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

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Comprehensive Framework for Computer-Aided Prostate Cancer Detection in Multi-Parametric MRI

Prostate cancer is the most diagnosed form of cancer and one of the leading causes of cancer death in men, but survival rates are relatively high with sufficiently early diagnosis. The current clinical model for initial prostate cancer screening is invasive and subject to overdiagnosis. As such, the use of magnetic resonance imaging (MRI) has recently grown in popularity as a non-invasive imaging-based prostate cancer screening method. In particular, the use of high volume quantitative radiomic features extracted from multi-parametric MRI is gaining attraction for the auto-detection of prostate tumours since it provides a plethora of mineable data which can be used for both detection and prognosis of prostate cancer. Current image-based cancer detection methods, however, face notable challenges that include noise in MR images, variability between different MRI modalities, weak contrast, and non-homogeneous texture patterns, making it difficult for diagnosticians to identify tumour candidates. In this thesis, a comprehensive framework for computer-aided prostate cancer detection using multi-parametric MRI was introduced. The framework consists of two parts: i) a saliency-based method for identifying suspicious regions in multi-parametric MR prostate images based on statistical texture distinctiveness, and ii) automatic prostate tumour candidate detection using a radiomics-driven conditional random field (RD-CRF). The framework was evaluated using real clinical prostate multi-parametric MRI data from 20 patients, and both parts were compared against state-of-the-art approaches. The suspicious region detection method achieved a 1.5% increase in sensitivity, and a 10% increase in specificity and accuracy over the state-of-the-art method, indicating its potential for more visually meaningful identification of suspicious tumour regions. The RD-CRF method was shown to improve the detection of tumour candidates by mitigating sparsely distributed tumour candidates and improving the detected tumour candidates via spatial consistency and radiomic feature relationships. Thus, the developed framework shows potential for aiding medical professionals with performing more efficient and accurate computer-aided prostate cancer detection

Intelligent Diagnosis Systems

This paper examines and compares several different approaches to the design of intelligent systems for diagnosis applications. These include expert systems (or knowledge-based systems), truth (or reason) maintenance systems, case-based reasoning systems, and inductive approaches like decision trees, artificial neural networks (or connectionist systems), and statistical pattern classification systems. Each of these approaches is demonstrated through the design of a system for a simple automobile fault diagnosis task. The paper also discusses the domain characteristics and design and performance requirements that influence the choice of a specific technique (or a combination of techniques) for a given application

Distributed fault detection of wireless sensor networks

Wireless Sensor Networks (WSNs) have become a new information collection and monitoring solution for a variety of applications. Faults occurring to sensor nodes are common due to the sensor device itself and the harsh environment where the sensor nodes are deployed. In order to ensure the network quality of service it is necessary for the WSN to be able to detect the faults and take actions to avoid further degradation of the service. The goal of this paper is to locate the faulty sensors in the wireless sensor networks. We propose and evaluate a localized fault detection algorithm to identify the faulty sensors. The implementation complexity of the algorithm is low and the probability of correct diagnosis is very high even in the existence of large fault sets. Simulation results show the algorithm can clearly identify the faulty sensors with high accuracy

