3D scene geometry estimation method of substation inspection robot based on lightweight neural network

2021 ◽  
Author(s):  
Hong Yu ◽  
Feng Shen
Keyword(s):  
Neural Network ◽  
Estimation Method ◽  
Inspection Robot ◽  
Geometry Estimation ◽  
3D Scene

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

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
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.

scholarly journals A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7559
Author(s):  
Lisha Li ◽  
Shuming Yuan ◽  
Yue Teng ◽  
Jing Shao
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Influencing Factors ◽  
Carbon Emission ◽  
Estimation Method ◽  
Civil Aviation ◽  
Accurate Estimation ◽  
Low Carbon ◽  
Fuel Price ◽  
Aircraft Fuel

Though the development of China’s civil aviation and the improvement of control ability have strengthened the safety operation and support ability effectively, the airlines are under the pressure of operation costs due to the increase of aircraft fuel price. With the development of optimization controlling methods in flight management systems, it becomes increasingly challenging to cut down flight fuel consumption by control the flight status of the aircraft. Therefore, the airlines both at home and abroad mainly rely on the accurate estimation of aircraft fuel to reduce fuel consumption, and further reduce its carbon emission. The airlines have to take various potential factors into consideration and load more fuel to cope with possible negative situation during the flight. Therefore, the fuel for emergency use is called PBCF (Performance-Based Contingency Fuel). The existing PBCF forecasting method used by China Airlines is not accurate, which fails to take into account various influencing factors. This paper aims to find a method that could predict PBCF more accurately than the existing methods for China Airlines.This paper takes China Eastern Airlines as an example. The experimental data of flight fuel of China Eastern Airlines Co, Ltd. were collected to find out the relevant parameters affecting the fuel consumption, which is followed by the establishment of the LSTM neural network through the parameters and collected data. Finally, through the established neural network model, the PBCF addition required by the airline with different influencing factors is output. It can be seen from the results that the all the four models are available for the accurate prediction of fuel consumption. The amount of data of A319 is much larger than that of A320 and A330, which leads to higher accuracy of the model trained by A319. The study contributes to the calculation methods in the fuel-saving project, and helps the practitioners to learn about a particular fuel calculation method. The study brought insights for practitioners to achieve the goal of low carbon emission and further contributed to their progress towards circular economy.

Mechanism of neural network training for forecasting the meteorological situation when using GIS

2021 ◽  
pp. 22-29
Author(s):  
M.R. Vagizov ◽  
◽  
E.P. Istomin ◽  
O.N. Kolbina ◽  
A.S. Kochnev ◽  
...  
Keyword(s):  
Neural Network ◽  
Estimation Method ◽  
Stochastic Method ◽  
Neural Network Training ◽  
Forecast Method ◽  
Network Training ◽  
Meteorological Situation ◽  
Previous Sample ◽  
Data Processor ◽  
Using Data

This article is devoted to the mechanisms of neural network training for forecasting the meteorological situation when using GIS. The structural scheme of the GIS under consideration is proposed as a project solution and the main elements allowing to implement neural networks and their training are defined. The stochastic method is chosen as a tool for neural network training as it suggests the most probable outcome of the event based on the previous sample. The article gives an example of testing neural network training as an application program «Data Processor». The results described in the article allow us to judge about the applicability of the selected neural network training method for forecasting meteorological conditions and using data in geoinformation decision-making systems. Keywords: geoinformation system, synoptic forecast method, hydrodynamic forecast method, aggregator, data processor, knowledge base, deterministic method, expert estimation method, stochastic method, neural network, sampling, probability dispersion.

scholarly journals A model-free hull deformation measurement method with time delay compensation

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881069 ◽  
Author(s):  
Ying He ◽  
Xiafu Peng ◽  
Xiaoli Zhang ◽  
Xiaoqiang Hu
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Deformation Measurement ◽  
Estimation Accuracy ◽  
Deformation Model ◽  
Delay Compensation ◽  
The Neural Network ◽  
Time Delay Compensation

Estimation and compensation for hull deformation is an indispensable step for the ship to establish a unified space attitude. The existing hull deformation measurement methods are dependent on the pre-established deformation model, and an inaccurate deformation model will reduce the deformation estimation accuracy. To solve this problem, a hull deformation estimation method without deformation model is proposed in this article, which utilizes the neural network to fit the hull deformation. To train the neural network online, connection weights of the neural network are regarded as system state variables which can be estimated by the Unscented Kalman Filter. Simultaneously, considering the time delay problem of inertial data, a time delay compensation method based on the quaternion attitude matrix is proposed. The simulation results show that the proposed method can obtain high estimation accuracy without any deformation model even when the inertial data are asynchronous.

Sign in / Sign up

Export Citation Format

Share Document