A New Continuum Robot With Crossed Elastic Strips: Extensible Sections With Only One Actuator per Section

2015 ◽  
Author(s):  
Andria A. Remirez ◽  
Robert J. Webster
Keyword(s):  
Average Error ◽  
Crossover Point ◽  
Circular Arcs ◽  
Continuum Robot ◽  
Model Predictions ◽  
Fixed Section

We propose a new kind of continuum robot based on crossed elastic strips. The actuator-specified location of the crossover point controls the lengths of the sections, enabling a wider range of configurations than would be possible with traditional fixed-section-length robots. Push-pull actuation of the crossed strips controls the curvature of the sections. We provide a model that describes the resulting configurations in terms of tangent circular arcs of varying lengths. Experiments with a prototype yield tip positions that agree with model predictions with an average error of 4.6% of the robot’s length.

An inspection continuum robot with tactile sensor based on electrical impedance tomography for exploration and navigation in unknown environment

2019 ◽  
Vol 47 (1) ◽  
pp. 121-130 ◽  
Author(s):  
Yaming Wang ◽  
Feng Ju ◽  
Yahui Yun ◽  
Jiafeng Yao ◽  
Yaoyao Wang ◽  
...  
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Aircraft Engine ◽  
Tactile Sensor ◽  
Average Error ◽  
Impedance Tomography ◽  
Content Type ◽  
Inspection Robot ◽  
Continuum Robot ◽  
The Continuum

Purpose This paper aims to introduce an aircraft engine inspection robot (AEIR) which can go in the internal of the aircraft engine without collision and detect damage for engine blades. Design/methodology/approach To obtain the position and pose information of the blades inside the engine, a novel tactile sensor based on electrical impedance tomography (EIT) is developed, which could provide location and direction information when it contacts with an unknown object. In addition, to navigate the continuum robot, a control method is proposed to control the continuum robot, which can control the continuum robot to move along the pre-planned path and reduce the deviation from the planned path. Findings Experiment results show that the average error of contact location measurement of the tactile sensor is 0.8 mm. The average error relative to the size (diameter of 18 mm) of the sensor is 4.4%. The continuum robot can successfully reach the target position through a gap of 30 mm and realize the spatial positioning of blades. The validity of the AEIR for engine internal blade detection is verified. Originality/value The aero-engine inspection robot developed in this paper can replace human to detect engine blades and complete different detection tasks with different kinds of sensors.

Modeling Opinion Formation in Social Networks: A Probabilistic Fuzzy Approach

2020 ◽  
Vol 28 (01) ◽  
pp. 99-116
Author(s):  
Masoud Bashari ◽  
M.-R. Akbarzadeh-T.
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
System Modeling ◽  
Fuzzy Model ◽  
Average Error ◽  
Dynamical Process ◽  
Formation Model ◽  
Opinion Formation ◽  
The Social ◽  
Model Predictions

Opinion formation in social networks is an interesting dynamical process from the perspective of system modeling due to its large scale as well as the variety of structural and parametric uncertainties that it entails. This paper proposes a probabilistic fuzzy opinion formation model for predicting the opinions of communities in the social networks. In this regard, the opinions of a group of individuals about a given topic in a Telegram pilot group, as a popular social network, are collected and presented in the framework of the probabilistic fuzzy model. Based on the obtained data, the parameters of the model are extracted, and the model is tuned. Finally, the variations of the actual opinions throughout time are compared with the model predictions. The numerical results in this study show that, with appropriately tuned parameters, the model successfully represents the opinion formation process, with an average error that approaches zero.

scholarly journals A Comparison Between Theoretical Prediction and Experimental Measurement of the Dynamic Behavior of Spur Gears

1992 ◽  
Author(s):  
Brian Rebbechi ◽  
B. David Forrester ◽  
Fred B. Oswald ◽  
Dennis P. Townsend
Keyword(s):  
Experimental Data ◽  
Computer Model ◽  
High Speed ◽  
Contact Region ◽  
Experimental Results ◽  
Average Error ◽  
Spur Gears ◽  
Gear Noise ◽  
Gear Teeth ◽  
Model Predictions

