The Vehicle Adjustable Suspension System Based on Data Mining

2013 ◽  
Vol 416-417 ◽  
pp. 790-795
Author(s):  
Gu Xiong Li ◽  
Kai Huang ◽  
Kan Feng Huang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Bp Neural Network ◽  
Ride Comfort ◽  
Suspension System ◽  
Experimental Results ◽  
Sensor Data ◽  
Pavement Condition ◽  
Good Outcome ◽  
Control Accuracy

One being developed vehicle adjustable suspension system, need to predict pavement condition then automatically adjust each suspension height, in order to ensure control accuracy and ride comfort. This paper proposed a method using BP neural network to predict the vehicle height sensor data of each wheel suspension. The experimental results show that, the proposed algorithm is practical and reliable, and good outcome have been achieved in the application of instruction carriage.

scholarly journals Research and Improvement of the Hydraulic Suspension System for a Heavy Hydraulic Transport Vehicle

2020 ◽  
Vol 10 (15) ◽  
pp. 5220 ◽  
Author(s):  
Jianjun Wang ◽  
Jingyi Zhao ◽  
Wenlei Li ◽  
Xing Jia ◽  
Peng Wei
Keyword(s):  
Mathematical Model ◽  
Nonlinear System ◽  
Ride Comfort ◽  
Impact Force ◽  
Suspension System ◽  
Experimental Results ◽  
Hydraulic Transport ◽  
Transport Vehicle ◽  
Set Up ◽  
The Impact

In order to ensure the ride comfort of a hydraulic transport vehicle in transportation, it is important to account for the effects of the suspension system. In this paper, an improved hydraulic suspension system based on a reasonable setting of the accumulator was proposed for a heavy hydraulic transport vehicle. The hydraulic transport vehicle was a multi-degree nonlinear system, and the establishment of an appropriate vehicle dynamical model was the basis for the improvement of the hydraulic suspension system. The hydraulic suspension system was analyzed, and a mathematical model of the hydraulic suspension system with accumulator established and then analyzed. The results revealed that installing the appropriate accumulator can absorb the impact pressure on the vehicle, while a hydraulic suspension system with an accumulator can be designed. Further, it was proved that a reasonable setting for the accumulator can reduce the impact force on the transport vehicle through simulation, and the optimal accumulator parameters can be obtained. Finally, an experiment in the field was set up and carried out, and the experimental results presented to prove the viability of the proposed method.

Study on Method of Recognizing Characteristics of Pipeline Leakage Acoustic Signals

2006 ◽  
Author(s):  
Likun Wang ◽  
Dongjie Tan ◽  
Yongjun Cai ◽  
SongGuang Fu ◽  
Jian Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Field Experiment ◽  
Frequency Band ◽  
Bp Neural Network ◽  
Acoustic Signals ◽  
Pressure Variation ◽  
Experimental Results ◽  
Acoustic Sensors ◽  
Experiment Data

Wavelet package and neural network are used to recognize the characteristics of pipeline leakage acoustic signals. Acoustic signals produced by pressure variation of pipelines can be detected by the acoustic sensors installed on the pipelines. The detecting accuracy can be increased with recognizing the acoustic signals correctly. The method to detect acoustic signals by combining the wavelet package and neural network is introduced in this paper. The signal is decomposed with wavelet package firstly, then the decomposed coefficients in each frequency band are obtained through reconstruction. As a result, the parameters of the new sequences reconstructed on every decomposed node are acquired, and then these parameters are input to BP neural network to recognize the fault reason intelligently. At the end of the paper, field experiment data and their analyzed results are studied. The experimental results are provided to show that the proposed method can increase the accuracy efficiently.

Classification Methods in the Detection of New Suspicious Emails

2008 ◽  
Vol 07 (03) ◽  
pp. 209-217 ◽  
Author(s):  
S. Appavu Alias Balamurugan ◽  
G. Athiappan ◽  
M. Muthu Pandian ◽  
R. Rajaram
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Binary Tree ◽  
Experimental Results ◽  
Classification Methods ◽  
Detection Rates ◽  
Naive Bayesian ◽  
The Past ◽  
Naïve Bayesian

Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of suspicious emails during the past few years. This paper proposes to apply classification data mining for the task of suspicious email detection based on deception theory. In this paper, email data was classified using four different classifiers (Neural Network, SVM, Naïve Bayesian and Decision Tree). The experiment was performed using weka on the basis of different data size by which the suspicious emails are detected from the email corpus. Experimental results show that simple ID3 classifier which make a binary tree, will give a promising detection rates.

