Optimal load shedding for microgrids with unlimited DGs

Recent years, increasing trends on electrical supply demand, make us to search for the new alternative in supplying the electrical power. A study in micro grid system with embedded Distribution Generations (DGs) to the system is rapidly increasing. Micro grid system basically is design either operate in islanding mode or interconnect with the main grid system. In any condition, the system must have reliable power supply and operating at low transmission power loss. During the emergency state such as outages of power due to electrical or mechanical faults in the system, it is important for the system to shed any load in order to maintain the system stability and security. In order to reduce the transmission loss, it is very important to calculate best size of the DGs as well as to find the best positions in locating the DG itself.. Analytical Hierarchy Process (AHP) has been applied to find and calculate the load shedding priorities based on decision alternatives which have been made. The main objective of this project is to optimize the load shedding in the micro grid system with unlimited DG’s by applied optimization technique Gravitational Search Algorithm (GSA). The technique is used to optimize the placement and sizing of DGs, as well as to optimal the load shedding. Several load shedding schemes have been proposed and studied in this project such as load shedding with fixed priority index, without priority index and with dynamic priority index. The proposed technique was tested on the IEEE 69 Test Bus Distribution system

Source identification for mobile devices, based on wavelet transforms combined with sensor imperfections

One of the most relevant applications of digital image forensics is to accurately identify the device used for taking a given set of images, a problem called source identification. This paper studies recent developments in the field and proposes the mixture of two techniques (Sensor Imperfections and Wavelet Transforms) to get better source identification of images generated with mobile devices. Our results show that Sensor Imperfections and Wavelet Transforms can jointly serve as good forensic features to help trace the source camera of images produced by mobile phones. Furthermore, the model proposed here can also determine with high precision both the brand and model of the device

Cost-effective HPC clustering for computer vision applications

We will present a cost-effective and flexible realization of high performance computing (HPC) clustering and its potential in solving computationally intensive problems in computer vision. The featured software foundation to support the parallel programming is the GNU parallel Knoppix package with message passing interface (MPI) based Octave, Python and C interface capabilities. The implementation is especially of interest in applications where the main objective is to reuse the existing hardware infrastructure and to maintain the overall budget cost. We will present the benchmark results and compare and contrast the performances of Octave and MATLAB