• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.295-302
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• 2022

The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.839-849
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• 2023

The development of automation technology to reduce human error by minimizing human intervention is accelerating with artificial intelligence and big data processing technology, even in the nuclear field. Among nuclear power plant operation modes, the startup and shutdown operations are still performed manually and thus have the potential for human error. As part of the development of an autonomous operation system for startup operation, this paper proposes an action coordinating strategy to obtain the optimal actions. The lower level of the system consists of operating blocks that are created by analyzing the operation tasks to achieve local goals through soft actor-critic algorithms. However, when multiple agents try to perform conflicting actions, a method is needed to coordinate them, and for this, an action coordination strategy was developed in this work as the upper level of the system. Three quantification methods were compared and evaluated based on the future plant state predicted by plant parameter prediction models using long short-term memory networks. Results confirmed that the optimal action to satisfy the limiting conditions for operation can be selected by coordinating the action sets. It is expected that this methodology can be generalized through future research.

• The Journal of Bigdata
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• v.7 no.2
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• pp.217-224
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• 2022

Since online shopping has become common, people can easily buy fashion goods anytime, anywhere. Therefore, consumers quickly respond to various environmental variables such as weather and sales prices. Therefore, utilizing big data for efficient inventory management has become very important in the fashion industry. In this paper, the changes in sales volume of fashion goods due to changes in temperature is analyzed via the proposed big data analysis algorithm by utilizing actual big data from Korean fashion company 'A'. According to the simulation results, it was confirmed that Bidirectional-LSTM(Bi-LSTM) compared to LSTM(Long Short-Term Memory) takes more simulation time about more than 50%, but the prediction accuracy of non-periodic time series data such as clothing product sales data is the same.

• 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 of Transportation
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• v.17 no.1
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• pp.141-157
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• 1999

This paper first reviews current practices of traveler information providing and provides some perspectives regarding the possible near term milestones in traveler information providing. Then, reviews of four types of prediction models: 1) dynamic traffic assignment (DTA) model; 2) statistical model; 3) simulation model; and 4) heuristic model are described in the sense that various prediction models are needed to support providing predictive traveler information in the near future. Next, the functional requirements and capabilities of the four types of prediction models are discussed and summarized along with some advantages and disadvantages of these models with reference to short-term travel time prediction. Furthermore, a comprehensive prediction procedure, which combines the four types of prediction models, is presented, together with the data requirements for each type of prediction model.

• Journal of the Korean earth science society
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• v.38 no.1
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• pp.11-23
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• 2017

The short term predictability of wind resources is an important factor in evaluating the economic feasibility of a wind power plant. As a method of improving the predictability, a Bayesian Kalman filter is applied as the model data postprocessing. At this time, a statistical training period is needed to evaluate the correlation between estimated model and observation data for several Kalman training periods. This study was quantitatively analyzes for the prediction characteristics according to different training periods. The prediction of the temperature and wind speed with 3-day short term Bayesian Kalman training at Taebaek area is more reasonable than that in applying the other training periods. In contrast, it may produce a good prediction result in Ieodo when applying the training period for more than six days. The prediction performance of a Bayesian Kalman filter is clearly improved in the case in which the Weather Research Forecast (WRF) model prediction performance is poor. On the other hand, the performance improvement of the WRF prediction is weak at the accurate point.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.9
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• pp.108-116
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• 2009

In this paper, we develop an ELM(Extreme Learning Machine) based short-tenn water demand prediction algorithm which solves overfitting problem of MLP(Multi Layer Perceptron) and has quick training time. To show effectiveness of proposed method, we analyzed time series data collected in A water treatment plant at Chung-Nam province during $2007{\sim}2008$ years and used the selected data for the verification of developed algorithm. According to the experimental results, MLP model showed 5.82[%], but the proposed ELM based model showed 5.61[%] with respect to MAPE, respectively. Also, MLP model needed 7.57s training time, but ELM based model was 0.09s. Therefore, the proposed ELM based short-term water demand prediction model can be used to operate the water treatment plant effectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.101-107
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• 2013

Is generated in real time in the real world, a large amount of time series data from a wide range of business areas. But it is not easy to determine the optimal model for the description and understanding of the time series data is represented as a dynamic feature. In this study, through the HMM suitable for estimating the short and long-term forecasting model of time-series data to estimate a model that can explain the characteristics of these time series data, it was estimated to predict future patterns of movement. The actual stock market through various materials, information criterion and optimal model estimation for the length of the most efficient data was found to accurately estimate the state of the model. Similar movement patterns predictive than the long-term prediction is more similar to the short-term prediction of the experimental result were found to be.

• Journal of Advanced Navigation Technology
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• v.26 no.2
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• pp.119-124
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• 2022

The international gnss service (IGS) provides real-time service (RTS) orbit and clock correction applicable to the broadcast ephemeris of GNSS satellites. However, since the RTS correction cannot be received if the Internet connection is lost, the RTS correction should be predicted and used when a signal interruption occurs in order to perform stable precise point positioning (PPP). In this paper, PPP was performed by predicting orbit and clock correction using a long short-term memory (LSTM) algorithm in real-time during the signal loss. The prediction performance was analyzed by implementing the LSTM algorithm in RPI (raspberry pi), the processing speed of which is not high. Compared to the polynomial prediction model, LSTM showed excellent performance in long-term prediction.