• Journal of Integrative Natural Science
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• v.13 no.2
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• pp.47-57
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• 2020

The purpose of this study was to understand experiences of successful aging of community-dwelling elderly living in the Korean community using mixed methods. This study is a mixed methods research using convergent parallel design. 483 elderly people living in the 2 cities were selected as subjects to collect quantitative data. For qualitative data collection, 6 elderly people participated. The level of successful aging for elderly people living at home scored an average of 4.30 ± .59 out of 5 points (4.46 ± .64 for self-efficacy, 4.31 ± .65 for good self-control, 4.29 ± .70 for satisfaction with children's success, and 4.26 ± .61 for partnership with spouses). The main themes of successful aging derived by interview were as follows: 1) fulfill responsibilities of raising children as parents, 2) establish a stable life to overcome difficulties, 3) accept the body being different from the past, and 4) live in harmony with people around them. Based on these results, it is necessary to provide useful health care services that can be used in the local community and to provide service utilization information through various routes in order to achieve a successful aging.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1045-1055
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• 2007

Latent topic models are statistical models which automatically captures salient patterns or correlation among features underlying a data collection in a probabilistic way. They are gaining an increased popularity as an effective tool in the application of automatic semantic feature extraction from text corpus, multimedia data analysis including image data, and bioinformatics. Among the important issues for the effectiveness in the application of latent topic models to the massive data set is the efficient learning of the model. The paper proposes an accelerated learning technique for PLSA model, one of the popular latent topic models, by an incremental EM algorithm instead of conventional EM algorithm. The incremental EM algorithm can be characterized by the employment of a series of partial E-steps that are performed on the corresponding subsets of the entire data collection, unlike in the conventional EM algorithm where one batch E-step is done for the whole data set. By the replacement of a single batch E-M step with a series of partial E-steps and M-steps, the inference result for the previous data subset can be directly reflected to the next inference process, which can enhance the learning speed for the entire data set. The algorithm is advantageous also in that it is guaranteed to converge to a local maximum solution and can be easily implemented just with slight modification of the existing algorithm based on the conventional EM. We present the basic application of the incremental EM algorithm to the learning of PLSA and empirically evaluate the acceleration performance with several possible data partitioning methods for the practical application. The experimental results on a real-world news data set show that the proposed approach can accomplish a meaningful enhancement of the convergence rate in the learning of latent topic model. Additionally, we present an interesting result which supports a possible synergistic effect of the combination of incremental EM algorithm with parallel computing.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.351-359
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• 2014

The log data generated by security equipment have been synthetically analyzed on the ESM(Enterprise Security Management) base so far, but due to its limitations of the capacity and processing performance, it is not suited for big data processing. Therefore the another way of technology on the big data platform is necessary. Big Data platform can achieve a large amount of data collection, storage, processing, retrieval, analysis, and visualization by using Hadoop Ecosystem. Currently ESM technology has developed in the way of SIEM (Security Information & Event Management) technology, and to implement security technology in SIEM way, Big Data platform technology is essential that can handle large log data which occurs in the current security devices. In this paper, we have a big data platform Hadoop Ecosystem technology for analyzing the security log for sure how to implement the system model is studied.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1341-1346
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• 2017

In this paper, we designed the open service platform based on integrated type of individual customized service and intelligent information technology with individual's complex attributes and requests. First, the data collection phase is proceed quickly and accurately to repeat extraction, transformation and loading. The generated data from extraction-transformation-loading process module is stored in the distributed data system. The data analysis phase is generated a variety of patterns that used the analysis algorithm in the field. The data processing phase is used distributed parallel processing to improve performance. The data providing should operate independently on device-specific management platform. It provides a type of the Open API.

• Journal of Computing Science and Engineering
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• v.10 no.3
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• pp.95-101
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• 2016

In this study, we propose a new system for constructing parallel corpora for sign languages, which are generally under-resourced in comparison to spoken languages. In order to achieve scalability and accessibility regarding data collection and corpus construction, our system utilizes deep learning-based techniques and predicts depth information to perform pose estimation on hand information obtainable from video recordings by a single RGB camera. These estimated poses are then transcribed into expressions in SignWriting. We evaluate the accuracy of hand tracking and hand pose estimation modules of our system quantitatively, using the American Sign Language Image Dataset and the American Sign Language Lexicon Video Dataset. The evaluation results show that our transcription system has a high potential to be successfully employed in constructing a sizable sign language corpus using various types of video resources.

