• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.915-929
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• 2023

Improvements in people's quality of life, diversification of leisure activities, and changes in population structure have led to an increase in the demand for tourism and an expansion of the diversification of tourism activities. In particular, for coastal cities where land and marine tourism elements coexist, various factors influence their tourism demands. Tourism requires the construction of infrastructure and content development according to the demand at the tourist destination. This study aims to improve the prediction accuracy and explore influencing factors through time series analysis of tourism scale using agent-based data. Basic local governments in the Busan area were examined, and the data used were the number of tourists and the amount of tourism consumption on a monthly basis. The univariate time series analysis, which is a deterministic model, was used along with the SARIMAX analysis to identify the influencing factor. The tourism consumption propensity, focusing on the consumption amount according to business types and the amount of mentions on SNS, was set as the influencing factor. The difference in accuracy (RMSE standard) between the time series models that did and did not consider COVID-19 was found to be very wide, ranging from 1.8 times to 32.7 times by region. Additionally, considering the influencing factor, the tourism consumption business type and SNS trends were found to significantly impact the number of tourists and the amount of tourism consumption. Therefore, to predict future demand, external influences as well as the tourists' consumption tendencies and interests in terms of local tourism must be considered. This study aimed to predict future tourism demand in a coastal city such as Busan and identify factors affecting tourism scale, thereby contributing to policy decision-making to prepare tourism demand in consideration of government tourism policies and tourism trends.

• Ocean and Polar Research
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• v.25 no.1
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• pp.63-74
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• 2003

The build-up of the heat field in shallow coastal water due to a point source has been investigated using an analytical solution of a time-integral form derived by extending the solutions by Holley(1969) and also presented in Harleman (1971). The uniform water depth is assumed with non-isotropic turbulent dispersion. The alongshore-flow is assumed to be uni-directional, spatially uniform and oscillatory. Due to the presence of the oscillatory alongshore-flow, the heat build-up occurs in an oscillatory manner, and the excess temperature thereby fluctuates in that course and even in the quasi-steady state. A series of calculations reveal that proper choices of the decay coefficient as well as dispersion coefficients are critical to the reliable prediction of the excess temperature field. The dispersion coefficients determine the absolute values of the excess temperature and characterize the shoreline profile, particularly within the tidal excursion distance, while the decay coefficient determines the absolute value of the excess temperature and the convergence rate to that of the quasi-steady state. Within the e-folding time scale $1/k_d$ (where $k_d$ is the heat decay coefficient), heat build-up occurs more than 90% of the quasi-steady state values in a region within a tidal excursion distance (L), while occurs increasingly less the farther we go to the downstream direction (about 80% at 1.25L, and 70% at 1.5L). Calculations with onshore and offshore discharges indicate that thermal spreading in the direction of the shoreline is reduced as the shoreline constraint which controls the lateral mixing is reduced. The importance of collecting long-term records of in situ meteorological conditions and clarifying the definition of the heat loss coefficient is addressed. Interactive use of analytical and numerical modeling is recommended as a desirable way to obtain a reliable estimate of the far-field excess temperature along with extensive field measurements.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.123-132
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• 2016

We can achieve the principle of parsimony and efficiency if homogeneity for panel time series model is satisfied. We suggest a Rao test statistic and a Wald test statistic for the test of homogeneity for panel AR(1) and derived the limit distribution. We performed a simulation to examine statistics with the same chisquare distribution when number of the individual is small and in common with large. We also simulated to compare the empirical power of the statistics in a small panel. In application, we fit panel AR(1) model using regional monthly economical active population data and test homogeneity for panel AR(1). It is satisfied homogeneity, so it could be fitted AR(1) using the sample mean at the time point. We also compare the power of prediction between each individual and pooled model.

• Journal of Environmental Science International
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• v.19 no.1
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• pp.115-124
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• 2010

Urban watershed can be found in the visible changes in technology, the most realistic satellite images is to use the data. Satellite image data on the indicators for progress on the nature of the change of land use is consistent and repetitive information, regular observation makes possible the detailed analysis of space-time. These remote sensing techniques and the type of course and, by using the time series history, the past, the dynamic model and the randomized prediction methodology for the conversion process if the city and river basin cooperation of the space changes effectively will be able to extrapolate. For each of the main changes in river flow, depending on the area of urbanization as determined according to reproduce the duration of the relationship between the urbanization of the area and runoff can be represented as a linear polynomial expression was, if a linear expression in the two fast slew rate of 0.858 to 0.861 showed up, and fast slew rate of 0.934 to 0.974 for the polynomial are reported. Change of land use changes in the watershed of the flow is one of the most affecting elements. Therefore, changes in land use of the correct classification of rivers is a more accurate calculation of the amount of the floodgate. In particular, using the Landsat images through the image of the land use category, land use past data and calculated using the Markov Chain model and predict the future land use plan in the water control project will be used for large likely.

