• Journal of Korean Society of Coastal and Ocean Engineers
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• v.7 no.4
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• pp.289-304
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• 1995

Time series of the relative sea levels at the selected tide-gauge stations in the North Pacific and historical aerial photographs in the Hawaiian Islands are analyzed. Long-term rising trend of sea level ranges from +1 to +5 mm/yr at most of the stations, which is primarily due to global warming and tectonic motion of the plates. The annual and interannual fluctuations of sea level result from the thermal expansion/contraction of sea-surface layer due to the annual change of the solar radiation and possibly from a coupled ocean-atmosphere phenomenon associated with an ENSO event, respectively. Sea-level changes in three different time-scales (linear trend. annual oscillation, and interannual fluctuation) and their quantitative contribution to the shoreline changes as a result of long-term cross-shore sediment transport arc hypothesized.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.673-680
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• 2012

Long-term GPS data analysis was performed in order to analyze the seasonal variation of tropospheric delay and the success rate of the ambiguity resolution. For this analysis, a total of 57 stations including 10 IGS stations in East Asia were processed together with double-differenced observables using Bernese GPS Software V5.0. The time span for this study ranges from 2002.0 to 2012.5 (10.5 years). The average baseline length is 339.0 km and the maximum reaches up to 2,000 km. The analysis is focused on two things: the annual variation of the tropospheric delay and the ambiguity resolution rate. The tropospheric delay is closely related to the weather condition, especially relative humidity, therefore it was estimated that the maximum would be in summer, while reaching its minimum in winter with the apparent seasonal variations. On the contrary, however, the success rate of the ambiguity resolution shows the opposite pattern: its maximum was in winter and minimum in summer. The fact seems to be induced by the surrounding conditions; that is, the trees thick with leaves near the GPS antenna interfere with GPS signals in summer. This seems to confirm partly that there is a distinct trend in the decreasing success rate since 2006 because the trees are growing every year. It is necessary to eliminate the factors that degrade the GPS quality and the tropospheric modeling for Korea needs to be studied further.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.119-130
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• 2022

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

• Geomechanics and Engineering
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• v.34 no.1
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• pp.43-49
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• 2023

Knowledge of minimum horizontal stress (S_hmin) is a significant step in determining full stress tensor. It provides crucial information for the production of sand, hydraulic fracturing, determination of safe mud weight window, reservoir production behavior, and wellbore stability. Calculating the S_hmin using indirect methods has been proved to be awkward because a lot of data are required in all of these models. Also, direct techniques such as hydraulic fracturing are costly and time-consuming. To figure these problems out, this work aims to apply the long-short-term memory (LSTM) algorithm to S_hmin time-series prediction. 13956 datasets obtained from an oil well logging operation were applied in the models. 80% of the data were used for training, and 20% of the data were used for testing. In order to achieve the maximum accuracy of the LSTM model, its hyper-parameters were optimized significantly. Through different statistical indices, the LSTM model's performance was compared with with other machine learning methods. Finally, the optimized LSTM model was recommended for S_hmin prediction in the well logging operation.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.61-68
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• 2021

The exchange rate is considered a tool improving the volume of exports and reducing imports. This paper aims to determine the impact of the exchange rate on exports and imports between Vietnam and the United States in the context of the trade war. The research uses Autoregressive Distributed Lag (ARDL) and Nonlinear Autoregressive Distributed Lag (NARDL) Model in the time-series data from 2010:1 to 2020:9. The ARDL's results support that real exchange rate impact on export and import volumes, but less than the trade war. The trade war helps trade balance increase 0.35%, while the exchange rate increases trade balance 0.191% when the Vietnamese currency devalues 1% in the long run. In the short term, the real exchange rate makes the trade balance decrease. Therefore, the J curve exists between Vietnam and the U.S. The NARDL expresses that the exchange rate is asymmetric both in the short term and the long term. The findings of this study point to two important elements. Firstly, the exchange rate plays a minor role in exports and imports. Secondly, trade war plays a vital role in increasing exports and imports volume between two countries, and the J curve exists between the two countries.

• Water for future
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• v.28 no.1
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• pp.81-90
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• 1995

This study attempts to develop a stochastic system model for extension and prediction of monthly runoff series in river basins where the observed runoff data are insufficient although there are long-term hydrometeorological records. For this purpose, univariate models of a seasonal ARIMA type are derived from the time series analysis of monthly runoff, monthly precipitation and monthly evaporation data with trend and periodicity. Also, a causual model of multiple input-single output relationship that take monthly precipitation and monthly evaporation as input variables-monthly runoff as output variable is built by the cross-correlation analysis of each series. The performance of the univariate model and the multiple input-output model were examined through comparisons between the historical and the generated monthly runoff series. The results reveals that the multiple input-output model leads to the improved accuracy and wide range of applicability when extension and prediction of monthly runoff series is required.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.305-312
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• 2014

This paper discusses real-time peak shaving algorithms for a large-scale battery energy storage system (BESS). Although several transmission and distribution functions could be implemented for diverse purposes in BESS applications, this paper focuses on a real-time peak shaving algorithm for an energy time shift, considering wind power generation. In a high wind penetration environment, the effective load levels obtained by subtracting the wind generation from the load time series at each long-term cycle time unit are needed for efficient peak shaving. However, errors can exist in the forecast load and wind generation levels, and the real-time peak shaving operation might require a method for wind generation that includes comparatively large forecasting errors. To effectively deal with the errors of wind generation forecasting, this paper proposes a real-time peak shaving algorithm for threshold value-based peak shaving that considers fuzzy wind power generation.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.987-998
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• 2013

This paper considers the statistical characteristics on the air quality (PM10) of Korea collected hourly in 2011. PM10 in Korea exhibits very strong correlations even for higher lags, namely, long range dependence. It is power-law tailed in marginal distribution, and generalized Pareto distribution successfully captures the thicker tail than log-normal distribution. However, slowly decaying autocorrelations may confuse practitioners since a non-stationary model (such as changes in mean) can produce spurious long term correlations for finite samples. We conduct a statistical testing procedure to distinguish two models and argue that the high persistency can be explained by non-stationary changes in mean model rather than long range dependent time series models.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.159-168
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• 2022

The time series of foreign trade turnover is complex and variable and contains linear and nonlinear information. This paper proposes preprocessing the dataset by the EMD algorithm and combining the linear prediction advantage of the SARIMA model with the nonlinear prediction advantage of the EMD-LSTM model to construct the SARIMA-EMD-LSTM hybrid model by the weight assignment method. The forecast performance of the single models is compared with that of the hybrid models by using MAPE and RMSE metrics. Furthermore, it is confirmed that the weight assignment approach can benefit from the hybrid models. The results show that the SARIMA model can capture the fluctuation pattern of the time series, but it cannot effectively predict the sudden drop in foreign trade turnover caused by special reasons and has the lowest accuracy in long-term forecasting. The EMD-LSTM model successfully resolves the hysteresis phenomenon and has the highest forecast accuracy of all models, with a MAPE of 7.4304%. Therefore, it can be effectively used to forecast the Sino-Russia foreign trade turnover time series post-epidemic. Hybrid models cannot take advantage of SARIMA linear and LSTM nonlinear forecasting, so weight assignment is not the best method to construct hybrid models.

• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.