water security and support (WSS), economic damage and impact (EDI), and environmental and sanitation impact (ESI) with 27, 15, 13, and 18 drought-related keywords in each category. The WD, WSS, EDI, and ESI occupied 41.4 %, 34.5 %, 14.8 %, and 9.3 % respectively. The drought articles were mostly posted in June 2015 and June 2017 with 22.7 % (15,097) and 15.9 % (10,619) respectively. The drought news articles were spatiotemporally compared with SPI (Standardized Precipitation Index) and RDI (Reservoir Drought Index) were calculated. They were classified into administration boundaries of 8 main cities and 9 provinces in South Korea because the drought response works based on local government unit. The space-time clustering between news articles (WD, WSS, EDI, and ESI) and indices (SPI and RDI) were tried how much they have correlation each other. The spatiotemporal clusters detection was applied using SaTScan software (Kulldorff, 2015). The retrospective and prospective cluster analyses were conducted for past and present time to understand how much they are intensive in clusters. The news articles of WD, WSS and EDI had strong clusters in provinces, and ESI in cities.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.9-13
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• 2023

In this study, we explored the potential of integrating interactive AI callbot technology into the medical consultation domain as part of a broader service development initiative. Aimed at enhancing patient satisfaction, the AI callbot was designed to efficiently address queries from hospitals' primary users, especially the elderly and those using phone services. By incorporating an AI-driven callbot into the hospital's customer service center, routine tasks such as appointment modifications and cancellations were efficiently managed by the AI Callbot Agent. On the other hand, tasks requiring more detailed attention or specialization were addressed by Human Agents, ensuring a balanced and collaborative approach. The deep learning model for voice recognition for this study was based on the Transformer model and fine-tuned to fit the medical field using a pre-trained model. Existing recording files were converted into learning data to perform SSL(self-supervised learning) Model was implemented. The ANN (Artificial neural network) neural network model was used to analyze voice signals and interpret them as text, and after actual application, the intent was enriched through reinforcement learning to continuously improve accuracy. In the case of TTS(Text To Speech), the Transformer model was applied to Text Analysis, Acoustic model, and Vocoder, and Google's Natural Language API was applied to recognize intent. As the research progresses, there are challenges to solve, such as interconnection issues between various EMR providers, problems with doctor's time slots, problems with two or more hospital appointments, and problems with patient use. However, there are specialized problems that are easy to make reservations. Implementation of the callbot service in hospitals appears to be applicable immediately.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.13-22
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• 2024

There is a need for and positive aspects of article-based advertising, but as exaggerated and disguised information is delivered due to some indiscriminate 'article-based advertisements', readers have difficulty distinguishing between general articles and article-based advertisements, leading to a lot of misinterpretation and confusion of information. is doing Since readers will continue to acquire new information and apply this information at the right time and place to bring a lot of value, it is judged to be even more important to distinguish between accurate general articles and article-like advertisements. Therefore, as differentiated information between general articles and article-like advertisements is needed, as part of this, for readers who have difficulty identifying accurate information due to such indiscriminate article-like advertisements in Internet newspapers, this paper introduces IT and AI technologies. We attempted to present a method that can be solved in terms of a system that incorporates, and this method was designed to extract articleable advertisements using a knowledge-based natural language processing method that finds and refines advertising keywords and deep learning technology.