• International Journal of High-Rise Buildings
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• v.3 no.4
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• pp.255-271
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• 2014

A method of smart system identification of super high-rise buildings is proposed in which super high-rise buildings are modeled by a shear-bending system. The method is aimed at finding the story shear and bending stiffnesses of a specific story only from the horizontal floor accelerations. The proposed method uses a set of closed-form expressions for the story shear and bending stiffnesses in terms of the limited floor accelerations and utilizes a reduced shear-bending system with the same number of elements as the observation points. A difficulty of prediction of an unstable specific function in a low frequency range can be overcome by introducing an ARX model and discussing its relation with the Taylor series expansion coefficients of a transfer function. It is demonstrated that the shear-bending system can simulate the vibration records with a reasonable accuracy. It is also shown that the vibration records at two super high-rise buildings during the 2011 Tohoku (Japan) earthquake can be simulated with the proposed method including a technique of inserting degrees of freedom between the vibration recording points. Finally it is discussed further that the time-varying identification of fundamental natural period and stiffnesses can be conducted by setting an appropriate duration of evaluation in the batch least-squares method.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.377-383
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• 2018

Internet of Things (IoT) is producing various data as the smart environment comes. The IoT data collection is used as important data to judge systems's status. Therefore, it is important to monitor the anomaly state of the sensor in real-time and to detect anomaly data. However, it is necessary to convert the IoT data into a normalized data structure for anomaly detection because of the variety of data structures and protocols. Thus, we can expect a good quality effect such as accurate analysis data quality and service quality. In this paper, we propose an anomaly detection system based on big data from collected sensor data. The proposed system is applied to ensure anomaly detection and keep data quality. In addition, we applied the machine learning model of support vector machine using anomaly detection based on time-series data. As a result, machine learning using preprocessed data was able to accurately detect and predict anomaly.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.223-228
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• 2020

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.

• Nuclear Engineering and Technology
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• v.52 no.1
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• pp.37-50
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• 2020

A passive containment cooling system (PCCS) has been developed as advanced safety feature for innovative power reactor (iPOWER). Passive systems are inherently less stable than active systems and the PCCS encountered the flashing-induced instability previously identified. The objective of this study is to develop stability maps for flashing-induced instability using MARS (Multi-dimensional Analysis of Reactor Safety) code. Firstly, we conducted a series of sensitivity analysis to see the effects of time step size, nodalization, and alternative MARS user options on the onset of flashing-induced instability. The riser nodalization strongly affects the prediction of flashing in a long riser of the PCCS, while time step size and alternative user options do not. Based on the sensitivity analysis, a standard input and an analysis methodology were set up to develop the stability maps of PCCS. We found out that the calculated equilibrium quality at the exit of the riser as a stability boundary above 5 kW/㎡ was approximately 1.2%, which was in good agreement with Furuya's results. However, in case of a very low heat flux condition, the onset of instability occurred at the lower equilibrium quality. In addition, it was confirmed that inlet throttling reduces the unstable region.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1891-1900
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• 2021

Fine particulate matter (FPM; diameter ≤ 2.5 ㎛) is frequently found in metropolitan areas due to activities associated with rapid urbanization and population growth. Many adolescents spend a substantial amount of time at school where, for various reasons, FPM generated outdoors may flow into indoor areas. The aims of this study were to estimate FPM concentrations and categorize types of FPM in schools. Meteorological and chemical variables as well as satellite-based aerosol optical depth were analyzed as input data in a random forest model, which applied 10-fold cross validation and a grid-search method, to estimate school FPM concentrations, with four statistical indicators used to evaluate accuracy. Loose and strict standards were established to categorize types of FPM in schools. Under the former classification scheme, FPM in most schools was classified as type 2 or 3, whereas under strict standards, school FPM was mostly classified as type 3 or 4.

• Journal of Navigation and Port Research
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• v.45 no.6
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• pp.284-297
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• 2021

A simple kinematic model for the prediction of ship manoeuvres based on trial data is proposed in this study. The model consists of first order differential equations in surge, sway, and yaw directions which simulate the time series of each velocity component. Actually instead of sea trial data, dynamic model simulations are conducted with randomly varied control inputs such as propeller revolution rates and rudder angles. Based on learning of control inputs and velocity outputs of dynamic model simulations in sufficient time, kinematic model coefficients are optimized so that the kinematic model can be approximately reproduce the velocity outputs of dynamic model simulations with arbitrary control inputs. The resultant kinematic model is verified with new dynamic simulation sets.