Automatic Screening of Sonar Imagery Using Artificial Intelligence Techniques John Tran.

Slides:

Advertisements

Similar presentations

A Brief Overview of Neural Networks By Rohit Dua, Samuel A. Mulder, Steve E. Watkins, and Donald C. Wunsch.

Advertisements

NEURAL NETWORKS Backpropagation Algorithm

1 Machine Learning: Lecture 4 Artificial Neural Networks (Based on Chapter 4 of Mitchell T.., Machine Learning, 1997)

Artificial Neural Networks (1)

NEURAL NETWORKS Perceptron

Multilayer Perceptrons 1. Overview  Recap of neural network theory  The multi-layered perceptron  Back-propagation  Introduction to training  Uses.

An Overview of the Application of Neural Networks to the Monitoring of Civil Engineering Structures By Brian Walsh & Arturo González With thanks thanks.

S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks / Fall 2004 Shreekanth Mandayam ECE Department Rowan University.

S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks ECE /ECE Fall 2008 Shreekanth Mandayam ECE Department Rowan University.

Prénom Nom Document Analysis: Artificial Neural Networks Prof. Rolf Ingold, University of Fribourg Master course, spring semester 2008.

1 Chapter 11 Neural Networks. 2 Chapter 11 Contents (1) l Biological Neurons l Artificial Neurons l Perceptrons l Multilayer Neural Networks l Backpropagation.

S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks / Fall 2004 Shreekanth Mandayam ECE Department Rowan University.

Prénom Nom Document Analysis: Artificial Neural Networks Prof. Rolf Ingold, University of Fribourg Master course, spring semester 2008.

S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks ECE /ECE Fall 2010 Shreekanth Mandayam ECE Department Rowan University.

S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks / Spring 2002 Shreekanth Mandayam Robi Polikar ECE Department.

Presenting: Itai Avron Supervisor: Chen Koren Characterization Presentation Spring 2005 Implementation of Artificial Intelligence System on FPGA.

S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks ECE /ECE Fall 2006 Shreekanth Mandayam ECE Department Rowan University.

September 28, 2010Neural Networks Lecture 7: Perceptron Modifications 1 Adaline Schematic Adjust weights i1i1i1i1 i2i2i2i2 inininin …  w 0 + w 1 i 1 +

Image Compression Using Neural Networks Vishal Agrawal (Y6541) Nandan Dubey (Y6279)

Presented by: Kamakhaya Argulewar Guided by: Prof. Shweta V. Jain

Multiple-Layer Networks and Backpropagation Algorithms

Artificial Neural Networks

Intrusion Detection Using Hybrid Neural Networks Vishal Sevani ( )

Artificial Neural Nets and AI Connectionism Sub symbolic reasoning.

IE 585 Introduction to Neural Networks. 2 Modeling Continuum Unarticulated Wisdom Articulated Qualitative Models Theoretic (First Principles) Models Empirical.

Explorations in Neural Networks Tianhui Cai Period 3.

Appendix B: An Example of Back-propagation algorithm

 Diagram of a Neuron  The Simple Perceptron  Multilayer Neural Network  What is Hidden Layer?  Why do we Need a Hidden Layer?  How do Multilayer.

Lecture 3 Introduction to Neural Networks and Fuzzy Logic President UniversityErwin SitompulNNFL 3/1 Dr.-Ing. Erwin Sitompul President University

LINEAR CLASSIFICATION. Biological inspirations  Some numbers…  The human brain contains about 10 billion nerve cells ( neurons )  Each neuron is connected.

Artificial Intelligence Techniques Multilayer Perceptrons.

From Machine Learning to Deep Learning. Topics that I will Cover (subject to some minor adjustment) Week 2: Introduction to Deep Learning Week 3: Logistic.

1 Chapter 11 Neural Networks. 2 Chapter 11 Contents (1) l Biological Neurons l Artificial Neurons l Perceptrons l Multilayer Neural Networks l Backpropagation.

Well Log Data Inversion Using Radial Basis Function Network Kou-Yuan Huang, Li-Sheng Weng Department of Computer Science National Chiao Tung University.

