Indiana University-Purdue University Indianapolis (IUPUI) / The electrical disturbances in the power system have threatened the stability of the system. In the first step, it is necessary to detect these electrical disturbances or events. In the next step, a proper control should apply to the system to decrease the consequences of the disturbances. One-shot control is one of the effective methods for stabilizing the events. In this method, a proper amount of loads are increased or decreased to the electrical system. Determining the amounts of loads, and the location for shedding is crucial. Moreover, some control combinations are more effective for some events and less effective for some others. Therefore, this project is completed in two different sections. First, finding the effective control combinations, second, finding an algorithm for applying different control combinations to different contingencies in real-time. To find effective control combinations, sensitivity analysis is employed to locate the most effective loads in the system. Then to find the control combination commands, gradient descent, and PSO algorithm are used in this project. In the next step, a pattern recognition method is used to apply the appropriate control combination for every event. The decision tree is selected as the pattern recognition method. The three most effective control combinations found by sensitivity analysis and the PSO method are used in the remainder of this study. A decision tree is trained for each of the three control combinations, and their outputs are combined into an algorithm for selecting the best control in real-time. Finally, the algorithm is evaluated using a test set of contingencies. The final results reveal a 30\% improvement in comparison to the previous studies.

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The feedforward neural network (NN) is a mathematical model capable of representing any non-linear relationship between input and output data. It has been succesfully applied to a wide variety of classification and function approximation problems. Various neural network training algorithms were developed, including the particle swarm optimiser (PSO), which was shown to outperform the standard back propagation training algorithm on a selection of problems. However, it was usually assumed that the environment in which a NN operates is static. Such an assumption is often not valid for real life problems, and the training algorithms have to be adapted accordingly. Various dynamic versions of the PSO have already been developed. This work investigates the applicability of dynamic PSO algorithms to NN training in dynamic environments, and compares the performance of dynamic PSO algorithms to the performance of back propagation. Three popular dynamic PSO variants are considered. The extent of adaptive properties of back propagation and dynamic PSO under different kinds of dynamic environments is determined. Dynamic PSO is shown to be a viable alternative to back propagation, especially under the environments exhibiting infrequent gradual changes. Copyright 2011, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. Please cite as follows: Rakitianskaia, A 2011, Using particle swarm optimisation to train feedforward neural networks in dynamic environments, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-02132012-233212 / > C12/4/406/gm / Dissertation (MSc)--University of Pretoria, 2011. / Computer Science / Unrestricted

Indiana University-Purdue University Indianapolis (IUPUI) / Plug-in Hybrid Electric Vehicles (PHEV) are new generation Hybrid Electric Vehicles (HEV) with larger battery capacity compared to Hybrid Electric Vehicles. They can store electrical energy from a domestic power supply and can drive the vehicle alone in Electric Vehicle (EV) mode. According to the U.S. Department of Transportation 80 % of the American driving public on average drives under 50 miles per day. A PHEV vehicle that can drive up to 50 miles by making maximum use of cheaper electrical energy from a domestic supply can significantly reduce the conventional fuel consumption. This may also help in improving the environment as PHEVs emit less harmful gases. However, the Energy Management System (EMS) of PHEVs would have to be very different from existing EMSs of HEVs. In this thesis, three different Energy Management Systems have been designed specifically for PHEVs using simulated study. For most of the EMS development mathematical vehicle models for powersplit drivetrain configuration are built and later on the results are tested on advanced vehicle modeling tools like ADVISOR or PSAT. The main objective of the study is to design EMSs to reduce fuel consumption by the vehicle. These EMSs are compared with existing EMSs which show overall improvement. x In this thesis the final EMS is designed in three intermediate steps. First, a simple rule based EMS was designed to improve the fuel economy for parametric study. Second, an optimized EMS was designed with the main objective to improve fuel economy of the vehicle. Here Particle Swarm Optimization (PSO) technique is used to obtain the optimum parameter values. This EMS has provided optimum parameters which result in optimum blended mode operation of the vehicle. Finally, to obtain optimum charge depletion and charge sustaining mode operation of the vehicle an advanced PSO EMS is designed which provides optimal results for the vehicle to operate in charge depletion and charge sustaining modes. Furthermore, to implement the developed advanced PSO EMS in real-time a possible real time implementation technique is designed using neural networks. This neural network implementation provides sub-optimal results as compared to advanced PSO EMS results but it can be implemented in real time in a vehicle. These EMSs can be used to obtain optimal results for the vehicle driving conditions such that fuel economy is improved. Moreover, the optimal designed EMS can also be implemented in real-time using the neural network procedure described.

