An alignment based similarity measure for hand detection in cluttered sign language video

Locating hands in sign language video is challenging due to a number of factors. Hand appearance varies widely across signers due to anthropometric variations and varying levels of signer proficiency. Video can be captured under varying illumination, camera resolutions, and levels of scene clutter, e.g., high-res video captured in a studio vs. low-res video gathered by a web cam in a user’s home. Moreover, the signers’ clothing varies, e.g., skin-toned clothing vs. contrasting clothing, short-sleeved vs. long-sleeved shirts, etc. In this work, the hand detection problem is addressed in an appearance matching framework. The Histogram of Oriented Gradient (HOG) based matching score function is reformulated to allow non-rigid alignment between pairs of images to account for hand shape variation. The resulting alignment score is used within a Support Vector Machine hand/not-hand classifier for hand detection. The new matching score function yields improved performance (in ROC area and hand detection rate) over the Vocabulary Guided Pyramid Match Kernel (VGPMK) and the traditional, rigid HOG distance on American Sign Language video gestured by expert signers. The proposed match score function is computationally less expensive (for training and testing), has fewer parameters and is less sensitive to parameter settings than VGPMK. The proposed detector works well on test sequences from an inexpert signer in a non-studio setting with cluttered background.

Policy-induced technology adoption: Evidence from the U.S. lead phasedown

Theory suggests that economic instruments, such as pollution taxes or tradable permits, can provide more efficient technology adoption incentives than conventional regulatory standards. We explore this issue for an important industry undergoing dramatic decreases in allowed pollution - the U.S. petroleum industry's phasedown of lead in gasoline. Using a duration model applied to a panel of refineries from 1971-1995, we find that the pattern of technology adoption is consistent with an economic response to market incentives, plant characteristics, and alternative policies. Importantly, evidence suggests that the tradable permit system used during the phasedown provided incentives for more efficient technology adoption decisions.

Implications of shale gas development for climate change.

Advances in technologies for extracting oil and gas from shale formations have dramatically increased U.S. production of natural gas. As production expands domestically and abroad, natural gas prices will be lower than without shale gas. Lower prices have two main effects: increasing overall energy consumption, and encouraging substitution away from sources such as coal, nuclear, renewables, and electricity. We examine the evidence and analyze modeling projections to understand how these two dynamics affect greenhouse gas emissions. Most evidence indicates that natural gas as a substitute for coal in electricity production, gasoline in transport, and electricity in buildings decreases greenhouse gases, although as an electricity substitute this depends on the electricity mix displaced. Modeling suggests that absent substantial policy changes, increased natural gas production slightly increases overall energy use, more substantially encourages fuel-switching, and that the combined effect slightly alters economy wide GHG emissions; whether the net effect is a slight decrease or increase depends on modeling assumptions including upstream methane emissions. Our main conclusions are that natural gas can help reduce GHG emissions, but in the absence of targeted climate policy measures, it will not substantially change the course of global GHG concentrations. Abundant natural gas can, however, help reduce the costs of achieving GHG reduction goals.

Fitting host-parasitoid models with CV² > 1 using hierarchical generalized linear models

The powerful general Pacala-Hassell host-parasitoid model for a patchy environment, which allows host density–dependent heterogeneity (HDD) to be distinguished from between-patch, host density–independent heterogeneity (HDI), is reformulated within the class of the generalized linear model (GLM) family. This improves accessibility through the provision of general software within well–known statistical systems, and allows a rich variety of models to be formulated. Covariates such as age class, host density and abiotic factors may be included easily. For the case where there is no HDI, the formulation is a simple GLM. When there is HDI in addition to HDD, the formulation is a hierarchical generalized linear model. Two forms of HDI model are considered, both with between-patch variability: one has binomial variation within patches and one has extra-binomial, overdispersed variation within patches. Examples are given demonstrating parameter estimation with standard errors, and hypothesis testing. For one example given, the extra-binomial component of the HDI heterogeneity in parasitism is itself shown to be strongly density dependent.

