Abstract: A new transmission loss allocation tool based on artificial neural network has been developed and presented in this paper. The proposed artificial neural network computes loss allocation much faster than other methods. A relatively short execution time of the proposed method makes it a suitable candidate for being a part of a real time decision making process. Most independent system variables can be used as inputs to this neural network which in turn makes the loss allocation procedure responsive to practical situations. Moreover, transmission line status (available or failed) was included in neural network inputs to make the proposed network capable of allocating loss even during the failure of a transmission line. The proposed neural networks were utilized to allocate losses in two types of energy transactions: bilateral contracts and power pool operation. Circuit Theory and Orthogonal Projection loss allocation methods were utilized to develop training and testing patterns. The 6-b

• Publication Date: 21-Apr-2013
• DOI: 10.15224/978-981-07-6184-4-21
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Abstract: The incredible evolution of Internet technologies and its applications requires high level of security for data over the unsecured communication channel. Digital image steganography is technique for hiding information into a cover image. Least Significant-Bit (LSB) based approach is popular steganographic technique in spatial domain due to its simplicity and robustness. All the existing methods of steganography focuson embedding mechanism with less consideration to the pre-processing, such as encryption of secrete image. The conventional steganographic model does not provide the preprocessing, needed in image basedsteganography for better security. The proposed work presents a unique technique for Image steganography based on the S- Box mapping. The preprocessing of secrete image is carried by shuffling of secrete image blocks using three 4×4 unique S-boxes. The preprocessing provide high level of security as extraction is not possible without the knowledge of mapping rules of Substitu

• Publication Date: 21-Apr-2013
• DOI: 10.15224/978-981-07-6184-4-22
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• Downloads: 0