A data-driven adaptive reduced order modelling approach is presented for the reconstruction of impulsively started and vortex-dominated flows. residual-based error metric first time in framework approach. The Reduced Order Modelling selects locally most accurate model on basis lowest residual produced by substituting reconstructed flow field into a finite volume discretisation Navier–Stokes equ...

Behavioural Model Fusion Shiva Nejati Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2008 In large-scale model-based development, developers periodically need to combine collections of interrelated models. These models may capture different features of a system, describe alternative perspectives on a single feature, or express ways in which different features...

A model-driven design approach that provides a unified way of modeling real-time systems, covering both functional and timing characteristics, enables designers to reason about different properties. Refinements of the model should be easy to construct in order to obtain a complete specification from which the implementation may be automatically generated. Previous work has shown how, in the Sof...

A key area of application for Learning Analytics (LA) and Educational Data Mining (EDM) is lifelong learner modeling. The aim is that data gathered from different learning environments would be fed into a personal lifelong learner model that can be used to foster personalized learning experiences. As learning is increasingly happening in open and networked environments beyond the classroom and ...

This paper focuses on the comprehensive restoration of both voltage and frequency in a standalone microgrid (SAMG). In SAMG, power balance is achieved through traditional methods such as droop control for sharing among distributed generators (DGs). However, when microgrids (MGs) are subjected to perturbations coming from stochastic renewables, parameters deviate their specified values. this pap...

We present a method for simulating individual pedestrian motion based on empirical data. Our model keeps track of the pedestrian’s position and body confi guration (pose) and uses motion capture data to produce plausible motion. While our ultimate goal is creating 3D animations of crowds, our initial efforts focus on 2D simulations. In this paper, we present a 2D model for an able-bodied male. ...

Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that “the sensornet is a database” is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this pap...

We present a generative model for spontaneous smiles that preserves their dynamics and can thus be used to generate genuine animations. We use a high-resolution motion capture dataset of spontaneous smiles to represent the accurate temporal information present in spontaneous smiles. The smile model consists of data-driven interpolation functions generated from a Principal Component Analysis mod...

Management of computing infrastructure in data centers is an important and challenging problem, that needs to: i) ensure availability of services conforming to the Service Level Agreements (SLAs); and ii) reduce the Power Usage Efficiency (PUE), i.e. the ratio of total power, up to half of which is attributed to data center cooling, over the computing power to service the workloads. The cooling...

We present an overview of an ongoing project to build DDDAS to use all available data for a short term wildfire prediction. The project involves new data assimilation methods to inject data into a running simulation, a physics based model coupled with weather prediction, on-site data acquisition using sensors that can survive a passing fire, and on-line visualization using Google Earth.