• Journal of Korean Medicine Rehabilitation
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• v.28 no.1
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• pp.121-131
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• 2018

Objectives We analyzed the trends of government-funded research on Kampo medicine in Japan to provide advanced evidence to R&D support policy for Korean medicine, and to introduce new research fields and trends to the researchers. Methods We reviewed the researches on Kampo medicine through 'research-er.jp' and 'KAKEN' database which contain R&D status in Japan and scientific research funding project issued by the Japan Ministry of Education, Culture, Sports, Science and Technology. Results Since 1976, government-funded research on Kampo medicine has been continuously announced, and now 533 tasks have been completed or are in progress. The average duration of the study is 2.54 years, but it has been prolonged to 3.52 years in recent years. 4~5 million yen was supported per project for laboratory research, and an average of 44,342 thousand yen was supported per project for specialized laboratory research and clinical research. Conclusions Despite the absence of systematically supporting departments, the researches on Kampo medicine in Japan were qualitatively superior since they focused on providing the scientific basis for clinical application. As competition in the world's traditional medicine market becomes more intense, it is necessary to improve the competitiveness of Korean medicine. Therefore, a keen interest in Korean medicine and active support from the government is needed.

• Korean Chemical Engineering Research
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• v.43 no.3
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• pp.331-343
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• 2005

Development of hydrogen station system is an important technology to commercialize fuel cells and fuel cell powered vehicles. Generally, hydrogen station consists of hydrogen production process including desulfurizer, reformer, water gas shift (WGS) reactor and pressure swing adsorption (PSA) apparatus, and post-treatment process including compressor, storage and distributer. In this review, we investigate the R&D trends and prospects of hydrogen station in domestic and foreign countries for opening the hydrogen economy society. Indeed, the reforming of fossil fuels for hydrogen production will be essential technology until the ultimate process that may be water hydrolysis using renewable energy source such as solar energy, wind force etc, will be commercialized in the future. Hence, we also review the research trends on unit technologies such as the desulfurization, reforming reaction of fossil fuels, water gas shift reaction and hydrogen separation for hydrogen station applications.

• Tribology and Lubricants
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• v.35 no.5
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• pp.274-285
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• 2019

Chemical mechanical polishing (CMP), which is a material removal process involving chemical surface reactions and mechanical abrasive action, is an essential manufacturing process for obtaining high-quality semiconductor surfaces with ultrahigh precision features. Recent rapid growth in the industries of digital devices and semiconductors has accelerated the demands for processing of various substrate and film materials. In addition, to solve many issues and challenges related to high integration such as micro-defects, non-uniformity, and post-process cleaning, it has become increasingly necessary to approach and understand the processing mechanisms for various substrate materials and abrasive particle behaviors from a tribological point of view. Based on these backgrounds, we review recent CMP R&D trends in this study. We examine experimental and analytical studies with a focus on substrate materials and abrasive particles. For the reduction of micro-scratch generation, understanding the correlation between friction and the generation mechanism by abrasive particle behaviors is critical. Furthermore, the contact stiffness at the wafer-particle (slurry)-pad interface should be carefully considered. Regarding substrate materials, recent research trends and technologies have been introduced that focus on sapphire (${\alpha}$-alumina, $Al_2O_3$), silicon carbide (SiC), and gallium nitride (GaN), which are used for organic light emitting devices. High-speed processing technology that does not generate surface defects should be developed for low-cost production of various substrates. For this purpose, effective methods for reducing and removing surface residues and deformed layers should be explored through tribological approaches. Finally, we present future challenges and issues related to the CMP process from a tribological perspective.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.74-86
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• 2019

As many existing research fields are matured academically, researchers have encountered numbers of academic, social and other problems that cannot be addressed by internal knowledge and methodologies of existing disciplines. Earlier, pioneers of researchers thus are following a new paradigm that breaks the boundaries between the prior disciplines, fuses them and seeks new approaches. Moreover, developed countries including Korea are actively supporting and fostering the convergence research at the national level. Nevertheless, there is insufficient research to analyze convergence trends in national R&D support projects and what kind of content the projects mainly deal with. This study, therefore, collected and preprocessed the research proposal data of National Research Foundation of Korea, transforming the proposal documents to term-frequency matrices. Based on the matrices, this study derived detailed research topics through Latent Dirichlet Allocation, a kind of topic modeling algorithm. Next, this study identified the research topics each proposal mainly deals with, visualized the convergence relationships, and quantitatively analyze them. Specifically, this study analyzed the centralities of the detailed research topics to derive clues about the convergence of the near future, in addition to visualizing the convergence relationship and analyzing time-varying number of research proposals per each topic. The results of this study can provide specific insights on the research direction to researchers and monitor domestic convergence R&D trends by year.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.61-70
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• 2021

This study aimed to analyze the research trends of the abstract data of ergonomic studies registered in MEDLINE, a medical bibliographic database, using word embedding. Medical-related ergonomic studies mainly focus on work-related musculoskeletal disorders, and there are no studies on the analysis of words as data using natural language processing techniques, such as word embedding. In this study, the abstract data of ergonomic studies were extracted with a program written with selenium and BeutifulSoup modules using python. The word embedding of the abstract data was performed using the word2vec model, after which the data found in the abstract were vectorized. The vectorized data were visualized in two dimensions using t-Distributed Stochastic Neighbor Embedding (t-SNE). The word "ergonomics" and ten of the most frequently used words in the abstract were selected as keywords. The results revealed that the most frequently used words in the abstract of ergonomics studies include "use", "work", and "task". In addition, the t-SNE technique revealed that words, such as "workplace", "design", and "engineering," exhibited the highest relevance to ergonomics. The keywords observed in the abstract of ergonomic studies using t-SNE were classified into four groups. Ergonomics studies registered with MEDLINE have investigated the risk factors associated with workers performing an operation or task using tools, and in this study, ergonomics studies were identified by the relationship between keywords using word embedding. The results of this study will provide useful and diverse insights on future research direction on ergonomic studies.

