Development of a time and space resolved sampling probe diagnostic for engine exhaust hydrocarbons

In order to understand how unburned hydrocarbons emerge from SI engines and, in particular, how non-fuel hydrocarbons are formed and oxidized, a new gas sampling technique has been developed. A sampling unit, based on a combination of techniques used in the Fast Flame Ionization Detector (FFID) and wall-mounted sampling valves, was designed and built to capture a sample of exhaust gas during a specific period of the exhaust process and from a specific location within the exhaust port. The sampling unit consists of a transfer tube with one end in the exhaust port and the other connected to a three-way valve that leads, on one side, to a FFID and, on the other, to a vacuum chamber with a high-speed solenoid valve. Exhaust gas, drawn by the pressure drop into the vacuum chamber, impinges on the face of the solenoid valve and flows radially outward. Once per cycle during a specified crank angle interval, the solenoid valve opens and traps exhaust gas in a storage unit, from which gas chromatography (GC) measurements are made. The port end of the transfer tube can be moved to different locations longitudinally or radially, thus allowing spatial resolution and capturing any concentration differences between port walls and the center of the flow stream. Further, the solenoid valve's opening and closing times can be adjusted to allow sampling over a window as small as 0.6 ms during any portion of the cycle, allowing resolution of a crank angle interval as small as 15°CA. Cycle averaged total HC concentration measured by the FFID and that measured by the sampling unit are in good agreement, while the sampling unit goes one step further than the FFID by providing species concentrations. Comparison with previous measurements using wall-mounted sampling valves suggests that this sampling unit is fully capable of providing species concentration information as a function of air/fuel ratio, load, and engine speed at specific crank angles. © Copyright 1996 Society of Automotive Engineers, Inc.

Use of contexts in language model interpolation and adaptation

Language models (LMs) are often constructed by building multiple individual component models that are combined using context independent interpolation weights. By tuning these weights, using either perplexity or discriminative approaches, it is possible to adapt LMs to a particular task. This paper investigates the use of context dependent weighting in both interpolation and test-time adaptation of language models. Depending on the previous word contexts, a discrete history weighting function is used to adjust the contribution from each component model. As this dramatically increases the number of parameters to estimate, robust weight estimation schemes are required. Several approaches are described in this paper. The first approach is based on MAP estimation where interpolation weights of lower order contexts are used as smoothing priors. The second approach uses training data to ensure robust estimation of LM interpolation weights. This can also serve as a smoothing prior for MAP adaptation. A normalized perplexity metric is proposed to handle the bias of the standard perplexity criterion to corpus size. A range of schemes to combine weight information obtained from training data and test data hypotheses are also proposed to improve robustness during context dependent LM adaptation. In addition, a minimum Bayes' risk (MBR) based discriminative training scheme is also proposed. An efficient weighted finite state transducer (WFST) decoding algorithm for context dependent interpolation is also presented. The proposed technique was evaluated using a state-of-the-art Mandarin Chinese broadcast speech transcription task. Character error rate (CER) reductions up to 7.3 relative were obtained as well as consistent perplexity improvements. © 2012 Elsevier Ltd. All rights reserved.

Near optimal combination of sensory and motor uncertainty in time during a naturalistic perception-action task.

Most behavioral tasks have time constraints for successful completion, such as catching a ball in flight. Many of these tasks require trading off the time allocated to perception and action, especially when only one of the two is possible at any time. In general, the longer we perceive, the smaller the uncertainty in perceptual estimates. However, a longer perception phase leaves less time for action, which results in less precise movements. Here we examine subjects catching a virtual ball. Critically, as soon as subjects began to move, the ball became invisible. We study how subjects trade-off sensory and movement uncertainty by deciding when to initiate their actions. We formulate this task in a probabilistic framework and show that subjects' decisions when to start moving are statistically near optimal given their individual sensory and motor uncertainties. Moreover, we accurately predict individual subject's task performance. Thus we show that subjects in a natural task are quantitatively aware of how sensory and motor variability depend on time and act so as to minimize overall task variability.

A comparison of losses in small (<1 kW) drives using sine and space vector pulse width modulation schemes

this paper quantifies effects of using three different pulse width modulation (PWM) schemes on the losses in the inverter and induction motor of a 1 kW drive. Direct measurements of losses have been made with a calorimeter. Results show that for the inverter, discontinuous PWM excitation reduces losses by up to 15% compared to sine and symmetrical space vector PWM methods. However, at a low modulation index the greater harmonic content with discontinuous PWM increased motor losses by nearly 20%. This study demonstrates the importance of careful choice of modulation scheme to achieve high overall drive efficiency. © 2005 IEEE.

Fracture and energy partitioning in uncooked and cooked noodles

Humans perform fascinating science experiments at home on a daily basis when they undertake the modification of natural and naturally-derived materials by a cooking process prior to consumption. The material properties of such foods are of interest to food scientists (texture is often fundamental to food acceptability), oral biologists (foods modulate feeding behavior), anthropologists (cooking is probably as old as the genus Homo and distinguishes us from all other creatures) and dentists (foods interact with tooth and tooth replacement materials). Materials scientists may be interested in the drastic changes in food properties observed over relatively short cooking times. In the current study, the mechanical properties of one of the most common (and oldest at 4,000+ years) foods on earth, the noodle, were examined as a function of cooking time. Two types of noodles were studied, each made from natural materials (wheat flour, salt, alkali and water) by kneading dough and passing them through a pasta-making machine. These were boiled for between 2-14 min and tested at regular intervals from raw to an overcooked state. Cyclic tensile tests at small strain levels were used to examine energy dissipation characteristics. Energy dissipation was >50% per cycle in uncooked noodles, but decreased by an order of magnitude with cooking. Fractional dissipation values remained approximately constant at cooking times greater than 7 min. Overall, a greater effect of cooking was on viscoplastic dissipation characteristics rather than on fracture resistance. The results of the current study plot the evolution of a viscoplastic mixture into an essentially elastic material in the space of 7 minutes and have broad implications for understanding what cooking does to food materials. In particular, they suggest that textural assessment by consumers of the optimally cooked state of food has a definite physical definition. © 2007 Materials Research Society.

Impacts of concurrency, iteration, design review, and problem complexity on design project lead time and error generation

A case study of an aircraft engine manufacturer is used to analyze the effects of management levers on the lead time and design errors generated in an iteration-intensive concurrent engineering process. The levers considered are amount of design-space exploration iteration, degree of process concurrency, and timing of design reviews. Simulation is used to show how the ideal combination of these levers can vary with changes in design problem complexity, which can increase, for instance, when novel technology is incorporated in a design. Results confirm that it is important to consider multiple iteration-influencing factors and their interdependencies to understand concurrent processes, because the factors can interact with confounding effects. The article also demonstrates a new approach to derive a system dynamics model from a process task network. The new approach could be applied to analyze other concurrent engineering scenarios. © The Author(s) 2012.