Composite materials impact damage detection using neural networks

This thesis considers two basic aspects of impact damage in composite materials, namely damage severity discrimination and impact damage location by using Acoustic Emissions (AE) and Artificial Neural Networks (ANNs). The experimental work embodies a study of such factors as the application of AE as Non-destructive Damage Testing (NDT), and the evaluation of ANNs modelling. ANNs, however, played an important role in modelling implementation. In the first aspect of the study, different impact energies were used to produce different level of damage in two composite materials (T300/914 and T800/5245). The impacts were detected by their acoustic emissions (AE). The AE waveform signals were analysed and modelled using a Back Propagation (BP) neural network model. The Mean Square Error (MSE) from the output was then used as a damage indicator in the damage severity discrimination study. To evaluate the ANN model, a comparison was made of the correlation coefficients of different parameters, such as MSE, AE energy, AE counts, etc. MSE produced an outstanding result based on the best performance of correlation. In the second aspect, a new artificial neural network model was developed to provide impact damage location on a quasi-isotropic composite panel. It was successfully trained to locate impact sites by correlating the relationship between arriving time differences of AE signals at transducers located on the panel and the impact site coordinates. The performance of the ANN model, which was evaluated by calculating the distance deviation between model output and real location coordinates, supports the application of ANN as an impact damage location identifier. In the study, the accuracy of location prediction decreased when approaching the central area of the panel. Further investigation indicated that this is due to the small arrival time differences, which defect the performance of ANN prediction. This research suggested increasing the number of processing neurons in the ANNs as a practical solution.

A comparison between static and kinetic visual attention as a means of detecting age-related deterioration of the visual system and driving performance

A critical review of previous research revealed that visual attention tests, such as the Useful Field of View (UFOV) test, provided the best means of detecting age-related changes to the visual system that could potentially increase crash risk. However, the question was raised as to whether the UFOV, which was regarded as a static visual attention test, could be improved by inclusion of kinetic targets that more closely represent the driving task. A computer program was written to provide more information about the derivation of UFOV test scores. Although this investigation succeeded in providing new information, some of the commercially protected UFOV test procedures still remain unknown. Two kinetic visual attention tests (DRTS1 and 2), developed at Aston University to investigate inclusion of kinetic targets in visual attention tests, were introduced. The UFOV was found to be more repeatable than either of the kinetic visual attention tests and learning effects or age did not influence these findings. Determinants of static and kinetic visual attention were explored. Increasing target eccentricity led to reduced performance on the UFOV and DRTS1 tests. The DRTS2 was not affected by eccentricity but this may have been due to the style of presentation of its targets. This might also have explained why only the DRTS2 showed laterality effects (i.e. better performance to targets presented on the left hand side of the road). Radial location, explored using the UFOV test, showed that subjects responded best to targets positioned to the horizontal meridian. Distraction had opposite effects on static and kinetic visual attention. While UFOV test performance declined with distraction, DRTS1 performance increased. Previous research had shown that this striking difference was to be expected. Whereas the detection of static targets is attenuated in the presence of distracting stimuli, distracting stimuli that move in a structured flow field enhances the detection of moving targets. Subjects reacted more slowly to kinetic compared to static targets, longitudinal motion compared to angular motion and to increased self-motion. However, the effects of longitudinal motion, angular motion, self-motion and even target eccentricity were caused by target edge speed variations arising because of optic flow field effects. The UFOV test was more able to detect age-related changes to the visual system than were either of the kinetic visual attention tests. The driving samples investigated were too limited to draw firm conclusions. Nevertheless, the results presented showed that neither the DRTS2 nor the UFOV tests were powerful tools for the identification of drivers prone to crashes or poor driving performance.

