This paper presents a practical algorithm for facial age estimation from frontal face image. Facial age estimation generally comprises two key steps including age image representation and age estimation. The anthropometric model used in this study includes computation of eighteen craniofacial ratios and a new accurate skin wrinkles analysis in the first step and a pairwise binary support vector...

In this paper principles of extended Kalman filtering theory is developed and applied to simulated on-line electric power systems state estimation in order to trace the operating condition changes through the redundant and noisy measurements. Test results on IEEE 14 - bus test system are included. Three case systems are tried; through the comparing of their results, it is concluded that the pro...

The standard robot identification method is based on the use of inverse dynamic model (IDM) and application Least Squares (LS) estimation while tracking trajectories. Although this approach has been successfully applied to several industrial robots, friction assumed be linear. In paper, a two-step LS proposed that corrects limitation. first step, State-Dependent-Parameter (SDP) combined with ID...

The performance of Vector Controlled Induction Motor drive depends on the accuracy of rotor resistance which will vary with temperature and frequency. The MRAS approach using reactive power and flux as a state variable for rotor resistance estimation makes MRAS computationally simpler and easy to design. In this paper, Rotor Flux based MRAS (RF-MRAS) and Reactive Power based MRAS (RP-MRAS) for ...

Theoretically, in order to describe the behavior of a spectrum, a mathematical model whichcould predict the spectrum characteristics is needed. Since in this study a Two-state system has beenused like models which was introduced previously past and could couple with the environment, theformer ideas have been extended in this study. we use the second quantized version for writing thisHamiltonian...

Optimal filtering: The filtering problem involves the estimation of the state vector at time k, given all the measurements up to and including time k, which we denote by z1:k. In a Bayesian setting, this problem can be formalized as the computation of the distribution p(xk|z1:k), which can be done recursively in two steps. In the prediction step, p(xk|z1:k−1) is computed from the filtering dist...

We introduce a formalism for optimal sensor parameter selection for iterative state estimation in static systems. Our optimality criterion is the reduction of uncertainty in the state estimation process, rather than an estimator-specific metric (e.g., minimum mean squared estimate error). The claim is that state estimation becomes more reliable if the uncertainty and ambiguity in the estimation...

Many contact-rich tasks in factories are still executed manually because it is often difficult for robots to adapt variable environmental conditions. To incorporate the adaptability of humans environment into robots, a machine learning technique called from demonstration (LfD) has been well studied. A popular approach automating complex involves selecting appropriate action several segmented mo...