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AUTOCONFIGURING ARTIFICIAL NEURAL NETWORK APPLIED TO FAULT DIAGNOSIS IN POWER SYSTEMS


Posted by: computer science technology
Created at: Sunday 24th of January 2010 06:39:12 AM
Last Edited Or Replied at :Monday 06th of February 2012 12:24:21 AM
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processing, capacitor installation and EMTP problems.
An attempt has been made here to solve the fault diagnosis problem in
power systems using ANN.
The principal functions of these diagnosis systems are:
1) Detection of fault occurrence
2) Identification of faulted sections
3) Classification of faults into types:
HIFs (high impedance faults) or LIFs(low impedance faults)
This has been achieved through a cascade, multilayered ANN structure.
Using these FDS accurately identifies HIFs, which are relatively
difficult to identify in the other methods.
FAULT ANALYSIS AND PROTEC..................[:=> Show Contents <=:]



TACTILE SENSING SYSTEM USING ARTIFICIAL NEURAL NETWORKS full report


Posted by: computer science technology
Created at: Sunday 24th of January 2010 04:06:02 AM
Last Edited Or Replied at :Sunday 24th of January 2010 04:06:02 AM
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of the object. The system has well support of software for processing the tactile data in different forms and taking any decision after the identification of the object. Finally it is observed that this system is intelligent enough for the identification of different types of the object like square, rectangle ,circle, bar, triangle independent to their position ,orientation and scale and giving its many physical properties .This system has various applications in robotics, medical and tele- operations and in computer vision.
REFERENCES:
.Prasant Kumar Patra, Neura..................[:=> Show Contents <=:]



cellular neural networks


Posted by: electronics seminars
Created at: Monday 28th of December 2009 05:07:40 AM
Last Edited Or Replied at :Monday 02nd of May 2011 03:07:21 AM
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two sense:
As spatial logic, it is equivalent to a Turing machine and as a local logic it may compute any Boolean function.
As a nonlinear dynamic operator, it can realize any local operator of fading memory. the CNN UM is a common computational paradigm for as diverse fields of spatio-temporal computing as for example, retinal model, reaction diffusion equations, mathematical morphology, etc. The stored program ,as a sequence of templates is considered as a genetic code of CNN UM. The elementary genes are the templates.



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Digit Recognition using Neural Network


Posted by: Electrical Fan
Created at: Saturday 14th of November 2009 05:33:37 AM
Last Edited Or Replied at :Thursday 19th of January 2012 10:33:35 PM
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ode recognition (Automatic sorting of mail by destination ZIP code), Digitizing hand written spreadsheets, tax forms etc. Patterns slightly shifted, distorted and even overwritten can be correctly recognized. Neural network aids in efficient recognition. A Multilayer Neural network trained with Backpropagation algorithm is used. Kirsch masks are adopted for extracting feature vectors and a multy layer clustered neural network is used for classifying numerals efficiently. The neural network will be trained with a database consisting of handwritten digits provided by writers of various ages with..................[:=> Show Contents <=:]



Face Recognition Using Artificial Neural Networks


Posted by: nit_cal
Created at: Saturday 31st of October 2009 04:58:30 AM
Last Edited Or Replied at :Saturday 20th of April 2013 12:55:41 AM
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properties of human brain enable it to analyze complex patterns consisting of a number of elements, those individually reveal little of the total pattern, yet collectively represent easily recognizable objects. The concepts of Neural Networks have been motivated right from its inception, by the recognition that the human brain computes in an entirely different way from the conventional digital computers. The brain modeling techniques opens a new era of Computer System that learns, from experience and uses its experiential knowledge next time. This biologically inspired method is being touted a..................[:=> Show Contents <=:]



Emerging Trends In Robotics Downlaod Seminar Report


Posted by: Computer Science Clay
Created at: Thursday 30th of July 2009 06:46:58 AM
Last Edited Or Replied at :Thursday 30th of July 2009 06:46:58 AM
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hine that can perform this instruction is truly a robot. So, the conclusion is that to be called a robot; you really need an on-board brain and a way to program it.

Classification of ROBOTS:
Robot like devices:
Prostheses
Exoskeletons
Telecherics
Locomotive mechanism
Classification by coordinate system:
Cylindrical coordinate robots
Spherical coordinate robots
Jointed arm robots
Cartesian coordinate robots
Classification by control method:
Non-servo controlled robots
Servo controlled robots
Point-to-point servo controlled robots


http://www.seminarprojects.net/downloads..................[:=> Show Contents <=:]



Cellular Neural Network


Posted by: Computer Science Clay
Created at: Tuesday 24th of February 2009 12:56:26 PM
Last Edited Or Replied at :Sunday 21st of November 2010 10:46:10 PM
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0's, second revolution led to internet industry in 1990's cheap sensors & mems arrays in desired forms of artificial eyes, nose, ears etc. this third revolution owes due to C.N.N.This technology is implemented using CNN-UM and is also used in imageprocessing.It can also implement any Boolean functions.

ARCHITECTURE OF CNN

A standard CNN architecture consists of an m*n rectangular array of cells c(i,j) with Cartesian coordinates (i,j) i=1,2¦..M, j=12¦...N.

A class -1 m*n standard CNN is defined by a m*n rectangular array of cells cij located at site (i,j) i= 1,2 ¦¦.m ,j=1,2..................[:=> Show Contents <=:]



Industrial Applications using Neural Networks


Posted by: computer science crazy
Created at: Monday 22nd of September 2008 12:08:04 AM
Last Edited Or Replied at :Thursday 05th of August 2010 12:43:13 PM
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c process model of the system can be integrated within a variety of process monitoring and control algorithms. In process control, for example, the model can be incorporated within a model based predictive control (MBPC) algorithm, such as Generalised Predictive Control.

Alternatively, for process monitoring, the residuals (prediction errors) from such models can be analyzed to detect abnormal operation. Such monitoring and control schemes have found widespread application in industry and have led to significant improvements in process operations. Unfortunately, the models employed within t..................[:=> Show Contents <=:]



Transient Stability Assessment using Neural Networks


Posted by: computer science crazy
Created at: Monday 22nd of September 2008 12:07:56 AM
Last Edited Or Replied at :Tuesday 19th of October 2010 05:02:49 AM
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tages etc., by examining the swing curves, separation of one or more generators from the rest of the system indicating loss of synchronism is detected. Even for small power network and simple mathematical model possible this method is slow and cumbersome.

The second method of Lyapunov:
In this method, the integration off post fall system equation is replaced by stability criterion. The value of a suitably designed lyapunov function v is calculated at the instant of last switching in the system and compared with the previously determined critical value Vcr of this function if V is smaller ..................[:=> Show Contents <=:]



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