• Journal of Digital Convergence
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• v.18 no.12
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• pp.87-95
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• 2020

The Incheon airport is a gateway to and from the Republic of Korea and has a great influence on the image of the country. Therefore, it is necessary to predict the number of airport passengers in the long term in order to maintain the quality of service at the airport. In this study, we compared the predictive performance of various time series models to predict the air passenger demand at Incheon Airport. From 2002 to 2019, passenger data include trend and seasonality. We considered the naive method, decomposition method, exponential smoothing method, SARIMA, PROPHET. In order to compare the capacity and number of passengers at Incheon Airport in the future, the short-term, mid-term, and long-term was forecasted by time series models. For the short-term forecast, the exponential smoothing model, which weighted the recent data, was excellent, and the number of annual users in 2020 will be about 73.5 million. For the medium-term forecast, the SARIMA model considering stationarity was excellent, and the annual number of air passengers in 2022 will be around 79.8 million. The PROPHET model was excellent for long-term prediction and the annual number of passengers is expected to be about 99.0 million in 2024.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.505-511
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• 2023

In the present study, a deep learning model was established to predict the motion response of small fishing vessels. Hydrodynamic performances were evaluated for two small fishing vessels for the dataset of deep learning model. The deep learning model of the Long Short-Term Memory (LSTM) which is one of the recurrent neural network was utilized. The input data of LSTM model consisted of time series of six(6) degrees of freedom motions and wave height and the output label was selected as the time series data of six(6) degrees of freedom motions. The hyperparameter and input window length studies were performed to optimize LSTM model. The time series motion response according to different wave direction was predicted by establised LSTM. The predicted time series motion response showed good overall agreement with the analysis results. As the length of the time series increased, differences between the predicted values and analysis results were increased, which is due to the reduced influence of long-term data in the training process. The overall error of the predicted data indicated that more than 85% of the data showed an error within 10%. The established LSTM model is expected to be utilized in monitoring and alarm systems for small fishing vessels.

• Journal of Ocean Engineering and Technology
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• v.25 no.6
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• pp.110-115
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• 2011

Numerouslong-term consolidation and secondary compression settlements may occur in Busan clay, which is astructured soft clay and consists of a thick clay deposit. As a surcharge load is applied to soils, soils experience different stress paths with depth. Therefore, it is necessary to study the long-term consolidation behavior of Busan clay considering stress conditions such as OC or NC states. In this study, a series of long-term consolidation tests were performed to investigate the consolidation characteristics of Busan clay for 20 days. The undisturbed clay samples were taken from 3 sites located in the Nakdong River estuary. The results showed that the creep rate of the Busan clay gradually decreased with time, which indicated that the secondary compression settlement decreased with time. In addition, the experimental results for 3 samples showed that the ratios were about 0.0363 and 0.051, respectively.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.469-476
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• 2022

This study developed a Recurrent Neural Network model implemented through Long Short-Term Memory (LSTM) that generates long-term tidal level data at Busan Port using tide observation data. The tide levels in Busan Port were predicted by the Korea Hydrographic and Oceanographic Administration (KHOA) using the tide data observed at Busan New Port and Tongyeong as model input data. The model was trained for one month in January 2019, and subsequently, the accuracy was calculated for one year from February 2019 to January 2020. The constructed model showed the highest performance with a correlation coefficient of 0.997 and a root mean squared error of 2.69 cm when the tide time series of Busan New Port and Tongyeong were inputted together. The study's finding reveal that long-term tidal level data prediction of an arbitrary port is possible using the deep learning recurrent neural network model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1271-1279
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• 2013

In this paper, a study on the prediction method of basic wind speed at the construction site of long-span bridge using short-term measurements was conducted. To determine the basic wind speed in the wind resistant design for the long-span bridge away from the weather station, statistical analysis of long-term data at site is required. Wind observation mast was installed at site, and short-term measurements were gathered and the correlation analysis between the site and the station was done using regression analysis and MCP(Measure-Correlate-Predict). The long-term wind data of the site was obtained from correlation formula after topographical revision of long-term data of the station. And basic wind speed could be estimated by extreme probability distribution analysis. The research results show that the wind speed by regression analysis is predicted lower than by MCP and after this study a series of correlation analyses at several sites will show clearly the difference two methods. And also a quality control of long-term wind data is very important in estimation of wind speed.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.4
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• pp.413-422
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• 2005