Abstract A comparison was made between computer model predictions of gear dynamic behaviour and experimental results. The experimental data were derived from the NASA gear noise rig, which was used to record dynamic tooth loads and vibration. The experimental results were compared with predictions from the Australian Defence Science and Technology Organisation Aeronautical Research Laboratory’s gear dynamics code, for a matrix of 28 load-speed points. At high torque the peak dynamic load predictions agree with experimental results with an average error of 5 percent in the speed range 800 to 6000 rpm. Tooth separation (or bounce), which was observed in the experimental data for light-torque, high-speed conditions, was simulated by the computer model. The model was also successful in simulating the degree of load sharing between gear teeth in the multiple-tooth-contact region.

Low-voltage, high-resolution scanning electron microscopy of polymers

1987 ◽  
Vol 45 ◽  
pp. 466-467 ◽  
Author(s):  
S. J. Krause ◽  
W.W. Adams ◽  
S. Kumar ◽  
T. Reilly ◽  
T. Suziki
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Low Voltage ◽  
Chromatic Aberration ◽  
Image Resolution ◽  
Resolution Limit ◽  
Crossover Point ◽  
New Generation ◽  
Beam Damage ◽  
Scanning Electron

Scanning electron microscopy (SEM) of polymers at routine operating voltages of 15 to 25 keV can lead to beam damage and sample image distortion due to charging. Imaging polymer samples with low accelerating voltages (0.1 to 2.0 keV), at or near the “crossover point”, can reduce beam damage, eliminate charging, and improve contrast of surface detail. However, at low voltage, beam brightness is reduced and image resolution is degraded due to chromatic aberration. A new generation of instruments has improved brightness at low voltages, but a typical SEM with a tungsten hairpin filament will have a resolution limit of about 100nm at 1keV. Recently, a new field emission gun (FEG) SEM, the Hitachi S900, was introduced with a reported resolution of 0.8nm at 30keV and 5nm at 1keV. In this research we are reporting the results of imaging coated and uncoated polymer samples at accelerating voltages between 1keV and 30keV in a tungsten hairpin SEM and in the Hitachi S900 FEG SEM.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Regular estimates of paddock pasture mass can improve profitability on New Zealand dairy farms

2015 ◽  
Vol 77 ◽  
pp. 29-34 ◽  
Author(s):  
P.C. Beukes ◽  
S. Mccarthy ◽  
C.M. Wims ◽  
A.J. Romera
Keyword(s):  
Supply And Demand ◽  
Grazing Management ◽  
Average Error ◽  
Value Proposition ◽  
Perfect Knowledge ◽  
Operating Profit ◽  
A Value ◽  
Main Driver ◽  
Imperfect Knowledge ◽  
Farm Scale

Paddock selection is an important component of grazing management and is based on either some estimate of pasture mass (cover) or the interval since last grazing for each paddock. Obtaining estimates of cover to guide grazing management can be a time consuming task. A value proposition could assist farmers in deciding whether to invest resources in obtaining such information. A farm-scale simulation exercise was designed to estimate the effect of three levels of knowledge of individual paddock cover on profitability: 1) "perfect knowledge", where cover per paddock is known with perfect accuracy, 2) "imperfect knowledge", where cover per paddock is estimated with an average error of 15%, 3) "low knowledge", where cover is not known, and paddocks are selected based on longest time since last grazing. Grazing management based on imperfect knowledge increased farm operating profit by approximately $385/ha compared with low knowledge, while perfect knowledge added a further $140/ha. The main driver of these results is the level of accuracy in daily feed allocation, which increases with improving knowledge of pasture availability. This allows feed supply and demand to be better matched, resulting in less incidence of under- and over-feeding, higher milk production, and more optimal post-grazing residuals to maximise pasture regrowth. Keywords: modelling, paddock selection, pasture cover

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