Agricultural Machinery Operation Posture Rapid Detection Intelligent Sensor Calibration Method Based on RBF Neural Network

2013 ◽  
Vol 373-375 ◽  
pp. 932-935 ◽  
Author(s):  
Nan Feng Zhang ◽  
Jing Feng Yang ◽  
Yue Ju Xue ◽  
Zhong Li ◽  
Xiao Lin Huang
Keyword(s):  
Neural Network ◽  
Rapid Detection ◽  
Rbf Neural Network ◽  
Calibration Method ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Test Results ◽  
Agricultural Machinery ◽  
Intelligent Sensor ◽  
Status Data

Based on agricultural machinery body posture detection parameters and wheels gesture detection parameters collected by gyro inertial measurement unit, an agricultural machinery operation posture rapid detection method is proposed in this paper. The test results calibrated by RBF neural network show that, the test results of the method are accurate and available, and the method is effective and available for real-time body and wheel status data to further understand the agricultural machinery.

Agricultural Machinery Operation Posture Rapid Detection Method Based on Intelligent Sensor

2013 ◽  
Vol 373-375 ◽  
pp. 936-939
Author(s):  
Nan Feng Zhang ◽  
Jing Feng Yang ◽  
Yue Ju Xue ◽  
Zhong Li ◽  
Xiao Lin Huang
Keyword(s):  
Real Time ◽  
Rapid Detection ◽  
Inertial Measurement Unit ◽  
Detection Method ◽  
Measurement Unit ◽  
Test Results ◽  
Agricultural Machinery ◽  
Intelligent Sensor ◽  
Inertial Measurement ◽  
Status Data

Based on agricultural machinery body posture detection parameters and wheels gesture detection parameters collected by gyro inertial measurement unit, an agricultural machinery operation posture rapid detection method is proposed in this paper. The test results show that, the test results of the method are accurate and available, and the method is effective and available for real-time body and wheel status data to further understand the agricultural machinery.

Damage Identification of Structure Based on RBF Neural Network

2013 ◽  
Vol 753-755 ◽  
pp. 2356-2359
Author(s):  
Cheng Gang Zhen ◽  
Xiang Ting Chong
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Health Monitoring ◽  
Damage Identification ◽  
Rbf Neural Network ◽  
High Accuracy ◽  
Learning Method ◽  
Test Results ◽  
Health Monitoring System ◽  
Orthogonal Least Squares

Health monitoring of the structure is a topic widely concerned and researched in the fields of technology and engineering at home and abroad. Damage identification of structure is an important aspect of the whole health monitoring system. In this paper, the RBF neural network with the effect of bionic is used to the extent, location and area recognition of the damage on the structure with single damage. The method of orthogonal least squares (OLS) is used as the learning method of the network. The test results show that the RBF neural network and the learning method of OLS can identify the damage status of the structure quickly and effectively with high accuracy.

scholarly journals Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Li Wang ◽  
Shimin Lin ◽  
Jingfeng Yang ◽  
Nanfeng Zhang ◽  
Ji Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Traffic Congestion ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Rbf Neural Network ◽  
Calibration Method ◽  
Theory Model ◽  
Point Of View ◽  
Urban Traffic

Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole.

scholarly journals Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS System

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xuewen Chen ◽  
Huaqing Chen ◽  
Huan Xu
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Region Of Interest ◽  
Vehicle Detection ◽  
Test Results ◽  
Driver Assistance ◽  
Rbf Network ◽  
The Neural Network ◽  
Stable Performance ◽  
High Recognition Accuracy

A region of interest (ROI) that may contain vehicles is extracted based on the composite features on vehicle’s bottom shadow and taillights by setting a gray threshold on vehicle shadow region and a series of constraints on taillights. In order to identify the existence of target vehicle in front of Advanced Driver Assistance System (ADAS) for the extracted ROI, a neural network recognizer of the Radial Basis Function (RBF) is found by extracting a series of parameters on the vehicle’s edge and region features. Using a large amount of collected images, the ROI that may contain vehicles is verified to be effective by extracting composite features of the shadow at the bottom of vehicle and taillights. Based on the positive and negative sample base of vehicles, the neural network recognizer is trained and learned, which can quickly realize network convergence. Furthermore, the vehicle can be effectively identified in the region of interest using the trained network. Test results show that the vehicle detection method based on multifeature extraction and recognition method based on RBF network have stable performance and high recognition accuracy.

scholarly journals Automatic Transmission Fault Symptom Identification by Apply of Neural Network and D-S Evidence Theory