Impact of senescence and disease on the structural and functional performance of the visual pathway

The diagnosis and monitoring of ocular disease presents considerable clinical difficulties for two main reasons i) the substantial physiological variation of anatomical structure of the visual pathway and ii) constraints due to technical limitations of diagnostic hardware. These are further confounded by difficulties in detecting early loss or change in visual function due to the masking of disease effects, for example, due to a high degree of redundancy in terms of nerve fibre number along the visual pathway. This thesis addresses these issues across three areas of study: 1. Factors influencing retinal thickness measures and their clinical interpretation As the retina is the principal anatomical site for damage associated with visual loss, objective measures of retinal thickness and retinal nerve fibre layer thickness are key to the detection of pathology. In this thesis the ability of optical coherence tomography (OCT) to provide repeatable and reproducible measures of retinal structure at the macula and optic nerve head is investigated. In addition, the normal physiological variations in retinal thickness and retinal nerve fibre layer thickness are explored. Principal findings were: • Macular retinal thickness and optic nerve head measurements are repeatable and reproducible for normal subjects and diseased eyes • Macular and retinal nerve fibre layer thickness around the optic nerve correlate negatively with axial length, suggesting that larger eyes have thinner retinae, potentially making them more susceptible to damage or disease • Foveola retinal thickness increases with age while retinal nerve fibre layer thickness around the optic nerve head decreases with age. Such findings should be considered during examination of the eye with suspect pathology or in long-term disease monitoring 2. Impact of glucose control on retinal anatomy and function in diabetes Diabetes is a major health concern in the UK and worldwide and diabetic retinopathy is a major cause of blindness in the working population. Objective, quantitative measurements of retinal thickness. particularly at the macula provide essential information regarding disease progression and the efficacy of treatment. Functional vision loss in diabetic patients is commonly observed in clinical and experimental studies and is thought to be affected by blood glucose levels. In the first study of its kind, the short term impact of fluctuations in blood glucose levels on retinal structure and function over a 12 hour period in patients with diabetes are investigated. Principal findings were: • Acute fluctuations in blood glucose levels are greater in diabetic patients than normal subjects • The fluctuations in blood glucose levels impact contrast sensitivity scores. SWAP visual fields, intraocular pressure and diastolic pressure. This effect is similar for type 1 and type 2 diabetic patients despite the differences in their physiological status. • Long-term metabolic control in the diabetic patient is a useful predictor in the fluctuation of contrast sensitivity scores. • Large fluctuations in blood glucose levels and/or visual function and structure may be indicative of an increased risk of development or progression of retinopathy 3. Structural and functional damage of the visual pathway in glaucomatous optic neuropathy The glaucomatous eye undergoes a number of well documented pathological changes including retinal nerve fibre loss and optic nerve head damage which is correlated with loss of functional vision. In experimental glaucoma there is evidence that glaucomatous damage extends from retinal ganglion cells in the eye, along the visual pathway, to vision centres in the brain. This thesis explores the effects of glaucoma on retinal nerve fibre layer thickness, ocular anterior anatomy and cortical structure, and its correlates with visual function in humans. Principal findings were: • In the retina, glaucomatous retinal nerve fibre layer loss is less marked with increasing distance from the optic nerve head, suggesting that RNFL examination at a greater distance than traditionally employed may provide invaluable early indicators of glaucomatous damage • Neuroretinal rim area and retrobulbar optic nerve diameter are strong indicators of visual field loss • Grey matter density decreases at a rate of 3.85% per decade. There was no clear evidence of a disease effect • Cortical activation as measured by fMRI was a strong indicator of functional damage in patients with significant neuroretinal rim loss despite relatively modest visual field defects These investigations have shown that the effects of senescence are evident in both the anterior and posterior visual pathway. A variety of anatomical and functional diagnostic protocols for the investigation of damage to the visual pathway in ocular disease are required to maximise understanding of the disease processes and thereby optimising patient care.

Near-ML detection for MDL-impaired few-mode fiber transmission

Few-mode fiber transmission systems are typically impaired by mode-dependent loss (MDL). In an MDL-impaired link, maximum-likelihood (ML) detection yields a significant advantage in system performance compared to linear equalizers, such as zero-forcing and minimum-mean square error equalizers. However, the computational effort of the ML detection increases exponentially with the number of modes and the cardinality of the constellation. We present two methods that allow for near-ML performance without being afflicted with the enormous computational complexity of ML detection: improved reduced-search ML detection and sphere decoding. Both algorithms are tested regarding their performance and computational complexity in simulations of three and six spatial modes with QPSK and 16QAM constellations.

