Problem statement: Significant movement has been made in handwriting recognition technology over the last few years. Up until now, Arabic handwriting recognition systems have been limited to small and medium vocabulary applications, since most of them often rely on a database during the recognition process. The facility of dealing with large database, however, opens up many more applications. Approach: This study presented a complete system to recognize off-line Arabic handwriting image and Arabic handwriting and printed text database AHPD-UTM that used to implement and test the system. That system start from preprocessing and segmentation phases that deepened on thinning the image and found the V and H projection profile until recognition phase by genetic algorithm. Results: The genetic algorithm stand on feature extraction algorithm that defined six feature for each segment beak. The system can be recognized Arabic handwriting with 87% accuracy. The confusion and rejection rates are 8.4, those causes for several problems like characters with broken loops and character segmentation problem. Conclusion: Peak connection solved some of the segmentation problems and helped to provide better accuracy
The development of Neural-network (NN) technology stemmed from the desire to create an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. In this paper the performance of NN to the structural optimization concept of frame structure is presented. The optimum set of frame designs is obtained using Finite Element (FE) software where stress and displacement constraints has been chosen as the optimum criteria. The optimized data then used to train the NN through Back Propagation Neural-network technique (BPNN) to identify the capability of this strategy to predict the exact data. Three case studies were performed with different complexity of structural configuration. Result indicates the Neural-network capable of predicting the exact solution with proper training but this ability depends on the complexity of the frame structural optimization itself.
Stiffened panels are the structure used in the aircraft wing skin panels. Stiffened panels are often critical in compression load due to its thin structural configuration. This paper analyzes the critical loads of a multi configuration stiffened panels under axial compressive loading. The study comprised three main sections; theoretical analysis, numerical analysis and experimental analysis. The present paper deals only with the theoretical analysis. This first part of analysis is very important since the results will be the main input parameter for the subsequent numerical and experimental analysis. The analysis was done on the buckling properties of the panels. Four panel configurations were investigated. Results showed that even though the stiffened panels have the same cross-sectional area, their critical loads were not identical.
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