2021 ◽  
pp. 778-799
Author(s):  
RyongSik O ◽  
Jiangwei Chu ◽  
Zhenwei Sun ◽  
Myongchol Ri ◽  
MyongSu Sim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Neural Network ◽  
Rbf Neural Network ◽  
Automatic Transmission ◽  
Evidence Theory ◽  
Test Results ◽  
The Neural Network ◽  
Initial Symptoms ◽  
Fuzzy Neural

At present, the method of identifying the fault symptoms of various machines by combining the neural network and the D-S evidence theory is attracting attention from researchers because the identification time is fast and the diagnosis is accurate. In this paper, it was mentioned a method for identifying the fault symptoms of automatic transmission by combining these two theories. First, it was mentioned a method for identifying fault symptoms of the automatic transmission by combining a fuzzy neural network and an RBF neural network. Next, it was newly described a method to improve the accuracy of fault symptom identification by the D-S evidence theory. In addition, the accuracy of this method was verified by an experimental method. In the experiment Firstly, two sub neural networks are established to recognize the initial symptoms. That is, the first sub-neural network E1 be used as the fuzzy neural network, the second sub-neural network E2 be used as RBF neural network, respectively, for preliminary symptom recognition. And then, these outputs of the two sub neural networks are used as the evidence space of D-S evidence theory, so the global diagnosis is carried out. The results show that the test results are consistent with the actual fault symptoms. The success rate of fault diagnosis up to 96.3%, therefore, on the identification of the automatic transmission fault symptom, effectiveness, and feasibility of the D-S evidence theory based on information fusion is verified.

scholarly journals Calibration and Noise Identification of a Rolling Shutter Camera and a Low-Cost Inertial Measurement Unit

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2345 ◽  
Author(s):  
Chang-Ryeol Lee ◽  
Ju Yoon ◽  
Kuk-Jin Yoon
Keyword(s):  
Inertial Measurement Unit ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Real Data ◽  
Calibration Method ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Calibration Error ◽  
Inertial Measurement ◽  
Noise Density

A low-cost inertial measurement unit (IMU) and a rolling shutter camera form a conventional device configuration for localization of a mobile platform due to their complementary properties and low costs. This paper proposes a new calibration method that jointly estimates calibration and noise parameters of the low-cost IMU and the rolling shutter camera for effective sensor fusion in which accurate sensor calibration is very critical. Based on the graybox system identification, the proposed method estimates unknown noise density so that we can minimize calibration error and its covariance by using the unscented Kalman filter. Then, we refine the estimated calibration parameters with the estimated noise density in batch manner. Experimental results on synthetic and real data demonstrate the accuracy and stability of the proposed method and show that the proposed method provides consistent results even with unknown noise density of the IMU. Furthermore, a real experiment using a commercial smartphone validates the performance of the proposed calibration method in off-the-shelf devices.

The Research on the Static Calibration of Fingertip Force Sensor for Underwater Dexterous Hand on RBF Neural Network

2007 ◽  
Vol 10-12 ◽  
pp. 267-270
Author(s):  
Peng Jia ◽  
Qing Xin Meng ◽  
Hua Wang ◽  
Hai Bo Wang
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Force Sensor ◽  
Calibration Method ◽  
Complex Task ◽  
Calibration System ◽  
Static Calibration ◽  
Fingertip Force ◽  
Calibration Experiment ◽  
Dexterous Hand

The fingertip force sensor is the key for the complex task of the dexterous underwater hand, in order to safely grasp an unknown object using the dexterous underwater hand and accurately perceive its position in the fingers, a sensor should be developed, which can detect the force and position simultaneously. Furthermore, this sensor should be used underwater. It is difficult to employ the accustomed calibration method for the characteristic of the fingertip force sensor, and the accustomed method is not able to assure the precision. A calibration method based on RBF (Radial-Basis Function) neural network is introduced. Furthermore, the calibration system and program are also designed. The calibration experiment of the sensor is carried out. The results show the nonlinear calibration method based on RBF neural network assure the precision of the sensor, which meets the demand of research on the underwater dexterous hand.

Prediction of Tensile Strength Based on RBF Neural Network

2012 ◽  
Vol 476-478 ◽  
pp. 1309-1312
Author(s):  
Hui Jun Li ◽  
Li Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Tensile Strength ◽  
Prediction Models ◽  
Rbf Neural Network ◽  
Rbf Neural Networks ◽  
Test Results ◽  
Strength Based

The objective of this research is to predict yarn tensile strength. The model of predicting yarn tensile strength is built based on RBF neural network. The RBF neural networks are trained with HVI test results of cotton and USTER TENSOJET 5-S400 test results of yarn. The results show prediction models based on RBF neural network are very precise and efficient.

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