• 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.

• Steel and Composite Structures
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• v.30 no.6
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• pp.535-550
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• 2019

Prestressed concrete (PC) bridges using corrugated steel webbing have emerged as one of the most promising forms of steel-concrete composite bridge. However, their long-term behavior is not well understood, especially in the case of large-span bridges. In order to study the time-dependent performance, a large three-span PC bridge with corrugated steel webbing was compared to a similar conventional PC bridge to examine their respective time-dependent characteristics. In addition, a three-dimensional finite element method with step-by-step time integration that takes into account cantilever construction procedures was used to predict long-term behaviors such as deflection, stress distribution and prestressing loss. These predictions were based upon four well-established empirical creep prediction models. PC bridges with a corrugated steel web were observed to have a better long-term performance relative to conventional PC bridges. In particular, it is noted that the pre-cambering for PC bridges with a corrugated steel web could be smaller than that of conventional PC bridges. The ratio of side-to-mid span has great influence on the long-term deformation of PC bridges with a corrugated steel web, and it is suggested that the design value should be between 0.4 and 0.6. However, the different creep prediction models still showed a weak homogeneity, thus, the further experimental research and the development of health monitoring systems are required to further progress our understanding of the long-term behavior of PC bridges with corrugated steel webbing.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.495-500
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• 2001

After the completion of an apartment housing project, deterioration of building materials will commence over time. Building maintenance consists of short-time and long-term repair projects. A long-term repair plan for organized maintenance management should be developed and implemented to maintain the longevity of the building. When forming such a plan, one should carefully predict when each part of the building will need repairs and how often subsequent repairs of each part will occur. The plan should be based on these predictions. Many apartment buildings in Korea have poor long-term maintenance management plans. This is because many of these plans are developed as mere legal formalities rather than as serious attempts to maintain the longevity of buildings. In this study, the materials used in the construction of each part of an apartment building we selected are taken into account to predict when the repairs will be required and how frequently subsequent repairs will be required. Furthermore, We suggest a long-term maintenance management plan for elongation so that apartment house managers can use to periodically check, diagnose, and replace old or malfunctioning parts using a web-based maintenance calender.

• International Journal of Concrete Structures and Materials
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• v.7 no.3
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• pp.183-191
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• 2013

Prestress losses assumed for bridge girder design and deflection analyses are dependent on the concrete modulus of elasticity (MOE). Most design specifications, such as the American Association of State Highways and Transportation Officials (AASHTO) bridge specifications, contain a constant value for the MOE based on the unit weight of concrete and the concrete compressive strength at 28 days. It has been shown in the past that that the concrete MOE varies with the age of concrete. The purpose of this study was to evaluate the effect of a time-dependent and variable MOE on the prestress losses assumed for bridge girder design. For this purpose, three different variable MOE models from the literature were investigated: Dischinger (Der Bauingenieur 47/48(20):563-572, 1939a; Der Bauingenieur 5/6(20):53-63, 1939b; Der Bauingenieur, 21/22(20):286-437, 1939c), American Concrete Institute (ACI) 209 (Tech. Rep. ACI 209R-92, 1992) and CEB-FIP (CEB-FIP Model Code, 2010). A typical bridge layout for the Dallas, Texas, USA, area was assumed herein. A prestressed concrete beam design and analysis program from the Texas Department of Transportation (TxDOT) was utilized to determine the prestress losses. The values of the time dependent MOE and also specific prestress losses from each model were compared. The MOE predictions based on the ACI and the CEB-FIP models were close to each other; in long-term, they approach the constant AASHTO value. Dischinger's model provides for higher MOE values. The elastic shortening and the long term losses from the variable MOE models are lower than that using a constant MOE up to deck casting time. In long term, the variable MOE-based losses approach that from the constant MOE predictions. The Dischinger model would result in more conservative girder design while the ACI and the CEB-FIP models would result in designs more consistent with the AASHTO approach.

• 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.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• Journal of Soil and Groundwater Environment
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• v.21 no.6
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• pp.67-79
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• 2016

Gaussian process regression (GPR) is proposed as a tool of long-term groundwater quality predictions. The major advantage of GPR is that both prediction and the prediction related uncertainty are provided simultaneously. To demonstrate the applicability of the proposed tool, GPR and a conventional non-parametric trend analysis tool are comparatively applied to synthetic examples. From the application, it has been found that GPR shows better performance compared to the conventional method, especially when the groundwater quality data shows typical non-linear trend. The GPR model is further employed to the long-term groundwater quality predictions based on the data from two domestically operated groundwater monitoring stations. From the applications, it has been shown that the model can make reasonable predictions for the majority of the linear trend cases with a few exceptions of severely non-Gaussian data. Furthermore, for the data shows non-linear trend, GPR with mean of second order equation is successfully applied.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.5
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• pp.60-67
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• 2003

For risk or reliability assessment of ship structures against particular hazardous situations such as total loss or sinking due to hull girder collapse, the short-term based response analysis rather than the long-term response analysis is required to determine wave-induced bending moments when the ship encounters a storm of specific duration and with a specified small encounter probability. In the present study, the effects of operational condition and sea states on wave-induced bending moments of large merchant vessels are investigated. A series of the short-term response analyses for a hypothetical VLCC and a Capesize bulk carrier (CSBC) are carried out with varying operational condition and sea states which include ship speed, significant wave height and wave persistence time, using the linear-strip theory based program ABS/SHIPMOTION and the MIT sea-keeping tables. The computed results are also compared with the IACS design formula predictions. The results and insights developed from the present study are summarized.

• Atmosphere
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• v.29 no.5
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• pp.671-687
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• 2019

This study evaluates the long-term seasonal predictability of summer (June, July and August) heatwaves over South Korea using 30-year (1989~2018) Hindcast data of the Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. Heatwave indices such as Number of Heatwave days (HWD), Heatwave Intensity (HWI) and Heatwave Warning (HWW) are used to explore the long-term seasonal predictability of heatwaves. The prediction skills for HWD, HWI, and HWW are evaluated in terms of the Temporal Correlation Coefficient (TCC), Root Mean Square Error (RMSE) and Skill Scores such as Heidke Skill Score (HSS) and Hit Rate (HR). The spatial distributions of daily maximum temperature simulated by WRF are similar overall to those simulated by NCEP-R2 and PNU CGCM. The WRF tends to underestimate the daily maximum temperature than observation because the lateral boundary condition of WRF is PNU CGCM. According to TCC, RMSE and Skill Score, the predictability of daily maximum temperature is higher in the predictions that start from the February and April initial condition. However, the PNU CGCM-WRF chain tends to overestimate HWD, HWI and HWW compared to observations. The TCCs for heatwave indices range from 0.02 to 0.31. The RMSE, HR and HSS values are in the range of 7.73 to 8.73, 0.01 to 0.09 and 0.34 to 0.39, respectively. In general, the prediction skill of the PNU CGCM-WRF chain for heatwave indices is highest in the predictions that start from the February and April initial condition and is lower in the predictions that start from January and March. According to TCC, RMSE and Skill Score, the predictability is more influenced by lead time than by the effects of topography and/or terrain feature because both HSS and HR varies in different leads over the whole region of South Korea.