Data Mining with BP Neural Network Algorithm Based MapReduce

2013 ◽  
Vol 380-384 ◽  
pp. 2915-2919 ◽  
Author(s):  
Jian Ming Cui ◽  
Yan Xin Ye
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Bp Neural Network ◽  
Resource Constraints ◽  
Training Time ◽  
Computing Platform ◽  
Network Training ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Cloud Computing Platform

Traditional massive data mining with BP neural network algorithm, resource constraints of the ordinary stand-alone platform and scalability bottlenecks and classification process serialization due to classification inefficient results, and also have an impact on the classification accuracy. In this paper, the Detailed description of the flow of execution of the BP neural network parallel algorithm in Hadoop's MapReduce programming model.Experimental results show that: the BP neural network under the cloud computing platform can greatly shorten the network training time, better parallel efficiency and good scalability.

Creep Behavior Prediction Research of T92 Steel Based on BP Neural Network

2013 ◽  
Vol 467 ◽  
pp. 203-207
Author(s):  
Jian Liu
Keyword(s):  
Neural Network ◽  
Creep Behavior ◽  
Bp Neural Network ◽  
Network Theory ◽  
Experimental Results ◽  
Behavior Prediction ◽  
Forecast Precision ◽  
Network Method ◽  
Simulation Results ◽  
Neural Network Theory

Based on the BP neural network theory, the creep rate prediction model of T92 steel was established under multiple stress levels. Obtained the experimental results and using the model, the experimental results were trained. The results show that the simulation results match the measured results well with a high forecast precision. The BP neural network method can serve as research on T92 steel creep behavior.

Prediction of Ride Comfort Values Based on GA-BP Neural Network

ICTIS 2011 ◽  
2011 ◽  
Author(s):  
Song Liu ◽  
Yong Qin ◽  
Zong-yi Xing
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Ride Comfort

Fault Diagnosis for Electrical Control System of Automobile Based on Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 203-207
Author(s):  
Jian Hua Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Electrical Control ◽  
Diagnosis Method ◽  
The Common

This paper is to present a fault diagnosis method for electrical control system of automobile based on support vector machine. We collect the common fault states of electrical control system of automobile to analyze the fault diagnosis ability of electrical control system of automobile based on support vector machine. It can be seen that the accuracy of fault diagnosis for electrical control system of automobile by support vector machine is 92.31%; and the accuracy of fault diagnosis for electrical control system of automobile by BP neural network is 80.77%. The experimental results show that the accuracy of fault diagnosis for electrical control system of automobile of support vector machine is higher than that of BP neural network.

Research on Closed-Loop Safety Production System of Hot Oil Pipeline Based on Big Data Mining

2019 ◽  
Author(s):  
Yu Tao ◽  
Li Chuanxian ◽  
Liu Lijun ◽  
Chen Hongjun ◽  
Guo Peng ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Big Data ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Closed Loop ◽  
Long Distance ◽  
Oil Pipeline ◽  
Big Data Mining ◽  
Actual Production

Abstract The process of long-distance hot oil pipeline is complicated, and its safety and optimization are contradictory. In actual production and operation, the theoretical calculation model of oil temperature along the pipeline has some problems, such as large error and complex application. This research relies on actual production data and uses big data mining algorithms such as BP neural network, ARMA, seq2seq to establish oil temperature prediction model. The prediction result is less than 0.5 C, which solves the problem of accurate prediction of dynamic oil temperature during pipeline operation. Combined with pigging, the friction prediction model of standard pipeline section is established by BP neural network, and then the economic pigging period of 80 days is given; and after the friction database is established, the historical friction data are analyzed by using the Gauss formula, and 95% of the friction is set as the threshold data to effectively monitor the variation of the friction due to the long period of waxing in pipelines. The closed loop operation system of hot oil pipeline safety and optimization was formed to guide the daily process adjustment and production arrangement of pipeline with energy saving up to 92.4%. The prediction model and research results based on production big data have good adaptability and generalization, which lays a foundation for future intelligent control of pipelines.

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