scholarly journals Tire–Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2027 ◽  
Author(s):  
Zhixin Jia ◽  
Kaiya Fu ◽  
Mengxiang Lin
Keyword(s):  
Neural Network ◽  
Estimation Error ◽  
Backpropagation Neural Network ◽  
Moving Vehicle ◽  
Multiple Sensors ◽  
Public Road ◽  
Weigh In Motion ◽  
Normal Speed ◽  
Gross Vehicle Weight ◽  
Multiple Sensor

Accurately estimating the weight of a moving vehicle at normal speed remains a challenging problem due to the complex vehicle dynamics and vehicle–pavement interaction. The weighing technique based on multiple sensors has proven to be an effective approach to this task. To improve the accuracy of weigh-in-motion (WIM) systems, this paper proposes a neural network-based method integrating identification and predication. A backpropagation neural network for signal classification (BPNN-i) was designed to identify ideal samples acquired by load sensors closest to the tire-pavement contact area. After that, ideal samples were used to predict the gross vehicle weight by using another backpropagation neural network (BPNN-e). The dataset for training and evaluation was collected from a multiple-sensor WIM (MS-WIM) system deployed in a public road. In our experiments, 96.89% of samples in the test set had an estimation error of less than 5%.

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.

scholarly journals Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2998
Author(s):  
Xinyong Zhang ◽  
Liwei Sun
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Bayesian Regularization ◽  
Backpropagation Neural Network ◽  
Swarm Optimization ◽  
Modal Frequency ◽  
Supporting Structure ◽  
First Order ◽  
Machine Structure

Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.

scholarly journals Convolutional Neural Network for Crowd Counting on Metro Platforms

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 703
Author(s):  
Jun Zhang ◽  
Jiaze Liu ◽  
Zhizhong Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Estimation Error ◽  
Image Features ◽  
Urban Rail Transit ◽  
Crowd Counting ◽  
Passenger Flow ◽  
Urban Rail ◽  
Density Map ◽  
Flow Detection

Owing to the increased use of urban rail transit, the flow of passengers on metro platforms tends to increase sharply during peak periods. Monitoring passenger flow in such areas is important for security-related reasons. In this paper, in order to solve the problem of metro platform passenger flow detection, we propose a CNN (convolutional neural network)-based network called the MP (metro platform)-CNN to accurately count people on metro platforms. The proposed method is composed of three major components: a group of convolutional neural networks is used on the front end to extract image features, a multiscale feature extraction module is used to enhance multiscale features, and transposed convolution is used for upsampling to generate a high-quality density map. Currently, existing crowd-counting datasets do not adequately cover all of the challenging situations considered in this study. Therefore, we collected images from surveillance videos of a metro platform to form a dataset containing 627 images, with 9243 annotated heads. The results of the extensive experiments showed that our method performed well on the self-built dataset and the estimation error was minimum. Moreover, the proposed method could compete with other methods on four standard crowd-counting datasets.

scholarly journals A Novel Neuron in Kernel Domain

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zahra Khandan ◽  
Hadi Sadoghi Yazdi
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Mean Square ◽  
Least Mean Square ◽  
Backpropagation Neural Network ◽  
Adaptive Parameters ◽  
Kernel Parameter ◽  
Online Learning Algorithms ◽  
Adaptive Kernel ◽  
Kernel Least Mean Square

Kernel-based neural network (KNN) is proposed as a neuron that is applicable in online learning with adaptive parameters. This neuron with adaptive kernel parameter can classify data accurately instead of using a multilayer error backpropagation neural network. The proposed method, whose heart is kernel least-mean-square, can reduce memory requirement with sparsification technique, and the kernel can adaptively spread. Our experiments will reveal that this method is much faster and more accurate than previous online learning algorithms.

Virtual Monitoring of orthotropic steel deck using bridge weigh-in-motion algorithm: Case study

2018 ◽  
Vol 18 (2) ◽  
pp. 610-620 ◽  
Author(s):  
Longwei Zhang ◽  
Hua Zhao ◽  
Eugene J OBrien ◽  
Xudong Shao
Keyword(s):  
Field Tests ◽  
Life Assessment ◽  
Dynamic Strain ◽  
Orthotropic Steel Deck ◽  
Remaining Service Life ◽  
Fatigue Life Assessment ◽  
Weigh In Motion ◽  
Gross Vehicle Weight ◽  
Bridge Weigh In Motion ◽  
Steel Deck

This article outlines a Virtual Monitoring approach for fatigue life assessment of orthotropic steel deck bridges. Bridge weigh-in-motion was used to calculate traffic loads which were then used to calculate “virtual” strains. Some of these strains were checked through long-term monitoring of dynamic strain data. Field tests, incorporating calibration with pre-weighed trucks and monitoring the response to regular traffic, were conducted at Fochen Bridge, which has an orthotropic steel deck and is located in Foshan City, China. In the calibration tests, a 45-t 3-axle truck ran repeatedly across Lane 2, the middle lane in a 3-lane carriageway. The results show that using an influence surface to weigh vehicles can improve the accuracy of the weights and, by inference, of remaining service life calculations. The most fatigue-prone position was found to be at the cutout in the diaphragms. Results show that many vehicles are overweight—the maximum gross vehicle weight recorded was 148 t, nearly 3.6 times heavier than the fatigue design truck.

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