Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS

Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main features are (a) encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks, (b) accessibility of all of the SNNS algorithmic functionality from R using a low-level interface, and (c) a high-level interface for convenient, R-style usage of many standard neural network procedures. The package also includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNS file formats.This work was supported in part by the Spanish Ministry of Science and Innovation (MICINN) under Project TIN-2009-14575. C. Bergmeir holds a scholarship from the Spanish Ministry of Education (MEC) of the \Programa de Formación del Profesorado Universitario (FPU)"

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

Optimization Of Network Parameters And Semi-supervision In Gaussian Art Architectures

In this thesis we extensively experiment with two ART (adaptive resonance theory) architectures called Gaussian ARTMAP (GAM) and Distributed Gaussian ARTMAP (dGAM). Both of these classifiers have been successfully used in the past on a variety of applications. One of our contributions in this thesis is extensively experiments with the GAM and dGAM network parameters and appropriately identifying ranges for these parameters for which these architectures attain good performance (good classification performance and small network size). Furthermore, we have implemented novel modifications of these architectures, called semi-supervised GAM and dGAM architectures. Semi-supervision is a concept that has been used effectively before with the FAM and EAM architectures and in this thesis we are answering the question of whether semi-supervision has the same beneficial effect on the GAM architectures too. Finally, we compared the performance of GAM, dGAM, EAM, FAM and their semi-supervised versions on a number of datasets (simulated and real datasets). These experiments allowed us to draw appropriate conclusions regarding the comparative performance of these architectures

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

Design Simulation and Assessment of Computer Based Cancer Diagnosis Accuracy using ART 1.0 Algorithm

Today Cancer is spreading heavily and become the most dangerous disease in the world. This disease causes death if not diagnoses before the major stage. Small changes or illness in the human body may transform to the cancer in the body. The main thing in this disease is it is not easily detected in its earlier stage. So this causes the aim to design a computer operated system that can make distinguish between benign (non-cancerous) and malignant (cancerous) mammogram. The proposed system helps doctors to increase the diagnosis accuracy. The above propose system shall be simulated by MATLAB. The ART 1.0 algorithm shall be studied and modified to improve the accuracy of existing ART 1.0 system. The simulation shall be done by obtaining cancer data set from UCI repository. The reason behind choosing ART algorithm because of its characteristic to work on three phases i.e., Recognition, Comparison, Search Phase.  The winning neuron is obtained by finding the dot product of input and weight vector. The neuron having largest dot product be the winner.&nbsp

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

This paper presents a review of clustering and mathematical programming methods and their impacts on cell forming (CF) and scheduling problems. In-depth analysis is carried out by reviewing 105 dominant research papers from 1972 to 2017 available in the literature. Advantages, limitations and drawbacks of 11 clustering methods in addition to 8 meta-heuristics are also discussed. The domains of studied methods include cell forming, material transferring, voids, exceptional elements, bottleneck machines and uncertain product demands. Since most of the studied models are NP-hard, in each section of this research, a deep research on heuristics and metaheuristics beside the exact methods are provided. Outcomes of this work could determine some existing gaps in the knowledge base and provide directives for objectives of this research as well as future research which would help in clarifying many related questions in cellular manufacturing systems (CMS)

Image Compression Using Cascaded Neural Networks

Images are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Many techniques developed for this purpose include transform coding, vector quantization and neural networks. In this thesis, a new neural network method is used to achieve image compression. This work extends the use of 2-layer neural networks to a combination of cascaded networks with one node in the hidden layer. A redistribution of the gray levels in the training phase is implemented in a random fashion to make the minimization of the mean square error applicable to a broad range of images. The computational complexity of this approach is analyzed in terms of overall number of weights and overall convergence. Image quality is measured objectively, using peak signal-to-noise ratio and subjectively, using perception. The effects of different image contents and compression ratios are assessed. Results show the performance superiority of cascaded neural networks compared to that of fixedarchitecture training paradigms especially at high compression ratios. The proposed new method is implemented in MATLAB. The results obtained, such as compression ratio and computing time of the compressed images, are presented