Usually, probabilistic automata and probabilistic grammars have crisp symbols as inputs, which can be viewed as the formal models of computing with values. In this paper, we first introduce probabilistic automata and probabilistic grammars for computing with (some special) words in a probabilistic framework, where the words are interpreted as probabilistic distributions or possibility distribut...

Source localization by electroencephalography (EEG) requires an accurate model of head geometry and tissue conductivity. The estimation of source time courses from EEG or from EEG in conjunction with magnetoencephalography (MEG) requires a forward model consistent with true activity for the best outcome. Although MRI provides an excellent description of soft tissue anatomy, a high resolution mo...

the objective of this paper is to determine the drift demand hazard curves of steel moment-resisting frames with different number of stories in territory of tehran this is done through the combination of the results obtained from probabilistic seismic hazard analysis and the demand estimated through the best probabilistic seismic demand models. to select the best demand model, in this paper, a ...

Topic Detection and Tracking (TDT) is a variant of classiication in which the classes are not known or xed in advance. Consider for example an incoming stream of news articles or email messages that are to be classiied by topic; new classes must be created as new topics arise. The problem is a challenging one for machine learning. Instances of new topics must be recognized as not belonging to a...

In this paper a novel technique for random vector sampling starting from rare data is presented. This model-free sampling technique is developed to operate without a probabilistic model. Instead of estimating a distribution function, the information contained in a given small sample is extracted directly to produce the sampling result as a second sample of considerably larger size that complete...

We present a method for real-time monitoring of workflows in a constrained environment. The monitoring system should not only be able to recognise the current step but also provide the instructions about the possible next steps in an ongoing workflow. In this paper, we address this issue by using a robust approach (HMM-pLSA) which relies on Hidden Markov Model (HMM) and generative model such as...

Verification of PCTL properties of MDPs with convex uncertainties has been investigated recently by Puggelli et al. However, model checking algorithms typically suffer from state space explosion. In this paper, we address probabilistic bisimulation to reduce the size of such an MDPs while preserving PCTL properties it satisfies. We discuss different interpretations of uncertainty in the models ...

Verification of PCTL properties of MDPs with convex uncertainties has been investigated recently by Puggelli et al. However, model checking algorithms typically suffer from the state space explosion problem. In this paper, we discuss the use of probabilistic bisimulation to reduce the size of such an MDP while preserving the PCTL properties it satisfies. As a core part, we show that deciding bi...

robust control design of vehicles addresses the effect of uncertainties on the vehicle’s performance. in present study, the robust optimal multi-objective controller design on a non-linear full vehicle dynamic model with 8-degrees of freedom having parameter with probabilistic uncertainty considering two simultaneous conflicting objective functions has been made to prevent the rollover. the obj...

We develop an abstraction-based framework to check probabilistic specifications of Markov Decision Processes (MDPs) using the stochastic two-player game abstractions (i.e. “games”) developed by Kwiatkowska et al. as a foundation. We define an abstraction preorder for these game abstractions which enables us to identify many new game abstractions for each MDP — ranging from compact and imprecise...