Simulation of the Unsteady Gas Flow through a Three-way Automotive Catalyst

This paper describes a model of a 1.8-litre four-cylinder four-stroke gasoline engine fitted with a close-coupled three-way catalyst (TWC). Designed to meet EURO 3 emissions standards, the engine includes some advanced emission control features in addition to the TWC, namely: variable valve timing (VVT), swirl control plates, and exhaust gas recirculation (EGR). Gas flow is treated as one-dimensional (1D) and unsteady in the engine ducting and in the catalyst. Reflection and transmission of pressure waves at the boundaries of the catalyst monolith are modelled. In-cylinder combustion is represented by a two-zone burn model with dissociation and reaction kinetics. A single Wiebe analysis of measured in-cylinder pressure data is used to determine the mass fraction burned as a function of crank angle (CA) at each engine speed. Measured data from steady-state dynamometer tests are presented for operation at wide open throttle (WOT) over a range of engine speeds. These results include CA-resolved traces of pressure at various locations throughout the engine together with cycle-averaged traces of gas composition entering the catalyst as indicated by a fast-response emissions analyser. Simulated engine performance and pressure wave action throughout the engine are well validated by the measured data.

Deactivation of Oxidation Catalysts by Oil-Derived Sulphur

The most common mode of deactivation suffered by catalysts fitted to two-stroke engines has traditionally been thermal degradation, or even meltdown, of the washcoat and substrate. The high temperatures experienced by these catalysts are caused by excessively high concentrations of HC and CO in the exhaust gas which are, in turn, caused by a rich AFR and the loss of neat fuel to the exhaust during the scavenging period. The effects of catalyst poisoning due to additives in the oil is often regarded as a secondary, or even negligible, deactivating mechanism in two-stroke catalysts and has therefore received little attention. However, with the introduction of direct in-cylinder fuel injection to some larger versions of this engine, the quantities of HC escaping to the exhaust can be reduced to levels similar to those found on four-stroke gasoline engines. Under these conditions, the effects of poisoning are much more significant to catalyst durability, particularly for crankcase scavenged derivatives which allow considerable quantities of oil to escape into the exhaust in a neat, or partially burned form. In this paper the effects of oil-derived sulphur on catalyst performance are examined using specialised test apparatus. The oil used throughout the study was formulated specifically for a two-stroke engine fitted with direct in-cylinder fuel injection. The sulphur content of this oil was 0.21% by mass and particular attention was paid to the role of this element in the resulting deactivation. The catalyst was also designed for two-stroke applications and contained a high palladium loading of 300g/ft3 (28g/l) to prolong the life of the catalyst. It was found that the sulphur caused permanent deactivation of the CO reaction and increased the light-off temperature by around 40oC after oiling for 60 hours. This deactivation was progressive and led to a reduction in surface area of the washcoat, particularly in the micropores of around 5Å diameter. By using a validated catalyst model the change in surface area of the precious metal was estimated. It was found that the simulated palladium surface area had to be reduced by a factor of around 7.5 to produce the light-off temperature of the deactivated catalyst. Conversely, the light-off temperature of the C3H6 reaction was barely affected by the deactivation.

Chemical Sensing Using a Polymer Coated Long-Period Fiber Grating Interrogated by Ring-Down Spectroscopy

An etched long-period grating was used as a refractive index sensor for vapours of four volatile organic compounds, i.e. m-xylene, cyclohexane, trichloroethylene and commercial gasoline. The sensitivity to the vapours was further increased by solid-phase microextraction into a coating made of polydimethylsiloxane (PDMS)/polymethyl-octylsiloxane (PMOS) co-polymer. By further amplification of the optical loss in an optical cavity made of two identical fiber-Bragg gratings and interrogation by phase-shift cavity ring-down spectroscopy we could detect and distinguish xylene (detection limit: 134ppm) from trichloroethylene (3300ppm), cyclohexane (1850ppm) and gasoline (10,500ppm).

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.

Action research and reflective practice: towards a holistic view

Two concepts that have captured the imagination of the educational community in the last 60 years have been those of ‘reflective practice’ and ‘action research’. Both, in their various forms, are considered to be critical dimensions of the professional development of teachers. However, whilst both were receiving academic attention during the 1930s and 1940s (Lewin, 1934, cited in Adelman, 1993; Lewin, 1946; Dewey, 1933), it was not until Stenhouse’s (1975) notion of the teacher-as-researcher that the two came most compellingly into relationship and educational action research as a process, which held at its centre different kinds of reflection, began to be reformulated in Britain (Carr, 1993). This article considers the important part played in teachers’ development by different kinds of action research. Its central thesis is that, although action research has a critical role to play not least as a means of building the capacity of teachers as researchers of their own practice, there has been insufficient attention given to both the nature of reflection in the action research process, and its relationship to the purposes, processes and outcomes. The article challenges the rational, cognitive models of reflection that are implicit in much of the action research literature. It suggests that more attention needs to be given to the importance of the role of emotion in understanding and developing the capacities for reflection which facilitates personal, professional and ultimately system change.

