126,298 research outputs found
A verification framework for hybrid systems
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 193-205) and index.Combining; discrete state transitions with differential equations, Hybrid system models provide an expressive formalism for describing software systems that interact with a physical environment. Automatically checking properties, such as invariance and stability, is extremely hard for general hybrid models, and therefore current research focuses on models with restricted expressive power. In this thesis we take a complementary approach by developing proof techniques that are not necessarily automatic, but are applicable to a general class of hybrid systems. Three components of this thesis, namely, (i) semantics for ordinary and probabilistic hybrid models, (ii) methods for proving invariance, stability, and abstraction, and (iii) software tools supporting (i) and (ii), are integrated within a common mathematical framework. (i) For specifying nonprobabilistic hybrid models, we present Structured Hybrid I/O Automata (SHIOAs) which adds control theory-inspired structures, namely state models, to the existing Hybrid I/O Automata, thereby facilitating description of continuous behavior. We introduce a generalization of SHIOAs which allows both nondeterministic and stochastic transitions and develop the trace-based semantics for this framework. (ii) We present two techniques for establishing lower-bounds on average dwell time (ADT) for SHIOA models. This provides a sufficient condition of establishing stability for SHIOAs with stable state models. A new simulation-based technique which is sound for proving ADT-equivalence of SHIOAs is proposed. We develop notions of approximate implementation and corresponding proof techniques for Probabilistic I/O Automata. Specifically, a PIOA A is an E-approximate implementation of B, if every trace distribution of A is c-close to some trace distribution of B-closeness being measured by a metric on the space of trace distributions.(cont.) We present a new class of real-valued simulation functions for proving c-approximate implementations, and demonstrate their utility in quantitatively reasoning about probabilistic safety and termination. (iii) We introduce a specification language for SHIOAs and a theorem prover interface for this language. The latter consists of a translator to typed high order logic and a set of PVS-strategies that partially automate the above verification techniques within the PVS theorem prover.by Sayan Mitra.Ph.D
Weak Singular Hybrid Automata
The framework of Hybrid automata, introduced by Alur, Courcourbetis,
Henzinger, and Ho, provides a formal modeling and analysis environment to
analyze the interaction between the discrete and the continuous parts of
cyber-physical systems. Hybrid automata can be considered as generalizations of
finite state automata augmented with a finite set of real-valued variables
whose dynamics in each state is governed by a system of ordinary differential
equations. Moreover, the discrete transitions of hybrid automata are guarded by
constraints over the values of these real-valued variables, and enable
discontinuous jumps in the evolution of these variables. Singular hybrid
automata are a subclass of hybrid automata where dynamics is specified by
state-dependent constant vectors. Henzinger, Kopke, Puri, and Varaiya showed
that for even very restricted subclasses of singular hybrid automata, the
fundamental verification questions, like reachability and schedulability, are
undecidable. In this paper we present \emph{weak singular hybrid automata}
(WSHA), a previously unexplored subclass of singular hybrid automata, and show
the decidability (and the exact complexity) of various verification questions
for this class including reachability (NP-Complete) and LTL model-checking
(PSPACE-Complete). We further show that extending WSHA with a single
unrestricted clock or extending WSHA with unrestricted variable updates lead to
undecidability of reachability problem
On Synchronous and Asynchronous Monitor Instrumentation for Actor-based systems
We study the impact of synchronous and asynchronous monitoring
instrumentation on runtime overheads in the context of a runtime verification
framework for actor-based systems. We show that, in such a context,
asynchronous monitoring incurs substantially lower overhead costs. We also show
how, for certain properties that require synchronous monitoring, a hybrid
approach can be used that ensures timely violation detections for the important
events while, at the same time, incurring lower overhead costs that are closer
to those of an asynchronous instrumentation.Comment: In Proceedings FOCLASA 2014, arXiv:1502.0315
Hamilton-Jacobi Reachability Analysis for Hybrid Systems with Controlled and Forced Transitions
Hybrid dynamical systems with non-linear dynamics are one of the most general
modeling tools for representing robotic systems, especially contact-rich
systems. However, providing guarantees regarding the safety or performance of
such hybrid systems can still prove to be a challenging problem because it
