The manufacturing and the application of polycrystalline diamond tools – A comprehensive review

Advanced materials such as titanium alloys and metal matrix composites are extensively used in the aerospace industry and biomedical engineering. They are difficult to machine because of the severe abrasion and high temperature at the tool/chip and tool/workpiece interfaces which cause severe tool wear and premature tool rejection. Compared with conventional cutting tools, polycrystalline diamond (PCD) tools are promising in machining refractory metals and hard-to-machine materials because of the outstanding mechanical properties of PCD. This paper reviewed the manufacturing and application of PCD cutting tools. The researches on manufacturing process of PCD tools and the application in cutting hard-to-machine materials were analysed, and the results and findings were comprehensively discussed. Two most widely used refining methods including abrasive grinding and electrical discharge grinding (EDG) as well as the defects caused by the processes were presented. The wear process of PCD tools in different industrial cutting methods and the wear mechanism of different PCD materials were explained in both micro-scale and macro-scale. Research directions and the trend of the application of PCD cutting tools were introduced

Wavelet methods in speech recognition

In this thesis, novel wavelet techniques are developed to improve parametrization of speech signals prior to classification. It is shown that non-linear operations carried out in the wavelet domain improve the performance of a speech classifier and consistently outperform classical Fourier methods. This is because of the localised nature of the wavelet, which captures correspondingly well-localised time-frequency features within the speech signal. Furthermore, by taking advantage of the approximation ability of wavelets, efficient representation of the non-stationarity inherent in speech can be achieved in a relatively small number of expansion coefficients. This is an attractive option when faced with the so-called 'Curse of Dimensionality' problem of multivariate classifiers such as Linear Discriminant Analysis (LDA) or Artificial Neural Networks (ANNs). Conventional time-frequency analysis methods such as the Discrete Fourier Transform either miss irregular signal structures and transients due to spectral smearing or require a large number of coefficients to represent such characteristics efficiently. Wavelet theory offers an alternative insight in the representation of these types of signals. As an extension to the standard wavelet transform, adaptive libraries of wavelet and cosine packets are introduced which increase the flexibility of the transform. This approach is observed to be yet more suitable for the highly variable nature of speech signals in that it results in a time-frequency sampled grid that is well adapted to irregularities and transients. They result in a corresponding reduction in the misclassification rate of the recognition system. However, this is necessarily at the expense of added computing time. Finally, a framework based on adaptive time-frequency libraries is developed which invokes the final classifier to choose the nature of the resolution for a given classification problem. The classifier then performs dimensionaIity reduction on the transformed signal by choosing the top few features based on their discriminant power. This approach is compared and contrasted to an existing discriminant wavelet feature extractor. The overall conclusions of the thesis are that wavelets and their relatives are capable of extracting useful features for speech classification problems. The use of adaptive wavelet transforms provides the flexibility within which powerful feature extractors can be designed for these types of application

ROBUST FAULT ANALYSIS FOR PERMANENT MAGNET DC MOTOR IN SAFETY CRITICAL APPLICATIONS

Robust fault analysis (FA) including the diagnosis of faults and predicting their level of severity is necessary to optimise maintenance and improve reliability of Aircraft. Early diagnosis of faults that might occur in the supervised process renders it possible to perform important preventative actions. The proposed diagnostic models were validated in two experimental tests. The first test concerned a single localised and generalised roller element bearing fault in a permanent magnet brushless DC (PMBLDC) motor. Rolling element bearing defect is one of the main reasons for breakdown in electrical machines. Vibration and current are analysed under stationary and non-stationary load and speed conditions, for a variety of bearing fault severities, and for both local and global bearing faults. The second test examined the case of an unbalance rotor due to blade faults in a thruster, motor based on a permanent magnet brushed DC (PMBDC) motor. A variety of blade fault conditions were investigated, over a wide range of rotation speeds. The test used both discrete wavelet transform (DWT) to extract the useful features, and then feature reduction techniques to avoid redundant features. This reduces computation requirements and the time taken for classification by the application of an orthogonal fuzzy neighbourhood discriminant analysis (OFNDA) approach. The real time monitoring of motor operating conditions is an advanced technique that presents the real performance of the motor, so that the dynamic recurrent neural network (DRNN) proposed predicts the conditions of components and classifies the different faults under different operating conditions. The results obtained from real time simulation demonstrate the effectiveness and reliability of the proposed methodology in accurately classifying faults and predicting levels of fault severity.the Iraqi Ministry of Higher Education and Scientific Researc

In-situ health monitoring for wind turbine blade using acoustic wireless sensor networks at low sampling rates

