• Health Policy and Management
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• v.20 no.3
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• pp.104-122
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• 2010

Long-term care insurance has been introduced in Korea a year ago, and we are in a stage requiring to set principles regarding the generosity of coverage and how to gradually extend the coverage. This study empirically analyzes how the long-term care insurance in Korea is operated. Special attention is given to who is the main beneficiary of the long-term care insurance introduction, and what is the factors influencing the elderly's decision to apply for or use long-term care services. Use of a detailed information of individuals' public health insurance and long-term care insurance from administration data made it possible to control for health status, socioeconomic status including family type, housing tenure, income level. Logit models were employed to analyze the effects of various socioeconomic factors on the likelihood of applying and using long-term care services. Also, this study employed a survey questioning whether to ever willing to take other option as a alternative to residential care or home-care and the level of cash benefit for which they are willing to replace the formal care with informal care. The result indicated that although the poorest elderly population groups are in the greatest need for the long-term care service, they are in difficulty using the service due to economic burden. This implies the copayment amount needs to be adjusted in order for the poor elderly group to be able to get the benefit of the long-term care service.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.81-86
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• 2016

Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.

• Journal of the Korean Society of Costume
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• v.56 no.3 s.102
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• pp.91-106
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• 2006

The purpose of this study is to examine the differences of long-term relationship intention process according to consumer characteristics. Data for this research was collected from 540 women older than 20 years old in Seoul, Gwangju, and Gyeonggi-do. The results are as follows. First, relationship consumers having relationship with stores were different from customers having relationship with a brand in specific store in the long-term relationship intention process. Second, consumers having relationship for a long period and consumers having relationship for a short period showed differences in long-term relationship intention process. Third, strong relationship consumers and weak relationship consumers showed differences in long-term relationship intention process. Forth, involvement groups showed differences in long-term relationship intention process. These results imply that fashion retailers need set up the relationship strategy for subdivision groups along consumer characteristics.

• Journal of the Korean Graphic Arts Communication Society
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• v.30 no.3
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• pp.77-88
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• 2012

In this report, we performed the measurements of physical properties of optical discs as a long term preservation electronic recording device and showed how to improve the preservation method of them. We collect the 1,993 optical discs from the archives of the National Archives of Korea and tested various measurements. We used DVDT-SD4 equipment to measure the quality of data, deformation of disc, the various writing strategy and manufacturer derives, which can be happened in optical discs by physical factors. We found that th quality of data are closely related with write strategy between discs and drives. This relation gives us information about data quality in optical discs for long term preservation that can be obtained from the state between empty discs and optical drives before recording. Thus, the initial selection of optimal discs and drives is critical for long term recording data preservation and the data quality after long time preservation will not be much different from that of the initial ones.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.271-280
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• 2018

Recently, big data has been growing rapidly due to the development of IT technology. Especially in the medical field, big data is utilized to provide services such as patient-customized medical care, disease management and disease prediction. In Korea, 'National Health Alarm Service' is provided by National Health Insurance Corporation. However, the prediction model has a problem of short-term prediction within 3 days and unreliability of social data used in prediction model. In order to solve these problems, this paper proposes a disease prediction model using medicine prescription data generated from actual patients. This model predicts the total number of patients and the risk of disease in each region and uses the ARIMA model for long-term predictions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2612-2633
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• 2020

Due to the explosive growth of multimedia speech data, how to protect the privacy of speech data and how to efficiently retrieve speech data have become a hot spot for researchers in recent years. In this paper, we proposed an encrypted speech retrieval scheme based on long short-term memory (LSTM) neural network and deep hashing. This scheme not only achieves efficient retrieval of massive speech in cloud environment, but also effectively avoids the risk of sensitive information leakage. Firstly, a novel speech encryption algorithm based on 4D quadratic autonomous hyperchaotic system is proposed to realize the privacy and security of speech data in the cloud. Secondly, the integrated LSTM network model and deep hashing algorithm are used to extract high-level features of speech data. It is used to solve the high dimensional and temporality problems of speech data, and increase the retrieval efficiency and retrieval accuracy of the proposed scheme. Finally, the normalized Hamming distance algorithm is used to achieve matching. Compared with the existing algorithms, the proposed scheme has good discrimination and robustness and it has high recall, precision and retrieval efficiency under various content preserving operations. Meanwhile, the proposed speech encryption algorithm has high key space and can effectively resist exhaustive attacks.

• New & Renewable Energy
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• v.19 no.4
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• pp.98-107
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• 2023

A study was conducted to analyze data from 1981 to 2020 for understanding the impact of climate on solar energy generation. A significant increase of 104.6 kWhm^-2 was observed in the annual cumulative solar radiation over this period. Notably, the distribution of solar radiation shifted, with the solar radiation in Busan rising from the seventh place in 1981 to the second place in 2020 in South Korea. This study also examined the correlation between long-term temperature trends and solar radiation. Areas with the highest solar radiation in 2020, such as Busan, Gwangju, Daegu, and Jinju, exhibited strong positive correlations, suggesting that increased solar radiation contributed to higher temperatures. Conversely, regions like Seosan and Mokpo showed lower temperature increases due to factors such as reduced cloud cover. To evaluate the impact on solar energy production, simulations were conducted using climate data from both years. The results revealed that relying solely on historical data for solar energy predictions could lead to overestimations in some areas, including Seosan or Jinju, and underestimations in others such as Busan. Hence, considering long-term climate variability is vital for accurate solar energy forecasting and ensuring the economic feasibility of solar projects.

• International Journal of Advanced Culture Technology
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• v.5 no.4
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• pp.57-62
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• 2017

In this paper, we applied long short term memory(LSTM) for classifying political polarity in cyber public sphere. The data collected from the cyber public sphere is transformed into word corpus data through word embedding. Based on this word corpus data, we train recurrent neural network (RNN) which is connected by LSTM's. Softmax function is applied at the output of the RNN. We conducted our proposed system to obtain experimental results, and we will enhance our proposed system by refining LSTM in our system.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.675-683
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• 2015

In the 21st century, we now face the serious problems of the enormous consumption of the energy resources. Depending on the power consumption increases, both China and South Korea face a reduction in available resources. This paper considers the regression models and time-series models to compare the performance of the forecasting accuracy based on Mean Absolute Percentage Error (MAPE) in order to forecast the electricity demand accurately on the short-term period (68 months) data in Northeast China and find the relationship with Korea. Among the models the support vector regression (SVR) model shows superior performance than time-series models for the short-term period data and the time-series models show similar results with the SVR model when we use long-term period data.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.495-497
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• 2000

The KEPCO is developing the load forecasting sysetm using land use simulation method and distribution planning system. Distribution planning needs the data of presents loads, forecasted loads sub-statin, and distribution lines. Using the data, determine the sub-station and feeder lines according to the load forecasting data. This paper presents the method of formulation processfor the long term load forecasting and optimal distribution planning and optimal distribution planning. And describes the case study of long term distribution planning of Kwangju city accord to the newly applied method.