Philip Van Every

Van Every, Philip M. M.S., Purdue University, May 2018. Multicore Xinu. Major
Professor: Douglas E. Comer.
Multicore architectures employ multiple processing cores that work together for
greater processing power. Shared memory, symmetric multiprocessor (SMP) systems
are ubiquitous. All software must be explicitly designed to support SMP processing,
including operating systems. XINU is a simple, lightweight, elegant operating system
that has existed for several decades and has… Show more

Van Every, Philip M. M.S., Purdue University, May 2018. Multicore Xinu. Major
Professor: Douglas E. Comer.
Multicore architectures employ multiple processing cores that work together for
greater processing power. Shared memory, symmetric multiprocessor (SMP) systems
are ubiquitous. All software must be explicitly designed to support SMP processing,
including operating systems. XINU is a simple, lightweight, elegant operating system
that has existed for several decades and has been ported to many platforms. However,
XINU has never been extended to support multicore processing. This project
incrementally adds SMP support to the XINU operating system. Core kernel modules,
including the scheduler and memory manager, have been successfully extended
without overhauling or completely redesigning XINU. A multicore methodology is
laid out for the remaining kernel modules. Show less

A novel fault detection algorithm based on machine learning is introduced in this paper, that is applied to the detection of faults in heater, ventilation and air conditioning (HVAC) systems. The algorithm is based on the use of a set of nonlinear regressors intended to estimate the response of the HVAC to the external variables. The regression algorithm is the well known Gaussian process regression, which, through a Gaussian modeling of the parameter priors and the conditional likelihood of… Show more

A novel fault detection algorithm based on machine learning is introduced in this paper, that is applied to the detection of faults in heater, ventilation and air conditioning (HVAC) systems. The algorithm is based on the use of a set of nonlinear regressors intended to estimate the response of the HVAC to the external variables. The regression algorithm is the well known Gaussian process regression, which, through a Gaussian modeling of the parameter priors and the conditional likelihood of the observations, is able to produce a probabilistic model of the prediction. We use the prediction error and its estimated variance as an input to a support vector machine novelty detector that, in an unsupervised way, is able to detect the faults of the HVAC. This algorithm improves the standard novelty detection, as it can be seen in the experiments. Show less

PHILIP M. VAN EVERY∗, ANDREW D. BACZEWSKI†
, AND RUDOLPH J. MAGYAR‡
Abstract. Using Ehrenfest time-dependent density functional theory (TDDFT), we simulate
a proton passing through several elemental metals under ambient conditions. Convergence in the
force acting on the proton with varying k-point grids is analyzed. We also investigate the inclusion
of a gauge correction that remedies issues with charge non-conservation in the projector augmented
wave (PAW) formalism. The… Show more

PHILIP M. VAN EVERY∗, ANDREW D. BACZEWSKI†
, AND RUDOLPH J. MAGYAR‡
Abstract. Using Ehrenfest time-dependent density functional theory (TDDFT), we simulate
a proton passing through several elemental metals under ambient conditions. Convergence in the
force acting on the proton with varying k-point grids is analyzed. We also investigate the inclusion
of a gauge correction that remedies issues with charge non-conservation in the projector augmented
wave (PAW) formalism. The computational speed of varying band parallelization parameters in
different system sizes is also explored. We simulate stopping power curves in Lithium, Beryillium,
and Aluminum. All simulations are performed with a modified version of VASP and post-processed
with local scripts. Show less