A typical recognition system consists of a sequential combination of two experts, called a detector and classifier respectively. The two stages are usually designed independently, but we show that this may be suboptimal due to interaction between the stages. In this paper we consider the two stages holistically, as components of a multiple classifier system. This allows for an optimal design th...

Combining the computational fluid dynamics (CFD) and the design of experiments (DOE) methods, as a mixed approach in modeling was proposed so that to simultaneously benefit from the advantages of both modeling methods. The presented method was validated using a coal hydraulic classifier in an industrial scale. Effects of operating parameters including feed flow rate, solid content and baffle le...

This paper presents a hybrid ensemble method that is comprised of a sequential and a parallel architecture for the classification of tree genus using LiDAR (Light Detection and Ranging) data. The two classifiers use different sets of features: (1) features derived from geometric information, and (2) features derived from vertical profiles using Random Forests as the base classifier. This classi...

There have been many recent efforts in contentbased retrieval to perform automatic classification of images/visual objects. Most approaches, however, have focused on using individual classifiers. In this paper, we study the way in which, in a dynamic framework, multiple classifiers can be combined when applying Visual Object Detectors. We propose a hybrid classifier combination approach, in whi...

in this paper, optimal design of hybrid solar-wind- diesel energy system with battery storage for reliable load in arsoon village in ardabil is presented. the village has 35 households that mean daily energy consumption is estimated 63.4 kwh. in this design optimization is implemented, considering loss of power supply probability (lpsp) index for reliable load providing, with minimum total cons...