Air borne or liquid-laden solid particle transport is a common phenomenon in various industrial applications. Solid particles, transported at severe operating conditions such as high flow velocity, can cause concerns for structural integrity through wear originated from particle impacts with structure. To apply Acoustic Emission (AE) in particle impact monitoring, previous researchers focused primarily on dry particle impacts on dry target plate and/or wet particle impacts on wet or dry target plate. For dry particle impacts on dry target plate, AE events energy, calculated from the recorded free falling or air borne particle impact AE signals, were correlated with particle size, concentration, height, target material and thickness. For a given system, once calibrated for a specific particle type and operating condition, this technique might be sufficient to serve the purpose. However, if more than one particle type present in the system, particularly with similar size, density and impact velocity, calculated AE event energy is not unique for a specific particle type. For wet particle impacts on dry or wet target plate (either submerged or in a flow loop), AE event energy was related to the particle size, concentration, target material, impact velocity and angle between the nozzle and the target plate. In these studies, the experimental arrangements and the operating conditions considered either did not allow any bubble formation in the system or even if there is any at least an order of magnitude lower in amplitude than the sand particle impact and so easily identifiable. In reality, bubble formation can be comparable with particle impacts in terms of AE amplitude in process industries, for example, sand production during oil and gas transportation from reservoir. Current practice is to calibrate an installed AE monitoring system against a range of sand free flow conditions. In real time monitoring, for a specific calibrated flow, the flow generated AE amplitude/energy is deducted from the recorded AE amplitude/energy and the difference is attributed to the sand particle impacts. However, if the flow condition changes, which often does in the process industry, the calibration is not valid anymore and AE events from bubble can be misinterpreted as sand particle impacts and vice versa. In this research, sand particles and glass beads with similar size, density and impact velocity have been studied dropping from 200 mm on a small cylindrical stepped mild steel coupon as a target plate. For signal recording purposes, two identical broadband AE sensors are installed, one at the centre and one 30 mm off centred, on the opposite of the impacting surface. Signal analysis have been carried out by evaluating 7 standard AE parameters (amplitude, energy, rise time, duration, power spectral density(PSD), peak frequency at PSD and spectral centroid) in the time and frequency domain and time-frequency domain analysis have been performed applying Gabor Wavelet Transform. The signal interpretation becomes difficult due to reflections, dispersions and mode conversions caused by close proximity of the boundaries. So, a new signal analysis parameter - frequency band energy ratio - has been proposed. This technique is able to distinguish between population of two very similar groups (in terms of size and mass and energy) of sand particles and glass beads, impacting on mild steel based on the coefficient of variation (Cv) of the frequency band AE energy ratios. To facilitate individual particle impact identification, further analysis has been performed using Support Vector Machine (SVM) based classification algorithm using 7 standard AE parameters, evaluated in both the time and frequency domain. Available data set has been segmented into two parts of training set (80%) and test set (20%). The developed model has been applied on the test data for model performance evaluation purpose. The overall success rate of individually identifying each category (PLB, Glass bead and Sand particle impacts) at S1 has been found as 86% and at S2 as 92%. To study wet particle impacts on wet target surface, in presence of bubbles, the target plate has been sealed to a cylindrical perspex tube. Single and multiple sand particles have been introduced in the system using a constant speed blower to impact the target surface under water loading. Two sensor locations, used in the previous sets of experiments, have been monitored. From frequency domain analysis it has been observed that characteristic frequency for particle impacts are centred at 300-350 kHz and for bubble formations are centred at 135 – 150 kHz. Based upon this, two frequency bands 100 – 200 kHz (E1) and 300 – 400 kHz (E3) and the frequency band energy ratio (E3E1,) have been identified as optimal for identification particle impacts for the given system. E3E1, > 1 has been associated with particle impacts and E3E1, < 1 has been associated with bubble formations. Applying these frequency band energy ratios and setting an amplitude threshold, an automatic event identification technique has been developed for identification of sand particle impacts in presence of bubbles. The method developed can be used to optimize the identification of sand particle impacts. The optimal setting of an amplitude threshold is sensitive to number of particles and noise levels. A high threshold of say 10% will clearly identify sand particle impacts but for multiparticle tests is likely to not detect about 20% of lower (impact) energy particles. A threshold lower than 3% is likely to result in detection of AE events with poor frequency content and wrong classification of the weakest events. Optimal setting of the parameters used in the framework such as thresholds, frequency bands and ratios of AE energy is likely to make identification of sand particle impacts in the laboratory environment within 10% possible. For this technique, once the optimal frequency bands and ratios have been identified, then an added advantage is that calibration of the signal levels is not required.

The Support Vector Machine (SVM) classifier seeks to find the separating hyperplane wx=r that maximizes the margin distance 1/||w||2^2. It can be formalized as an optimization problem that minimizes the hinge loss Ʃ[subscript i](1-y[subscript i] f(x[subscript i]))₊ plus the L₂-norm of the weight vector. SVM is now a mainstay method of machine learning. The goal of this dissertation work is to solve different biomedical data analysis problems efficiently using extensions of SVM, in which we augment the standard SVM formulation based on the application requirements. The biomedical applications we explore in this thesis include: cancer diagnosis, biomarker discovery, and energy function learning for protein structure prediction. Ovarian cancer diagnosis is problematic because the disease is typically asymptomatic especially at early stages of progression and/or recurrence. We investigate a sample set consisting of 44 women diagnosed with serous papillary ovarian cancer and 50 healthy women or women with benign conditions. We profile the relative metabolite levels in the patient sera using a high throughput ambient ionization mass spectrometry technique, Direct Analysis in Real Time (DART). We then reduce the diagnostic classification on these metabolic profiles into a functional classification problem and solve it with functional Support Vector Machine (fSVM) method. The assay distinguished between the cancer and control groups with an unprecedented 99\% accuracy (100\% sensitivity, 98\% specificity) under leave-one-out-cross-validation. This approach has significant clinical potential as a cancer diagnostic tool. High throughput technologies provide simultaneous evaluation of thousands of potential biomarkers to distinguish different patient groups. In order to assist biomarker discovery from these low sample size high dimensional cancer data, we first explore a convex relaxation of the L₀-SVM problem and solve it using mixed-integer programming techniques. We further propose a more efficient L₀-SVM approximation, fractional norm SVM, by replacing the L₂-penalty with L[subscript q]-penalty (q in (0,1)) in the optimization formulation. We solve it through Difference of Convex functions (DC) programming technique. Empirical studies on the synthetic data sets as well as the real-world biomedical data sets support the effectiveness of our proposed L₀-SVM approximation methods over other commonly-used sparse SVM methods such as the L₁-SVM method. A critical open problem in emph{ab initio} protein folding is protein energy function design. We reduce the problem of learning energy function for extit{ab initio} folding to a standard machine learning problem, learning-to-rank. Based on the application requirements, we constrain the reduced ranking problem with non-negative weights and develop two efficient algorithms for non-negativity constrained SVM optimization. We conduct the empirical study on an energy data set for random conformations of 171 proteins that falls into the {it ab initio} folding class. We compare our approach with the optimization approach used in protein structure prediction tool, TASSER. Numerical results indicate that our approach was able to learn energy functions with improved rank statistics (evaluated by pairwise agreement) as well as improved correlation between the total energy and structural dissimilarity.