Research on Cognition of a Soft Robot Based on Amorphous Computational Material

2014 ◽  
Vol 602-605 ◽  
pp. 1177-1180
Author(s):  
Jun Qiang Wang ◽  
Shu Qiang Yang ◽  
Jing Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distributed Cognition ◽  
Active Material ◽  
Water Hammer ◽  
Soft Robot ◽  
Computational Material ◽  
Artificial Neural ◽  
Novel Concept ◽  
Neuromorphic Architecture

Amorphous Computational Material (ACM) is a concept of an active material that can sense its environment and, due to its cognitive capabilities, react “intelligently” to those changes. In this paper, We demonstrate the feasibility of utilizing water hammer as a form of directed actuation. We show a novel concept of a Synthetic Neural Network, a type of an organic neuromorphic architecture modeled after Artificial Neural Network, which is used for a distributed cognition purposes for ACM. A simulation of the SNN is shown to accurately predict the directionality of water hammer propulsion.

Novel Concept of Modelling Embryology for Structuring an Artificial Neural Network

2010 ◽  
Vol 20 (2) ◽  
pp. 25-32 ◽  
Author(s):  
Ronald Thenius ◽  
Thomas Schmickl ◽  
Karl Crailsheim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Novel Concept

scholarly journals SOFT ROBOT POSITIONING USING ARTIFICIAL NEURAL NETWORK

2019 ◽  
Vol 18 (1) ◽  
pp. 019
Author(s):  
Marko Kovandžić ◽  
Vlastimir Nikolić ◽  
Miloš Simonović ◽  
Ivan Ćirić ◽  
Abdulathim Al-Noori
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Building Blocks ◽  
Inverse Kinematic ◽  
Step Motor ◽  
Future Research ◽  
Hydraulic Pressure ◽  
Soft Robot ◽  
Inverse Kinematic Problem ◽  
Artificial Neural

The experiment investigated the performance of an artificial neural network in solving the inverse kinematic problem of a soft robot. For this purpose, a simple soft robot was designed of building blocks, stringed on three rubber hoses, and an actuating system, to provide the hydraulic pressure. An axial extending of a hose, while the others are in the relaxed state, results in bending of the robot. The network was employed, as a black box, to approximate the behavior of the system. In accordance with the purpose, the input consisted of the desired spatial coordinates and the output of the step motor angular displacements. The network was trained and tested using records collected at 200 randomly chosen robot positions. The relative testing error of positioning, about 5%, confirmed a predictable robot behavior. The solution proposed is competitive in terms of simplicity, safety and price of realization. The experiment provided basics for the future research of the design of modular soft robots.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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