Indiana University-Purdue University Indianapolis (IUPUI) / Finding the optimal placement of receiving assets among transmitting targets in a three-dimensional (3D) space is a complex and dynamic problem that is solved in this work. The placement of assets in R^6 to optimize the best coverage of transmitting targets requires the placement in 3D-spatiality, center frequency assignment, and antenna azimuth and elevation orientation, with respect to power coverage at the receiver without overloading the feed-horn, maintaining suficient power sensitivity levels, and maintaining terrain constraints. Further complexities result from the human-user having necessary and time-constrained knowledge to real-world conditions unknown to the problem space, such as enemy positions or special targets, resulting in the requirement of the user to interact with the solution convergence in some fashion. Particle Swarm Optimization (PSO) approaches this problem with accurate and rapid approximation to the electronic warfare asset allocation problem (EWAAP) with near-real-time solution convergence using a linear combination of weighted components for tness comparison and particles representative of asset con- gurations. Finally, optimizing the weights for the tness function requires the use of unsupervised machine learning techniques to reduce the complexity of assigning a tness function using a Meta-PSO. The result of this work implements a more realistic asset allocation problem with directional antenna and complex terrain constraints that is able to converge on a solution on average in 488.7167+-15.6580 ms and has a standard deviation of 15.3901 for asset positions across solutions.

Indiana University-Purdue University Indianapolis (IUPUI) / The contribution of this research work can be divided into two main tasks: 1) implementing this Electronic Warfare Asset Allocation Problem (EWAAP) with the Genetic Algorithm (GA); 2) Comparing performance of Genetic Algorithm to Particle Swarm Optimization (PSO) algorithm. This research problem implemented Genetic Algorithm in C++ and used QT Data Visualization for displaying three-dimensional space, pheromone, and Terrain. The Genetic algorithm implementation maintained and preserved the coding style, data structure, and visualization from the PSO implementation. Although the Genetic Algorithm has higher fitness values and better global solutions for 3 or more receivers, it increases the running time. The Genetic Algorithm is around (15-30\%) more accurate for asset counts from 3 to 6 but requires (26-82\%) more computational time. When the allocation problem complexity increases by adding 3D space, pheromones and complex terrains, the accuracy of GA is 3.71\% better but the speed of GA is 121\% slower than PSO. In summary, the Genetic Algorithm gives a better global solution in some cases but the computational time is higher for the Genetic Algorithm with than Particle Swarm Optimization.

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Optimization techniques are widely applied in the power system planning and operation to achieve more efficient and reliable power supply. With the introduction of new technologies, the complexity of today’s power system increased significantly. Intelligent optimization techniques, such as Particle Swarm Optimization (PSO), can efficiently deal with the new challenges compared to conventional optimization techniques. This thesis presents applications of discrete PSO in two specific environments. The first one is for day-ahead optimal scheduling of the reconfigurable gird with distributed energy resources. The second one is a two-step method for rapid reconfiguration of shipboard power system. Effective techniques, such as graph theory, optimal power flow and heuristic mutation, are employed to make the PSO algorithm more suitable to application environments and achieve better performance.

Short-term wind power forecasting is crucial for the efficient operation of power systems with high wind power penetration. Many forecasting approaches have been developed in the past to forecast short-term wind power. In recent years, artificial neural network-based approaches (ANNs) have been one of the most effective and popular approaches for short-term wind power forecasting because of the availability of large amounts of historical data and strong computational power. Although ANNs usually perform well for short-term wind power forecasting, further improvement can be obtained by selecting suitable input features, model parameters, and using forecasting techniques like spatial correlation and ensemble for ANNs. In this research, the effect of input features, model parameters, spatial correlation and ensemble techniques on short-term wind power forecasting performance of the ANNs models was evaluated. Pearson correlation coefficients between wind speed and other meteorological variables, together with a basic ANN model, were used to determine the impact of different input features on the forecasting performance of the ANNs. The effect of training sample resolution and training sample size on the forecasting performance was also investigated. To separately investigate the impact of the number of hidden layers and the number of hidden neurons on short-term wind power forecasting and to keep a single variable for each experiment, the same number of hidden neurons was used in each hidden layer. The ANNs with a total of 20 hidden neurons are shown to be sufficient for the nonlinear multivariate wind power forecasting problems faced in this dissertation. The ANNs with two hidden layers performed better than the one with a single hidden layer because additional hidden layer adds nonlinearity to the model. However, the ANNs with more than two hidden layers have the same or worse forecasting performance than the one with two hidden layers. ANNs with too many hidden layers and hidden neurons can overfit the training data. Spatial correlation technique was used to include meteorological variables from highly correlated neighbouring stations as input features to provide more surrounding information to the ANNs. The advantages of input features, model parameters, and spatial correlation and ensemble techniques were combined to form an ANN-based ensemble model to further enhance the forecasting performance from an individual ANN model. The simulation results show that all the available meteorological variables have different levels of impact on forecasting performance. Wind speed has the most significant impact on both short-term wind speed and wind power forecasting, whereas air temperature, barometric pressure, and air density have the smallest effects. The ANNs perform better with a higher data resolution and a significantly larger training sample size. However, one requires more computational power and a longer training time to train the model with a higher data resolution and a larger training sample size. Using the meteorological variables from highly related neighbouring stations do significantly improve the forecasting accuracy of target stations. It is shown that an ANNs-based ensemble model can further enhance the forecasting performance of an individual ANN by obtaining a large amount of surrounding meteorological information in parallel without encountering the overfitting issue faced by a single ANN model.