MAP detection for impairment compensation in coherent WDM systems

We propose a novel recursive-algorithm based maximum a posteriori probability (MAP) detector in spectrally-efficient coherent wavelength division multiplexing (CoWDM) systems, and investigate its performance in a 1-bit/s/Hz on-off keyed (OOK) system limited by optical-signal-to-noise ratio. The proposed method decodes each sub-channel using the signal levels not only of the particular sub-channel but also of its adjacent sub-channels, and therefore can effectively compensate deterministic inter-sub-channel crosstalk as well as inter-symbol interference arising from narrow-band filtering and chromatic dispersion (CD). Numerical simulation of a five-channel OOK-based CoWDM system with 10Gbit/s per channel using either direct or coherent detection shows that the MAP decoder can eliminate the need for phase control of each optical carrier (which is necessarily required in a conventional CoWDM system), and greatly relaxes the spectral design of the demultiplexing filter at the receiver. It also significantly improves back-to-back sensitivity and CD tolerance of the system.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Goal detection from natural language queries

This paper aims to identify the communication goal(s) of a user's information-seeking query out of a finite set of within-domain goals in natural language queries. It proposes using Tree-Augmented Naive Bayes networks (TANs) for goal detection. The problem is formulated as N binary decisions, and each is performed by a TAN. Comparative study has been carried out to compare the performance with Naive Bayes, fully-connected TANs, and multi-layer neural networks. Experimental results show that TANs consistently give better results when tested on the ATIS and DARPA Communicator corpora.

A comparative study of bayesian models for unsupervised sentiment detection

This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentiment-topic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint sentiment topic detection.

Feature LDA:a supervised topic model for automatic detection of web API documentations from the web

Web APIs have gained increasing popularity in recent Web service technology development owing to its simplicity of technology stack and the proliferation of mashups. However, efficiently discovering Web APIs and the relevant documentations on the Web is still a challenging task even with the best resources available on the Web. In this paper we cast the problem of detecting the Web API documentations as a text classification problem of classifying a given Web page as Web API associated or not. We propose a supervised generative topic model called feature latent Dirichlet allocation (feaLDA) which offers a generic probabilistic framework for automatic detection of Web APIs. feaLDA not only captures the correspondence between data and the associated class labels, but also provides a mechanism for incorporating side information such as labelled features automatically learned from data that can effectively help improving classification performance. Extensive experiments on our Web APIs documentation dataset shows that the feaLDA model outperforms three strong supervised baselines including naive Bayes, support vector machines, and the maximum entropy model, by over 3% in classification accuracy. In addition, feaLDA also gives superior performance when compared against other existing supervised topic models.

Acoustic emission detection using optical fibre sensors for aerospace applications

Structural Health Monitoring (SHM) ensures the structural health and safety of critical structures covering a wide range of application areas. This thesis presents novel, low-cost and good-performance fibre Bragg grating (FBG) based systems for detection of Acoustic Emission (AE) in aircraft structures, which is a part of SHM. Importantly a key aim, during the design of these systems, was to produce systems that were sufficiently small to install in an aircraft for lifetime monitoring. Two important techniques for monitoring high frequency AE that were developed as a part of this research were, Quadrature recombination technique and Active tracking technique. Active tracking technique was used extensively and was further developed to overcome the limitations that were observed while testing it at several test facilities and with different optical fibre sensors. This system was able to eliminate any low frequency spectrum shift due to environmental perturbation and keeps the sensor always working at optimum operation point. This is highly desirable in harsh industrial and operationally active environments. Experimental work carried out in the laboratory has proved that such systems can be used for high frequency detection and have capability to detect up to 600 kHz. However, the range of frequency depends upon the requirement and design of the interrogation system as the system can be altered accordingly for different applications. Several optical fibre configurations for wavelength detection were designed during the course of this work along with industrial partners. Fibre Bragg grating Fabry-Perot (FBG-FP) sensors have shown higher sensitivity and usability than the uniform FBGs to be used with such system. This was shown experimentally. The author is certain that further research will lead to development of a commercially marketable product and the use of active tracking systems can be extended in areas of healthcare, civil infrastructure monitoring etc. where it can be deployed. Finally, the AE detection system has been developed to aerospace requirements and was tested at NDT & Testing Technology test facility based at Airbus, Filton, UK on A350 testing panels.

