Electric load forecasting has always been a key component of power grids. Many countries have opened up electricity markets and facilitated the participation of multiple agents, which create a competitive environment and reduce costs to consumers. In the electricity market, multi-step short-term load forecasting becomes increasingly significant for electricity market bidding and spot price calculation, but the performances of traditional algorithms are not robust and unacceptable enough. In recent years, the rise of deep learning gives us the opportunity to improve the accuracy of multi-step forecasting further. In this paper, we propose a novel model multi-scale convolutional neural network with time-cognition (TCMS-CNN). At first, a deep convolutional neural network model based on multi-scale convolutions (MS-CNN) extracts different level features that are fused into our network. In addition, we design an innovative time coding strategy called the periodic coding strengthening the ability of the sequential model for time cognition effectively. At last, we integrate MS-CNN and periodic coding into the proposed TCMS-CNN model with an end-to-end training and inference process. With ablation experiments, the MS-CNN and periodic coding methods had better performances obviously than the most popular methods at present. Specifically, for 48-step point load forecasting, the TCMS-CNN had been improved by 34.73%, 14.22%, and 19.05% on MAPE than the state-of-the-art methods recursive multi-step LSTM (RM-LSTM), direct multi-step MS-CNN (DM-MS-CNN), and the direct multi-step GCNN (DM-GCNN), respectively. For 48-step probabilistic load forecasting, the TCMS-CNN had been improved by 3.54% and 6.77% on average pinball score than the DM-MS-CNN and the DM-GCNN. These results show a great promising potential applied in practice. INDEX TERMS Short-term load forecasting, probabilistic load forecasting, multi-step, multi-scale convolution, time cognition, deep learning.

Maxillary sinus segmentation plays an important role in the choice of therapeutic strategies for nasal disease and treatment monitoring. Difficulties in traditional approaches deal with extremely heterogeneous intensity caused by lesions, abnormal anatomy structures, and blurring boundaries of cavity. 2D and 3D deep convolutional neural networks have grown popular in medical image segmentation due to utilization of large labeled datasets to learn discriminative features. However, for 3D segmentation in medical images, 2D networks are not competent in extracting more significant spacial features, and 3D ones suffer from unbearable burden of computation, which results in great challenges to maxillary sinus segmentation. In this paper, we propose a deep neural network with an end-to-end manner to generalize a fully automatic 3D segmentation. At first, our proposed model serves a symmetrical encoder-decoder architecture for multitask of bounding box estimation and in-region 3D segmentation, which cannot reduce excessive computation requirements but eliminate false positives remarkably, promoting 3D segmentation applied in 3D convolutional neural networks. In addition, an overestimation strategy is presented to avoid overfitting phenomena in conventional multitask networks. Meanwhile, we introduce residual dense blocks to increase the depth of the proposed network and attention excitation mechanism to improve the performance of bounding box estimation, both of which bring little influence to computation cost. Especially, the structure of multilevel feature fusion in the pyramid network strengthens the ability of identification to global and local discriminative features in foreground and background achieving more advanced segmentation results. At last, to address problems of blurring boundary and class imbalance in medical images, a hybrid loss function is designed for multiple tasks. To illustrate the strength of our proposed model, we evaluated it against the state-of-the-art methods. Our model performed better significantly with an average Dice 0.947±0.031, VOE 10.23±5.29, and ASD 2.86±2.11, respectively, which denotes a promising technique with strong robust in practice.

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