• Economic Analysis
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• v.26 no.3
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• pp.1-47
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• 2020

This paper surveys research since the 1990s on the ability of the yield spread and its components (i.e., expectation spread and term premium components) for future economic activity, and also conducts an empirical analysis of their forecasting ability using the yield data of Korean government bonds. This paper's survey, particularly for the US, shows that the yield spread has significant predictive power for some macroeconomic variables, but since the mid-1980s, its predictive power seems to have declined, possibly due to stronger inflation targeting. Next, this paper's empirical analysis using Korean data indicates that the yield spread, and the term premium component in particular, has significant predictive power for industrial production (IP) growth, consumer price index growth, and the IP gap. An out-of-sample analysis shows that the prediction equations are unstable over time, and that in predicting IP growth, the yield spread decomposition makes a significant contribution to the prediction of IP growth.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.105-117
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• 2022

This study analyzes the effects between stock returns and interest rate spread, difference between long-term and short-term interest rate through the polynomial linear regression analysis. The existing research concentrated on the business forecast through the interest rate spread focusing on the US market. The previous studies verified the interest rate spread based on the leading indicators of business forecast by moderating the period of long-term/short-term interest rates and analyzing the degree of leading. After the 7th reform of composite indices of business indicators in Korea of 2006, the interest rate spread was included in the items of composing the business leading indicators, which is utilized till today. Nevertheless, there are a few research on stock returns of each industry and interest rate spread in domestic stock market. Therefore, this study analyzed the stock returns of each industry and interest rate spread targeting Korean stock market. This study selected the long-term/short-term interest rates with high causality through the regression analysis, and then understood the correlations with each leading period and industry. To overcome the limitation of the simple linear regression analysis, polynomial linear regression analysis is used, which raised explanatory power. As a result, the high causality was verified when using differences between returns of corporate bond(AA-) without guarantee for three years by leading six months and call rate returns as interest rate spread. In addition, analyzing the stock returns of each industry, the relation between the relevant interest rate spread and returns of the automobile industry was the closest. This study is significant in the aspect of verifying the causality of interest rate spread, business forecast, and stock returns in Korea. Even though it could be limited to forecast the stock price by using only the interest rate spread, it would be working as a strong factor when it is properly utilized with other various factors.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.21 no.6
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• pp.480-494
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• 2009

Long term extreme wave data are essential for planning and designing coastal structures. Since the availability of the field data for the waters around Korean peninsula is limited to provide a reliable wave statistics, the wave climate information has been generated by means of long-term wave hindcasting using available meteorological data. KORDI(2005) has proposed extreme wave data at 106 stations off the Korean coast from 1979 to 2003. In this paper, extreme data sets of wave(KORDI, 2005) have been analyzed for best-fitting distribution functions, for which the spread parameter proposed by Goda(2004) is evaluated. The calculated values of the spread parameter are in good agreement with the values based on method of moment for parameter estimation. However, the spread parameter of extreme wave data has a representative value ranging from about 1.0 to 2.8 which is larger than some foreign coastal waters, it is necessary to review deep water design wave.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.147-155
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• 2012

We propose some properties for a fuzzy correlation test by reduced-spread fuzzy variance for sample fuzzy data. First, we define the condition of fuzzy data for repeatedly observed data or that which includes error term data. By using the average of spreads for fuzzy numbers, we reduce the spread of fuzzy variance and define the agreement index for the degree of acceptance and rejection. Given a non-normal random fuzzy sample, we have bivariate normal distribution by apply Box-Cox power fuzzy transformation and test the fuzzy correlation for independence between the variables provided by the agreement index.

• 한국전산유체공학회:학술대회논문집
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• 2010.05a
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• pp.109-114
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• 2010

Interface tracking of two phase is significant to analyze multi-phase phenomena. The VOF(Volume of Fluid) and level set are well known interface tracking method. However, they have limitations to solve compressible flow and incompressible flow at the same time. CIP(Cubic Interpolate Propagation) method is appropriate for considering compressible and incompressible flow at once by solving the governing equation which is divided up into advection and non-advection term. In this article, we analyze the droplet impingement according to various We number using improved CIP method which treats nonlinear term once more comparison with original CIP method. Furthermore, we compare spread radius after droplet impingement on the wall with the experimental data and original CIP original CIP method, and it reduces the mass conservation error which is generated in the numerical analysis comparison with original CIP method.

