• 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 Engineering Education Research
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• v.21 no.3
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• pp.56-65
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• 2018

With rapid advance of technologies including information and communication technologies, jobs are evolving faster than ever. Architectural engineering is no exception in this regard, and the green architectural engineering is emerging fast as a promising new field. In this study, a Delphi study of expert architectural engineers are conducted to find out (1) near future prospects of the field, (2) near future emerging jobs, (3) competencies needed for these jobs, and (4) educational content necessary to build these competencies with regards to the green architectural engineering. Initial Delphi survey consisting of open-ended questions in the above four areas were conducted and came out with 65 items after duplicate removal and semantic refinements. Further refinements via second and third wave of Delphi results into 40 items that the 13 architectural engineering experts may largely agree upon as future prospects with regards to the green architectural engineering. Findings indicate that it is expected that the demand for green architectural engineering and needs for automatic energy control system increase. Also, collaborations with other fields is becoming more and more important in green architectural engineering. The professional work management skills such as knowledge convergence, problem solving, collaboration skills, and creativity linking components from various related areas seem to also be on the increasing need. Near future ready critical skills are found to be the building environment control techniques (thermal, light, sound, and air), the data processing techniques like data mining, energy monitoring, and the control and utilization of environmental analysis software. Experts also agree on new curriculum for green building architecture to be developed with more of converging subjects across disciplines for future ready professional skills and experiences. Major topics to be covered in the near future includes building environment studies, building energy management, energy reduction systems, indoor air quality, global environment and natural phenomena, and machinery and electrical facility. Architectural engineering community should be concerned with building up the competencies identified in this Delphi preparing for fast advancing future.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.13-25
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• 2021

In the age of digital innovation based on the Internet, Information and Communication and Artificial Intelligence technologies, huge amounts of datasets are being generated, collected, accumulated, and opened on the web by various public institutions providing useful and public information. In order to analyse, gain useful insights and information from data, Formal Concept Analysis(FCA) has been successfully used for analyzing, classifying, clustering and visualizing data based on the binary relation between objects and attributes in the dataset. In this paper, we present an approach for enhancing the analysis of relational attributes of data within the extended framework of FCA, which is designed to classify, conceptualize and visualize sets of objects described not only by attributes but also by relations between these objects. By using the proposed tool, RCA wizard, several experiments carried out on some public open datasets demonstrate the validity and usability of our approach on generating and visualizing conceptual hierarchies for extracting more useful knowledge from datasets. The proposed approach can be used as an useful tool for effective data analysis, classifying, clustering, visualization and exploration.

• Mycobiology
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• v.49 no.2
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• pp.151-160
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• 2021

Despite recent studies, relatively few are known about the diversity of fungal communities in the deep Atlantic Ocean. In this study, we investigated the diversity of fungal communities in 15 different deep-sea sediments from the South Atlantic Ocean with a culture-dependent approach followed by phylogenetic analysis of ITS sequences. A total of 29 fungal strains were isolated from the 15 deep-sea sediments. These strains belong to four fungal genera, including Aspergillus, Cladosporium, Penicillium, and Alternaria. Penicillium, accounting for 44.8% of the total fungal isolates, was a dominant genus. The antiaflatoxigenic activity of these deep-sea fungal isolates was studied. Surprisingly, most of the strains showed moderate to strong antiaflatoxigenic activity. Four isolates, belonging to species of Penicillium polonicum, Penicillium chrysogenum, Aspergillus versicolor, and Cladosporium cladosporioides, could completely inhibit not only the mycelial growth of Aspergillus parasiticus mutant strain NFRI-95, but also the aflatoxin production. To our knowledge, this is the first report to investigate the antiaflatoxigenic activity of culturable deep-sea fungi. Our results provide new insights into the community composition of fungi in the deep South Atlantic Ocean. The high proportion of strains that displayed antiaflatoxigenic activity demonstrates that deep-sea fungi from the Atlantic Ocean are valuable resources for mining bioactive compounds.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.166-171
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• 2020

Big data processing technology that can store and analyze data and obtain new knowledge has been adjusted for importance in many fields of the society. Big data is emerging as an important problem in the field of information and communication technology, but the mind of continuous technology is rising. the R, a tool that can analyze big data, is a language and environment that enables information analysis of statistical bases. In this paper, we use this to analyze the Bible data. We analyze the four Gospels of the New Testament in the Bible. We collect the Bible data and perform filtering for analysis. The R is used to investigate the frequency of what text is distributed and analyze the Bible through social network analysis, in which words from a sentence are paired and analyzed between words for accurate data analysis.

