77,343 research outputs found
Transfer Learning for Improving Model Predictions in Highly Configurable Software
Modern software systems are built to be used in dynamic environments using
configuration capabilities to adapt to changes and external uncertainties. In a
self-adaptation context, we are often interested in reasoning about the
performance of the systems under different configurations. Usually, we learn a
black-box model based on real measurements to predict the performance of the
system given a specific configuration. However, as modern systems become more
complex, there are many configuration parameters that may interact and we end
up learning an exponentially large configuration space. Naturally, this does
not scale when relying on real measurements in the actual changing environment.
We propose a different solution: Instead of taking the measurements from the
real system, we learn the model using samples from other sources, such as
simulators that approximate performance of the real system at low cost. We
define a cost model that transform the traditional view of model learning into
a multi-objective problem that not only takes into account model accuracy but
also measurements effort as well. We evaluate our cost-aware transfer learning
solution using real-world configurable software including (i) a robotic system,
(ii) 3 different stream processing applications, and (iii) a NoSQL database
system. The experimental results demonstrate that our approach can achieve (a)
a high prediction accuracy, as well as (b) a high model reliability.Comment: To be published in the proceedings of the 12th International
Symposium on Software Engineering for Adaptive and Self-Managing Systems
(SEAMS'17
PID control system analysis, design, and technology
Designing and tuning a proportional-integral-derivative
(PID) controller appears to be conceptually intuitive, but can
be hard in practice, if multiple (and often conflicting) objectives
such as short transient and high stability are to be achieved.
Usually, initial designs obtained by all means need to be adjusted
repeatedly through computer simulations until the closed-loop
system performs or compromises as desired. This stimulates
the development of "intelligent" tools that can assist engineers
to achieve the best overall PID control for the entire operating
envelope. This development has further led to the incorporation
of some advanced tuning algorithms into PID hardware modules.
Corresponding to these developments, this paper presents a
modern overview of functionalities and tuning methods in patents,
software packages and commercial hardware modules. It is seen
that many PID variants have been developed in order to improve
transient performance, but standardising and modularising PID
control are desired, although challenging. The inclusion of system
identification and "intelligent" techniques in software based PID
systems helps automate the entire design and tuning process to
a useful degree. This should also assist future development of
"plug-and-play" PID controllers that are widely applicable and
can be set up easily and operate optimally for enhanced productivity,
improved quality and reduced maintenance requirements
Intelligent agent for formal modelling of temporal multi-agent systems
Software systems are becoming complex and dynamic with the passage of time, and to provide better fault tolerance and resource management they need to have the ability of self-adaptation. Multi-agent systems paradigm is an active area of research for modeling real-time systems. In this research, we have proposed a new agent named SA-ARTIS-agent, which is designed to work in hard real-time temporal constraints with the ability of self-adaptation. This agent can be used for the formal modeling of any self-adaptive real-time multi-agent system. Our agent integrates the MAPE-K feedback loop with ARTIS agent for the provision of self-adaptation. For an unambiguous description, we formally specify our SA-ARTIS-agent using Time-Communicating Object-Z (TCOZ) language. The objective of this research is to provide an intelligent agent with self-adaptive abilities for the execution of tasks with temporal constraints. Previous works in this domain have used Z language which is not expressive to model the distributed communication process of agents. The novelty of our work is that we specified the non-terminating behavior of agents using active class concept of TCOZ and expressed the distributed communication among agents. For communication between active entities, channel communication mechanism of TCOZ is utilized. We demonstrate the effectiveness of the proposed agent using a real-time case study of traffic monitoring system
Automation Process for Morphometric Analysis of Volumetric CT Data from Pulmonary Vasculature in Rats
With advances in medical imaging scanners, it has become commonplace to generate large multidimensional datasets. These datasets require tools for a rapid, thorough analysis. To address this need, we have developed an automated algorithm for morphometric analysis incorporating A Visualization Workshop computational and image processing libraries for three-dimensional segmentation, vascular tree generation and structural hierarchical ordering with a two-stage numeric optimization procedure for estimating vessel diameters. We combine this new technique with our mathematical models of pulmonary vascular morphology to quantify structural and functional attributes of lung arterial trees. Our physiological studies require repeated measurements of vascular structure to determine differences in vessel biomechanical properties between animal models of pulmonary disease. Automation provides many advantages including significantly improved speed and minimized operator interaction and biasing. The results are validated by comparison with previously published rat pulmonary arterial micro-CT data analysis techniques, in which vessels were manually mapped and measured using intense operator intervention
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