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Noting the vital importance of localization using wireless sensor networks in real-world applications, many limitations of existing techniques urge us to seek more advanced localization algorithms. This paper presents a new range-free algorithm which takes advantages of genetic algorithms (GAs) to optimize multi-objective functions used in calculating an unknown position of normal node. The proposed algorithm, so far has improved the typical rage-free algorithms. It has good impact on the solving of localization problems with high accuracy. The first part illustrates typical based DV-hop localization algorithms. The principle of position estimation via genetic algorithms is introduced later. A proposed objective function to be optimized is defined in a next part, and its optimization based on GAs allows the unknown position’s computation. The new algorithm has been proved functional by theoretical analysis and simulation results. We have also proved the efficient performance of the proposed approach by comparing it to some state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

This paper characterizes an approach to data compression and defect isolation for the multi-variable process by introducing the wavelet principal component analysis (PCA) and the genetic algorithms. In the defect analysis process, data compression is a critical phase. In our case of study, this can be achieved through the wavelet PCA. We can, through the use of such a technique, filter the data noise and the measurement errors by combining the PCA and the wavelet analysis. The genetic algorithm optimization feature is then employed in structuring the residues of the defect isolation. We present the principle of the PCA as well as the wavelet compression property. Then, we describe both the defect isolation classical method by structuring the residues and the principle of optimizing a problem by the genetic algorithms. The proposed approach is implemented on a statistic system and the Tennessee Eastman process. In comparison with other previous methods, the obtained results mirror the performance of the approach to defect isolation. [ABSTRACT FROM AUTHOR]

This work is dedicated to the synthesis of a new fault detection and identification scheme for the actuator and/or sensor faults modeled as unknown inputs of the system. The novelty of this scheme consists in the synthesis of a new structure of proportional-integral observer (PIO) reformulated from the new linear ARX-Laguerre representation with filters on system input and output in order to estimate the unknown inputs presented as faults. The designed observer exploits the input/output measurements to reconstruct the Laguerre filter outputs where the stability and the convergence properties are ensured by using Linear Matrix Inequality. However, a significant reduction of this model is subject to an optimal choice of both Laguerre poles which is achieved by a new proposed identification approach based on a genetic algorithm. The performances of the proposed identification approach and the resulting PIO are tested on numerical simulation and validated on a 2 order electrical linear system. [ABSTRACT FROM AUTHOR]

While defect localization is vital in real-world systems, some limitations inherent to the existing techniques urge us to seek more advanced methods. This paper presents a new approach which takes advantages of genetic algorithms for optimization of non-convex objective function employed in calculating structured residues. The proposed approach so far improved the current principal component analysis (PCA) based on one of the defect localization. It has excellent impact on problem solving while dealing with optimization of residues structuring. The first part illustrates both the PCA model and the traditional residues structuring approach. The principle of optimizing a problem via genetic algorithms is explained later. A proposed objective function to be optimized is defined in the next part, and its optimization via genetic algorithms allows the structured residues computation. The new approach has been applied and proved functional for monitoring the Tennessee Eastman process. We have also proved the efficient performance of the proposed method in comparing it with some state-of-the-art methods. [ABSTRACT FROM AUTHOR]