The Experts below are selected from a list of 19893 Experts worldwide ranked by ideXlab platform
Enrico Vicario - One of the best experts on this subject based on the ideXlab platform.
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transient analysis of non markovian models using stochastic state classes
Performance Evaluation, 2012Co-Authors: Andras Horvath, Marco Paolieri, Lorenzo Ridi, Enrico VicarioAbstract:The method of stochastic state classes approaches the analysis of Generalised Semi Markov Processes (GSMPs) through the symbolic derivation of probability density functions over supports described by Difference Bounds Matrix (DBM) zones. This makes steady state analysis viable, provided that at least one Regeneration Point is visited by every cyclic behaviour of the model. We extend the approach providing a way to derive transient probabilities. To this end, stochastic state classes are extended with a supplementary timer that enables the symbolic derivation of the distribution of time at which a class can be entered. The approach is amenable to efficient implementation when model timings are given by expolynomial distributions, and it can be applied to perform transient analysis of GSMPs within any given time bound. In the special case of models underlying a Markov Regenerative Process (MRGP), the method can also be applied to the symbolic derivation of local and global kernels, which in turn provide transient probabilities through numerical integration of generalised renewal equations. Since much of the complexity of this analysis is due to the local kernel, we propose a selective derivation of its entries depending on the specific transient measure targeted by the analysis.
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aggregated stochastic state classes in quantitative evaluation of non markovian stochastic petri nets
Quantitative Evaluation of Systems, 2009Co-Authors: Andras Horvath, Enrico VicarioAbstract:The method of stochastic state classes provides a new approach for theanalysis of non-Markovian stochastic Petri Nets, which relies on thestochastic expansion of the graph of non-deterministic state classes basedon Difference Bounds Matrix (DBM) which is usually employed in qualitativeverification.In so doing, the method is able to manage multiple concurrentnon-exponential (GEN) transitions and largely extends the class of modelsthat are amenable to quantitative evaluation.However, its application requires that every cycle in the graph ofnon-deterministic state classes visits at least a Regeneration Point whereall GEN transitions are newly enabled.In particular, this rules out models whose non-deterministic class graphincludes cycles within a Continuous Time Markov Chain (CTMC) subordinatedto the activity period of one or more GEN transitions.In this paper, we propose an extension that overcomes thislimitation by aggregating together classes that are reachedthrough firings that do not change the enabling status of GENtransitions.This enlarges the class of models that can be analysed through themethod of stochastic state classes and makes it become a properextension of the class of models that satisfies the so calledenabling restriction.
Andras Horvath - One of the best experts on this subject based on the ideXlab platform.
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transient analysis of non markovian models using stochastic state classes
Performance Evaluation, 2012Co-Authors: Andras Horvath, Marco Paolieri, Lorenzo Ridi, Enrico VicarioAbstract:The method of stochastic state classes approaches the analysis of Generalised Semi Markov Processes (GSMPs) through the symbolic derivation of probability density functions over supports described by Difference Bounds Matrix (DBM) zones. This makes steady state analysis viable, provided that at least one Regeneration Point is visited by every cyclic behaviour of the model. We extend the approach providing a way to derive transient probabilities. To this end, stochastic state classes are extended with a supplementary timer that enables the symbolic derivation of the distribution of time at which a class can be entered. The approach is amenable to efficient implementation when model timings are given by expolynomial distributions, and it can be applied to perform transient analysis of GSMPs within any given time bound. In the special case of models underlying a Markov Regenerative Process (MRGP), the method can also be applied to the symbolic derivation of local and global kernels, which in turn provide transient probabilities through numerical integration of generalised renewal equations. Since much of the complexity of this analysis is due to the local kernel, we propose a selective derivation of its entries depending on the specific transient measure targeted by the analysis.
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aggregated stochastic state classes in quantitative evaluation of non markovian stochastic petri nets
Quantitative Evaluation of Systems, 2009Co-Authors: Andras Horvath, Enrico VicarioAbstract:The method of stochastic state classes provides a new approach for theanalysis of non-Markovian stochastic Petri Nets, which relies on thestochastic expansion of the graph of non-deterministic state classes basedon Difference Bounds Matrix (DBM) which is usually employed in qualitativeverification.In so doing, the method is able to manage multiple concurrentnon-exponential (GEN) transitions and largely extends the class of modelsthat are amenable to quantitative evaluation.However, its application requires that every cycle in the graph ofnon-deterministic state classes visits at least a Regeneration Point whereall GEN transitions are newly enabled.In particular, this rules out models whose non-deterministic class graphincludes cycles within a Continuous Time Markov Chain (CTMC) subordinatedto the activity period of one or more GEN transitions.In this paper, we propose an extension that overcomes thislimitation by aggregating together classes that are reachedthrough firings that do not change the enabling status of GENtransitions.This enlarges the class of models that can be analysed through themethod of stochastic state classes and makes it become a properextension of the class of models that satisfies the so calledenabling restriction.
