DYNAMIC MODEL BASED STATE ESTIMATION IN A HEAVY DUTY DIESEL AFTERTREATMENT SYSTEM FOR ONBOARD DIAGNOSTICS AND CONTROLS

Estimating un-measurable states is an important component for onboard diagnostics (OBD) and control strategy development in diesel exhaust aftertreatment systems. This research focuses on the development of an Extended Kalman Filter (EKF) based state estimator for two of the main components in a diesel engine aftertreatment system: the Diesel Oxidation Catalyst (DOC) and the Selective Catalytic Reduction (SCR) catalyst. One of the key areas of interest is the performance of these estimators when the catalyzed particulate filter (CPF) is being actively regenerated. In this study, model reduction techniques were developed and used to develop reduced order models from the 1D models used to simulate the DOC and SCR. As a result of order reduction, the number of states in the estimator is reduced from 12 to 1 per element for the DOC and 12 to 2 per element for the SCR. The reduced order models were simulated on the experimental data and compared to the high fidelity model and the experimental data. The results show that the effect of eliminating the heat transfer and mass transfer coefficients are not significant on the performance of the reduced order models. This is shown by an insignificant change in the kinetic parameters between the reduced order and 1D model for simulating the experimental data. An EKF based estimator to estimate the internal states of the DOC and SCR was developed. The DOC and SCR estimators were simulated on the experimental data to show that the estimator provides improved estimation of states compared to a reduced order model. The results showed that using the temperature measurement at the DOC outlet improved the estimates of the CO , NO , NO2 and HC concentrations from the DOC. The SCR estimator was used to evaluate the effect of NH3 and NOX sensors on state estimation quality. Three sensor combinations of NOX sensor only, NH3 sensor only and both NOX and NH3 sensors were evaluated. The NOX only configuration had the worst performance, the NH3 sensor only configuration was in the middle and both the NOX and NH3 sensor combination provided the best performance

AIDS Treatment and Intrahousehold Resource Allocations: Children's Nutrition and Schooling in Kenya

The provision of life-saving antiretroviral (ARV) treatment has emerged as a key component of the global response to HIV/AIDS, but very little is known about the impact of this intervention on the welfare of children in the households of treated persons. We estimate the impact of ARV treatment on children’s schooling and nutrition outcomes using longitudinal household survey data collected in collaboration with a treatment program in western Kenya. We find that children’s weekly hours of school attendance increase by over 20 percent within six months after treatment is initiated for the adult household member. For boys in treatment households, these increases closely follow their reduced market labor supply. Similarly, young children’s short-term nutritional status—as measured by their weight-for-height Z-score—also improves dramatically. We argue that these treatment effects will be considerably larger when compared to the counterfactual scenario of no ARV treatment. The results provide evidence on how intrahousehold resource allocation is altered in response to significant health improvements. Since the improvements in children’s schooling and nutrition at these critical early ages will affect their socio-economic outcomes in adulthood, the widespread provision of ARV treatment is likely to generate significant long-run macroeconomic benefits.

Using Chandra to Unveil the High-Energy Properties of the High-Magnetic Field Radio Pulsar J1119-6127

(shortened) PSR J1119-6127 is a high magnetic field (B=4.1E13 Gauss), young (<=1,700 year-old), and slow (P=408 ms) radio pulsar associated with the supernova remnant (SNR) G292.2-0.5. In 2003, Chandra allowed the detection of the X-ray counterpart of the radio pulsar, and provided the first evidence for a compact pulsar wind nebula (PWN). We here present new Chandra observations which allowed for the first time an imaging and spectroscopic study of the pulsar and PWN independently of each other. The PWN is only evident in the hard band and consists of jet-like structures extending to at least 7" from the pulsar, with the southern `jet' being longer than the northern `jet'. The spectrum of the PWN is described by a power law with a photon index~1.1 for the compact PWN and ~1.4 for the southern long jet (at a fixed column density of 1.8E22/cm2), and a total luminosity of 4E32 ergs/s (0.5-7 keV), at a distance of 8.4 kpc. The pulsar's spectrum is clearly softer than the PWN's spectrum. We rule out a single blackbody model for the pulsar, and present the first evidence of non-thermal (presumably magnetospheric) emission that dominates above ~3keV. A two-component model consisting of a power law component (with photon index ~1.5--2.0) plus a thermal component provides the best fit. The thermal component can be fit by either a blackbody model with a temperature kT~0.21 keV, or a neutron star atmospheric model with a temperature kT~0.14 keV. The efficiency of the pulsar in converting its rotational power, Edot, into non-thermal X-ray emission from the pulsar and PWN is ~5E-4, comparable to other rotation-powered pulsars with a similar Edot. We discuss our results in the context of the X-ray manifestation of high-magnetic field radio pulsars in comparison with rotation-powered pulsars and magnetars.Comment: 26 pages including 3 tables and 7 figures. Accepted for publication in Ap

Machine Learning Approach for Smile Detection in Real Time Images

Recognizing facial expressions of human beings by a computer is an interesting and challenging problem. A system that performs the operation of face detection and facial feature extraction accurately and in real time would form a big step in achieving a humanlike interaction between man and machine. In this paper, we propose a method for detecting Smile in real time Images by machine learning approach. Machine learning method involves training a classifier and using it in real time images to determine smile. Our implemented approach has been tested on several Images from different databases and the achieved results were found to be very satisfactory