• Journal of the Korean Society for Railway
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• v.15 no.4
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• pp.323-328
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• 2012

The purpose of this study is to develop automated structural design and analysis aided-program of aluminum extrusion carbody structures for railway vehicle. This developed program is called "AUTO-RAP" and could perform simultaneously structural design and verification for railway carbody structures made of aluminum extrusion independent of expertise and experience of design engineers. Design engineers are able to conduct the knowledge-based design by providing database of existing aluminum extrusion or user-defined function. The design verification is automatically programmed to evaluate its structural integrity according to Korean Railway Safety Law or Urban Transit Safety Law. In addition, this program could automatically generate an executable file of various commercial finite element programs such as ANSYS and ABAQUS and CAD files such as .stp and .iges by GUI environment applications using MFC(Microsoft Foundation Classes). In conclusion, it is expected to contribute to reduce product design cost and time of carbody structures aluminum extrusions in railway industry.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.