Multi-objective GA with fuzzy decision making for security enhancement in power system

Power system security enhancement is a major concern in the operation of power system. In this paper, the task of security enhancement is formulated as a multi-objective optimization problem with minimization of fuel cost and minimization of FACTS device investment cost as objectives. Generator active power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors (TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the decision variables are represented as floating point numbers in the GA population. The MOGA emphasize non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation results show the effectiveness of the proposed approach for solving the multi-objective security enhancement problem.     

A Hybrid RF and Vision Aware Fusion Scheme for Multi-Sensor Wireless Capsule Endoscopic Localization

A huge torrent of data traffic is generated from various heterogeneous applications and services at the Internet backbone. In general, at the backbone, all such applications and services are allocated spectral resources under a shared spectrum environment within elastic optical networks (EONs). In such a fully shared environment, connection requests (CRs) belonging to different traffic profiles compete for spectral resources. Hence, it is very challenging for network operators to resolve resource conflict that occur at the time of provisioning resources to such CRs. The heterogeneous traffic profile (HTP) considered in this work includes permanent lightpath demands (PLDs) and scheduled lightpath demands (SLDs). We propose various distance adaptive routing and spectrum assignment (DA-RSA) heuristics to resolve resource conflict among these two traffic profiles in EONs under a full sharing environment. Conventionally, preemption was the only technique to resolve such resource conflict among HTPs. Since preemption involves the overhead of selecting CRs to be preempted and then deallocating the resources given to those CRs, excessive preemption adversely affects the performance of the network. Therefore, in this work, we utilized bandwidth splitting as a solution to resolve resource conflict among HTPs under such a shared environment in EONs. Moreover, an integrated solution consisting of splitting and preemption is also proposed. We refer to this new integration as flow-based preemption. Our simulation results demonstrate that bandwidth splitting-based heuristics yield significant improvement in terms of the amount of bandwidth accepted in the network, link and node utilization ratio, number of transponders utilized and the amount of bandwidth dropped due to preemption. Moreover, the flow-based preemption approach is proved to be superior in performance amongst all proposed strategies.

     

Lightweight Self-Compacting Concrete Incorporating Industrial Rejects and Mineral Admixtures: Strength and Durability Assessment

By the recent global research developments, a lot of natural and artificial materials that are normally discarded and landfilled, are continually investiga     

The new block pixel sort algorithm for TVC-encrypted medical image

To transfer the medical image from one place to another place or to store a medical image in a particular place with secure manner has become a challenge. In order to solve those problems, the medical image is encrypting and compressing before sending or saving at a place. In this paper, a new block pixel sort algorithm has been proposed for compressing the encrypted medical image. The encrypted medical image acts as an input for this compression process. During the compression, encrypted secret image E₁₂(;) is compressed by the pixel block sort encoding (PBSE). The image is divided into four identical blocks, similar to 2×2 matrix. The minimum occurrence pixel(s) are found out from every block and the positions of the minimum occurrence pixel(s) are found using the verdict occurrence process. The pixel positions are shortened with the help of a shortening process. The features (symbols and shortened pixel positions) are extracted from each block and the extracted features are stored in a particular place, and the values of these features put together as a compressed medical image. The next process of PBSE is pixel block short decoding (PBSD) process. In the decoding process, there are nine steps involved while decompressing the compressed encrypted medical image. The feature extraction value of compressed information is found out from the feature extraction, the symbols are split and the positions are shortened in a separate manner. The position is retrieved from the rescheduled process and the symbols and reconstructed positions of the minimum occurrence pixels are taken block wise. Every symbol is placed based on the position in each block: if the minimum occurrence pixel is ‘0’, then the rest of the places are automatically allocated as ‘1’ or if the minimum occurrence pixel is ‘1’ the remaining place is automatically allocated as ‘0’. Both the blocks are merged as per order 2×2. The final output is the reconstructed encrypted medical image. From this compression method, we can achieve the high compression ratio, minimum time, less compression size and lossless compression, which are the things experimented and proved.