• The Research Journal of the Costume Culture
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• v.24 no.6
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• pp.873-885
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• 2016

It is common knowledge that a conventionally woven textile consists of two assemblies of parallel threads (warp and weft), one interlaced with the other at ninety degrees. Where each of the two assemblies is arranged in a particular colour sequence, a check design, known as a 'tartan', may be created. Although similar check-type cloths have been produced worldwide, it is the tartans of Scotland which have received most attention and it is here that a complex set of rules evolved and tartans of different types became associated traditionally with different regions, family groups or 'clans'. There is an impressive array of publications focused on the identification of tartans and their clan associations. This paper explains the nature of tartans, analyses typical surface structures, ratios and proportions, and suggests possible avenues of use for modern designers. The principal sources of data were a collection of tartans held at ULITA - An Archive of International Textiles (University of Leeds, UK) and Stewart's 1974 publication The Setts of Scottish Tartans. Based on the observation that divisions into halfs and thirds were dominant, a series of templates is presented with the intention of developing an awareness among designers that ratios and proportions used in familiar or traditional frameworks can be employed in a modern context.

• KIEE International Transactions on Power Engineering
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• v.4A no.2
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• pp.100-105
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• 2004

Installed capacity markets in the northeast of the United States ensure that adequate generation exists to satisfy regional loss of load probability (LOLP) criterion. LOLP studies are conducted to determine the amount of capacity that is needed, but they do not consider several factors that substantially affect the calculated distribution of available capacity. These studies do not account for the fact that generation availability increases during periods of high demand and therefore prices, common-cause failures that result in multiple generation units being unavailable at the same time, and the negative correlation between load and available capacity due to temperature and humidity. A categorization of incidents in an existing bulk power reliability database is proposed to analyze the existence and frequency of independent failures and those associated with resource dynamics. Findings are augmented with other empirical findings. Monte Carlo methods are proposed to model these resource dynamics. Using the IEEE Reliability Test System as a single-bus case study, the LOLP results change substantially when these factors are considered. Better data collection is necessary to support the more comprehensive modeling of resource adequacy that is proposed. In addition, a parallel processing method is used to offset the increase in computational times required to model these dynamics.

• Journal of Preventive Medicine and Public Health
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• v.40 no.2
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• pp.95-101
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• 2007

Focusing on complex diseases of public health significance, strategic issues regarding the on-going Korean Genome Cohort were reviewed: target size and diseases, measurements, study design issues, and follow-up strategy of the cohort. Considering the epidemiologic characteristics of Korean population as well as strengths and drawbacks of current research environment, we tried to tailor the experience of other existing cohorts into proposals for this Korean study. Currently 100,000 individuals have been participating the new Genome Cohort in Korea. Target size of de novo collection is recommended to be set as between 300,000 to 500,000. This target size would allow acceptable power to detect genetic and environmental factors of moderate effect size and possible interactions between them. Family units and/or special subgroups are recommended to parallel main body of adult individuals to increase the overall efficiency of the study. Given that response rate to the conventional re-contact method may not be satisfactory, successful follow-up is the main key to the achievement of the Korean Genome Cohort. Access to the central database such as National Health Insurance data can provide enormous potential for near-complete case detection. Efforts to build consensus amongst scientists from broad fields and stakeholders are crucial to unleash the centralized database as well as to refine the commitment of this national project.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.73-85
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• 2022

In the era of the 4th industrial revolution, where automation and connectivity are maximized with artificial intelligence, the importance of data collection and utilization for model update is increasing. In order to create a model using artificial intelligence technology, it is usually necessary to gather data in one place so that it can be updated, but this can infringe users' privacy. In this paper, we introduce federated learning, a distributed machine learning method that can update models in cooperation without directly sharing distributed stored data, and introduce a study to optimize distributed consensus among participants without an existing server. In addition, we propose a pattern and group-based distributed consensus optimization algorithm that uses an algorithm for generating patterns and groups based on the Kirkman Triple System, and performs parallel updates and communication. This algorithm guarantees more privacy than the existing distributed consensus optimization algorithm and reduces the communication time until the model converges.

• Journal of Korean Academy of Nursing Administration
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• v.11 no.1
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• pp.89-104
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• 2005

Purpose: The authors reviewed researches related to performance appraisal(32 researches), in order to identify the direction for future research and to establish a credible performance appraisal system. Method: Almost all of the theses and research published in 9 major journals of nursing in korea were reviewed. Results: Most of research(84.4%) were theses and all researches were used to non-experimental design; survey 81.3%, methodological research 15.6%, review 3.1%. The major subjects of study were appraisees and appraisers. Psychosocial data collection which only used questionnaire were 81.3% and carried out interview(12.5%) and delphi-method(3.1%). Data analysis methods were used frequency 78.1%, t-test 62.5%, mean/SD 59.4%, Pearson's correlation 50%, and Cronbach α 50%. Appraisal tool used or developed in studies was graphic rating scales in nine studies and ran parallel with forced distribution in 2. Also, MBO and BSC were developed. Total number of korean terms in performance measurement were 11, and english terms 15. The tendency of the terms was toward performance appraisal or evaluation. Conclusion: In the light of results, we expect development of corporate appraisal tool that can evaluate nurse competence and performance. Total performance management system also should be established.