Multi-Layer Perceptron

Akram Bitar and Larry Manevitz Department of Computer Science

SUPERVISED LEARNING NETWORK

Chapter 8: Adaptive Networks

Introduction to Neural Networks Freek Stulp. 2 Overview Biological Background Artificial Neuron Classes of Neural Networks 1. Perceptrons 2. Multi-Layered.

Neural Networks 2nd Edition Simon Haykin

Artificial Neural Networks (ANN). Artificial Neural Networks First proposed in 1940s as an attempt to simulate the human brain’s cognitive learning processes.

Bab 5 Classification: Alternative Techniques Part 4 Artificial Neural Networks Based Classifer.

Operation and Control Strategy of PV/WTG/EU Hybrid Electric Power System Using Neural Networks Faculty of Engineering, Elminia University, Elminia, Egypt.

1 Neural Networks MUMT 611 Philippe Zaborowski April 2005.

Neural networks.

Multiple-Layer Networks and Backpropagation Algorithms

Neural Network Architecture Session 2

ANN-based program for Tablet PC character recognition

Data Mining, Neural Network and Genetic Programming

MATLAB Implementation of the Optimal Brain Surgeon (OBS) Algorithm

Ranga Rodrigo February 8, 2014

CSE 473 Introduction to Artificial Intelligence Neural Networks

with Daniel L. Silver, Ph.D. Christian Frey, BBA April 11-12, 2017

Final Year Project Presentation --- Magic Paint Face

FUNDAMENTAL CONCEPT OF ARTIFICIAL NETWORKS

CSE P573 Applications of Artificial Intelligence Neural Networks

Prof. Carolina Ruiz Department of Computer Science

BACKPROPOGATION OF NETWORKS

System Level Diesel Engine Emission Modeling Using Neural Networks

CSC 578 Neural Networks and Deep Learning

Artificial Neural Network & Backpropagation Algorithm

of the Artificial Neural Networks.

CSE 573 Introduction to Artificial Intelligence Neural Networks

Artificial Neural Networks

Lecture Notes for Chapter 4 Artificial Neural Networks

Zip Codes and Neural Networks: Machine Learning for

Artificial Neural Networks

Artificial Neural Networks / Spring 2002

Prof. Carolina Ruiz Department of Computer Science

Outline Announcement Neural networks Perceptrons - continued

Presentation transcript:

Automatic Screening of Sonar Imagery Using Artificial Intelligence Techniques John Tran

Contents Introduction and Background Development Results and Conclusions

Introduction and Background

Functionality Analyze sonar images and produce signals of interest Use processed image data instead of raw image Revolves around the application of neural networks with various modifications

Background Neural Network –Takes in a number of inputs –Calculates the net internal activity at each node –Apply activation functions to these values –Continue to do this until the end of the network Multilayer Perceptron –Involves multiple layers and possible hidden nodes

Related Projects The Truck Backer-Upper (Nguyen and Widrow) –Used a multilayer perceptron network to simulate a trailer truck backing into a loading dock

Expected Results Training error over time < 1 Consistent detection spaces Good adaptability of the neural network

Related Research The research project being done here was built off of my mentor’s original research on the subject.

Development

Procedures/Methods Initialization Repeated Review Update

Programming Language MATLab –Handles manipulation of matrices quickly and efficiently

Algorithms Back Propagation Sigmoidal Nonlinerality Delta-Delta Learning Rule Nguyen-Widrow Initialization

Encountered Problems Incorrect image statistics –Statistics did not represent the original sonar image accurately Inconsistent training results –Detection space would fluctuate with each training

Results and Conclusions

Figures Original Sonar Image Detection Space Output Coordinate Tracker

Figures Good CaseAverage CaseBad Case Results

Thank you for listening.

Works Cited The formatting will be edited later. Simon Haykin - Neural Networks: A Comprehensive Foundation (2nd Edition). Clifford Lau - Neural Networks: Theoretical Foundations and Analysis Yu Hen Hu, Jenq-Neng Hwang - Introduction to Neural Networks for Signal Processing Cohen and Bray, Gram Analysis Research Derrick Nguyen and Prof. Bernard Widrow - The Truck Backer-Upper: An Example of Self- Learning in Neural Networks. Derrick Nguyen and Prof. Bernard Widrow - Improving the Learning Speed of 2-Layer Neural Networks by Choosing Initial Values of the Adaptive Weights.