Multiple-Sensor Weigh-in-Motion of Road Vehicles using a Neural Network Algorithm

2009 ◽  
Author(s):  
A. Shamseldin ◽  
B.J. Black ◽  
E. O'Brien
Keyword(s):  
Neural Network ◽  
Road Vehicles ◽  
Network Algorithm ◽  
Weigh In Motion ◽  
Multiple Sensor ◽  
Neural Network Algorithm

Evaluation of an Artificial Neural Network Technique Applied to Multiple-Sensor Weigh-in-Motion Systems

2003 ◽  
Vol 1855 (1) ◽  
pp. 151-159 ◽  
Author(s):  
Arturo González ◽  
A. Thomas Papagiannakis ◽  
Eugene J. O’Brien
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Algorithm ◽  
Weigh In Motion ◽  
Road Profile ◽  
Multiple Sensor ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Algorithm

Weigh-in-motion (WIM) accuracy in measuring static axle loads is affected by vehicle dynamics and noise. Neural networks can identify underlying relationships, such as the spatial repeatability in axle dynamics, and can efficiently remove noise. Furthermore, they can adapt to changing circumstances (e.g., traffic characteristics, road profile, or sensor failure), unlike conventional WIM calibration algorithms. The paper performance of a multilayer feed-forward artificial neural network algorithm applied to a multiple-sensor WIM is analyzed. Numerical simulations of the axle forces applied on a smooth road profile are used to train, validate, and test the artificial neural network algorithm. This dynamic axle load variation is predicted with the vehicle simulation model VESYM. The mechanical parameters of the truck models and their speeds are randomly varied over a range established from real traffic measurements. Once the theoretical WIM data are obtained at the sensor locations, the measurements are artificially corrupted with noise up to the typical level of WIM accuracy. Details are given on the process of the neural network design, the size of the training sample, and the length of the training period. The artificial neural networks approach resulted in higher accuracy than the traditional average-based calibration method, especially at high noise levels. As a result, it shows promise for estimating static axle loads from multiple WIM measurements.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

A fuzzy logic neural network algorithm for compressor crankshaft-rolling bearing system optimization

2021 ◽  
pp. 1-7
Author(s):  
Zheng Zhang ◽  
Jianrong Zheng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dynamic Analysis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
System Optimization ◽  
Simulation Software ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bearing System

Taking the crankshaft-rolling bearing system in a certain type of compressor as the research objective, dynamic analysis software is used to conduct detailed dynamic analysis and optimal design under the rated power of the compressor. Using Hertz mathematical formula and the analysis method of the superstatic orientation problem, the relationship expression between the bearing force and deformation of the rolling bearing is solved, and the dynamic analysis model of the elastic crankshaft-rolling bearing system is constructed in the simulation software ADAMS. The weighted average amplitude of the center of the neck between the main bearings is used as the target, and the center line of the compressor cylinder is selected as the design variable. Finally, an example analysis shows that by introducing the fuzzy logic neural network algorithm into the compressor crankshaft-rolling bearing system design, the optimal solution between the design variables and the objective function can be obtained, which is of great significance to the subsequent compressor dynamic design.

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