• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1945-1949
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• 2013

Esophageal cancer (EC) incidence rates in Kazakhstan were assessed by component analysis based on primary registered cases in 2001-2010. It was found that despite an apparent general decrease in the number of EC patients in Kazakhstan, a potential increase should be evaluated, due to changes in aging as well as the increase in population. Some problems of EC patients' registration were broached with an emphasis on the importance of the expected absolute number and reasons for undercounting in the country. Based on these, ways of improving the recording and registration of such patients in the country were suggested.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.354-357
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• 2020

DDoS(Distributed Denial of Service) 공격은 현재의 인터넷 환경뿐만 아니라 NDN에서도 정상적인 서비스를 저해시키는 주요 문제이며 이에 관련된 다양한 연구들이 진행되고 있다. 본 논문에서는 DDoS 공격이 가해질 때 NDN 라우터의 PIT(Pending Interest Table) 가용성 저해로 인해 발생하는 문제 해결에 중점을 둔다. 이를 위한 방안으로 RED(Random Early Detection) 알고리즘을 기반으로 하는 기법을 적용하고, 시뮬레이션을 통한 측정 결과를 보여준다.

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.99-108
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• 2012

Many methods had been developed to improve the accuracy of extracting information from a vast amount of data. This paper combined a number of natural language processing methods such as NER (named entity recognition), sentence extraction, and part of speech tagging to carry out text analysis. The data source is comprised of texts obtained from the web using a domain-specific data extraction agent. A framework for the extraction of information from unstructured data was developed using the aforementioned natural language processing methods. We simulated the performance of our work in the extraction and analysis of texts for the detection of organizational structures. Simulation shows that our study outperformed other NER classifiers such as MUC and CoNLL on information extraction.

• Atmosphere
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• v.25 no.2
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• pp.367-374
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• 2015

Korean Peninsula has high potential for occurrence of aviation turbulence. A Korean aviation Turbulence Guidance (KTG) system focused on the Korean Peninsula, named Korean-Peninsula KTG (KP-KTG) system, is developed using the high resolution (horizontal grid spacing of 1.5 km) Local Data Assimilation and Prediction System (LDAPS) of the Korea Meteorological Administration (KMA). The KP-KTG system is constructed first by selection of 15 best diagnostics of aviation turbulence using the method of probability of detection (POD) with pilot reports (PIREPs) and the LDAPS analysis data. The 15 best diagnostics are combined into an ensemble KTG predictor, named KP-KTG, with their weighting scores computed by the values of area under curve (AUC) of each diagnostics. The performance of the KP-KTG, represented by AUC, is larger than 0.84 in the recent two years (June 2012~May 2014), which is very good considering relatively small number of PIREPs. The KP-KTG can provide localized turbulence forecasting in Korean Peninsula, and its skill score is as good as that of the operational-KTG conducting in East Asia.

• Journal of the Korean Society of Earth Science Education
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• v.4 no.1
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• pp.66-73
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• 2011

This study aims to compare and analyze the types of teaching-learning activities, themes and percentage of STS contents in the area of "The Earth and the Universe" in elementary science subject following the 7th Curriculum and 2007 Revised Curriculum, identifying how STS education has changed and their features. First, the number of pages where STS appears in the 2007 revised science textbook has increased over 10% compared to the that of the 7th curriculum. In particular, the number of pages in the 5th and 6th graders increased substantially to 15% and 34%, respectively. Second, as a result of analysis on components of STS, 'applications of science', 'local and community relevance', 'social problem and issues', 'evaluation concerned fir getting and using information' were obtained in the order named for the 7th curriculum; while 'applications of science', 'local and community relevance', 'career awareness' and 'social problem and issues' were obtained in the order named for 2007 revised curriculum. Third, with regard to the analysis on theme areas, the 7th curriculum was found to cover the theme on use of natural resources most frequently, followed by environmental problem, while 2007 revised curriculum to cover environmental problem and effects of technical development most frequently, followed by space development and use of natural resources. Fourth, in the area of STS teaching activities, 'investigation activity' showed highest percentage in 7th curriculum, followed by 'analysis of data', and 'research design', while 'analysis of data' showed highest frequency of appearance, followed by "investigation activity' and 'actual activities' and 'research design' in the order named in 2007 revised curriculum, showing that the area of 'analysis of data' and 'actual activities' increased substantially compared to the 7th curriculum.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.12
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• pp.319-328
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• 2021

With popularization of services for massive content, the fundamental limitations of TCP/IP networking were discussed and a new paradigm called Information-centric networking (ICN) was presented. In ICN, content is addressed by the content identifier (content name) instead of the location identifier such as IP address, and network nodes can use the cache to store content in transit to directly service subsequent user requests. As the user request can be serviced from nearby network caches rather than from far-located content servers, advantages such as reduced service latency, efficient usage of network bandwidth, and service scalability have been introduced. However, these advantages are determined by how actively content stored in the cache can be utilized. In this paper, we 1) introduce content access schemes in Named-data networking, one of the representative ICN architectures; 2) in particular, review the schemes that allow access to cached content away from routing paths; 3) conduct comparative study on the performance of the schemes using the ndnSIM simulator.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.13-20
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• 2022

This study is to develop a named entity recognition model specialized in criminal investigation domains using deep learning techniques. Through this study, we propose a system that can contribute to analysis of crime for prevention and investigation using data analysis techniques in the future by automatically extracting and categorizing crime-related information from text-based data such as criminal judgments and investigation documents. For this study, the criminal investigation domain text was collected and the required entity name was newly defined from the perspective of criminal analysis. In addition, the proposed model applying KoELECTRA, a pre-trained language model that has recently shown high performance in natural language processing, shows performance of micro average(referred to as micro avg) F1-score 98% and macro average(referred to as macro avg) F1-score 95% in 9 main categories of crime domain NER experiment data, and micro avg F1-score 98% and macro avg F1-score 62% in 56 sub categories. The proposed model is analyzed from the perspective of future improvement and utilization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.940-945
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• 2021

Future network architectures such as Named Data Networking (NDN) were born to change the way data can be transmitted from current host-centric network technologies to information-centric network technologies. Recently, many studies are being conducted to graft Vehicular NDN to the communication network technology of smart vehicles including connected vehicles. Explosion of data traffic due to Interest/Data packet broadcasting in Vehicular NDN environment is a very important problem to be solved in order to realize VNDN-based data communication. In this paper, the generation of data packet copies according to the increase in network size, vehicle speed, and frequency of interest packets in VNDN network is simulated and evaluated using ndnSIM, in order to show how severe the data broadcast storm phenomenon. The CDP(Copies of Data Packets) increased proportionally in the increase of network size or Interest frequency.

• The Research Journal of the Costume Culture
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• v.13 no.3 s.56
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• pp.329-343
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• 2005

This study purposed to classify the somatotype of early adolescent girls based on the physical characteristics. For this purpose, a total of 529 girls aged between 10 and 14 were measured and data were collected from 42 anthropometric measurements and 41 photographic measurements per a person. According to the results of classifying somatotype based of the factor analysis, 176 students ($33.3\%$) were type 1, which is short and thin. In students of this type, the breast did not develop, the belly was stuck out as in the body shape of latter childhood, and the contour of the body had not been formed yet. This somatotype was named Type A. Another 176 students ($33.3\%$) were type 2, which is tall and somewhat thin. In students of this type, the breast and the hip developed well, so the contour of the body was quite clear. This somatotype was named Type X. Lastly, 177 students ($33_4\%$) were type 3, which is fattest among the three types. In students of this type, the breast developed but the waist and the hip were not voluminous. This somatotype was named Type H.