Search

Predicting electrical power consumption is essential in many industries. This paper presents a method to predict total electrical power consumption in a certain period of Hot Rolling Mill (HRM) in steel mills. We adequately pre-processed operation data, including work schedule, work result, and power measurement. In addition, based on domain knowledge, we derived several useful variables relevant to power consumption. To predict power consumption, we developed a Multi-Level Stacked Regression model that combines the strengths of multiple prediction models. Real-world applications have dataset shift characteristics in which the covariance between features or distribution of the features changes over time due to frequent changes in the working environment. To verify the generalization accuracy of our model, we trained and tested our model for various training/test splits and compared it with several comparative models. Our model showed the best accuracy in all training/test splits. We verified whether the predictive model reflects the real-world environment well by analyzing the actual physical meaning of the essential features used in the predictive model. • A series of pre-processing methods are applied to real data from Hot Rolling Mill. • A Multi-Level Stacked Regression algorithm is proposed. • Performance on various training/test splits that contains dataset shifts is verified. [ABSTRACT FROM AUTHOR]

• A weakly supervised power line detection algorithm is proposed. • A line specialized refinement algorithm is applied for performance improvement. • A repeated learning framework with noise label develops the model output. Detection of power lines in aerial images is an important problem to prevent accidents of unmanned aerial vehicles operating at low altitudes in the electrical industry. Recently, pixel-level power line detection using deep learning has been studied but production of the pixel-level annotations for massive dataset is difficult. In this study, we propose a power line detection algorithm using weakly supervised learning method to reduce the labeling cost for dataset generation. The algorithm is divided into two stages. First, an approximately localized mask was generated based on a convolutional neural network which was trained with only patch-level labels. Second, recursive training of segmentation network with refined broken line segments was executed. A refinement algorithm, line segment connecting (LSC) is a power-line-specialized refinement module that connects broken lines by approximating the segments as partially straight. In proposed algorithm, predicted image at each recursive step was updated as a label of the next training and the label was developed by itself with LSC. The comprehensive experimental results of our algorithm showed state-of-art F 1 -score of 94.3% in weakly supervised learning approaches on public dataset. This result suggests that the proposed algorithm is useful for low labeling cost with high performance in line detection application. [ABSTRACT FROM AUTHOR]