Neural Network Applications

Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering area

Neural Networks: Implementations and Applications

Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering area

Fuzzy neural network pattern recognition algorithm for classification of the events in power system networks

This dissertation introduces advanced artificial intelligence based algorithm for detecting and classifying faults on the power system transmission line. The proposed algorithm is aimed at substituting classical relays susceptible to possible performance deterioration during variable power system operating and fault conditions. The new concept relies on a principle of pattern recognition and detects the existence of the fault, identifies fault type, and estimates the transmission line faulted section. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combined with fuzzy decision rule for interpretation of neural network outputs. Neural network learns the mapping between inputs and desired outputs through processing a set of example cases. Training of the neural network is based on the combined use of unsupervised and supervised learning methods. During training, a set of input events is transformed into a set of prototypes of typical input events. During application, real events are classified based on the interpretation of their matching to the prototypes through fuzzy decision rule. This study introduces several enhancements to the original version of the ART algorithm: suitable preprocessing of neural network inputs, improvement in the concept of supervised learning, fuzzyfication of neural network outputs, and utilization of on-line learning. A selected model of an actual power network is used to simulate extensive sets of scenarios covering a variety of power system operating conditions as well as fault and disturbance events. Simulation results show improved recognition capabilities compared to a previous version of ART neural network algorithm, Multilayer Perceptron (MLP) neural network algorithm, and impedance based distance relay. Simulation results also show exceptional robustness of the novel ART algorithm for all operating conditions and events studied, as well as superior classification capabilities compared to the other solutions. Consequently, it is demonstrated that the proposed ART solution may be used for accurate, high-speed distinction among faulted and unfaulted events, and estimation of fault type and fault section

Cutting tool condition monitoring of the turning process using artificial intelligence

This thesis relates to the application of Artificial Intelligence to tool wear monitoring. The main objective is to develop an intelligent condition monitoring system able to detect when a cutting tool is worn out. To accomplish this objective it is proposed to use a combined Expert System and Neural Network able to process data coming from external sensors and combine this with information from the knowledge base and thereafter estimate the wear state of the tool. The novelty of this work is mainly associatedw ith the configurationo f the proposeds ystem.W ith the combination of sensor-baseidn formation and inferencer ules, the result is an on-line system that can learn from experience and can update the knowledge base pertaining to information associated with different cutting conditions. Two neural networks resolve the problem of interpreting the complex sensor inputs while the Expert System, keeping track of previous successe, stimatesw hich of the two neuraln etworks is more reliable. Also, mis-classificationsa re filtered out through the use of a rough but approximate estimator, the Taylor's tool life equation. In this study an on-line tool wear monitoring system for turning processesh as been developed which can reliably estimate the tool wear under common workshop conditions. The system's modular structurem akesi t easyt o updatea s requiredb y different machinesa nd/or processesT. he use of Taylor's tool life equation, although weak as a tool life estimator, proved to be crucial in achieving higher performance levels. The application of the Self Organizing Map to tool wear monitoring is, in itself, new and proved to be slightly more reliable then the Adaptive Resonance Theory neural network

An Adaptive Resonance Theory Neural Network (ART NN)-based fault diagnosis system: A Case Study of gas turbine system in Resak Development Platform

The project introduces a case study of a real gas turbine system in Resak Development Platform. There are two main objectives of this project. The first objective is aimed to achieve an online fault diagnosis model using Adaptive Resonance Theorem (ART) as a considered option to avoid potential faults happen during plant system and process. The second objective is focused on a solution to improve the maintenance plan for the gas turbine system to be more economical yet still maintaining its safety level

Applications of clustering analysis to signal processing problems.

