32,961 research outputs found
Degradation Analysis of Probabilistic Parallel Choice Systems
Degradation analysis is used to analyze the useful lifetimes of systems,
their failure rates, and various other system parameters like mean time to
failure (MTTF), mean time between failures (MTBF), and the system failure rate
(SFR). In many systems, certain possible parallel paths of execution that have
greater chances of success are preferred over others. Thus we introduce here
the concept of probabilistic parallel choice. We use binary and -ary
probabilistic choice operators in describing the selections of parallel paths.
These binary and -ary probabilistic choice operators are considered so as to
represent the complete system (described as a series-parallel system) in terms
of the probabilities of selection of parallel paths and their relevant
parameters. Our approach allows us to derive new and generalized formulae for
system parameters like MTTF, MTBF, and SFR. We use a generalized exponential
distribution, allowing distinct installation times for individual components,
and use this model to derive expressions for such system parameters
Modelling drug coatings: A parallel cellular automata model of ethylcellulose-coated microspheres
Pharmaceutical companies today face a growing demand for more complex drug designs. In the past few decades, a number of probabilistic models have been developed, with the aim of improving insight on microscopic features of these complex designs. Of particular interest are models of controlled release systems, which can provide tools to study targeted dose delivery. Controlled release is achieved by using polymers with different dissolution characteristics. We present here an approach for parallelising a large-scale model of a drug delivery system based on Monte Carlo methods, as a framework for Cellular Automata mobility. The model simulates drug release in the gastro-intestinal tract, from coated ethylcellulose microspheres. The objective is high performance simulation of coated drugs for targeted delivery. The overall aim is to understand the importance of various molecular effects with respect to system evolution over time. Important underlying mechanisms of the process, such as erosion and diffusion, are described
Design Environments for Complex Systems
The paper describes an approach for modeling complex systems by hiding as much formal details as possible from the user, still allowing verification and simulation of the model. The interface is based on UML to make the environment available to the largest audience. To carry out analysis, verification and simulation we automatically extract process algebras specifications from UML models. The results of the analysis is then reflected back in the UML model by annotating diagrams. The formal model includes stochastic information to handle quantitative parameters. We present here the stochastic -calculus and we discuss the implementation of its probabilistic support that allows simulation of processes. We exploit the benefits of our approach in two applicative domains: global computing and systems biology
Evaluating the reliability of NAND multiplexing with PRISM
Probabilistic-model checking is a formal verification technique for analyzing the reliability and performance of systems exhibiting stochastic behavior. In this paper, we demonstrate the applicability of this approach and, in particular, the probabilistic-model-checking tool PRISM to the evaluation of reliability and redundancy of defect-tolerant systems in the field of computer-aided design. We illustrate the technique with an example due to von Neumann, namely NAND multiplexing. We show how, having constructed a model of a defect-tolerant system incorporating probabilistic assumptions about its defects, it is straightforward to compute a range of reliability measures and investigate how they are affected by slight variations in the behavior of the system. This allows a designer to evaluate, for example, the tradeoff between redundancy and reliability in the design. We also highlight errors in analytically computed reliability bounds, recently published for the same case study
Evaluating the reliability of NAND multiplexing with PRISM
Probabilistic-model checking is a formal verification technique for analyzing the reliability and performance of systems exhibiting stochastic behavior. In this paper, we demonstrate the applicability of this approach and, in particular, the probabilistic-model-checking tool PRISM to the evaluation of reliability and redundancy of defect-tolerant systems in the field of computer-aided design. We illustrate the technique with an example due to von Neumann, namely NAND multiplexing. We show how, having constructed a model of a defect-tolerant system incorporating probabilistic assumptions about its defects, it is straightforward to compute a range of reliability measures and investigate how they are affected by slight variations in the behavior of the system. This allows a designer to evaluate, for example, the tradeoff between redundancy and reliability in the design. We also highlight errors in analytically computed reliability bounds, recently published for the same case study
Supporting group maintenance through prognostics-enhanced dynamic dependability prediction
Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry
Phonetic Temporal Neural Model for Language Identification
Deep neural models, particularly the LSTM-RNN model, have shown great
potential for language identification (LID). However, the use of phonetic
information has been largely overlooked by most existing neural LID methods,
although this information has been used very successfully in conventional
phonetic LID systems. We present a phonetic temporal neural model for LID,
which is an LSTM-RNN LID system that accepts phonetic features produced by a
phone-discriminative DNN as the input, rather than raw acoustic features. This
new model is similar to traditional phonetic LID methods, but the phonetic
knowledge here is much richer: it is at the frame level and involves compacted
information of all phones. Our experiments conducted on the Babel database and
the AP16-OLR database demonstrate that the temporal phonetic neural approach is
very effective, and significantly outperforms existing acoustic neural models.
It also outperforms the conventional i-vector approach on short utterances and
in noisy conditions.Comment: Submitted to TASL
Chance-Constrained Outage Scheduling using a Machine Learning Proxy
Outage scheduling aims at defining, over a horizon of several months to
years, when different components needing maintenance should be taken out of
operation. Its objective is to minimize operation-cost expectation while
satisfying reliability-related constraints. We propose a distributed
scenario-based chance-constrained optimization formulation for this problem. To
tackle tractability issues arising in large networks, we use machine learning
to build a proxy for predicting outcomes of power system operation processes in
this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains
cheaper and more reliable plans than other candidates
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