scholarly journals Calculation of Effective Structural Number Using Simple Neural Network for Some Road Links in Indonesia

2018 ◽  
Vol 14 (2) ◽  
pp. 45
Author(s):  
Siegfried Syafier
Keyword(s):  
Neural Network ◽  
System Analysis ◽  
Falling Weight Deflectometer ◽  
Acceptable Error ◽  
Pavement Maintenance ◽  
The Neural Network ◽  
Network Method ◽  
Simple Neural Network ◽  
Falling Weight ◽  
Layer Composition

In the pavement maintenance system, the parameter of effective structural number (SNeff) would be a considered factor in deciding whether a road link would be repaired or not. To calculate this parameter, it is required the testing of Falling Weight Deflectometer (FWD) and information of layer composition and thicknesses. The combination of these information and using the method of AASHTO’93, it can be calculated the SNeff. These two information generally would be gained through the testings of core drill and test pit which would take time and cost. To overcome these problems, the neural network method or precisely the artificial neural network is developed for analysis of pavement structure. From the analysis, it can be said that the neural network of single perceptron can be used for predicting the SNeff with an acceptable error. In general the value of SNeff obtained from neural network calculation is lower than that of AASHTO’93. In this paper it is also recommended to develop the neural network using multi layer perceptron for the use on pavement system analysis that might be decreasing the error.

Time Series Analysis of Slope Displacement Using Neural Network Method

2013 ◽  
Vol 405-408 ◽  
pp. 129-132
Author(s):  
Zhi Qiang Zhang ◽  
Yan Liang Wen ◽  
Guo Jian Zhang ◽  
Lai Shan Chang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Theory ◽  
System Analysis ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Network Method ◽  
Neural Network System ◽  
Neural Network Theory ◽  
Slope Displacement

Based on the artificial neural network theory, a neural network approach is proposed for the analysis of slope displacement time series, the neural network system analysis of slope displacement time series is developed, it is proved that this method is scientific and reasonable.

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

DIPLOBACK: Neural-Network-Based Backcalculation Program for Composite Pavements

1997 ◽  
Vol 1570 (1) ◽  
pp. 143-150 ◽  
Author(s):  
Lev Khazanovich ◽  
Jeffery Roesler
Keyword(s):  
Neural Network ◽  
Winkler Foundation ◽  
Multilayer Composite ◽  
Falling Weight Deflectometer ◽  
Subgrade Reaction ◽  
Elastic Parameters ◽  
The Neural Networks ◽  
Elastic Layers ◽  
Falling Weight ◽  
Moduli Of Elasticity

A neural-network-based backcalculation procedure is developed for multilayer composite pavement systems. The constructed layers are modeled as compressible elastic layers, whereas the subgrade is modeled as a Winkler foundation. The neural networks are trained to find moduli of elasticity of the constructed layers and a coefficient of subgrade reaction to accurately match a measured deflection profile. The method was verified by theoretically generated deflection profiles and falling weight deflectometer data measurements conducted at Edmonton Municipal Airport, Canada. For the theoretical deflection basins, the results of backcalculation were compared with actual elastic parameters, and excellent agreement was observed. The results of backcalculation using field test data were compared with the results obtained using WESDEF. Similar trends were observed for elastic parameters of all the pavement layers. The backcalculation procedure is implemented in a computer program called DIPLOBACK.

scholarly journals Comparative analysis of the geometrized histogram method and the neural network method for road markings recognition

2021 ◽  
pp. 1-22
Author(s):  
Aleksei Valerievich Podoprosvetov ◽  
Dmitry Anatolevich Anokhin ◽  
Konstantin Ivanovich Kiy ◽  
Igor Aleksandrovich Orlov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Comparative Analysis ◽  
Computational Complexity ◽  
Video Sequences ◽  
Histogram Method ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Accuracy Speed

This paper compares two approaches to determining road markings from video sequences, namely, the method of finding the markings using geometrized histograms and the method based on neural networks. An independent open dataset TuSimple is used to conduct a comparative analysis of the algorithms. Since the investigated methods have different architectures, their work is evaluated according to the following metrics: Accuracy, speed (relative FPS), general computational complexity of the algorithm (TFlops).

Predicting the French Stock Market Using Social Media Analysis

2015 ◽  
Vol 7 (2) ◽  
pp. 70-84
Author(s):  
Vincent Martin ◽  
Emmanuel Bruno ◽  
Elisabeth Murisasco
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Percentage Error ◽  
Social Media Analysis ◽  
Single Output ◽  
The Neural Network ◽  
Simple Neural Network

In this article, the authors try to predict the next-day CAC40 index. They apply the idea of Johan Bollen et al. from (Bollen, Mao, & Zeng, 2011) on the French stock market and they conduct their experiment using French tweets. Two analyses are applied on tweets: sentiment analysis and subjectivity analysis. Results of these analyses are then used to train a simple neural network. The input features are the sentiment, the subjectivity and the CAC40 closing value at day-1 and day-0. The single output value is the predicted CAC40 closing value at day+1. The authors propose an architecture using the JEE framework resulting in a better scalability and an easier industrialization. The main experiments are conducted over 5 months of data. The authors train their neural network on the first of the data and they test predictions on the remaining quarter. Their best run gives a direction accuracy of 80% and a mean absolute percentage error (MAPE) of 2.97%. In another experiment, the authors retrain the neural network each day which decreases the MAPE to 1.14%.

scholarly journals Neural Network Application for Netted Radar Information Processing Systems

2013 ◽  
Vol 347-350 ◽  
pp. 2156-2159
Author(s):  
Jian Hu ◽  
Fan Jun Hu
Keyword(s):  
Neural Network ◽  
Information Processing ◽  
Simulation Method ◽  
Tracking Systems ◽  
Radar Tracking ◽  
Radar Systems ◽  
The Neural Network ◽  
Network Method ◽  
Network Application ◽  
Radar Information

This paper discusses the neural network application for the information processing in the netted radar tracking systems compared with the problems of the conventional radar information processing. And then test the neural network using simulation method. The simulation result shows that the neural network method can perfectly solve the target tracking problems in the netted radar systems.

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