• Convergence Security Journal
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• v.24 no.2
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• pp.31-38
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• 2024

Recent expansion in cloud service usage has heightened the importance of cloud security. The purpose of this study is to analyze current research trends in the field of cloud security and to derive implications. To this end, R&D project data provided by the National Science and Technology Knowledge Information Service (NTIS) from 2010 to 2023 was utilized to analyze trends in cloud security research. Fifteen core topics in cloud security research were identified using LDA topic modeling and ARIMA time series analysis. Key areas identified in the research include AI-powered security technologies, privacy and data security, and solving security issues in IoT environments. This highlights the need for research to address security threats that may arise due to the proliferation of cloud technologies and the digital transformation of infrastructure. Based on the derived topics, the field of cloud security was divided into four categories to define a technology reference model, which was improved through expert interviews. This study is expected to guide the future direction of cloud security development and provide important guidelines for future research and investment in academia and industry.

• Journal of the Korean Magnetics Society
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• v.5 no.5
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• pp.376-379
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• 1995

Employing the muffin-tin-orbital theory combined with pseudo-potential concepts, we have evaluated hybridization matrix elements between R and T sites in $RT_{2}$ compounds. The matrix elements are calculated with two parameters, the interatomic distance between R and T atoms from the crystal structure data, and the expectation values of the radial distances for the radial wave functions of the ground state charge densities, which are obtained from the linearized muffin-tin orbital band method within the local density approximation. It is found that the R 4f/T 3d hybridization matrix elements decrease with an increasing atomic number from R=Ce to Gd, and that they are smaller in $RNi_{2}$ than in $RCo_{2}$, which are consistent with trends observed in recent photoemission spectroscopy experiments. It is also found that the magnitudes of the hybridization matrix elements in $RFe_{2}$ are comparable to those in $RNi_{2}$.

• Korean Journal of Ecology and Environment
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• v.42 no.4
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• pp.441-451
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• 2009

The major objectives of this study were to evaluate trophic state of reservoirs using major water quality variables and its relations in terms of trophic guilds and tolerance guilds with dominant lentic fishes. For this study, we selected 6 artificial reservoirs such as Namyang Reservoir ($N_yR$), Youngsan Reservoir ($Y_sR$), Daechung Reservoir ($D_cR$), Chungju Reservoir ($Cj_R$), Chungpyung Reservoir ($C_pR$), and Paldang Reservoir ($P_dR$), and collected fish during 2000~2007 along with data analysis of water quality monitored by the ministry of environment, Korea. Biological oxygen demand (BOD) and chemical oxygen demand (COD), indicators of organic matter pollution, varied depending on types of the reservoirs and the spatial patterns in terms of trophic gradients were similar to patterns of nutrients, Secchi depth and chlorophyll-a. Analysis of trophic state index (TSI) showed that reservoirs of $D_cR$ and $C_jR$ were mesotrophy and other 4 reservoirs were eutrophic state. The relations of trophic relations showedthat TSI (Chl-a) had a positive linear function [TSI (CHL)=0.407 TSI (TP)+28.2, n=138, p<0.05] with TSI (TP) but had a weak relation with TSI (TN). Also, TSI (TP) were negatively correlated ($R^2=0.703$, p<0.05) with TSI (SD), whereas TSI (TN) was not significant (p>0.05) relations with TSI (SD). Tolerance guilds of lentic fishes, based on three types of the reservoirs, reflected the exactly water quality in the TN, TP, BOD, and COD, and similar trends were shown in the fish feeding/trophic guilds.

• The Journal of Society for e-Business Studies
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• v.25 no.1
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• pp.203-214
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• 2020

It is necessary to classify technical documents such as patents, R&D project reports in order to understand the trends of technology convergence and interdisciplinary joint research, technology development and so on. Text mining techniques have been mainly used to classify these technical documents. However, in the case of classifying technical documents by text mining algorithms, there is a disadvantage that the features representing technical documents must be directly extracted. In this study, we propose a BERT-based document classification model to automatically extract document features from text information of national R&D projects and to classify them. Then, we verify the applicability and performance of the proposed model for classifying documents.

• Journal of Digital Convergence
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• v.15 no.9
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• pp.231-239
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• 2017

Solar energy is attracting attention as an alternative to fossil fuels. However, there was a lack of discussion on the overall research direction and future direction of research in technology development. In order to develop more effective technology, we analyzed and discussed the technology trend of solar energy using patent data and thesis data. As an analysis method, topics were selected by using topic modeling and text mining, the increase of included keywords was analyzed, and the direction of development of solar technology was analyzed. Research on solar power generation technology is expected to proceed steadily, and it is analyzed that intensive research will be done especially on high efficiency and high performance technology. Future studies could be conducted by adding overseas patent data and various paper data.