The ozone concentration measured by HALOE (Ver 19) from Oct. 1991 to Dec. 2003 is used for analyzing the variation of ozone concentration. The HALOE loaded in UARS is observing several gases in the atmosphere, from 10km to 80km. Fourier analysis of these data in the middle latitude northern hemisphere is reported in this paper. To detect any possible long term trends, the fourier transformed time series was back transformed after removing signals with time periods of less than 6 months. Although the results clearly show the strong annual cycle, it is difficult to show any long term trends from the fourier series. We also compared the ozone volume mixing ratio's from HALOE with that from the ground-based radiometry to evaluate the accuracy of microwave observation at Sookmyung Women's University.

• Fisheries and Aquatic Sciences
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• v.2 no.2
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• pp.199-209
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• 1999

Before investigating the long-term variations in macrobenthic communities sampled in the Chokchon macrotidal flat in Inchon, Korea, from 1989 to 1996, we need to understand how environmental factors in the area vary. As potential governing agents of tidal flat communities, abiotic factors such as mean sea level, seawater, air temperature, and precipitation were considered. Data for these factors were collected at equal intervals from 1976 or 1980 to 1996, and were analyzed using a decomposition method. In this analysis, all the above variables showed strong seasonal nature, and yielded a significant trend and cyclical variation. Positive trends were seen in the seawater and air temperatures, and based upon this relationship, it was found that the biological sampling period of our program has been carried out during warmer periods in succession. This paper puts forth some hypotheses concerning the response of tidal flat macrobenthos communities to the changing environment including mild winters in succession.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.582-588
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• 2018

Accident diagnosis is one of the complex tasks for nuclear power plant (NPP) operators. In abnormal or emergency situations, the diagnostic activity of the NPP states is burdensome though necessary. Numerous computer-based methods and operator support systems have been suggested to address this problem. Among them, the recurrent neural network (RNN) has performed well at analyzing time series data. This study proposes an algorithm for accident diagnosis using long short-term memory (LSTM), which is a kind of RNN, which improves the limitation for time reflection. The algorithm consists of preprocessing, the LSTM network, and postprocessing. In the LSTM-based algorithm, preprocessed input variables are calculated to output the accident diagnosis results. The outputs are also postprocessed using softmax to determine the ranking of accident diagnosis results with probabilities. This algorithm was trained using a compact nuclear simulator for several accidents: a loss of coolant accident, a steam generator tube rupture, and a main steam line break. The trained algorithm was also tested to demonstrate the feasibility of diagnosing NPP accidents.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.290-304
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• 2018

The study proposes a discussion model for long-term care workers as a thought experiment, that strengthens speciality and presents an alternative education & training scheme for long-term care workers. First, the study unpacks the sociodemographic characteristics of license acquisitors and the employed as long-term care workers. In sequence, the study tries to present an alternative education & training scheme of the professional long-term care workers, that is comprised of a new education & training course with NCS in junior colleges for young peoples, intensification of speciality in education & training course through extension of times and deepening contents, introduction of legal refresher training, granting of roles of the NHIC as insurer in legal refresher training, introduction and legal employment of the professional tong-term care workers with career experience and speciality. At last, the study suggests a series of policy projects for realization of that alternative education & training scheme.

• Environmental and Resource Economics Review
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• v.26 no.3
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• pp.287-317
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• 2017

Asbestos has been actively used for various places. Since it was designated as the first grade carcinogen in the 1970s, strict regulations on using asbestos has been implemented globally. Considering long-term latent periods between asbestos exposure and environmental diseases, we analyze the time lag between asbestos consumption and the incidence of mesothelioma in Korea and estimate the long-run relationship. In addition, we conduct a comparative analysis on the effectiveness of asbestos regulations in the United Kingdom and the United States, which have accumulated long-term time-series observations. The latent period analysis indicates that the consumption of asbestos and the incidence of the disease are highly correlated in all three countries, being long-term lags of more than 30 years. Also, we find a long-run equilibrium relationship between asbestos consumption and the incidence of mesothelioma in the presence of long-term lags between the variables in all three countries. Furthermore, using a distributed lag model, asbestos consumption has statistically significant positive effects on mesothelioma with a long-term lag.