• Trans-
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• v.13
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• pp.141-169
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• 2022

Analysis of narrative texts has been regarded as academically and practically important, and has been made from various perspectives and methods. In this paper, the computational narrative analysis methodology from the perspective of information processing was examined. From the point of view of information processing, the creation and acceptance of narrative is a bidirectional coding process mediated by narrative text, and narrative text can be said to be a multi-layered structured code. In this paper, four methodologies that share this point of view - character network analysis, text mining and sentiment analysis, continuity analysis of event composition, and knowledge analysis of narrative agents - were examined together with cases. Through this, the mechanism and possibility of computational methodology in narrative analysis were confirmed. In conclusion, the significance and side effects of computational narrative analysis were examined, and the necessity of designing a human-computer collaboration model based on the consilience of the humanities and science/technology was discussed. Based on this model, it was argued that aesthetically creative, ethically good, politically progressive, and cognitively sophisticated narratives could be made more effectively.

• Journal of Environmental Impact Assessment
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• v.31 no.3
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• pp.173-183
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• 2022

The purpose of this study is to explore the purpose of introducing the system and the possibility of development by comparing the period before and after the implementation of the Korean Important Agricultural Heritage System (KIAHS) using big data. In terms of perception related to Ulleungdo Island, keywords related to accessibility were derived as higher keywords before and after designation, and in particular, keywords such as various approaches and new ports could be found after designation. It can be seen that positive perception increased after the designation of KIAHS, and the perception of good increased particularly. In addition, the exact name of wild greens and keywords for volcanic island appeared in common, but it was confirmed that the influence increased in the results of the centrality analysis after the designation. In other words, it was found that the designation of KIAHS was helpful in preserving traditional knowledge and developing traditional agricultural culture using it.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1800-1817
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• 2022

With the development of the economy and the improvement of living standards, the hot issues in the subject area have become the main research direction, and the mining of the hot issues in the subject currently has problems such as a large amount of data and a complex algorithm structure. Therefore, in response to this problem, this study proposes a method for extracting hot keywords in scientific journals based on the improved BERT model.It can also provide reference for researchers,and the research method improves the overall similarity measure of the ensemble,introducing compound keyword word density, combining word segmentation, word sense set distance, and density clustering to construct an improved BERT framework, establish a composite keyword heat analysis model based on I-BERT framework.Taking the 14420 articles published in 21 kinds of social science management periodicals collected by CNKI(China National Knowledge Infrastructure) in 2017-2019 as the experimental data, the superiority of the proposed method is verified by the data of word spacing, class spacing, extraction accuracy and recall of hot keywords. In the experimental process of this research, it can be found that the method proposed in this paper has a higher accuracy than other methods in extracting hot keywords, which can ensure the timeliness and accuracy of scientific journals in capturing hot topics in the discipline, and finally pass Use information technology to master popular key words.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.397-404
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• 2022

Accurate prediction of the undrained shaft resistance is essential for robust design of bored piles in undrained condition. The undrained shaft resistance is calculated using the undrained adhesion factor multiplied by the undrained cohesion of the soil. However, the available correlations to predict the undrained adhesion factor have been developed using simple regression techniques and the accuracy of these correlations has not been thoroughly assessed in previous studies. The lack of the assessment of these correlations made it difficult for geotechnical engineers to select the most accurate correlation in routine designs. Furthermore, limited attempts have been made in previous studies to use advanced data mining techniques to develop simple and accurate correlation to predict the undrained adhesion factor. This research, therefore, has been conducted to fill these gaps in knowledge by developing novel and robust correlation to predict the undrained adhesion factor. The development of the new correlation has been conducted using the multi-objective evolutionary polynomial regression analysis. The new correlation outperformed the available empirical correlations, where the new correlation scored lower mean absolute error, mean square error, root mean square error and standard deviation of measured to predicted adhesion factor, and higher mean, a20-index and coefficient of correlation. The correlation also successfully showed the influence of the undrained cohesion and the effective stress on the adhesion factor. Hence, the new correlation enhances the design accuracy and can be used by practitioner geotechnical engineers to ensure optimized designs of bored piles in undrained conditions.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.785-792
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• 2021

Since the Fourth Industrial Revolution, various changes have occurred in society as a whole due to advance in technologies such as artificial intelligence and big data. The amount of data that can be collect in the process of applying important technologies tends to increase rapidly. Especially in academia, existing generated literature data is analyzed in order to grasp research trends, and analysis of these literature organizes the research flow and organizes some research methodologies and themes, or by grasping the subjects that are currently being talked about in academia, we are making a lot of contributions to setting the direction of future research. However, it is difficult to access whether data collection is necessary for the analysis of document data without the expertise of ordinary programs. In this paper, propose a text mining-based topic modeling Web application model. Even if you lack specialized knowledge about data analysis methods through the proposed model, you can perform various tasks such as collecting, storing, and text-analyzing research papers, and researchers can analyze previous research and research trends. It is expect that the time and effort required for data analysis can be reduce order to understand.