• Electronics and Telecommunications Trends
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• v.28 no.1
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• pp.42-51
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• 2013

The R language is an open source programming language and a software environment for statistical computing and data visualization. The R language is widely used among a lot of statisticians and data scientists to develop statistical software and data analysis. The R language provides a variety of statistical and graphical techniques, including basic descriptive statistics, linear or nonlinear modeling, conventional or advanced statistical tests, time series analysis, clustering, simulation, and others. In this paper, we first introduce the R language and investigate its features as a data analytics tool. As results, we may explore the application possibility of the R language in the field of data analytics.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.1
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• pp.387-394
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• 2019

Recently, the importance of big data has been increased, and demand for data analysis for the big data is also increased. R language is developed for data analysis, and users are analyzing data by using algorithms of various statistics, machine learning and data mining packages in R language. However, it is difficult to develop an application using R. Early study proposed a method to call R script through another language such as PHP, Java, and so on. However, it is troublesome to write such a development method in addition to R in combination with other languages. In this study, we introduce how to write API using only R language without using another language by using Plumber package. We also propose a solution for security issues related with R API. If we use propose technology for developing web application, we can expect high productivity, easy of use, and easy of maintenance.

• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.31-55
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• 2007

R is an open source language for statistical computing and graphics based on the ACM software award-winning S language. R is widely used for data analysis and has become a major vehicle for making available new statistical methodology. This paper presents an overview of the design philosophy and the development model for R, reviews the basic capabilities of the system, and outlines some current projects that will influence future developments of R.

• MALSORI
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• no.4
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• pp.20-30
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• 1982

The aim of this paper is to analyse difficulties of the pronunciation in swedish vowels encountered by Koreans learners and to seek solutions in order to correct the possible errors. In the course of the analysis the swedish and Korean vowels in question are compared with the purpose of describing differences aha similarities between these two systems. This contrastive description is largely based on the students' articulatory speech level ana the writer's auditory , judgement . The following points are discussed : 1 ) Vowel length as a distinctive feature in Swedish compared with that of Korean. 2) A special attention is paid on the Swedish vowel [w:] that is characterized by its peculiar type of lip rounding. 3) The six pairs of Swedish vowels that are phonologically contrastive but difficult for Koreans to distinguish one from the other: [y:] ~ [w:], [i:] ~ [y:], [e:] ~ [${\phi}$:], [w;] ~ [u:] [w:] ~ [$\theta$], [$\theta$] ~ [u] 4) The r-colored vowel in the case of the postvocalic /r/ that is very common in American English is not allowed in English sound sequences. The r-colored vowel in the American English pattern has to be broken up and replaced hi-segmental vowel-consonant sequences . Korean accustomed to the American pronunciation are warned in this respect. For a more distinct articulation of the postvocalic /r/ trill [r] is preferred to fricative [z]. 5) The front vowels [e, $\varepsilon, {\;}{\phi}$) become opener variants (${\ae}, {\;}:{\ae}$] before / r / or supradentals. The results of the analysis show that difficulties of the pronunciation of the target language (Swedish) are mostly due to the interference from the Learner's source language (Korean). However, the Learner sometimes tends to get interference also from the other foreign language with which he or she is already familiar when he or she finds in that language more similarity to the target language than in his or her own mother tongue. Hence this foreign language (American English) in this case functions as a second language for Koreans in Learning Swedish.

• The Journal of Information Systems
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• v.25 no.4
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• pp.121-139
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• 2016

Purpose In this study, we propose the special purposed R package named ""new_Noun()" to process nonstandard texts appeared in various social networks. As the Big data is getting interested, R - analysis tool and open source software is also getting more attention in many fields. Design/methodology/approach With more than 9,000 R packages, R provides a user-friendly functions of a variety of data mining, social network analysis and simulation functions such as statistical analysis, classification, prediction, clustering and association analysis. Especially, "KoNLP" - natural language processing package for Korean language - has reduced the time and effort of many researchers. However, as the social data increases, the informal expressions of Hangeul (Korean character) such as emoticons, informal terms and symbols make the difficulties increase in natural language processing. Findings In this study, to solve the these difficulties, special algorithms that upgrade existing open source natural language processing package have been researched. By utilizing the "KoNLP" package and analyzing the main functions in noun extracting command, we developed a new integrated noun processing package "new_Noun()" function to extract nouns which improves more than 29.1% compared with existing package.