The rapidly increasing demands for high data-rate applications and the growth of wireless devices connected to the internet overcrowded the radio frequency spectrum. This necessitates researchers to examine higher frequencies for wireless communication. Recently, visible light communication (VLC) has received significant attention as a viable solution to complement the RF technologies, thanks to the abundant unregulated/unlicensed spectrum it occupies while utilizing the existing lighting infrastructure. However, due to the physical properties of light, the signal cannot penetrate through obstacles, and the VLC system heavily relies on the existence of a line-of-sight (LoS) link between VLC transmitters and receivers. Optical reconfigurable intelligent surfaces (RISs) are recently proposed with the ability to dynamically control the wireless channel, which offers opportunities to enhance the VLC system performance by exploiting the non-LoS components of the VLC link. In this thesis, we highlight the recent developments in optical RISs and the various reflection characteristics they provide for the incident optical beams. Then, we investigate RIS-assisted VLC systems for both indoor and outdoor setups. Firstly, in indoor VLC systems, we study multi-user RIS-assisted VLC systems while considering specular and diffuse reflecting RISs. As the channel gain varies significantly between users in VLC systems, a large gap in performance is observed between users. We aim to maximize the VLC system achievable sum rate while ensuring network fairness. We formulate multi-objective optimization problems for both specular and diffuse reflecting RISs and propose a solution utilizing low complex genetic algorithm (GA) and particle swarm optimization (PSO). We highlight the advantages provided by the proposed algorithms in terms of achievable sum rate and network fairness performance. In addition, we assess the link outage ratio for specular reflecting RISs and assess the gains provided by diffuse RISs while considering an environment with mobile users. Secondly, in the context of outdoor VLC systems, we provide an overview of outdoor RIS-assisted VLC systems. In particular, we highlight the benefits of optical RISs to mitigate LoS blockage and VLC transceivers misalignment. More specifically, we focus on RIS-assisted unmanned aerial vehicles (UAVs)-based VLC, RIS-assisted vehicular VLC, and RIS-assisted streetlight-based communication. In addition, we highlight the use of RISs to support VLC outdoor-to-indoor communications.

Dynamic pricing is a difficult problem for machine learning. The environment is noisy, dynamic and has a measurable cost associated with exploration that necessitates that learning be done in short-time horizons. These short-time horizons force the learning algorithms to make pricing decisions based on scarce data. In this work, various machine learning algorithms are compared in the context of dynamic pricing. These algorithms include the Kalman filter, artificial neural networks, particle swarm optimization and genetic algorithms. The majority of these algorithms have been modified to handle the pricing problem. The results show that these adaptations allow the learning algorithms to handle the noisy dynamic conditions and to learn quickly.

Particle swarm optimization (PSO) has previously been parallelized primarily by distributing the computation corresponding to particles across multiple processors. In this thesis we present a speculative approach to the parallelization of PSO that we refer to as SEPSO. In our approach, we refactor PSO such that the computation needed for iteration t+1 can be done concurrently with the computation needed for iteration t. Thus we can perform two iterations of PSO at once. Even with some amount of wasted computation, we show that this approach to parallelization in PSO often outperforms the standard parallelization of simply adding particles to the swarm. SEPSO produces results that are exactly equivalent to PSO; this is not a new algorithm or variant, only a new method of parallelization. However, given this new parallelization model we can relax the requirement of exactly reproducing PSO in an attempt to produce better results. We present several such relaxations, including keeping the best speculative position evaluated instead of the one corresponding to the standard behavior of PSO, and speculating several iterations ahead instead of just one. We show that these methods dramatically improve the performance of parallel PSO in many cases, giving speed ups of up to six times compared to previous parallelization techniques.

Function optimization---finding the minimum or maximum of a given function---is an extremely challenging problem with applications in physics, economics, machine learning, engineering, and many other fields. While optimization is an active area of research, only a portion of this work acknowledges parallel computation, which is now widely available. Today, anyone with a modest budget can buy a cluster with hundreds of cores, pay for access to a supercomputer with thousands of processors, or at least purchase a laptop with 8 cores. Thus, an algorithm that works well in serial but cannot be parallelized is needlessly inefficient in real-life computationalenvironments.We address these issues in three connected threads of development: a high-level programming framework that makes it possible to create flexible and efficient implementations of optimization algorithms; improvements to an existing algorithm, Particle Swarm Optimization, to make it take better advantage of parallel resources; and a statistical model designed to efficiently use available information in parallel optimization by inferring search directions. Each of these is an essential step toward effective parallel optimization. First, without a suitable high-level programming model, expediency leads to purely serial development with parallel issues only an afterthought. Second, PSO has proven effective for optimization and is an excellent candidate to consider for efficient parallel implementations. Third, a model for inference of search directions is useful for understanding communication in the context of parallel optimization and provides a flexible base for continuing optimization research.

Optical filters encompass a vast array of devices and structures for a wide variety of applications. Generally speaking, an optical filter is some structure that applies a designed amplitude and phase transform to an incident signal. Different classes of filters have vastly divergent characteristics, and one of the challenges in the optical design process is identifying the ideal filter for a given application and optimizing it to obtain a specific response. In particular, it is highly advantageous to obtain a filter that can be seamlessly integrated into an overall device package without requiring exotic fabrication steps, extremely sensitive alignments, or complicated conversions between optical and electrical signals. This dissertation explores three classes of nano-scale optical filters in an effort to obtain different types of dispersive response functions. First, dispersive waveguides are designed using a sub-wavelength periodic structure to transmit a single TE propagating mode with very high second order dispersion. Next, an innovative approach for decoupling waveguide trajectories from Bragg gratings is outlined and used to obtain a uniform second-order dispersion response while minimizing fabrication limitations. Finally, high Q-factor microcavities are coupled into axisymmetric pillar structures that offer extremely high group delay over very narrow transmission bandwidths. While these three novel filters are quite diverse in their operation and target applications, they offer extremely compact structures given the magnitude of the dispersion or group delay they introduce to an incident signal. They are also designed and structured as to be formed on an optical wafer scale using standard integrated circuit fabrication techniques. A number of frequency-domain numerical simulation methods are developed to fully characterize and model each of the different filters. The complete filter response, which includes the dispersion and delay characteristics and optical coupling, is used to evaluate each filter design concept. However, due to the complex nature of the structure geometries and electromagnetic interactions, an iterative optimization approach is required to improve the structure designs and obtain a suitable response. To this end, a Particle Swarm Optimization algorithm is developed and applied to the simulated filter responses to generate optimal filter designs.