requires simultaneous reasoning about continuous state evolution and discrete
mode switching. In this work, we address this problem by extending classical
Hamilton-Jacobi (HJ) reachability analysis, a formal verification method for
continuous non-linear dynamics in the presence of bounded inputs and
disturbances, to hybrid dynamical systems. Our framework can compute reachable
sets for hybrid systems consisting of multiple discrete modes, each with its
own set of non-linear continuous dynamics, discrete transitions that can be
directly commanded or forced by a discrete control input, while still
accounting for control bounds and adversarial disturbances in the state
evolution. Along with the reachable set, the proposed framework also provides
an optimal continuous and discrete controller to ensure system safety. We
demonstrate our framework in simulation on an aircraft collision avoidance
problem, as well as on a real-world testbed to solve the optimal mode planning
problem for a quadruped with multiple gaits
Hybrid 2D and 3D face verification
Face verification is a challenging pattern recognition problem. The face is a biometric that, we as humans, know can be recognised. However, the face is highly deformable and its appearance alters significantly when the pose, illumination or expression changes. These changes in appearance are most notable for texture images, or two-dimensional (2D) data. But the underlying structure of the face, or three dimensional
(3D) data, is not changed by pose or illumination variations.
Over the past five years methods have been investigated to combine 2D and
3D face data to improve the accuracy and robustness of face verification. Much of this research has examined the fusion of a 2D verification system and a 3D verification system, known as multi-modal classifier score fusion. These verification systems usually compare two feature vectors (two image representations), a and b, using distance or angular-based similarity measures. However, this does not provide the most complete description of the features being compared as the distances describe at best the covariance of the data, or the second order statistics (for instance Mahalanobis based measures).
A more complete description would be obtained by describing the distribution of the feature vectors. However, feature distribution modelling is rarely applied to face verification because a large number of observations is required to train the models. This amount of data is usually unavailable and so this research examines two methods for overcoming this data limitation:
1. the use of holistic difference vectors of the face, and
2. by dividing the 3D face into Free-Parts.
The permutations of the holistic difference vectors is formed so that more observations are obtained from a set of holistic features. On the other hand, by dividing the face into parts and considering each part separately many observations are obtained from each face image; this approach is referred to as the Free-Parts approach. The extra observations from both these techniques are used to perform holistic feature distribution modelling and Free-Parts feature distribution modelling respectively. It is shown that the feature distribution modelling of these features leads to an improved 3D face verification system and an effective 2D face verification system. Using these two feature distribution techniques classifier score fusion is then examined.
This thesis also examines methods for performing classifier fusion score fusion.
Classifier score fusion attempts to combine complementary information from multiple classifiers. This complementary information can be obtained in two ways: by using different algorithms (multi-algorithm fusion) to represent the same face data for instance the 2D face data or by capturing the face data with different sensors (multimodal fusion) for instance capturing 2D and 3D face data. Multi-algorithm fusion is approached as combining verification systems that use holistic features and local features (Free-Parts) and multi-modal fusion examines the combination of 2D and 3D face data using all of the investigated techniques.
The results of the fusion experiments show that multi-modal fusion leads to a consistent improvement in performance. This is attributed to the fact that the data being fused is collected by two different sensors, a camera and a laser scanner. In deriving the multi-algorithm and multi-modal algorithms a consistent framework for fusion was developed.
The consistent fusion framework, developed from the multi-algorithm and multimodal experiments, is used to combine multiple algorithms across multiple modalities. This fusion method, referred to as hybrid fusion, is shown to provide improved performance over either fusion system on its own. The experiments show that the final hybrid face verification system reduces the False Rejection Rate from 8:59% for the best 2D verification system and 4:48% for the best 3D verification system to 0:59% for the hybrid verification system; at a False Acceptance Rate of 0:1%
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