Deep Learning and Quantum-computing Based Optimization in Medical Imaging and Power Dispatcing

兵庫県立大学大学院202

Deep Learning and Quantum-computing Based Optimization in Medical Imaging and Power Dispatcing

兵庫県立大学大学院202

Face recognition-based real-time system for surveillance

The ability to automatically recognize human faces based on dynamic facial images is important in security, surveillance and the health/independent living domains. Specific applications include access control to secure environments, identification of individuals at a particular place and intruder detection. This research proposes a real-time system for surveillance using cameras. The process is broken into two steps: (1) face detection and (2) face recognition to identify particular persons. For the first step, the system tracks and selects the faces of the detected persons. An efficient recognition algorithm is then used to recognize detected faces with a known database. The proposed approach exploits the Viola-Jones method for face detection, the Kanade-Lucas-Tomasi algorithm as a feature tracker and Principal Component Analysis (PCA) for face recognition. This system can be implemented at different restricted areas, such as at the office or house of a suspicious person or at the entrance of a sensitive installation. The system works almost perfectly under reasonable lighting conditions and image depths

Quantum Inspired Computational Intelligence Techniques for Combined Economic Emission Dispatch Problem

The rapid increase in the demand of electricity, shortage of fossil fuel supply and environmental concerns make economic load dispatch (ELD) and emission dispatch problem as the main concerns of electrical power generation system. ELD refers to find an optimal combination of power generation in order to minimize the total generation cost, while the goal of emission dispatch is to minimize the amount of pollutants by satisfying all other constraints. The goal of combined economic emission dispatch (CEED) is to minimize the total generation cost as well as the emission of pollutants, while satisfying all other constraints. Previously, different classical methods like LR, LP and EP, stand-alone methods such as PSO, GA and ABC, and different hybrid methods have been used to solve CEED problem. But, due to their different weaknesses like not suitable for nonlinear cost function, trapping into local optima and high computational time, researchers are now looking for alternative powerful optimization tools in order to address the challenges found to solve this problem. In this research work, we at first separately optimize ELD and emission dispatch problem using particle swarm optimization (PSO), quantum-behaved bat algorithm (QBA) and quantum particle swarm optimization (QPSO) for different number of units. Later, we consider both of the objectives simultaneously as a multiobjective optimization problem. We have considered cubic function to represent both ELD and emission dispatch problem as well as CEED problem. Emission dispatch problem is divided into three different objectives as minimization of SO2, NOX and CO2. Thus, making CEED problem as a four objectives optimization problem. We consider a unit-wise price penalty factor to convert all the objectives into a single objective. The main goal of this research is to attain a balanced trade-off between secured and profitable energy choices, and maintaining healthy and sound environment. Quantum computing phenomenon is integrated with swarm intelligence-based PSO and bat algorithm (BA) to make these algorithms computationally more powerful and robust