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Because of environmental pollution, forces the automotive industry constantly reduced emissions requirements legislated by the authorities. Improved techniques for engine control are a must for bringing down emissions. The use of an Exhaust Gas Recirculation (EGR) reduces NOx emissions significantly. Faulty EGR valves affect the emissions negative and therefore needs to be eliminated. It is possible to create malfunctions on the EGR valve by modifying the software of the control unit (ECU), but it does not create realistic malfunctions. The problem by modifying the software is that flags and various parameters are set to confirm the malfunction of the ECU. To create actual failure of the EGR valve an external tool to modify the control signal is needed. The project's main objective is on a flexible way creating malfunctions on the EGR valve in a truck engine. By investigating engine behavior in a realistic and credible way, one can eliminate malfunctions on the EGR valve. The aim was achieved by a model that has been developed that can, using electronics and a microprocessor read and create a control signal. The electronic circuit is controlled by the microprocessor, which can modify the signal and create malfunctions in the form of a slow valve "slow-response". A graphical user interface is used to change and influence the error signal. The circuit with the microprocessor is placed safely in a box to both protect and preserve the components. Simulation of Slow response has resulted in an incorrect operated valve being created. Using two different methods a Slow-response can be created. One method is a delay in time, which occurs when the new position is given, the second method is a ramp function when the control signal is gradually increasing. The software can also create an error that mimics a stuck valve of a fixed value. With the above listed methods it is possible in theory to find unknown malfunctions on the EGR valve that influence emissions negatively.

Bock and Aitkin (1981) developed an EM algorithm for the maximum marginal likelihood estimation of parametric item response curves, such that these estimates could be obtained in the absence of the estimation of examinee parameters. Using functional data analytic techniques described by Ramsay and Silverman (1997), this algorithm is extended to achieve nonparametric estimates of item response functions. Unlike their parametric counterparts, nonparametric functions have the freedom to adopt any possible shape, making the current approach an attractive alternative to the popular three-parameter logistic model. A basis function expansion is described for the item response functions, as is a roughness penalty which mediates a compromise between the fit of the data and the smoothness of the estimate. The algorithm is developed and applied to both actual and simulated data to illustrate its performance, and how the nonparametric estimates compare to results obtained through more classical methods.

Typescript. / Thesis (Ph. D.)--University of Hawaii at Manoa, 1977. / Bibliography: leaves [83]-92. / Microfiche. / viii, 92 leaves ill

Typescript. / Thesis (Ph. D.)--University of Hawaii, 1970. / Bibliography: leaves [50]-53. / v, 53 l tables

Typescript. / Thesis (Ph. D.)--University of Hawaii, 1966. / Bibliography: leaves [103]-107. / viii, 107 l illus., tables

iii, 258 leaves : graphs ; 30 cm / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--Dept. of Microbiology and Immunology, University of Adelaide, 1981

v, 275 leaves : appendices / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.1972) from the Dept. of Psychology, University of Adelaide

Thesis (Ph.D. 1972) from the Dept. of Psychology, University of Adelaide.

Thesis (Ph.D.) -- Dept. of Microbiology and Immunology, University of Adelaide, 1981.