Theory of the Kinetics of Chemical Potentials in Heterogeneous Catalysis

Simple and powerful: The reaction kinetics at surfaces of heterogeneous catalysts is reformulated in terms of the involved chemical potentials. Based on this formulism, an approach of searching for good catalysts is proposed without recourse to extensive calculations of reaction barriers and detailed kinetic analyses. (see picture; R=reactant, I=surface intermediate, P=product, and =standard chemical potential).

Performance and characteristics of platinum, palladium/rhodium and palladium automotive catalysts when subjected to ethanol, acetaldehyde and a synthetic E85 exhaust gas mixture

The performance optimisation of automotive catalysts has been the focus of a great deal of research for many years as the automotive industry has endeavored to reduce the emission of toxic and pollutant gases generated from internal combustion engines. Just as the emissions from diesel and gasoline combustion vary so do the emissions from combustion of alternative fuels such as ethanol; the variation is in both quantity and chemical composition. In particular, when ethanol is contained in the fuel, ethanol and acetaldehyde are present in the exhaust gas stream and these are two compounds which the catalytic converter has not traditionally been designed to manage. The aim of the study outlined in this paper was to assess the performance of various catalyst formulations when subjected to a representative ethanol exhaust gas mixture. Three automotive catalytic converter formulations were tested including a fully Pt sample, a PdRh three-way catalyst sample and a fully Pd sample. Initially the samples were tested using single component hydrocarbon light-off tests followed by a set of tests with carbon monoxide included as an inlet gas to observe its effect on each individual hydrocarbon oxidation. Finally, each formulation was tested using a full E85 exhaust gas mixture. The study was carried out using a synthetic gas reactor along with FTIR and FID exhaust gas analysers. All formulations showed selectivity toward acetaldehyde formation from ethanol dehydrogenation which resulted in negative acetaldehyde conversion across each of the samples during the mixture tests. The fully Pt sample was the most detrimentally affected by the introduction of carbon monoxide into the gas feed. The Pd and PdRh samples exhibited a tendency toward acetaldehyde decomposition resulting in methane and carbon monoxide formation. The Pt sample did not form methane but did form ethylene as a result of ethanol dehydration.

Prospective control of manual interceptive actions:comparative simulations of extant and new model constructs

Two prospective controllers of hand movements in catching-both based on required velocity control-were simulated. Under certain conditions, this required velocity control led to overshoots of the future interception point. These overshoots were absent in pertinent experiments. To remedy this shortcoming, the required velocity model was reformulated in terms of a neural network, the Vector Integration To Endpoint model, to create a Required Velocity Integration To Endpoint model. Addition of a parallel relative velocity channel, resulting in the Relative and Required Velocity Integration To Endpoint model, provided a better account for the experimentally observed kinematics than the existing, purely behavioral models. Simulations of reaching to intercept decelerating and accelerating objects in the presence of background motion were performed to make distinct predictions for future experiments.

Generalized loopy 2U: A new algorithm for approximate inference in credal networks

Credal networks generalize Bayesian networks by relaxing the requirement of precision of probabilities. Credal networks are considerably more expressive than Bayesian networks, but this makes belief updating NP-hard even on polytrees. We develop a new efficient algorithm for approximate belief updating in credal networks. The algorithm is based on an important representation result we prove for general credal networks: that any credal network can be equivalently reformulated as a credal network with binary variables; moreover, the transformation, which is considerably more complex than in the Bayesian case, can be implemented in polynomial time. The equivalent binary credal network is then updated by L2U, a loopy approximate algorithm for binary credal networks. Overall, we generalize L2U to non-binary credal networks, obtaining a scalable algorithm for the general case, which is approximate only because of its loopy nature. The accuracy of the inferences with respect to other state-of-the-art algorithms is evaluated by extensive numerical tests.

Generalized Loopy 2U: A New Algorithm for Approximate Inference in Credal Networks

Credal nets generalize Bayesian nets by relaxing the requirement of precision of probabilities. Credal nets are considerably more expressive than Bayesian nets, but this makes belief updating NP-hard even on polytrees. We develop a new efficient algorithm for approximate belief updating in credal nets. The algorithm is based on an important representation result we prove for general credal nets: that any credal net can be equivalently reformulated as a credal net with binary variables; moreover, the transformation, which is considerably more complex than in the Bayesian case, can be implemented in polynomial time. The equivalent binary credal net is updated by L2U, a loopy approximate algorithm for binary credal nets. Thus, we generalize L2U to non-binary credal nets, obtaining an accurate and scalable algorithm for the general case, which is approximate only because of its loopy nature. The accuracy of the inferences is evaluated by empirical tests.