From upscaling techniques to hybrid models

2012 ◽  
pp. 57-82
Keyword(s):  
Hybrid Models

Combination of End-to-End and Hybrid Models for Speech Recognition

2020 ◽  
Author(s):  
Jeremy H.M. Wong ◽  
Yashesh Gaur ◽  
Rui Zhao ◽  
Liang Lu ◽  
Eric Sun ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Hybrid Models ◽  
End To End

Intercomparison of downscaling methods for daily precipitation with emphasis on wavelet-based hybrid models

2021 ◽  
pp. 126373
Author(s):  
Yeditha Pavan Kumar ◽  
Rathinasamy Maheswaran ◽  
Ankit Agarwal ◽  
Bellie Sivakumar
Keyword(s):  
Daily Precipitation ◽  
Hybrid Models

Spatial Prediction of Landslides Along National Highway-6, Hoa Binh Province, Vietnam Using Novel Hybrid Models

2021 ◽  
pp. 1-22
Author(s):  
Ha Thi Hang ◽  
Hoang Tung ◽  
Pham Duy Hoa ◽  
Nguyen Viet Phuong ◽  
Tran Van Phong ◽  
...  
Keyword(s):  
Spatial Prediction ◽  
Hybrid Models ◽  
National Highway ◽  
Hoa Binh

scholarly journals Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1794
Author(s):  
Eduardo Ramos-Pérez ◽  
Pablo J. Alonso-González ◽  
José Javier Núñez-Velázquez
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Risk Measures ◽  
Hybrid Models ◽  
Stock Volatility ◽  
Management Actions ◽  
Equity Risk ◽  
Hedging Strategies ◽  
Volatility Models

Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells.

Novel hybrid models for the design of upflow anaerobic filters separated in phases

2021 ◽  
Author(s):  
Julio Isaac Maldonado Maldonado ◽  
Adriana Mercedes Márquez Romance ◽  
Edilberto Guevara Pérez ◽  
Sergio Alejandro Pérez Pacheco ◽  
Demetrio José Rey Lago
Keyword(s):  
Hybrid Models

scholarly journals Hybrid Scour Depth Prediction Equations for Reliable Design of Bridge Piers

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2019
Author(s):  
Hossein Hamidifar ◽  
Faezeh Zanganeh-Inaloo ◽  
Iacopo Carnacina
Keyword(s):  
Flow Velocity ◽  
Critical Velocity ◽  
Depth Estimation ◽  
Hybrid Models ◽  
Velocity Estimation ◽  
Bridge Piers ◽  
Critical Flow ◽  
Scour Depth ◽  
Critical Flow Velocity ◽  
Estimation Equations

Numerous models have been proposed in the past to predict the maximum scour depth around bridge piers. These studies have all focused on the different parameters that could affect the maximum scour depth and the model accuracy. One of the main parameters individuated is the critical velocity of the approaching flow. The present study aimed at investigating the effect of different equations to determine the critical flow velocity on the accuracy of models for estimating the maximum scour depth around bridge piers. Here, 10 scour depth estimation equations, which include the critical flow velocity as one of the influencing parameters, and 8 critical velocity estimation equations were examined, for a total combination of 80 hybrid models. In addition, a sensitivity analysis of the selected scour depth equations to the critical velocity was investigated. The results of the selected models were compared with experimental data, and the best hybrid models were identified using statistical indicators. The accuracy of the best models, including YJAF-VRAD, YJAF-VARN, and YJAI-VRAD models, was also evaluated using field data available in the literature. Finally, correction factors were implied to the selected models to increase their accuracy in predicting the maximum scour depth.

scholarly journals Hybrid Models for Efficient Control, Optimization, and Monitoring of Thermo-Chemical Processes and Plants

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 515
Author(s):  
Thomas Freudenmann ◽  
Hans-Joachim Gehrmann ◽  
Krasimir Aleksandrov ◽  
Mohanad El-Haji ◽  
Dieter Stapf
Keyword(s):  
Artificial Intelligence ◽  
Expert Knowledge ◽  
Numerical Models ◽  
Integrated Control ◽  
Nox Reduction ◽  
Hybrid Models ◽  
Control Optimization ◽  
Pulverized Fuel ◽  
Renewable Energy Law ◽  
Institute Of Technology

This paper describes a procedure and an IT product that combine numerical models, expert knowledge, and data-based models through artificial intelligence (AI)-based hybrid models to enable the integrated control, optimization, and monitoring of processes and plants. The working principle of the hybrid model is demonstrated by NOx reduction through guided oscillating combustion at the pulverized fuel boiler pilot incineration plant at the Institute for Technical Chemistry, Karlsruhe Institute of Technology. The presented example refers to coal firing, but the approach can be easily applied to any other type of nitrogen-containing solid fuel. The need for a reduction in operation and maintenance costs for biomass-fired plants is huge, especially in the frame of emission reductions and, in the case of Germany, the potential loss of funding as a result of the Renewable Energy Law (Erneuerbare-Energien-Gesetz) for plants older than 20 years. Other social aspects, such as the departure of experienced personnel may be another reason for the increasing demand for data mining and the use of artificial intelligence (AI).

Sign in / Sign up

Export Citation Format

Share Document