Statistical model-checking is a recent technique used for both verification and performance analysis of hybrid systems. It does not suffer from decidability issues or state-space explosion compared to traditional model-checking. Furthermore, it is applicable to more powerful formalisms such as stochastic hybrid automata. Its principle is simple so how simple is it really to make it work in prac...

Statistical model-checking is a recent technique used for both verification and performance analysis of hybrid systems. It does not suffer from decidability issues or state-space explosion compared to traditional model-checking. Furthermore, it is applicable to more powerful formalisms such as stochastic hybrid automata. Its principle is simple so how simple is it really to make it work in prac...

Statistical model-checking is a recent technique used for both verification and performance analysis of hybrid systems. It does not suffer from decidability issues or state-space explosion compared to traditional model-checking. Furthermore, it is applicable to more powerful formalisms such as stochastic hybrid automata. Its principle is simple so how simple is it really to make it work in prac...

Statistical model-checking is a recent technique used for both verification and performance analysis of hybrid systems. It does not suffer from decidability issues or state-space explosion compared to traditional model-checking. Furthermore, it is applicable to more powerful formalisms such as stochastic hybrid automata. Its principle is simple so how simple is it really to make it work in prac...

Innovative methods have been developed for diagnosis, activity monitoring, and state estimation that achieve high accuracy through the use of stochastic models involving hybrid discrete and continuous behaviors. A key bottleneck is the automated acquisition of these hybrid models, and recent methods have focused predominantly on Jump Markov processes and piecewise autoregressive models. In this...

this research work is devoted to experimental and theoretical evaluations of a hybrid constitutive model which was designed to simulate the mechanical behavior of a tread rubber compound. the model is a combination of yeoh hyperelastic model with a strain-rate hysteresis model developed by bergstrom and boyce. the parameters of the yeoh model were calibrated from experimental data of the astm d...

the main goal of the present study was to implement a hybrid pattern of cellular automata model and particle swarm optimization algorithm based on tm and etm+ imagery of landsat satellite from 1988 to 2010 for simulating the urban sprawling. in this study, an alternative model was implemented in two ways: the first method was based on two images (1988 and 2010) and the second one was based on t...

in this paper, we propose a new residual analysis method using fourier series transform into fuzzy time series model for improving the forecasting performance. this hybrid model takes advantage of the high predictable power of fuzzy time series model and fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...

Fossil Fuels are always considered as environmental pollutants. On the other hand, the political and economic situations highly affect the price of these fuels. Offshore wind and wave, as renewable energy sources, represent the better alternatives for electricity generation. Therefore, it is necessary that wind speeds effectively be estimated due to the absence of field measurements of the wind...