• 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.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.300-308
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• 2018

Objectives: Prompt detection is a cornerstone in the control and prevention of infectious diseases. The Integrated Disease Surveillance Project of India identifies outbreaks, but it does not exactly predict outbreaks. This study was conducted to assess temporal correlation between Google Trends and Integrated Disease Surveillance Programme (IDSP) data and to determine the feasibility of using Google Trends for the prediction of outbreaks or epidemics. Methods: The Google search queries related to malaria, dengue fever, chikungunya, and enteric fever for Chandigarh union territory and Haryana state of India in 2016 were extracted and compared with presumptive form data of the IDSP. Spearman correlation and scatter plots were used to depict the statistical relationship between the two datasets. Time trend plots were constructed to assess the correlation between Google search trends and disease notification under the IDSP. Results: Temporal correlation was observed between the IDSP reporting and Google search trends. Time series analysis of the Google Trends showed strong correlation with the IDSP data with a lag of -2 to -3 weeks for chikungunya and dengue fever in Chandigarh (r > 0.80) and Haryana (r > 0.70). Malaria and enteric fever showed a lag period of -2 to -3 weeks with moderate correlation. Conclusions: Similar results were obtained when applying the results of previous studies to specific diseases, and it is considered that many other diseases should be studied at the national and sub-national levels.

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.82-89
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• 2021

The objective of this study is to propose a new Glide Path that dynamically adjusts the risky asset inclusion ratio of the Target Date Fund by simultaneously considering the market's forecast volatility as well as the time of investor retirement, and to compare the investment performance with the traditional Target Date Fund. Forecasts of market volatility utilize historical volatility, time series model GARCH volatility, and the volatility index VKOSPI. The investment performance of the new dynamic Glide Path, which considers stock market volatility has been shown to be excellent during the analysis period from 2003 to 2020. In all three volatility prediction models, Sharpe Ratio, an investment performance indicator, is improved with higher returns and lower risks than traditional static Glide Path, which considers only retirement date. The empirical results of this study present the potential for the utilization of the suggested Glide Path in the Target Date Fund management industry as well as retirees.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.341-346
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• 2022

Due to global warming and abnormal climate, the frequency and damage of floods are increasing, and the number of people exposed to flood-prone areas has increased by 25% compared to 2000. Floods cause huge financial and human losses, and in order to reduce the losses caused by floods, it is necessary to predict the flood in advance and decide to evacuate quickly. This paper proposes a flood risk determination model using a CNN-based classification model so that timely evacuation decisions can be made using rainfall and water level data, which are key data for flood prediction. By comparing the results of the CNN-based classification model proposed in this paper and the DNN-based classification model, it was confirmed that it showed better performance. Through this, it is considered that it can be used as an initial study to determine the risk of flooding, determine whether to evacuate, and make an evacuation decision at the optimal time.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.273-283
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• 2020

Quantitative forecasting of groundwater levels for the assessment of groundwater variation and vulnerability is very important. To achieve this purpose, various time series analysis and machine learning techniques have been used. In this study, we developed a prediction model based on LSTM (Long short term memory), one of the artificial neural network (ANN) algorithms, for predicting the daily groundwater level of 11 groundwater wells in Hankyung-myeon, Jeju Island. In general, the groundwater level in Jeju Island is highly autocorrelated with tides and reflected the effects of precipitation. In order to construct an input and output variables based on the characteristics of addressing data, the precipitation data of the corresponding period was added to the groundwater level data. The LSTM neural network was trained using the initial 365-day data showing the four seasons and the remaining data were used for verification to evaluate the fitness of the predictive model. The model was developed using Keras, a Python-based deep learning framework, and the NVIDIA CUDA architecture was implemented to enhance the learning speed. As a result of learning and verifying the groundwater level variation using the LSTM neural network, the coefficient of determination (R²) was 0.98 on average, indicating that the predictive model developed was very accurate.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.4
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• pp.217-227
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• 2007

The western cherry fruit fly, Rhagoletis indifferens Curran (Diptera:Tephritidae), is the most important pest of cultivated cherries in the Pacific Northwest area of the United States, being widely distributed throughout Oregon, Washington, Montana, Utah, Idaho, Colorado and parts of Nevada. The control of R. indifferens has been based on calendar sprays after its first emergence because of their zero tolerance for quarantine. Therefore, a good prediction model is needed for the spray timing. This study was conducted to obtain the empirical population dynamic information of R. indifferens after overwintering in the major cherry growing area of the Pacific Northwest of the United States, where the information is critically needed to develop and validate the prediction model of the fruit fly. Adult fly populations were monitored by using yellow sticky and emergence traps. Larvae growth and density in fruits were observed by fruit sampling and the pupal growth and density were monitored by pupal collection traps. The first adult was emerged around mid May and a large number of adults were caught in early June. A fruit had more than one larva from mid June to early July. A large number of pupae were caught in early July. The pupae were collected in various period of time to determine the effect of pupation timing and the soil moisture content during the winter. A series of population density data collected in each of the developmental stage were analyzed and organized to provide more reliable validation information for the population dynamic models.

• Journal of Korea Water Resources Association
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• v.39 no.3 s.164
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• pp.187-198
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• 2006

Korea Water Resources Corporation(KOWACO) has developed the Integrated Real-time Water Management System(IRWMS) that calculates monthly optimal ending target storages by using Sampling Stochastic Dynamic Programming(SSDP) with Ensemble Streamflow Prediction(ESP) running on the $1^{st}$ day of each month. This system, however, has a shortcoming: it cannot reflect the hydrolmeteorologic variations in the middle of the month. To overcome this drawback, in this study updated ESP forecasts three times each month by using the observed precipitation series from the $1^{st}$ day of the month to the forecast day and the historical precipitation ensemble for the remaining days. The improved accuracy and its effect on the reservoir operations were quantified as a result. SSDP/ESP21 that reflects within-a-month hydrolmeteorologic states saves $1\;X\;10^6\;m^3$ in water shortage on average than SSDP/ESP01. In addition, the simulation result demonstrated that the effect of ESP accuracy on the reduction of water shortage became more important when the total runoff was low during the drawdown period.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.17-26
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• 2023

In this paper, we propose a several smoothing techniques are compared and applied to increase the application of the LSTM-based learning model and its effectiveness. The applied smoothing technique is Savitky-Golay, exponential smoothing, and weighted moving average. Through this study, the LSTM algorithm with the Savitky-Golay filter applied in the preprocessing process showed significant best results in prediction performance than the result value shown when applying the LSTM model to Bitcoin data. To confirm the predictive performance results, the learning loss rate and verification loss rate according to the Savitzky-Golay LSTM model were compared with the case of LSTM used to remove complex factors from Bitcoin price prediction, and experimented with an average value of 20 times to increase its reliability. As a result, values of (3.0556, 0.00005) and (1.4659, 0.00002) could be obtained. As a result, since crypto-currencies such as Bitcoin have more volatility than stocks, noise was removed by applying the Savitzky-Golay in the data preprocessing process, and the data after preprocessing were obtained the most-significant to increase the Bitcoin prediction rate through LSTM neural network learning.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.