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.

The Calibration Method of a Large Six-Axis Force Sensors

2013 ◽  
Vol 753-755 ◽  
pp. 2091-2094 ◽  
Author(s):  
Min Yang
Keyword(s):  
Force Sensor ◽  
Calibration Method ◽  
Force Sensors ◽  
Static Calibration

Based on the accelerator calibration method of six-axis force sensors, the system of static calibration for a large six-axis force sensor is build, The accelerator calibration method are introduced detail. the designed six-axis force sensor in multi-dimensional accelerators field is calibrated and the result is well used. the system of static calibrations a contraption, which is smart cheap and practicality.

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.

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.

Decoupling algorithms for piezoelectric six-dimensional force sensor based on RBF neural network

2017 ◽  
Vol 25 (5) ◽  
pp. 1266-1271 ◽  
Author(s):  
李映君 LI Ying-jun ◽  
韩彬彬 HAN Bin-bin ◽  
王桂从 WANG Gui-cong ◽  
黄 舒 HUANG Shu ◽  
孙 杨 SUN Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Force Sensor

Improved IP2 calibration method using a perturbance-based algorithm

2009 ◽  
Vol 1 (1) ◽  
pp. 83-87 ◽  
Author(s):  
Karim Aoulad Ali ◽  
Philippe Barré ◽  
Guillaume Andrieux ◽  
Jean-François Diouris
Keyword(s):  
Calibration Method ◽  
System Dynamic ◽  
Automatic Calibration ◽  
Portable Devices ◽  
Calibration System ◽  
Online Calibration ◽  
Static Calibration ◽  
On Chip ◽  
Calibration Techniques ◽  
Intentional Introduction

To achieve a high IIP2 level on a mixer, static calibration techniques have been developed. Most of them are based on an intentional introduction of a calibrated mismatch in the structure of the mixer. They are performed at production stage. It is also possible to automate them but their activation is strongly limited in portable devices because of system restrictions. Furthermore, IIP2 is sensitive to system variations, thereby degrading the calibration operation. The challenge is so to make the calibration system dynamic, i.e. performing an online calibration. This paper presents a perturbance-based algorithm as part of an automatic calibration system to track the optimum IIP2 level. Measurements validate the algorithm behavior and indicate the feasibility of using it in a complete calibration system for a future on-chip implementation.

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