scholarly journals A novel spatial-temporal prediction method for unsteady wake flows based on hybrid deep neural network

2019 ◽  
Vol 31 (12) ◽  
pp. 127101 ◽  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Prediction Method ◽  
Wake Flows ◽  
Temporal Prediction ◽  
Unsteady Wake

A Building Energy Consumption Prediction Method Based on Integration of a Deep Neural Network and Transfer Reinforcement Learning

2020 ◽  
Vol 34 (10) ◽  
pp. 2052005
Author(s):  
Qiming Fu ◽  
QingSong Liu ◽  
Zhen Gao ◽  
Hongjie Wu ◽  
Baochuan Fu ◽  
...  
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Energy Consumption ◽  
Deep Neural Network ◽  
Layer Structure ◽  
Prediction Method ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Seamless Integration ◽  
Common Information

With respect to the problem of the low accuracy of traditional building energy prediction methods, this paper proposes a novel prediction method for building energy consumption, which is based on the seamless integration of the deep neural network and transfer reinforcement learning (DNN-TRL). The method introduces a stack denoising autoencoder to extract the deep features of the building energy consumption, and shares the hidden layer structure to transfer the common information between different building energy consumption problems. The output of the DNN model is used as the input of the Sarsa algorithm to improve the prediction performance of the target building energy consumption. To verify the performance of the DNN-TRL algorithm, based on the data recorded by American Power Balti Gas and Electric Power Company, and compared with Sarsa, ADE-BPNN, and BP-Adaboost algorithms, the experimental results show that the DNN-TRL algorithm can effectively improve the prediction accuracy of the building energy consumption.

scholarly journals Prediction Method of College Students’ Psychological Pressure Based on Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bing Wang ◽  
Sitong Liu
Keyword(s):  
Neural Network ◽  
College Students ◽  
Deep Neural Network ◽  
Prediction Method ◽  
Theory Model ◽  
Network Information ◽  
Physical Network ◽  
Gray Theory ◽  
Psychological Pressure ◽  
Current Psychological

Aiming at the problems of low prediction accuracy and efficiency and poor prediction effect in the current psychological pressure prediction methods, a psychological pressure prediction method for college students based on deep neural network is proposed. The structure and algorithm of depth neural network and gray theory model are analyzed. Using the deep neural network, this paper establishes the sample set data of college students’ psychological pressure prediction and constructs the college students’ psychological pressure prediction model combined with the deep neural network algorithm of gray theory. The physical network information model is formed through the relationship between neurons. According to the dynamic changes of college students’ psychological pressure in each neuron of the physical network, the prediction of college students’ psychological pressure is completed. The experimental results show that the proposed method is effective in predicting college students’ psychological pressure and can effectively improve the accuracy and efficiency of college students’ psychological pressure prediction.

Hybrid deep neural network based prediction method for unsteady flows with moving boundary

2021 ◽  
Author(s):  
Ren-Kun Han ◽  
Zhong Zhang ◽  
Yi-Xing Wang ◽  
Zi-Yang Liu ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Moving Boundary ◽  
Unsteady Flows ◽  
Prediction Method

scholarly journals An Adaptive Offloading Method for an IoT-Cloud Converged Virtual Machine System Using a Hybrid Deep Neural Network

2018 ◽  
Vol 10 (11) ◽  
pp. 3955 ◽  
Author(s):  
Yunsik Son ◽  
Junho Jeong ◽  
YangSun Lee
Keyword(s):  
Neural Network ◽  
Virtual Machine ◽  
Deep Neural Network ◽  
Estimation Method ◽  
Prediction Method ◽  
Context Information ◽  
Local Environments ◽  
Low Performance ◽  
Iot Devices ◽  
Synchronization Structure

A virtual machine with a conventional offloading scheme transmits and receives all context information to maintain program consistency during communication between local environments and the cloud server environment. Most overhead costs incurred during offloading are proportional to the size of the context information transmitted over the network. Therefore, the existing context information synchronization structure transmits context information that is not required for job execution when offloading, which increases the overhead costs of transmitting context information in low-performance Internet-of-Things (IoT) devices. In addition, the optimal offloading point should be determined by checking the server’s CPU usage and network quality. In this study, we propose a context management method and estimation method for CPU load using a hybrid deep neural network on a cloud-based offloading service that extracts contexts that require synchronization through static profiling and estimation. The proposed adaptive offloading method reduces network communication overheads and determines the optimal offloading time for low-computing-powered IoT devices and variable server performance. Using experiments, we verify that the proposed learning-based prediction method effectively estimates the CPU load model for IoT devices and can adaptively apply offloading according to the load of the server.

scholarly journals Tunnel Geology Prediction Using a Neural Network Based on Instrumented Drilling Test

2020 ◽  
Vol 11 (1) ◽  
pp. 217
Author(s):  
Yuwei Fang ◽  
Zhenjun Wu ◽  
Qian Sheng ◽  
Hua Tang ◽  
Dongcai Liang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Predictive Accuracy ◽  
Confusion Matrix ◽  
Prediction Method ◽  
Neural Network Models ◽  
Tunnel Face ◽  
The Neural Network ◽  
Geological Conditions ◽  
Tunnel Design

Reliable geology prediction is of great importance in ensuring the stability and safety of tunnels and other underground engineering projects. This paper presents basic neural network and deep neural network models using a genetic algorithm (GA) to predict geological conditions for tunneling. Batch normalization and GA optimization approaches are employed in the deep neural network. A case study of the Jiudingshan Tunnel on the Chuxiong–Dali Highway in Yunnan, China, shows that the neural network method can predict geological conditions well, especially for rock types with voluminous data, for which predictive accuracy exceeds 90%. These results suggest that an appropriately trained neural network can reliably and accurately predict the geological conditions behind the tunnel face. The area under the curve (AUC) and confusion matrix evaluations show that the accuracy performance of the deep neural network exceeds that of the basic neural network. The feature importance of each drilling parameter was also analyzed; the results indicate that a neural network model for geology prediction can achieve predictive accuracy with few drilling parameters. The neural network geology prediction method provides reliable results for dynamic tunnel design.

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