The market for tramp shipping is an industry characterized by volatility that is determined by the principle of perfect competition according to supply and demand. The factors affecting the supply and demand of the market are very diverse, and the mechanism of action is complicated, so forecasting in the shipping market remains a difficult task. To solve this problem, time series analysis has been conducted for a long time. However, there have been some limitations in the time series analysis which did not reflect various indicators. For this reason, most of the previous research has been stayed at an initial stage of research by comparing the prediction accuracy of two approaches : the time series analysis and the deep learning method. There has not been much research on how to improve the model performance. The purpose of this study is to improve the Baltic Dry Index(BDI) prediction performance by using Long Short Term Memory(LSTM) which is one of the deep neural networks and to consider additional variables that influence on the previous research. †
Induction furnaces are widely used for melting scrapped steel in small foundries and their use has recently become more frequent. The maintenance of induction furnaces is usually based on empirical decisions of the operator and an explosion can occur through operator error. To prevent an explosion, previous studies have utilized statistical models but have been unable to generalize the problem and have achieved a low accuracy. Herein, we propose a data-driven method for induction furnaces by proposing a novel 2D matrix called a sequential feature matrix(s-encoder) and multi-channel convolutional long short-term memory (s-ConLSTM). First, the sensor data and operation data are converted into sequential feature matrices. Then, N-sequential feature matrices are imported into the convolutional LSTM model to predict the residual life of the induction furnace wall. Based on our experimental results, our method outperforms general neural network models and enhances the safe use of induction furnaces.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.