PhD ThesisThe development of in-situ structural health monitoring (SHM) techniques represents a challenge for offshore wind turbines (OWTs) in order to reduce the cost of the operation and maintenance (O&M) of safety-critical components and systems. This thesis propos- es an in-situ wireless SHM system based on acoustic emission (AE) techniques. The proposed wireless system of AE sensor networks is not without its own challenges amongst which are requirements of high sampling rates, limitations in the communication bandwidth, memory space, and power resources. This work is part of the HEMOW- FP7 Project, ‘The Health Monitoring of Offshore Wind Farms’. The present study investigates solutions relevant to the abovementioned challenges. Two related topics have been considered: to implement a novel in-situ wireless SHM technique for wind turbine blades (WTBs); and to develop an appropriate signal pro- cessing algorithm to detect, localise, and classify different AE events. The major contri- butions of this study can be summarised as follows: 1) investigating the possibility of employing low sampling rates lower than the Nyquist rate in the data acquisition opera- tion and content-based feature (envelope and time-frequency data analysis) for data analysis; 2) proposing techniques to overcome drawbacks associated with lowering sampling rates, such as information loss and low spatial resolution; 3) showing that the time-frequency domain is an effective domain for analysing the aliased signals, and an envelope-based wavelet transform cross-correlation algorithm, developed in the course of this study, can enhance the estimation accuracy of wireless acoustic source localisa- tion; 4) investigating the implementation of a novel in-situ wireless SHM technique with field deployment on the WTB structure, and developing a constraint model and approaches for localisation of AE sources and environmental monitoring respectively. Finally, the system has been experimentally evaluated with the consideration of the lo- calisation and classification of different AE events as well as changes of environmental conditions. The study concludes that the in-situ wireless SHM platform developed in the course of this research represents a promising technique for reliable SHM for OWTBs in which solutions for major challenges, e.g., employing low sampling rates lower than the Nyquist rate in the acquisition operation and resource constraints of WSNs in terms of communication bandwidth and memory space are presente

A machine learning framework for automatic human activity classification from wearable sensors

Wearable sensors are becoming increasingly common and they permit the capture of physiological data during exercise, recuperation and everyday activities. This work investigated and advanced the current state-of-the-art in machine learning technology for the automatic classification of captured physiological data from wearable sensors. The overall goal of the work presented here is to research and investigate every aspect of the technology and methods involved in this field and to create a framework of technology that can be utilised on low-cost platforms across a wide range of activities. Both rudimentary and advanced techniques were compared, including those that allowed for both real-time processing on an android platform and highly accurate postprocessing on a desktop computer. State-of-the-art feature extraction methods such as Fourier and Wavelet analysis were also researched to ascertain how well they could extract discriminative physiological information. Various classifiers were investigated in terms of their ability to work with different feature extraction methods. Consequently, complex classification fusion models were created to increase the overall accuracy of the activity recognition process. Genetic algorithms were also employed to optimise classifier parameter selection in the multidimensional search space. Large annotated sporting activity datasets were created for a range of sports that allowed different classification models to be compared. This allowed for a machine learning framework to be constructed that could potentially create accurate models when applied to any unknown dataset. This framework was also successfully applied to medical and everyday-activity datasets confirming that the approach could be deployed in different application settings

Enhancement and optimization of a multi-command-based brain-computer interface

Brain-computer interfaces (BCI) assist disabled person to control many appliances without any physically interaction (e.g., pressing a button). SSVEP is brain activities elicited by evoked signals that are observed by visual stimuli paradigm. In this dissertation were addressed the problems which are oblige more usability of BCI-system by optimizing and enhancing the performance using particular design. Main contribution of this work is improving brain reaction response depending on focal approaches

Review of EEG-based pattern classification frameworks for dyslexia

Dyslexia is a disability that causes difficulties in reading and writing despite average intelligence. This hidden disability often goes undetected since dyslexics are normal and healthy in every other way. Electroencephalography (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. The aims of this paper are to examine pros and cons of existing EEG-based pattern classification frameworks for dyslexia and recommend optimisations through the findings to assist future research. A critical analysis of the literature is conducted focusing on each framework’s (1) data collection, (2) pre-processing, (3) analysis and (4) classification methods. A wide range of inputs as well as classification approaches has been experimented for the improvement in EEG-based pattern classification frameworks. It was uncovered that incorporating reading- and writing-related tasks to experiments used in data collection may help improve these frameworks instead of using only simple tasks, and those unwanted artefacts caused by body movements in the EEG signals during reading and writing activities could be minimised using artefact subspace reconstruction. Further, support vector machine is identified as a promising classifier to be used in EEG-based pattern classification frameworks for dyslexia

Public Engagement Technology for Bioacoustic Citizen Science

Inexpensive mobile devices offer new capabilities for non-specialist use in the field for the purpose of conservation. This thesis explores the potential for such devices to be used by citizen scientists interacting with bioacoustic data such as birdsong. This thesis describes design research and field evaluation, in collaboration with conservationists and educators, and technological artefacts implemented as mobile applications for interactive educational gaming and creative composition. This thesis considers, from a participant-centric collaborative design approach, conservationists' demand for interactive artefacts to motivate engagement in citizen science through gameful and playful interactions. Drawing on theories of motivation, frequently applied to the study of Human-Computer Interaction (HCI), and on approaches to designing for motivational engagement, this thesis introduces a novel pair of frameworks for the analysis of technological artefacts and for assessing participant engagement with bioacoustic citizen science from both game interaction design and citizen science project participation perspectives. This thesis reviews current theories of playful and gameful interaction developed for collaborative learning, data analysis, and ground-truth development, describes a process for design and analysis of motivational mobile games and toys, and explores the affordances of various game elements and mechanics for engaging participation in bioacoustic citizen science. This thesis proposes research into progressions for scaffolding engagement with citizen science projects where participants interact with data collection and analysis artefacts. The research process includes the development of multiple designs, analyses of which explore the efficacy of game interactions to motivate engagement through interaction progressions, given proposed analysis frameworks. This thesis presents analysed results of experiments examining the usability of, and data-quality from, several prototypes and software artefacts, in both laboratory conditions and the field. This thesis culminates with an assessment of the efficacy of proposed design analysis frameworks, an analysis of designed artefacts, and a discussion of how these designs increase intrinsic and extrinsic motivation for participant engagement and affect resultant bioacoustic citizen science data quantity and quality.Non

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure