Effect of Initial HMM Choices in Multiple Sequence Training for Gesture Recognition

We present several ways to initialize and train Hidden Markov Models (HMMs) for gesture recognition. These include using a single initial model for training (reestimation), multiple random initial models, and initial models directly computed from physical considerations. Each of the initial models is trained on multiple observation sequences using both Baum-Welch and the Viterbi Path Counting algorithm on three different model structures: Fully Connected (or ergodic), Left-Right, and Left-Right Banded. After performing many recognition trials on our video database of 780 letter gestures, results show that a) the simpler the structure is, the less the effect of the initial model, b) the direct computation method for designing the initial model is effective and provides insight into HMM learning, and c) Viterbi Path Counting performs best overall and depends much less on the initial model than does Baum-Welch training

Understanding HMM Training For Video Gesture Recognition

When developing a video gesture recognition system to recognise letters of the alphabet based on hidden Markov Model (HMM) pattern recognition, we observed that by carefully selecting the model structure we could obtain greatly improved recognition performance. This led us to the questions: Why do some HMMs work so well for pattern recognition? Which factors affect the HMM training process? In an attempt to answer these fundamental questions of learning, we used simple triangle and square video gestures where good HMM structure can be deduced analytically from knowledge of the physical process. We then compared these analytic models to models estimated from Baum-Welch training on the video gestures. This paper shows that with appropriate constraints on model structure, Baum-Welch reestimation leads to good HMMs which are very similar to those obtained analytically. These results corroborate earlier work where we show that the LR banded HMM structure is remarkably effective in recognising video gestures when compared to fully-connected (ergodic) or LR HMM structures

Speech Enhancement for Robust Speaker Verification

We examine the performance of Kalman filtering and smoothing techniques in the context of a working verification system to see the effect of interspeaker and intraspeaker variability

The effect of the degree of cure on the corrosion resistance of vinyl ester/glass fibre composites

Four different cure schedules were used to determine the effect of the degree of cure on the corrosion resistance of a vinyl ester/glass fibre reinforced composite. Specimens were immersed in four separate environments: 5 wt% NaOH, 32 wt% HCL, 25 wt% H2SO4, and uncut Kerosene, each at 66 degreesC (150 degreesF). It was found that the degree of cure did not affect the amount of degradation in flexural properties over time. Rubber-toughening of the resin matrix and the total volume fraction of resin in the composite were found to affect the amount of degradation exhibited. Sodium hydroxide was the only medium, which produced degradation of the flexural properties after 3 months. For all the other media, degradation occurred within the first 3 months of exposure. The analysis of variance technique provided a useful method for determining significant differences in measured mechanical properties

Seawater durability of glass- and carbon-polymer composites

The effect of seawater immersion on the durability of glass- and carbon-fibre reinforced polymer composites was experimentally investigated. The materials studied were glass/polyester, carbon/polyester, glass/vinyl ester and carbon/vinyl ester composites used in marine structures. When immersed in seawater at a temperature of 30 °C for over two years, the composites experienced significant moisture absorption and suffered chemical degradation of the resin matrix and fibre/matrix interphase region. This degraded the flexural modulus and strength of the composites, although the mode I interlaminar fracture toughness was only marginally affected by immersion

The role of secondary carbide precipitation on the fracture toughness of a reduced carbon white iron

The fracture toughness of a high chromium, reduced carbon white cast iron was measured using the KIc fracture toughness test. The toughness was found to increase with increasing heat treatment temperature for the temperature range of 1273–1423 K. Increases in the fracture toughness were due to crack deflection into the dendritic phase. Cracking in the dendrites was promoted by the presence of secondary carbides which formed during the high temperature heat treatment employed. The characteristic distance for brittle fracture as calculated by the Ritchie–Knott–Rice model correlated well with the centre to centre mean free path of the secondary carbides on the fracture plane

Stability of polyester and vinyl ester-based composites in seawater

Glass fiber-reinforced polymer composites are used in boats, yachts, ships, submarines and offshore drilling platforms due to their low cost, high specific strength, fatigue endurance and durability [1-6]. However, a problem with fiberglass laminates is their low Young's modulus, which makes it difficult to build ultralight marine structures with adequate stiffness. Consequently, marine composite structures requiring high stiffness are often built using carbon fiber composite. However, little published information is available on the effect of long-term seawater immersion on carbon fiber composites [4, 7]. Therefore, the aim of this research is to compare the stability of glass and carbon fiber composites in seawater. The materials studied are glass/polyester, carbon/polyester, glass/vinyl ester and carbon/vinyl ester, and these are representative of composite materials used in boats, yachts, ships, submarines and offshore drilling platforms

Tool vibration prediction and optimisation in face milling of Al 7075 and St 52 by using neural networks and genetic algorithm

Tool vibration generated under unsuitable cutting conditions is an extremely serious problem during face milling as it causes excessive tool wear, noise, tool breakage, and deterioration of the surface quality. In the current study, an artificial neural network (ANN) was used to predict tool vibration stability during face milling for different materials: Al 7075 and St 52. The testing of the ANN after training had a correlation of 99.206% with experimentally determined results. A generic algorithm (GA) was then used to minimise the vibration experienced during face milling and machining was performed using the GA recommended parameters. Measurement of the vibration during machining showed that the GA had a calculated error of 0.124%

The effect of the reduction of carbon content on the toughness of high chromium white irons in the as-cast state

Three high chromium white cast irons were examined in the as-cast state to determine the effect of the carbon content on the fracture toughness. The plane strain fracture toughness K-Ic and the fracture strength were measured for each alloy. X-ray mapping was used to identify the phases on the fracture surfaces. Scanning electron fractography and optical microscopy were used to determine the volume fraction of each phase on the fracture surfaces. It was found that most fracture occurred in the eutectic carbides, but that for the alloys with a reduced volume fraction of eutectic carbides, a small amount of crack propagation occurred in the austenitic dendrites. This change in crack path correlated with an increase in fracture toughness. The Ritchie-Knott-Rice model of brittle fracture was applied. It was found to sensibly predict the critical length for fracture for each alloy. Deep etching was employed to examine the distribution of eutectic carbides. It was found that the eutectic carbides formed a continuous network in each case. (C) 2004 Kluwer Academic Publishers