Wing-Keung Sim.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 109-114).Abstracts in English and Chinese.Abstract --- p.2摘要 --- p.3Acknowledgements --- p.4Contents --- p.5List of Figures --- p.8List of Tables --- p.9Introductions --- p.10Chapter 1.1 --- Motivation & Aims --- p.10Chapter 1.2 --- Contributions --- p.11Chapter 1.3 --- Structure of Thesis --- p.11Electrophysiological Spike Discrimination --- p.13Chapter 2.1 --- Introduction --- p.13Chapter 2.2 --- Cellular Physiology --- p.13Chapter 2.2.1 --- Action Potential --- p.13Chapter 2.2.2 --- Recording of Spikes Activities --- p.15Chapter 2.2.3 --- Demultiplexing of Multi-Neuron Recordings --- p.17Chapter 2.3 --- Application of Clustering for Mixed Spikes Train Separation --- p.17Chapter 2.3.1 --- Design Principles for Spike Discrimination Procedures --- p.17Chapter 2.3.2 --- Clustering Analysis --- p.18Chapter 2.3.3 --- Comparison of Clustering Techniques --- p.19Chapter 2.4 --- Literature Review --- p.19Chapter 2.4.1 --- Template Spike Matching --- p.19Chapter 2.4.2 --- Reduced Feature Matching --- p.20Chapter 2.4.3 --- Artificial Neural Networks --- p.21Chapter 2.4.4 --- Hardware Implementation --- p.21Chapter 2.5 --- Summary --- p.22Correlation of Perceived Headphone Sound Quality with Physical Parameters --- p.23Chapter 3.1 --- Introduction --- p.23Chapter 3.2 --- Sound Quality Evaluation --- p.23Chapter 3.3 --- Headphone Characterization --- p.26Chapter 3.3.1 --- Frequency Response --- p.26Chapter 3.3.2 --- Harmonic Distortion --- p.26Chapter 3.3.3 --- Voice-Coil Driver Parameters --- p.27Chapter 3.4 --- Statistical Correlation Measurement --- p.29Chapter 3.4.1 --- Correlation Coefficient --- p.29Chapter 3.4.2 --- t Test for Correlation Coefficients --- p.30Chapter 3.5 --- Summary --- p.31Algorithms --- p.32Chapter 4.1 --- Introduction --- p.32Chapter 4.2 --- Principal Component Analysis --- p.32Chapter 4.2.1 --- Dimensionality Reduction --- p.32Chapter 4.2.2 --- PCA Transformation --- p.33Chapter 4.2.3 --- PCA Implementation --- p.36Chapter 4.3 --- Traditional Clustering Methods --- p.37Chapter 4.3.1 --- Online Template Matching (TM) --- p.37Chapter 4.3.2 --- Online Template Matching Implementation --- p.40Chapter 4.3.3 --- K-Means Clustering --- p.41Chapter 4.3.4 --- K-Means Clustering Implementation --- p.44Chapter 4.4 --- Unsupervised Neural Learning --- p.45Chapter 4.4.1 --- Neural Network Basics --- p.45Chapter 4.4.2 --- Artificial Neural Network Model --- p.46Chapter 4.4.3 --- Simple Competitive Learning (SCL) --- p.47Chapter 4.4.4 --- SCL Implementation --- p.49Chapter 4.4.5 --- Adaptive Resonance Theory Network (ART). --- p.50Chapter 4.4.6 --- ART2 Implementation --- p.53Chapter 4.6 --- Summary --- p.55Experimental Design --- p.57Chapter 5.1 --- Introduction --- p.57Chapter 5.2 --- Electrophysiological Spike Discrimination --- p.57Chapter 5.2.1 --- Experimental Design --- p.57Chapter 5.2.2 --- Extracellular Recordings --- p.58Chapter 5.2.3 --- PCA Feature Extraction --- p.59Chapter 5.2.4 --- Clustering Analysis --- p.59Chapter 5.3 --- Correlation of Headphone Sound Quality with physical Parameters --- p.61Chapter 5.3.1 --- Experimental Design --- p.61Chapter 5.3.2 --- Frequency Response Clustering --- p.62Chapter 5.3.3 --- Additional Parameters Measurement --- p.68Chapter 5.3.4 --- Listening Tests --- p.68Chapter 5.3.5 --- Confirmation Test --- p.69Chapter 5.4 --- Summary --- p.70Results --- p.71Chapter 6.1 --- Introduction --- p.71Chapter 6.2 --- Electrophysiological Spike Discrimination: A Comparison of Methods --- p.71Chapter 6.2.1 --- Clustering Labeled Spike Data --- p.72Chapter 6.2.2 --- Clustering of Unlabeled Data --- p.78Chapter 6.2.3 --- Remarks --- p.84Chapter 6.3 --- Headphone Sound Quality Control --- p.89Chapter 6.3.1 --- Headphones Frequency Response Clustering --- p.89Chapter 6.3.2 --- Listening Tests --- p.90Chapter 6.3.3 --- Correlation with Measured Parameters --- p.90Chapter 6.3.4 --- Confirmation Listening Test --- p.92Chapter 6.4 --- Summary --- p.93Conclusions --- p.97Chapter 7.1 --- Future Work --- p.98Chapter 7.1.1 --- Clustering Analysis --- p.98Chapter 7.1.2 --- Potential Applications of Clustering Analysis --- p.99Chapter 7.2 --- Closing Remarks --- p.100Appendix --- p.101Chapter A.1 --- Tables of Experimental Results: (Spike Discrimination) --- p.101Chapter A.2 --- Tables of Experimental Results: (Headphones Measurement) --- p.104Bibliography --- p.109Publications --- p.11

Coordinated Machine Learning and Decision Support for Situation Awareness

For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator\u27s input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario

An Adaptive Resonance Theory Neural Network (ART NN)-based fault diagnosis system: A Case Study of gas turbine system in Resak Development Platform

The project introduces a case study of a real gas turbine system in Resak Development Platform. There are two main objectives of this project. The first objective is aimed to achieve an online fault diagnosis model using Adaptive Resonance Theorem (ART) as a considered option to avoid potential faults happen during plant system and process. The second objective is focused on a solution to improve the maintenance plan for the gas turbine system to be more economical yet still maintaining its safety level