Formation of Morphable 3D­model of Large Scale Natural Sites by Using Image Based Modeling and Rendering Techniques

No global 3D model of the environment needs to be assembled, a process which can be extremely cumbersome and error prone for large scale scenes e.g. the global registration of multiple local models can accumulate a great amount of error, while it also presumes a very accurate extraction of the underlying geometry. On the contrary, neither any such accurate geometric reconstruction of the individual local 3D models nor a very precise registration between them is required by our framework in order that it can produce satisfactory results. This paper presents an application of LP based MRF optimization techniques and also we have turned our attention to a different re­ search topic: the proposal of novel image based modeling and rendering methods, which are capable of automatically reproducing faithful (i.e. photorealistic) digital copies of complex 3D virtual environments, while also allowing the virtual exploration of these environments at interactive frame rates

Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO

A global parameter estimation method for a PMSM drive system is proposed, where the electrical parameters, mechanical parameters and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters and VSI of PMSM drive by using dynamic self learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process. Moreover, a nonlinear multi-scale based interactive learning operator is introduced for accelerating the convergence speed of the Pbest particles; meanwhile a dynamic opposition-based learning (OBL) strategy is designed to facilitate the gBest particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously

Paired Comparisons-based Interactive Differential Evolution

We propose Interactive Differential Evolution (IDE) based on paired comparisons for reducing user fatigue and evaluate its convergence speed in comparison with Interactive Genetic Algorithms (IGA) and tournament IGA. User interface and convergence performance are two big keys for reducing Interactive Evolutionary Computation (IEC) user fatigue. Unlike IGA and conventional IDE, users of the proposed IDE and tournament IGA do not need to compare whole individuals each other but compare pairs of individuals, which largely decreases user fatigue. In this paper, we design a pseudo-IEC user and evaluate another factor, IEC convergence performance, using IEC simulators and show that our proposed IDE converges significantly faster than IGA and tournament IGA, i.e. our proposed one is superior to others from both user interface and convergence performance points of view