An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

Computation of loop flows in electric grids with high wind energy penetration

2013 Fall.Includes bibliographical references.In a deregulated electricity market, the financial transmission rights (FTRs) and the bid-sell principle for energy trades are used to determine the expected power flows on transmission lines. Expected power flows are calculated by applying the superposition theorem on the approved electronic tags (e-tags). Multiple parallel paths in interconnected networks lead to division of power flows determined by the impedances of the parallel paths and the physical laws of electricity. The actual power flows in the network do not conform to the market expectations leading to unscheduled flows (USF) on transmission lines. USF have historically been estimated and accommodated deterministically for a given set of e-tags. However, wide-area interconnections experience variability and uncertainty due to a significant penetration of wind energy connected at the transmission level, thus imparting a stochastic nature to USF. A linear model, from the literature, has been adopted to model USF using a mathematical artifact called `minor loop flows'. This research develops an automated framework that provides accurate estimates of loop flows suitable for both market and network level accommodation of variable USF. This generic framework will be applicable to any power transmission network with intermittent energy resources. A loop detection algorithm (LDA) based on graph theory is proposed to detect loops in a transmission network of any size. The LDA is formulated as a modification of the A-star (A*) algorithm, the lowest ancestor theorem, and Dijkstra's algorithm. The LDA has an order of complexity of V2, where V is the total number of vertices or buses in the network under consideration. An application of a geographical information systems (GIS) technique has been established to obtain the transmission line layouts. The outcome of the LDA (i.e., minor loops) and line layouts (i.e., azimuth) are processed to compute the incidence matrix of the estimator. The variability due to the penetration of wind energy is accounted in the proposed framework using the probabilistic load flow analysis based on Monte Carlo simulations. Three techniques - ordinary least squares (OLS), analytic ridge regression (RR), and robust regression (M-estimators) - are used to estimate minor loop flows. The estimation techniques adhere to the auto-correction of the quality of estimates in case of ill-conditioning of the incidence matrix. Accuracy of loop flow estimates is highly significant, as they may be used for assigning economic responsibility of USF in electricity markets. Wind power generation companies (WGENCOs) employ forecasting models to participate in the primary electricity markets. Forecasting models used to predict the output of wind power plants are inherently erroneous and hence, their impacts on USF are studied. The impact of forecasting errors associated with the output of wind plants is investigated using the concept of prediction intervals rather than point accurate forecasts. Loop flow estimates corresponding to the prediction intervals of power output of wind power plants are computed to provide statistical bounds. The proposed framework is tested on the IEEE 14-bus and the IEEE 30-bus standard test systems with suitable modifications to represent wind energy penetration. Accurate loops are detected for the aforementioned test systems using the LDA. Thus, an automated and generic computation of loop flows is proposed along with a step-wise demonstration on IEEE test systems is provided. Future work and concluding remarks summarize the research work in this dissertation

Impaired Structural Motor Connectome in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease selectively affecting upper and lower motor neurons. Patients with ALS suffer from progressive paralysis and eventually die on average after three years. The underlying neurobiology of upper motor neuron degeneration and its effects on the complex network of the brain are, however, largely unknown. Here, we examined the effects of ALS on the structural brain network topology in 35 patients with ALS and 19 healthy controls. Using diffusion tensor imaging (DTI), the brain network was reconstructed for each individual participant. The connectivity of this reconstructed brain network was compared between patients and controls using complexity theory without - a priori selected - regions of interest. Patients with ALS showed an impaired sub-network of regions with reduced white matter connectivity (p = 0.0108, permutation testing). This impaired sub-network was strongly centered around primary motor regions (bilateral precentral gyrus and right paracentral lobule), including secondary motor regions (bilateral caudal middle frontal gyrus and pallidum) as well as high-order hub regions (right posterior cingulate and precuneus). In addition, we found a significant reduction in overall efficiency (p = 0.0095) and clustering (p = 0.0415). From our findings, we conclude that upper motor neuron degeneration in ALS affects both primary motor connections as well as secondary motor connections, together composing an impaired sub-network. The degenerative process in ALS was found to be widespread, but interlinked and targeted to the motor connectome

The Structural Basis for Brain Health

Cardiovascular disease (CVD) remains the leading cause of mortality in the United States. Stroke and dementia are the leading causes of adult disability worldwide, and the 5th and 6th leading causes of mortality respectively in the United States. Furthermore, CVD annually accounts for approximately $330 billion in direct and indirect costs in the United States: approximately one in seven health care dollars is spent on CVD. While these diseases have different etiologies, and present with different clinical manifestations and prognosis, converging evidence increasingly supports the idea of CVD as a common pathophysiological origin of cerebrovascular disease, potentially indicating a complex interplay between brain health and cardiovascular health. In this thesis, we leverage methodological advancements in systems and computational neurosciences related to the human brain connectome to assess individual topological network organization and integrity in acute and chronic stroke cohorts, and in a non-stroke cohort with varying CV risk factor burden, using graph theory and network analysis. We propose measures that underly neuroanatomical mechanisms that constitute efficient transfer of information and brain health. We demonstrate the impact of cardiovascular risk factors on brain health, even before overt clinical manifestation, and the resulting impact on cognitive performance, and further determine the underlying pathophysiology relating white matter disease and post-stroke outcomes