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 ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Attitude Estimation ◽  
Model Based ◽  
Electromagnetic Model

Surrogate Model based Co-Optimization of Deep Neural Network Hardware Accelerators

2021 ◽  
Author(s):  
Hendrik Wohrle ◽  
Mariela De Lucas Alvarez ◽  
Fabian Schlenke ◽  
Alexander Walsemann ◽  
Michael Karagounis ◽  
...  
Keyword(s):  
Neural Network ◽  
Surrogate Model ◽  
Deep Neural Network ◽  
Hardware Accelerators ◽  
Model Based ◽  
Neural Network Hardware

scholarly journals An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

2019 ◽  
Vol 9 (7) ◽  
pp. 1487 ◽  
Author(s):  
Fei Mei ◽  
Qingliang Wu ◽  
Tian Shi ◽  
Jixiang Lu ◽  
Yi Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Load Forecasting ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Forecasting Model ◽  
Model Based ◽  
Net Load

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.

scholarly journals A Novel Stock Index Intelligent Prediction Algorithm Based on Attention-Guided Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yangzi Zhao
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Stock Index ◽  
Prediction Algorithm ◽  
Short Term ◽  
Term Memory ◽  
Model Based

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.

scholarly journals A Recommendation Model Based on Deep Neural Network

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 9454-9463 ◽  
Author(s):  
Libo Zhang ◽  
Tiejian Luo ◽  
Fei Zhang ◽  
Yanjun Wu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Model Based
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