30,103 research outputs found
Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance
Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft
or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner.
Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''.
The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few.
This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage.
The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling
Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance
Heating, Ventilation and Air Conditioning (HVAC) consumes a significant
fraction of energy in commercial buildings. Hence, the use of optimization
techniques to reduce HVAC energy consumption has been widely studied. Model
predictive control (MPC) is one state of the art optimization technique for
HVAC control which converts the control problem to a sequence of optimization
problems, each over a finite time horizon. In a typical MPC, future system
state is estimated from a model using predictions of model inputs, such as
building occupancy and outside air temperature. Consequently, as prediction
accuracy deteriorates, MPC performance--in terms of occupant comfort and
building energy use--degrades. In this work, we use a custom-built building
thermal simulator to systematically investigate the impact of occupancy
prediction errors on occupant comfort and energy consumption. Our analysis
shows that in our test building, as occupancy prediction error increases from
5\% to 20\% the performance of an MPC-based HVAC controller becomes worse than
that of even a simple static schedule. However, when combined with a personal
environmental control (PEC) system, HVAC controllers are considerably more
robust to prediction errors. Thus, we quantify the effectiveness of PECs in
mitigating the impact of forecast errors on MPC control for HVAC systems.Comment: 21 pages, 13 figure
HyperLoom: A platform for defining and executing scientific pipelines in distributed environments
Real-world scientific applications often encompass end-to-end data processing pipelines composed of a large number of interconnected computational tasks of various granularity. We introduce HyperLoom, an open source platform for defining and executing such pipelines in distributed environments and providing a Python interface for defining tasks. HyperLoom is a self-contained system that does not use an external scheduler for the actual execution of the task. We have successfully employed HyperLoom for executing chemogenomics pipelines used in pharmaceutic industry for novel drug discovery.6
Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks
In order to robustly execute a task under environmental uncertainty, a robot
needs to be able to reactively adapt to changes arising in its environment. The
environment changes are usually reflected in deviation from expected sensory
traces. These deviations in sensory traces can be used to drive the motion
adaptation, and for this purpose, a feedback model is required. The feedback
model maps the deviations in sensory traces to the motion plan adaptation. In
this paper, we develop a general data-driven framework for learning a feedback
model from demonstrations. We utilize a variant of a radial basis function
network structure --with movement phases as kernel centers-- which can
generally be applied to represent any feedback models for movement primitives.
To demonstrate the effectiveness of our framework, we test it on the task of
scraping on a tilt board. In this task, we are learning a reactive policy in
the form of orientation adaptation, based on deviations of tactile sensor
traces. As a proof of concept of our method, we provide evaluations on an
anthropomorphic robot. A video demonstrating our approach and its results can
be seen in https://youtu.be/7Dx5imy1KcwComment: 8 pages, accepted to be published at the International Conference on
Robotics and Automation (ICRA) 201
A Time-Triggered Constraint-Based Calculus for Avionic Systems
The Integrated Modular Avionics (IMA) architec- ture and the Time-Triggered
Ethernet (TTEthernet) network have emerged as the key components of a typical
architecture model for recent civil aircrafts. We propose a real-time
constraint-based calculus targeted at the analysis of such concepts of avionic
embedded systems. We show our framework at work on the modelisation of both the
(IMA) architecture and the TTEthernet network, illustrating their behavior by
the well-known Flight Management System (FMS)
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