Objective: For monthly electricity consumption forecasting, a multi-time-scale transformation and temporal attention neural network for a temporal convolutional network is proposed. Methods: First, a multi-time-scale compression model of temporal convolutional network is proposed, which compresses data on...
To overcome these limitations and therefore enhance the spatial and temporal features extraction for action recognition, we propose a novel Spatial Attention-Enhanced Multi-Timescale Graph Convolutional Network (SA-MTGCN) for skeleton-based action recognition. Specifically, as the relation of non-adjacent...
Multi-graph attention networkWe present a stock index prediction model based on a multi-time scale learning (MTSL) and the multi-graph attention network (MGAT) approach. Instead of dealing with individual stock markets, we consider a group of stock markets simultaneously and exploit the effects ...
In the past decades, clean and renewable energy has gained increasing attention due to a global effort on carbon footprint reduction. In particular, Saudi ... Wang, Kesen,Kim, Minwoo,Castruccio, Stefano,... 被引量: 0发表: 2024年 加载更多来源...
[24] may not be suitable as it accounts for only the Sun's height which alone is not adequate. Hence, an accurate clear sky model taking into account the other important factors such as the Sun's direction and the angle of incidence of solar radiation deserves research attention. Similarly...
However, open issues include the linear assumptions of partial cross-correlation analysis20, as well as testing for significance, should also be paid special attention. In this section, we will discuss how to test the significance and finally summarize the steps of how to use the new methods ...
具体来说,我们将深度卷积自动编码器(Deep Convolutional Autoencoder,DCAE)作特征提取模块,将基于注意力的双向长短期记忆模型(Attention-based BidirectionalLSTM)[5]和自回归模型(Autoregressive,AR)用作预测模块。通过同时最小化重构误差和预测误差,可以共同优化 CAE-M模型。同时,使用了最大均值差异(Maximum Mean...
Because the iterative learning control (ILC) includes dynamic behavior along time and batch direction, which is similarity to the characteristic of batch processes, it has attracted more and more attention. There are many results [27], [28], [29], [30], [31], [32] on 2D control of ...
In the literature [25], the decomposition of electricity sequences into trend, seasonal, and residual information was input to the Bi-GRU model with the addition of multi-headed attention, which effectively converted the global and fine-grained features of the electricity consumption records, and ...
The automated classification of respiratory sound has gained increasing attention in recent years and has been the subject of a growing number of international scientific challenges for the development of accurate classification algorithms to support clinical practice. The COVID-19 pandemic has highlighted...