Bit-error probability for direct detection of optical RZ signal degraded by ASE noise and timing jitter

We analyze theoretically the interplay between optical return-to-zero signal degradation due to timing jitter and additive amplified-spontaneous-emission noise. The impact of these two factors on the performance of a square-law direct detection receiver is also investigated. We derive an analytical expression for the bit-error probability and quantitatively determine the conditions when the contributions of the effects of timing jitter and additive noise to the bit error rate can be treated separately. The analysis of patterning effects is also presented. © 2007 IEEE.

Improving the performance of FBG sensors in a WDM network using a simulated annealing technique

Simulated annealing technique is used to improve the performance of fiber Bragg grating (FBG) sensors in a wavelength-division-multiplexed network. Experiments demonstrated strain detection accuracy of ̃2.5 με when the spectrums of FBGs are fully or partially overlapped.

Detection of organic aromatic compounds in paraffin by a long-period fiber grating optical sensor with optimized sensitivity

A long-period grating (LPG) sensor is used to detect small variations in the concentration of an organic aromatic compound (xylene) in a paraffin (heptane) solution. A new design procedure is adopted and demonstrated to maximize the sensitivity of LPG (wavelength shift for a change in the surrounding refractive index, (dλ/dn3)) for a given application. The detection method adopted is comparable to the standard technique used in industry (high performance liquid chromatograph and UV spectroscopy) which has a relative accuracy between ∼±0.5% and 5%. The minimum detectable change in volumetric concentration is 0.04% in a binary fluid with the detection system presented. This change of concentration relates to a change in refractive index of Δn ∼ 6 × 10-5. © 2001 Elsevier Science B.V.

Performance management of event processing systems

This thesis is a study of performance management of Complex Event Processing (CEP) systems. Since CEP systems have distinct characteristics from other well-studied computer systems such as batch and online transaction processing systems and database-centric applications, these characteristics introduce new challenges and opportunities to the performance management for CEP systems. Methodologies used in benchmarking CEP systems in many performance studies focus on scaling the load injection, but not considering the impact of the functional capabilities of CEP systems. This thesis proposes the approach of evaluating the performance of CEP engines’ functional behaviours on events and develops a benchmark platform for CEP systems: CEPBen. The CEPBen benchmark platform is developed to explore the fundamental functional performance of event processing systems: filtering, transformation and event pattern detection. It is also designed to provide a flexible environment for exploring new metrics and influential factors for CEP systems and evaluating the performance of CEP systems. Studies on factors and new metrics are carried out using the CEPBen benchmark platform on Esper. Different measurement points of response time in performance management of CEP systems are discussed and response time of targeted event is proposed to be used as a metric for quality of service evaluation combining with the traditional response time in CEP systems. Maximum query load as a capacity indicator regarding to the complexity of queries and number of live objects in memory as a performance indicator regarding to the memory management are proposed in performance management of CEP systems. Query depth is studied as a performance factor that influences CEP system performance.

A rule-based approach to implicit emotion detection in text

Most research in the area of emotion detection in written text focused on detecting explicit expressions of emotions in text. In this paper, we present a rule-based pipeline approach for detecting implicit emotions in written text without emotion-bearing words based on the OCC Model. We have evaluated our approach on three different datasets with five emotion categories. Our results show that the proposed approach outperforms the lexicon matching method consistently across all the three datasets by a large margin of 17–30% in F-measure and gives competitive performance compared to a supervised classifier. In particular, when dealing with formal text which follows grammatical rules strictly, our approach gives an average F-measure of 82.7% on “Happy”, “Angry-Disgust” and “Sad”, even outperforming the supervised baseline by nearly 17% in F-measure. Our preliminary results show the feasibility of the approach for the task of implicit emotion detection in written text.