APPLICATION OF SENSOR FUSION FOR SI ENGINE DIAGNOSTICS AND COMBUSTION FEEDBACK

Shifting consumer mindsets and evolving government norms are forcing automotive manufacturers the world over to improve vehicle performance and also reduce greenhouse gas emissions. A critical aspect of achieving future fuel economy and emission targets is improved powertrain control and diagnostics. This study focuses on using a sensor fusion based approach to improving control and diagnostics in a gasoline engine. A four cylinder turbocharged engine was instrumented with a suite of sensors including ion sensors, exhaust pressure sensors, crank position sensors and accelerometers. The diagnostic potential of these sensors was studied in detail. The ability of these sensors to detect knock, misfires and also correlate with pressure and combustion metrics was also evaluated. Lastly a neural network based approach to combine individual sensor signal information was developed. The neural network was used to estimate mean effective pressure and location of fifty percent mass fraction fuel burn. Additionally, the influence of various neural network architectures was studied. Results showed that under pseudo transient conditions a recursive neural network could use information from the low cost sensors to estimate mean effective pressure within an error of 0.1bar and combustion phasing within 2.5 crank-angle degrees

Shift-invariant image reconstruction of speckle-degraded using bispectrum estimation

Coherent speckle noise is modeled as a multiplicative noise process that has a negative exponential probability density function. Using a homomorphic transfor mation, this speckle noise is converted to a signal-independent, additive process. The speckled images are randomly jittered from frame-to-frame against a uniform background to simulate image motion and/or platform jitter. Multiple images are logarithmically transformed and ensemble averaged in the bispectral domain. The bispectrum ignores this image motion so no blurring results from the ensemble averaging. Object Fourier magnitude and phase information are also retained in the bispectrum so that the resultant image can be uniquely reconstructed. This value is then exponentiated to complete the image reconstruc tion process. Since speckle masks the resolution of details in the noisy image and effectively destroys the object structure within the image, it is seen that image reconstruction using bispectrum estimation results in images that regain their object structure. Both one-dimensional and two-dimensional images were tested using separate bispectral signal reconstruction algorithms for each

A new algorithm for the loss distribution function with applications to Operational Risk Management

Operational risks inside banks and insurance companies is currently an important task. The computation of a risk measure associated to these risks lies on the knowledge of the so-called Loss Distribution Function. Traditionally this distribution function is computed via the Panjer algorithm which is an iterative algorithm. In this paper, we propose an adaptation of this last algorithm in order to improve the computation of convolutions between Panjer class distributions and continuous distributions. This new approach permits to reduce drastically the variance of the estimated VAR associated to the operational risks.Operational risk, Panjer algorithm, Kernel, numerical integration, convolution.