• Journal of Korean Neurosurgical Society
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• v.52 no.4
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• pp.372-376
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• 2012

Objective : The purpose of this large prospective study is to assess the association between the disappearance of the lateral spread response (LSR) before and after microvascular decompression (MVD) and clinical long term results over two years following hemifacial spasm (HFS) treatment. Methods : Continuous intra-operative monitoring during MVD was performed in 244 consecutive patients with HFS. Patients with persistent LSR after decompression (n=22, 9.0%), without LSR from the start of the surgery (n=4, 1.7%), and with re-operation (n=15, 6.1%) and follow-up loss (n=4, 1.7%) were excluded. For the statistical analysis, patients were categorized into two groups according to the disappearance of their LSR before or after MVD. Results : Intra-operatively, the LSR was checked during facial electromyogram monitoring in 199 (81.5%) of the 244 patients. The mean follow-up duration was $40.9{\pm}6.9$ months (range 25-51 months) in all the patients. Among them, the LSR disappeared after the decompression (Group A) in 128 (64.3%) patients; but in the remaining 71 (35.6%) patients, the LSR disappeared before the decompression (Group B). In the post-operative follow-up visits over more than one year, there were significant differences between the clinical outcomes of the two groups (p<0.05). Conclusion : It was observed that the long-term clinical outcomes of the intra-operative LSR disappearance before and after MVD were correlated. Thus, this factor may be considered a prognostic factor of HFS after MVD.

• Journal of Environmental Health Sciences
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• v.46 no.5
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• pp.599-609
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• 2020

Objectives: Coronavirus disease 2019 (COVID-19) first emerged in December 2019 in Wuhan, China, and has rapidly become a global pandemic with over 26.4 million confirmed cases and approximately 871,000 fatalities worldwide as of this writing. In the Republic of Korea, disease clusters frequently occurred in long-term care hospitals where the majority of residents are elderly with underlying medical conditions. Despite the fact that public health authorities and local community health centers have put tremendous efforts into preventing the spread of disease, positive cases have continued to occur. Thus, the Korea Centers for Disease Control & Prevention rapid response team decided to conduct an environmental investigation of a long-term care hospital to identify whether environmental contamination has remained and contributed to the spread of COVID-19. Methods: An environmental investigation was conducted at Hospital A. The characteristics of the facility and its HVAC system were assessed by checking the layout and interviewing the people in charge. A total of 64 surface samples were collected from areas of concern, including patient rooms, toilets, elevators, and nurses' station. These samples were tested by a regional health and environmental research institute using real-time reverse transcription polymerase chain reaction. Results: All samples from Hospital A were confirmed to be negative. Through interviews with high-level personnel at the regional community health center, we found that extensive disinfection is frequently performed on potentially contaminated areas in Hospital A in accordance with government guidelines. Conclusion: The environmental control measures implemented in Hospital A had been sufficient for mitigating the risk of further infection, suggesting that such measures may also be effective for other long-term health care facilities.

• The Journal of Bigdata
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• v.5 no.1
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• pp.1-9
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• 2020

The study was conducted with funding from the government (Ministry of Agriculture, Food and Rural Affairs) in 2018 with support from the Agricultural, Food, and Rural Affairs Agency, 318069-03-HD040, and in based on artificial intelligence-based HPAI spread analysis and patterning. The model that is actively used in time series and text mining recently is LSTM (Long Short-Term Memory Models) model utilizing deep learning model structure. The LSTM model is a model that emerged to resolve the Long-Term Dependency Problem that occurs during the Backpropagation Through Time (BPTT) process of RNN. LSTM models have resolved the problem of forecasting very well using variable sequence data, and are still widely used.In this paper study, we used the data of the Call Detailed Record (CDR) provided by KT to identify the migration path of people who are expected to be closely related to the virus. Introduce the results of predicting the path of movement by learning the LSTM model using the path of the person concerned. The results of this study could be used to predict the route of HPAI propagation and to select routes or areas to focus on quarantine and to reduce HPAI spread.

• Journal of Korean Academy of Rural Health Nursing
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• v.18 no.1
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• pp.27-39
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• 2023

Purpose: This study is a mixed methods research that was conducted to verify factors affecting the working experience of community-based long-term care hospital workers during the COVID-19 pandemic. Methods: The study was carried out from July 19 to November 3, 2021 for 340 nurses who worked at 10 long-term care hospitals located in G city. Results: As the study results, factors that affected job stress of the workers working at community-based long-term care hospitals included job satisfaction (β=-.27, p<.001), work demand (β=-.25, p<.001), fatigue (β=.19, p=.001), and cooperation and leadership (β=-.12, p=.049). It was found that the participants were struggling with physical and mental stress caused by the increased workload due to the preventative measures taken to stop the infection and spread of COVID-19. Despite this, they accepted their situation as necessary to overcome the pandemic and shared the quarantine guidelines of the government and community health centers while actively responding to prevent the spread of COVID-19 under the leadership of their supervisors. However, they were experiencing psychological and emotional burnout in the prolonged pandemic situation. Conclusion: It is considered necessary to help relieve their stress and provide psychological and mental support by adopting a policy to develop and apply comprehensive programs.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.22-28
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• 2021

This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.