• Knowledge Management Research
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• v.22 no.3
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• pp.91-106
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• 2021

With the recent rapid development of deep learning technology, the demand for analyzing huge text documents in the national R&D field from various perspectives is rapidly increasing. In particular, interest in the application of a BERT(Bidirectional Encoder Representations from Transformers) language model that has pre-trained a large corpus is growing. However, the terminology used frequently in highly specialized fields such as national R&D are often not sufficiently learned in basic BERT. This is pointed out as a limitation of understanding documents in specialized fields through BERT. Therefore, this study proposes a method to build an R&D KoBERT language model that transfers national R&D field knowledge to basic BERT using further pre-training. In addition, in order to evaluate the performance of the proposed model, we performed classification analysis on about 116,000 R&D reports in the health care and information and communication fields. Experimental results showed that our proposed model showed higher performance in terms of accuracy compared to the pure KoBERT model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.1
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• pp.80-90
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• 2010

Performance management is very popular in business area and seems to be an exciting topic. Despite significant research efforts and myriads of performance metrics, performance management today as a rigorous approach is still in an immature state and metrics are often selected based on intuitive and heuristic approach. In a R&D sector, the difficulty to select the proper performance metrics is even more increasing due to the natural characteristics of R&D such as unique or domain-specific problems. In this paper, we present a way of presenting R&D performance framework using ontology language. Based on this, the specific metrics can be derived by reusing or inheriting the context in the framework. The proposed ontological framework is formalized using OWL(Ontology Web Language) and metrics selection rules satisfying the characteristics of R&D are represented in SWRL(Semantic Web Rule Language). Actual metrics selection procedure is carried out using JESS rule engine, a plug-in to Prot$\acute{e}$g$\acute{e}$, and illustrated with an example, incorporating a prevalent R&D performance model : TVP(Technology Value Pyramid).

• Journal of Korean Neurosurgical Society
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• v.45 no.6
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• pp.329-335
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• 2009

Objective : Determining language lateralization is important for the presurgical evaluation of patients with medically intractable epilepsy. The Wada test has been the gold standard for lateralization of language dominance before epilepsy surgery. However, it is an invasive test with risk, and have some limitations. Methods : We compared the volumetric analysis with Wada test, and studied the clinical potential of volumetric analysis to assess language laterality in large surgical candidates with temporal lobe epilepsy (TLE). To examine the efficacy of volumetric analysis to determine language lateralization during presurgical evaluation, we compared the volumetric analysis of the bilateral planum temporale with the results of Wada test in 59 patients with chronic intractable TLE (rTLE, n=32; lTLE, n=27) who underwent epilepsy surgery. We measured the gray matter volumes of planum temporale (PT) of each patients using the VoxelPlus2 program (Mevisys, Daejeon, Korea). Results : Overall congruence of the volumetric analysis with the Wada test was 97.75% in rTLE patients and 81.5% in lTLE patients. There were more significant leftward asymmetry of the PT in rTLE patients than lTLE patients. In lTLE patients, relatively high proportion (37%) of the patients showed bilateral or right hemispheric language dominance. Conclusion : These results provide evidence that the volumetric analysis of the PT could be used as an alternatives in language lateralization. Also, the results of the Wada test suggested that there was considerable plasticity of language representation in the brains of patients with intractable TLE and it was associated with an earlier age of brain injury.

• Proceedings of the Korean Society for Language and Information Conference
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• 1996.02a
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• pp.383-392
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• 1996

• Electronics and Telecommunications Trends
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• v.35 no.3
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• pp.9-19
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• 2020

Recently, a technique for applying a deep learning language model pretrained from a large corpus to fine-tuning for each application task has been widely used as a language processing technology. The pretrained language model shows higher performance and satisfactory generalization performance than existing methods. This paper introduces the major research trends related to deep learning pretrained language models in the field of language processing. We describe in detail the motivations, models, learning methods, and results of the BERT language model that had significant influence on subsequent studies. Subsequently, we introduce the results of language model studies after BERT, focusing on SpanBERT, RoBERTa, ALBERT, BART, and ELECTRA. Finally, we introduce the KorBERT pretrained language model, which shows satisfactory performance in Korean language. In addition, we introduce techniques on how to apply the pretrained language model to Korean (agglutinative) language, which consists of a combination of content and functional morphemes, unlike English (refractive) language whose endings change depending on the application.