A Power Load Prediction by LSTM Model Based on the Double Attention Mechanism for Hospital Building

This work proposed a LSTM(long short-term memory)model based on the double attention mechanism for power load prediction,to further improve the energy-saving potential and accurately control the distribution of power load into each department of the hospital.Firstly,the key influencing factors of the power loads were screened based on the grey relational degree analysis.Secondly,in view of the characteristics of the power loads affected by various factors and time series changes,the feature attention mechanism and sequential attention mechanism were introduced on the basis of LSTM network.The former was used to analyze the relationship between the historical information and input variables autonomously to extract important features,and the latter was used to select the historical information at critical moments of LSTM network to improve the stability of long-term prediction effects.In the end,the experimental results from the power loads of Shanxi Eye Hospital show that the LSTM model based on the double attention mechanism has the higher forecasting accuracy and stability than the conventional LSTM,CNN-LSTM and attention-LSTM models.

Double attention U-Net for brain tumor MR image segmentation

Purpose-Automatic segmentation of brain tumor from medical images is a challenging task because of tumor’s uneven and irregular shapes.In this paper,the authors propose an attention-based nested segmentation network,named DAU-Net.In total,two types of attention mechanisms are introduced to make the U-Net network focus on the key feature regions.The proposed network has a deep supervised encoder-decoder architecture and a redesigned dense skip connection.DAU-Net introduces an attention mechanism between convolutional blocks so that the features extracted at different levels can be merged with a task-related selection.Design/methodology/approach-In the coding layer,the authors designed a channel attention module.It marks the importance of each feature graph in the segmentation task.In the decoding layer,the authors designed a spatial attention module.It marks the importance of different regional features.And by fusing features at different scales in the same coding layer,the network can fully extract the detailed information of the original image and learn more tumor boundary information.Findings-To verify the effectiveness of the DAU-Net,experiments were carried out on the BRATS2018 brain tumor magnetic resonance imaging(MRI)database.The segmentation results show that the proposed method has a high accuracy,with a Dice similarity coefficient(DSC)of 89%in the complete tumor,which is an improvement of 8.04 and 4.02%,compared with fully convolutional network(FCN)and U-Net,respectively.Originality/value-The experimental results show that the proposed method has good performance in the segmentation of brain tumors.The proposed method has potential clinical applicability.