Jyripekka Mikkola - One of the best experts on this subject based on the ideXlab platform.
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ionic liquid assisted extraction of nitrogen and sulphur containing air pollutants from model oil and Regeneration of the spent ionic liquid
Journal of Environmental Protection, 2011Co-Authors: Ikenna Anugwom, Paivi Makiarvela, Tapio Salmi, Jyripekka MikkolaAbstract:Removal of air pollutants, such as nitrogen and sulphur containing compounds from a model oil (dodecane) was studied. An ionic liquid (1-ethyl-3-methylimidazolium chloride [C2mim] [Cl]) was used as an extractant. Liquid-liquid extraction by using 1-ethyl-3-methylimidazolium chloride [C2mim] [Cl] was found to be a very promising method for the removal of N- and S-compounds. This was evaluated by using a model oil (dodecane) with indole as a neutral nitrogen compound and pyridine as a basic nitrogen compound. Dibenzothiophene (DBT) was used as a sulphur compound. An extraction capacity of up to 90 wt% was achieved for the model oil containing pyridine, while only 76 wt% of indole in the oil was extracted. The extraction capacity of a model sulphur compound DBT was found to be up to 99 wt%. Regeneration of the spent ionic liquid was carried out with toluene back-extraction. A 1:1 toluene-to-IL wt ratio was performed at room temperature. It was observed that, for the spent ionic liquid containing DBT as a model compound more than 85 wt% (corresponding 3852 mg/kg) could be removed from the oil. After the second Regeneration cycle, 86 wt% of the DBT was recovered from the ionic liquid to toluene. In the case of indole as the nitrogen containing species, more than 99 wt%, (corresponding to 2993 mg/kg) of the original indole was transferred from the model oil to the ionic liquid. After the first-Regeneration cycle of the spent ionic liquid, 54 wt% of the indole–in-IL was transferred to toluene. Thus, both extractions of nitrogen and sulphur model compounds were successfully carried out from model oil and the back-extraction of these compounds from the ionic liquids to toluene demonstrated the proved the concept of the Regeneration Point of view.
S. K. Singh - One of the best experts on this subject based on the ideXlab platform.
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stochastic analysis of a two unit cold standby system subject to maximum operation and repair time
Microelectronics Reliability, 1995Co-Authors: S. K. Singh, G K AgrafiotisAbstract:Abstract A two-unit standby system is considered under excess time stochastic behaviour, i.e. the failure and repair time of the on line (off line) unit is exceeding some prespecified value. Whenever an operating unit crosses a prespecified operation time, it is sent to preventive maintenance and when repair of a failed unit crosses a prespecified time, the unit is rejected and replaced by a new unit. Using the Regeneration Point technique, certain characteristics of the system are derived and the cost of the system is calculated. Particular cases of the system are also considered.
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on the expected revenue of a production system subject to operator condition
Microelectronics Reliability, 1994Co-Authors: S. K. Singh, A K MishraAbstract:Abstract This paper deals with the profit analysis of a production station that has one machine. The input to the production station follows a Poisson process while the service time of the station follows a negative exponential distribution. The production machine is operated by an operator whose physical condition may be good or poor and this affects the operation of the machine. The analysis has been carried out using the Regeneration Point technique and various parameters have been obtained. Graphical representation is used to explain the results.
A K Mishra - One of the best experts on this subject based on the ideXlab platform.
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on the expected revenue of a production system subject to operator condition
Microelectronics Reliability, 1994Co-Authors: S. K. Singh, A K MishraAbstract:Abstract This paper deals with the profit analysis of a production station that has one machine. The input to the production station follows a Poisson process while the service time of the station follows a negative exponential distribution. The production machine is operated by an operator whose physical condition may be good or poor and this affects the operation of the machine. The analysis has been carried out using the Regeneration Point technique and various parameters have been obtained. Graphical representation is used to explain the results.