• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2008년도 추계학술대회 논문집
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• pp.278-281
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• 2008

In these days the installation of wind turbines or wind parks includes a high financial risk. So for the planning and the constructing of wind farms, long-term data of wind speed and wind direction is required. However, in most cases only few data are available at the designated places. Traditional Measure-Correlate-Predict (MCP) can extend this data by using data of nearby meteorological stations. But also Neural Networks can create such long-term predictions. The key issue of this paper is to demonstrate the possibility and the quality of predictions using Neural Networks. Thereto this paper compares the results of different MCP Models and Neural Networks for creating long-term data with various indexes.

• 한국인쇄학회지
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• 제30권3호
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• pp.23-33
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• 2012

We studied simultaneously the electronically written data affected in the use of thermal transfer discs and the recoding strategy between recoding drives for the stable long term preservation of optical discs which are commonly used in an electronic data storage device. The most important thing in the archiving preservation might be a choice of a device, however the use of thermal transfer recording discs is not good for long term data preservation because the thermal effect on the recoding data is critical which means that the data are recorded not under best condition but under bad condition. We inspect the strategies of recoding data from 12 brands of optical discs and drives of 7 brands and it turns out the recoding strategy is needed first for the long term preservation of electronic recording data. Thus, without affecting data quality and deformation of optical discs, the choice of optimal disc and drive in recoding data will be a solution for the long term preservation of recoding data.

• 대한가정학회지
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• 제38권1호
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• pp.59-73
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• 2000

The purposes of this study were to determine the factors that influence consumer concerns for safety to cow's milk produced using food-related biotechnology and to find the similarity and difference among concern factors relating short-term and long-term risk perception. Telephone interviews were conducted and the data were collected from households(n=1,466) nationwide in the U.S. And the data were analyzed by probit model and LIMDEP softare package. The data demonstrated that consumers were concerned about food safety from consuming milk produced using food-related biotechnology. The concerns were found to be influenced by demographic factors(gender in short-term, gender and age in long-term) as well as psychological aspect such as outrage(heard about bGH, milk belief about naturalness, expected benefit in short-term, heard about bGH, expected benefit in long-term) and attitudinal factors(animal rights group, locus of control in short-term, animal rights group, cancer history, locus of control in long-term). The results suggest that consumers have concerns for safety to cow's milk produced by biotechnology and the most factors influencing consumer concerns were similar between short-term and long-term period, though a few factors such as cancer history, milk belief about naturalness and age were different.

• 디지털산업정보학회논문지
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• 제15권1호
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• pp.1-7
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• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.

• Journal of Computing Science and Engineering
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• 제5권2호
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• pp.150-160
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• 2011

Automatic text classification has a long history and many studies have been conducted in this field. In particular, many machine learning algorithms and information retrieval techniques have been applied to text classification tasks. Even though much technical progress has been made in text classification, there is still room for improvement in text classification. In this paper, we will discuss remaining issues in improving text classification. In this paper, three improvement issues are presented including automatic training data generation, noisy data treatment and term weighting and indexing, and four actual studies and their empirical results for those issues are introduced. First, the semi-supervised learning technique is applied to text classification to efficiently create training data. For effective noisy data treatment, a noisy data reduction method and a robust text classifier from noisy data are developed as a solution. Finally, the term weighting and indexing technique is revised by reflecting the importance of sentences into term weight calculation using summarization techniques.

• Journal of Information Technology Applications and Management
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• 제28권3호
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• pp.23-30
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• 2021

Korea currently provides long-term care benefits for the elderly with poor functionality, but most of the service providers are private businesses. This is the time when quality management of care services is required, which is just around the corner of the super-aged era. In this study, we would like to look at the case in which 'A company', which operates a long-term care institution, attempted to make voluntary changes ahead of social demands. The company tried to transform the social needs of quality management by judging them as opportunities, not threats, and establishing an integrated database of centers. First, the company processed data and built a cloud-based database system. Second, the company automatically linked data from existing systems for the efficiency of data utilization. Third, the company pursued visualization for the convenience of data utilization. This allowed the company to make data-driven strategic decisions internally. This is expected to increase sales as it will soon lead to securing new customers and pioneering new markets. It is also significant in that it can provide best practices for the long-term care industry.

• 한국병원경영학회지
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• 제28권3호
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• pp.58-67
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• 2023

Purpose: The purpose of this study was to analyze the factors associated with long-term hospitalized patients in long-term care hospitals using the quality assessment data for long-term care hospitals by the Health Insurance Review. Methods: Among 1,376 long-term care hospitals, frequency analysis and descriptive statistics were used to analyze the characteristics of these hospitals. Multiple linear regression was conducted to examine the associations between infrastructure characteristics, medical personnel characteristics, health outcomes and the proportion of long-term hospitalized patients. Results: The research findings indicate that the number of patients per doctor, the number of patients per nurse, and the number of patients per nursing staff were positively associated with the proportion of long-term hospitalized patients. Among health outcomes, a higher proportion of patients with more than a 5% weight loss compared to the previous month and the proportion of patients showing improvement in ADL, were more likely to have a lower proportion of long-term hospitalized patients. However the proportion of diabetic patients with HbA1c test results within the appropriate range was positively associated with the proportion of long-term hospitalized patients. Conclusion: The present study results provide fundamental data for the establishment of policies for long-term care hospitals. Based on this study, it is important to suggest screening methods for unnecessary long-term hospitalizations, such as sufficient medical personnel to improve the quality of care in long-term care hospitals. It is also necessary to clearly separate the roles of medical institutions and long-term care facilities and implement policies to support patients' social reintegration.

• 한국환경과학회지
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• 제22권10호
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• pp.1305-1316
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• 2013

A long-term wind resource map was made to provide the key design data for the 2.5 GW Korean West-South Offshore Wind Project, and its reliability was validated. A one-way dynamic downscaling of the MERRA reanalysis meteorological data of the Yeongwang-Gochang offshore was carried out using WindSim, a Computational Fluid Dynamics based wind resource mapping software, to establish a 33-year time series wind resource map of 100 m x 100 m spatial resolution and 1-hour interval temporal resolution from 1979 to 2012. The simulated wind resource map was validated by comparison with wind measurement data from the HeMOSU offshore meteorological tower, the Wangdeungdo Island meteorological tower, and the Gochang transmission tower on the nearby coastline, and the uncertainty due to long-term variability was analyzed. The long-term variability of the wind power was investigated in inter-annual, monthly, and daily units while the short-term variability was examined as the pattern of the coefficient of variation in hourly units. The results showed that the inter-annual variability had a maximum wind index variance of 22.3% while the short-term variability, i.e., the annual standard deviation of the hourly average wind power, was $0.041{\pm}0.001$, indicating steady variability.

• 신재생에너지
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• 제4권4호
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• pp.23-29
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• 2008

Wind farm development project contains high business risks because that a wind farm, which is to be operating for 20 years, has to be designed and assessed only relying on a year or little more in-situ wind data. Accordingly, long-term correction of short-term measurement data is one of most important process in wind resource assessment for project feasibility investigation. This paper shows comparison of general Measure-Correlate-Prediction models and neural network, and presents new method using neural network for increasing prediction accuracy by accommodating multiple reference data. The proposed method would be interim step to complete long-term correction methodology for Korea, complicated Monsoon country where seasonal and diurnal variation of local meteorology is very wide.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.