Yes / A confocal microscope provides a sequence of images of the corneal layers and structures at different depths from which medical clinicians can extract clinical information on the state of health of the patient’s cornea. Hybrid model based on snake and particle swarm optimisation (S-PSO) is proposed in this paper to analyse the confocal endothelium images. The proposed system is able to pre-process (quality enhancement, noise reduction), detect the cells, measure the cell density and identify abnormalities in the analysed data sets. Three normal corneal data sets acquired using confocal microscope, and two abnormal endothelium images associated with diseases have been investigated in the proposed system. Promising results are achieved and the performance of this system are compared with the performance of two morphological based approaches. The developed system can be deployed as clinical tool to underpin the expertise of ophthalmologists in analysing confocal corneal images.

The PMU has been found to carry great deal of value for applications in the wide area monitoring of power systems. Historically, deployment of these devices has been limited by the prohibitive cost of the device itself. Therefore, the objective of the conventional optimal PMU placement problem is to find the minimum number devices, which if carefully placed throughout the network, either maximize observability or completely observe subject to different constraints. Now due to improved technology and digital relays serving a dual use as relay & PMU, the cost of the PMU device itself is not the largest portion of the deployment cost, but rather the substation installation. In a recently completed large-scale deployment of PMUs on the EHV network, Virginia Electric & Power Company (VEPCO) has found this to be so. The assumption then becomes that if construction work is done in a substation, enough PMU devices will be placed such that everything at that substation is measured. This thesis presents a technique proposed to minimize the number of substation installations thus indirectly minimizing the synchrophasor deployment costs. Also presented is a brief history of the PMU and its applications along with the conventional Optimal PMU placement problem and the scope for expanding this work. / Master of Science

Over the years, phased array antennas have provided electronic scanning with high gain and low sidelobe levels for many radar and satellite applications. The need for higher bandwidth as well as greater scanning ability has led to research in the area of aperiodically spaced antenna arrays. Aperiodic arrays use variable spacing between antenna elements and generally require fewer elements than periodically spaced arrays to achieve similar far field pattern performance. This reduction in elements allows the array to be built at much lower cost than traditional phased arrays. This thesis introduces the concept of aperiodic phased arrays and their design via optimization algorithms, specifically Particle Swarm Optimization. An axial mode helix is designed as the antenna array element to obtain the required half power beamwidth and bandwidth. The final optimized aperiodic array is compared to a traditional periodic array and conclusions are made. / Master of Science

When suffering a stroke, the time to treatment is one of the key factors to increase the chance of desirable recovery. To ensure proper treatment, a diagnosis has to be made before treatment can begin. The potential consequences of treating a misdiagnosis can be severely harmful or even deadly. A Mobile Stroke Unit (MSU) is an ambulance equipped with the necessary tools to diagnose and begin treatment of stroke before reaching a hospital, reducing the time to initial treatment. We contribute a model to identify suitable locations of MSUs within a geographical region. We propose a Multi-State Particle Swarm Optimization (MBPSO) algorithm variation to solve this problem. Furthermore, we demonstrate the use of the model in a scenario created in the Southern Healthcare Region of Sweden in order to properly communicate and evaluate the model. The objective of our MBPSO variation is to find locations within a geographical region which are suitable for placing MSUs. The results of the solution shows that populations previously not covered by stroke care within one hour of an emergency call has the potential to be covered up to 81%.

Die Auslegung von Magnetlagern erfolgt in der Regel durch Fachpersonal in einem iterativen zeitaufwendigen Prozess. Dies stellt einen großen Kostenfaktor bei der Entwicklung magnetgelagerter Maschinen oder der Umrüstung konventionell gelagerter Maschinen dar. Aus diesem Grund wurde ein Softwarewerkzeug entwickelt, welches eine automatisierte, optimale Auslegung von Magnetlagern auf Basis der Particle-Swarm-Optimization ermöglicht. Dabei wurden auch Temperatureinflüsse berücksichtigt, sodass eine Auslegung von Magnetlagern für erweiterte Temperaturbereiche möglich ist (Hochtemperatur-Magnetlager). / The design of magnetic bearings is usually carried out by specialist personnel in an iterative time-consuming process. This represents a major cost factor in the development of machines with magnetic bearings or the retrofitting of machines with conventional bearings. For this reason, a software tool was developed that enables an automated, optimal design of magnetic bearings based on Particle-Swarm Optimization. Temperature influences were also taken into account, so that a design of magnetic bearings for extended temperature ranges is possible (high-temperature magnetic bearings).