39,308 research outputs found
PALS-Based Analysis of an Airplane Multirate Control System in Real-Time Maude
Distributed cyber-physical systems (DCPS) are pervasive in areas such as
aeronautics and ground transportation systems, including the case of
distributed hybrid systems. DCPS design and verification is quite challenging
because of asynchronous communication, network delays, and clock skews.
Furthermore, their model checking verification typically becomes unfeasible due
to the huge state space explosion caused by the system's concurrency. The PALS
("physically asynchronous, logically synchronous") methodology has been
proposed to reduce the design and verification of a DCPS to the much simpler
task of designing and verifying its underlying synchronous version. The
original PALS methodology assumes a single logical period, but Multirate PALS
extends it to deal with multirate DCPS in which components may operate with
different logical periods. This paper shows how Multirate PALS can be applied
to formally verify a nontrivial multirate DCPS. We use Real-Time Maude to
formally specify a multirate distributed hybrid system consisting of an
airplane maneuvered by a pilot who turns the airplane according to a specified
angle through a distributed control system. Our formal analysis revealed that
the original design was ineffective in achieving a smooth turning maneuver, and
led to a redesign of the system that satisfies the desired correctness
properties. This shows that the Multirate PALS methodology is not only
effective for formal DCPS verification, but can also be used effectively in the
DCPS design process, even before properties are verified.Comment: In Proceedings FTSCS 2012, arXiv:1212.657
Modelling and analyzing adaptive self-assembling strategies with Maude
Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA
Towards formal models and languages for verifiable Multi-Robot Systems
Incorrect operations of a Multi-Robot System (MRS) may not only lead to
unsatisfactory results, but can also cause economic losses and threats to
safety. These threats may not always be apparent, since they may arise as
unforeseen consequences of the interactions between elements of the system.
This call for tools and techniques that can help in providing guarantees about
MRSs behaviour. We think that, whenever possible, these guarantees should be
backed up by formal proofs to complement traditional approaches based on
testing and simulation.
We believe that tailored linguistic support to specify MRSs is a major step
towards this goal. In particular, reducing the gap between typical features of
an MRS and the level of abstraction of the linguistic primitives would simplify
both the specification of these systems and the verification of their
properties. In this work, we review different agent-oriented languages and
their features; we then consider a selection of case studies of interest and
implement them useing the surveyed languages. We also evaluate and compare
effectiveness of the proposed solution, considering, in particular, easiness of
expressing non-trivial behaviour.Comment: Changed formattin
A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability?
Predictability estimates of ensemble prediction systems are uncertain due to
limited numbers of past forecasts and observations. To account for such
uncertainty, this paper proposes a Bayesian inferential framework that provides
a simple 6-parameter representation of ensemble forecasting systems and the
corresponding observations. The framework is probabilistic, and thus allows for
quantifying uncertainty in predictability measures such as correlation skill
and signal-to-noise ratios. It also provides a natural way to produce
recalibrated probabilistic predictions from uncalibrated ensembles forecasts.
The framework is used to address important questions concerning the skill of
winter hindcasts of the North Atlantic Oscillation for 1992-2011 issued by the
Met Office GloSea5 climate prediction system. Although there is much
uncertainty in the correlation between ensemble mean and observations, there is
strong evidence of skill: the 95% credible interval of the correlation
coefficient of [0.19,0.68] does not overlap zero. There is also strong evidence
that the forecasts are not exchangeable with the observations: With over 99%
certainty, the signal-to-noise ratio of the forecasts is smaller than the
signal-to-noise ratio of the observations, which suggests that raw forecasts
should not be taken as representative scenarios of the observations. Forecast
recalibration is thus required, which can be coherently addressed within the
proposed framework.Comment: 36 pages, 10 figure
Initialization and Ensemble Generation for Decadal Climate Predictions: A Comparison of Different Methods
Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system âMittelfristige Klimaprognoseâ (MiKlip). Among the tested methods, three tackle aspects of modelâconsistent initialization using the ensemble Kalman filter, the filtered anomaly initialization, and the initialization method by partially coupled spinâup (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter corrects each ensemble member with the ensemble mean during model integration. And the bred vectors perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the lowâresolution configuration (PreopâLR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to PreopâLR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the ensemble Kalman filter and filtered anomaly initialization show the most distinct improvements over PreopâLR for surface temperatures and upper ocean heat content, followed by the bred vectors, the ensemble dispersion filter, and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basinâwide longâterm mean and variability and with respect to the temporal evolution at the 26° N latitude
Modelling and analyzing adaptive self-assembling strategies with Maude
Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA
International conference on software engineering and knowledge engineering: Session chair
The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing.
The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome
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