This paperexplores theapplicationof artificial neural networksfor online identification of a multimachinepower system.Arecurrent neural networkhas been proposedas the identifier of the two area, four machinesystemwhich is a benchmark system for studying electromechanical oscillations in multimachine powersystems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of thepaper is on investigating the performance of the variants of the Backpropagation algorithm in training theneural identifier. The paper also compares the performances of the neural identifiers trained usingvariantsof the Backpropagation algorithmover a wide range of operating conditions.The simulation resultsestablish a satisfactory performance of the trained neural identifiers in identification of the test powersyste
The OCR technology for Indian documents is in emerging stage and most of these Indian OCR systems can read the documents written in only a single script. As many commercial and official documents of different states of India are tri-lingual in nature, therefore identification of script and/ or language is one of the elementary tasks for multi-script document recognition. A script recognizer simplifies the task of multi-lingual OCR by improving the accuracy and reducing the computational complexity. This script recognition may be at line, word or character level depending on interlacing of different scripts at different levels. This paper presents the effectiveness of Gabor Filter banks with kNN, SVM and PNN classifiers to identify the scripts at line level from such trilingual documents. The experiments show that Gabor features with SVM classifier achieve a recognition rate of 99.85% for trilingual documents.
Abstract-Nature-inspired algorithms are recently being appreciated for solving complex optimization and engineering problems. Black hole algorithm is one of the recent nature-inspired algorithms that have obtained inspiration from black hole theory of universe. In this paper, four formulations of multi-objective black hole algorithm have been developed by using combination of weighted objectives, use of secondary storage for managing possible solutions and use of Genetic Algorithm (GA). These formulations are further applied for scheduling jobs on parallel machines while optimizing bi-criteria namely maximum tardiness and weighted flow time. It has been empirically verified that GA based multi-objective Black Hole algorithms leads to better results as compared to their counterparts. Also the use of combination of secondary storage and GA further improves the resulting job sequence. The proposed algorithms are further compared to some of the existing algorithms, and empirically found to be better. The results have been validated by numerical illustrations and statistical tests.
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