3-D electromagnetic-model-based absolute attitude estimation using a deep neural network

2021 ◽  
Vol 12 (10) ◽  
pp. 1015-1024
Author(s):  
Xiaoliang Yang ◽  
Weiping Ni ◽  
Weidong Yan ◽  
Hui Bian ◽  
Han Zhang ◽  
...  
Author(s):  
Hendrik Wohrle ◽  
Mariela De Lucas Alvarez ◽  
Fabian Schlenke ◽  
Alexander Walsemann ◽  
Michael Karagounis ◽  
...  

2019 ◽  
Vol 9 (7) ◽  
pp. 1487 ◽  
Author(s):  
Fei Mei ◽  
Qingliang Wu ◽  
Tian Shi ◽  
Jixiang Lu ◽  
Yi Pan ◽  
...  

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yangzi Zhao

The stock market is affected by economic market, policy, and other factors, and its internal change law is extremely complex. With the rapid development of the stock market and the expansion of the scale of investors, the stock market has produced a large number of transaction data, which makes it more difficult to obtain valuable information. Because deep neural network is good at dealing with the prediction problems with large amount of data and complex nonlinear mapping relationship, this paper proposes an attention-guided deep neural network stock prediction algorithm. This paper synthesizes the daily stock social media text emotion index and stock technology index as the data source and applies them to the long-term and short-term memory neural network (LSTM) model to predict the stock market. The stock emotion index is extracted by constructing a social text classification emotion model of bidirectional long-term and short-term memory neural network (Bi-LSTM) based on attention mechanism and glove word vector representation algorithm. In addition, a dimensionality reduction model based on decision tree (DT) and principal component analysis (PCA) is constructed to reduce the dimensionality of stock technical indicators and extract the main data information. Furthermore, this paper proposes a model based on nasNet for pattern recognition. The recognition results can be used to automatically identify short-term K-line patterns, predict reliable trading signals, and help investors customize short-term high-efficiency investment strategies. The experimental results show that the prediction accuracy of the proposed algorithm can reach 98.6%, which has high application value.


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 9454-9463 ◽  
Author(s):  
Libo Zhang ◽  
Tiejian Luo ◽  
Fei Zhang ◽  
Yanjun Wu

Sign in / Sign up

Export Citation Format

Share Document