Matching Items (214)
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Description
In this dissertation, two problems are addressed in the verification and control of Cyber-Physical Systems (CPS):

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

In this dissertation, two problems are addressed in the verification and control of Cyber-Physical Systems (CPS):

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.
ContributorsAbbas, Houssam Y (Author) / Fainekos, Georgios (Thesis advisor) / Duman, Tolga (Thesis advisor) / Mittelmann, Hans (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2015
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Description
To ensure system integrity, robots need to proactively avoid any unwanted physical perturbation that may cause damage to the underlying hardware. In this thesis work, we investigate a machine learning approach that allows robots to anticipate impending physical perturbations from perceptual cues. In contrast to other approaches that require knowledge

To ensure system integrity, robots need to proactively avoid any unwanted physical perturbation that may cause damage to the underlying hardware. In this thesis work, we investigate a machine learning approach that allows robots to anticipate impending physical perturbations from perceptual cues. In contrast to other approaches that require knowledge about sources of perturbation to be encoded before deployment, our method is based on experiential learning. Robots learn to associate visual cues with subsequent physical perturbations and contacts. In turn, these extracted visual cues are then used to predict potential future perturbations acting on the robot. To this end, we introduce a novel deep network architecture which combines multiple sub- networks for dealing with robot dynamics and perceptual input from the environment. We present a self-supervised approach for training the system that does not require any labeling of training data. Extensive experiments in a human-robot interaction task show that a robot can learn to predict physical contact by a human interaction partner without any prior information or labeling. Furthermore, the network is able to successfully predict physical contact from either depth stream input or traditional video input or using both modalities as input.
ContributorsSur, Indranil (Author) / Amor, Heni B (Thesis advisor) / Fainekos, Georgios (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017
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Description

Unidirectional glass fiber reinforced polymer (GFRP) is tested at four initial strain rates (25, 50, 100 and 200 s-1) and six temperatures (−25, 0, 25, 50, 75 and 100 °C) on a servo-hydraulic high-rate testing system to investigate any possible effects on their mechanical properties and failure patterns. Meanwhile, for

Unidirectional glass fiber reinforced polymer (GFRP) is tested at four initial strain rates (25, 50, 100 and 200 s-1) and six temperatures (−25, 0, 25, 50, 75 and 100 °C) on a servo-hydraulic high-rate testing system to investigate any possible effects on their mechanical properties and failure patterns. Meanwhile, for the sake of illuminating strain rate and temperature effect mechanisms, glass yarn samples were complementally tested at four different strain rates (40, 80, 120 and 160 s-1) and varying temperatures (25, 50, 75 and 100 °C) utilizing an Instron drop-weight impact system. In addition, quasi-static properties of GFRP and glass yarn are supplemented as references. The stress–strain responses at varying strain rates and elevated temperatures are discussed. A Weibull statistics model is used to quantify the degree of variability in tensile strength and to obtain Weibull parameters for engineering applications.

ContributorsOu, Yunfu (Author) / Zhu, Deju (Author) / Zhang, Huaian (Author) / Huang, Liang (Author) / Yao, Yiming (Author) / Li, Gaosheng (Author) / Mobasher, Barzin (Author) / Ira A. Fulton Schools of Engineering (Contributor)
Created2016-05-19
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Description
The meta-MAC protocol is a systematic and automatic method to dynamically combine any set of existing Medium Access Control (MAC) protocols into a single higher level MAC protocol. The meta-MAC concept was proposed more than a decade ago, but until now has not been implemented in a testbed environment due

The meta-MAC protocol is a systematic and automatic method to dynamically combine any set of existing Medium Access Control (MAC) protocols into a single higher level MAC protocol. The meta-MAC concept was proposed more than a decade ago, but until now has not been implemented in a testbed environment due to a lack of suitable hardware. This thesis presents a proof-of-concept implementation of the meta-MAC protocol by utilizing a programmable radio platform, the Wireless MAC Processor (WMP), in combination with a host-level software module. The implementation of this host module, and the requirements and challenges faced therein, is the primary subject of this thesis. This implementation can combine, with certain constraints, a set of protocols each represented as an extended finite state machine for easy programmability. To illustrate the combination principle, protocols of the same type but with varying parameters are combined in a testbed environment, in what is termed parameter optimization. Specifically, a set of TDMA protocols with differing slot assignments are experimentally combined. This experiment demonstrates that the meta-MAC implementation rapidly converges to non-conflicting TDMA slot assignments for the nodes, with similar results to those in simulation. This both validates that the presented implementation properly implements the meta-MAC protocol, and verifies that the meta-MAC protocol can be as effective on real wireless hardware as it is in simulation.
ContributorsFlick, Nathaniel Graham (Author) / Syrotiuk, Violet (Thesis director) / Fainekos, Georgios (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05