The prediction method by neural network and The estimation method by fractal dimension (Stock price's prediction).

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.

Download Full-text

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

Proc. Indon. Petrol. Assoc., Digital Technical Conference, 2020 ◽

10.29118/ipa20-g-307 ◽

2020 ◽

Author(s):

A. Syahputra

Keyword(s):

Neural Network ◽

Water Saturation ◽

Prediction Method ◽

Neural Model ◽

Training Model ◽

Acquisition Time ◽

Oil Saturation ◽

Full Field ◽

Remaining Oil ◽

Observation Wells

Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

Download Full-text

Performance Estimation Method on IoT-Cloud Environments Using Hybrid Deep Neural Network

The International Journal of Internet of Things and its Applications ◽

10.21742/ijiota.2018.2.2.01 ◽

2018 ◽

Vol 2 (2) ◽

pp. 1-6

Author(s):

Yunsik Son ◽

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Estimation Method ◽

Performance Estimation ◽

Cloud Environments

Download Full-text

Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽

10.3390/en13051094 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1094 ◽

Cited By ~ 3

Author(s):

Lanjun Wan ◽

Hongyang Li ◽

Yiwei Chen ◽

Changyun Li

Keyword(s):

Neural Network ◽

Working Conditions ◽

Bp Neural Network ◽

Evidence Theory ◽

Prediction Method ◽

Rolling Bearing ◽

Fault Prediction ◽

Fault Classification ◽

Bearing Fault ◽

Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

Download Full-text

Research on Substation Project Prediction Method in Power Transmission and Transformation by Improved Neural Network Intelligent Model

Journal of Physics Conference Series ◽

10.1088/1742-6596/1952/3/032052 ◽

2021 ◽

Vol 1952 (3) ◽

pp. 032052

Author(s):

Li Ma ◽

Lanfei He ◽

Xuefei Zhang ◽

Wei Wang ◽

Liping Sun ◽

...

Keyword(s):

Neural Network ◽

Power Transmission ◽

Prediction Method ◽

Intelligent Model

Download Full-text

The Monitoring of Factory Electrical System in Collapse Using Neural Network Prediction Method

2020 8th International Conference on Condition Monitoring and Diagnosis (CMD) ◽

10.1109/cmd48350.2020.9287204 ◽

2020 ◽

Author(s):

Chongrag Boonseng ◽

Nannam Nilnimitr ◽

Kunyanuth Kularbphettong

Keyword(s):

Neural Network ◽

Prediction Method ◽

Electrical System ◽

Neural Network Prediction ◽

Network Prediction

Download Full-text

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119837236 ◽

2019 ◽

Vol 2673 (4) ◽

pp. 108-116 ◽

Cited By ~ 1

Author(s):

Mostafa H. Tawfeek ◽

Karim El-Basyouny

Keyword(s):

Neural Network ◽

Linear Regression ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Safety Management ◽

Estimation Method ◽

Estimation Model ◽

The Neural Network ◽

Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

Download Full-text

Financial market trend analysis based on autoregressive conditional heteroscedasticity model and BP neural network prediction

International Journal of Electrical Engineering Education ◽

10.1177/0020720920988497 ◽

2021 ◽

pp. 002072092098849

Author(s):

Xinzhe Yin ◽

Jinghua Li

Keyword(s):

Neural Network ◽

Trend Analysis ◽

Financial Market ◽

Prediction Method ◽

Good Effect ◽

Trend Prediction ◽

Bp Network ◽

Conditional Heteroscedasticity ◽

Bp Model ◽

Autoregressive Conditional Heteroscedasticity

Many experts and scholars at home and abroad have studied this topic in depth, laying a solid foundation for the research of financial market prediction. At present, the mainstream prediction method is to use neural network and autoregressive conditional heteroscedasticity to build models, which is a more scientific way, and also verified the feasibility of the way in many studies. In order to improve the accuracy of financial market trend prediction, this paper studies in detail the neural network system represented by BP and the autoregressive conditional heterogeneous variance model represented by GARCH. Analyze its structure and algorithm, combine the advantages of both, create a GARCH-BP model, and transform its combination structure and optimize the algorithm according to the uniqueness of the financial market, so as to meet the market as much as possible Characteristics. The novelty of this paper is the construction of the autoregressive conditional heteroscedasticity model, which lays the foundation for the prediction of financial market trends through the construction of the model. However, there are some shortcomings in this article. The overall overview of the financial market is not very clear, and the prediction of the BP network is not so comprehensive. Finally, through the actual data statistics of market transactions, the effectiveness of the GARCH-BP model was tested, analyzed and researched. The final results show that model has a good effect on the prediction and trend analysis of market, and its accuracy and availability greatly improved compared with the previous conventional approach, which is worth further study and extensive research It is believed that the financial market prediction model will become one of the mainstream tools in the industry after its later improvement.

Download Full-text