This work addresses the problem of state estimation for Markovian switching systems with unknown transition probability matrix (TPM) of the embedded Markov chain governing the switching. Under the assumption of constant but random TPM, an approximate recursion of the TPM’s posterior probability density function (PDF) within the Bayesian framework is obtained. This general PDF recursion is then ...

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (R...

a wind farm is a collection of wind turbines built in an area to provide electricity. wind power is a renewable energy resource and an alternative to non-renewable fossil fuels. in this paper impact of wind farms in power system reliability is investigate and a new procedure for reliability assessment of wind farms in hl1 level is proposed. in proposed procedure, application of fuzzy – markov f...

In this paper, Burr-type XII ̄X synthetic schemes are proposed as an alternative to the classical ̄X synthetic schemes when the assumption of normality fails to hold. First, the basic design of the Burr-type XII ̄X synthetic scheme is developed and its performance investigated using exact formulae. Secondly, the non-side-sensitive and side-sensitive Burr-type XII ̄X synthetic schemes are int...

A wind farm is a collection of wind turbines built in an area to provide electricity. Wind power is a renewable energy resource and an alternative to non-renewable fossil fuels. In this paper impact of wind farms in power system reliability is investigate and a new procedure for reliability assessment of wind farms in HL1 level is proposed. In proposed procedure, application of Fuzzy – Markov f...

Addressed is the problem of state estimation for dynamic Markovian jump systems (MJS) with unknown transitional probability matrix (TPM) of the embedded Markov chain governing the system jumps. Based on recent authors’ results, proposed is a new TPM-estimation algorithm that utilizes stochastic simulation methods (viz. Bayesian sampling) for finite mixtures’ estimation. Monte Carlo simulation r...

Markov Chain models are often used to represent subsurface stratigraphy and to simulate likely representations of the subsurface. The data used are cores or boreholes. Rock types are classified into a finite set of states, and a state transition probability matrix is estimated for some vertical sampling interval, ∆z. The estimated transition probability matrix is sensitive to the ∆z used. No ob...

In this paper, a robust visual tracking method is proposed to track an object in dynamic conditions that include motion blur, illumination changes, pose variations, and occlusions. To cope with these challenges, multiple trackers with different feature descriptors are utilized, and each of which shows different level of robustness to certain changes in an object’s appearance. To fuse these inde...

A fuzzy logic-based multi-factor aided multiple-model filter (FLMAMMF) for General aviation (GA) maneuvering target tracking (MTT) is presented. The target category and meteorological information are introduced into the interacting multiple model (IMM) filter to perform GA target tracking. Fuzzy logic inference is employed in the proposed algorithm to reflect the complicated relationship betwee...