1) Falsification: given a CPS, and a property of interest that the CPS must satisfy under all allowed operating conditions, does the CPS violate, i.e. falsify, the property?
2) Conformance testing: given a model of a CPS, and an implementation of that CPS on an embedded platform, how can we characterize the properties satisfied by the implementation, given the properties satisfied by the model?
Both problems arise in the context of Model-Based Design (MBD) of CPS: in MBD, the designers start from a set of formal requirements that the system-to-be-designed must satisfy.
A first model of the system is created.
Because it may not be possible to formally verify the CPS model against the requirements, falsification tries to verify whether the model satisfies the requirements by searching for behavior that violates them.
In the first part of this dissertation, I present improved methods for finding falsifying behaviors of CPS when properties are expressed in Metric Temporal Logic (MTL).
These methods leverage the notion of robust semantics of MTL formulae: if a falsifier exists, it is in the neighborhood of local minimizers of the robustness function.
The proposed algorithms compute descent directions of the robustness function in the space of initial conditions and input signals, and provably converge to local minima of the robustness function.
The initial model of the CPS is then iteratively refined by modeling previously ignored phenomena, adding more functionality, etc., with each refinement resulting in a new model.
Many of the refinements in the MBD process described above do not provide an a priori guaranteed relation between the successive models.
Thus, the second problem above arises: how to quantify the distance between two successive models M_n and M_{n+1}?
If M_n has been verified to satisfy the specification, can it be guaranteed that M_{n+1} also satisfies the same, or some closely related, specification?
This dissertation answers both questions for a general class of CPS, and properties expressed in MTL.
Recent developments in computational software and public accessibility of gridded climatological data have enabled researchers to study Urban Heat Island (UHI) effects more systematically and at a higher spatial resolution. Previous studies have analyzed UHI and identified significant contributors at the regional level for cities, within the topology of urban canyons, and for different construction materials.
In UHIs, air is heated by the convective energy transfer from land surface materials and anthropogenic activities. Convection is dependent upon the temperature of the surface, temperature of the air, wind speed, and relative humidity. At the same time, air temperature is also influenced by greenhouse gases (GHG) in the atmosphere. Climatologists project a 1-5°C increase in near-surface air temperature over the next several decades, and 1-4°C specifically for Los Angeles and Maricopa during summertime due to GHG effects. With higher ambient air temperatures, we seek to understand how convection will change in cities and to what ends.
In this paper we develop a spatially explicit methodology for quantifying UHI by estimating the daily convection thermal energy transfer from land to air using publicly-available gridded climatological data, and we estimate how much additional energy will be retained due to lack of convective cooling in scenarios of higher ambient air temperature.