Master of Sciences, Case Western Reserve University, 2011, EMC - Mechanical Engineering

Two-dimensional shape of a wind turbine blade was optimized by means of Particle Swarm Optimization. By following blade element theory, lift coefficient Cl and drag coefficient Cd were used as objective functions. In order to compute the objective functions, flow field around airfoils were calculated by Re-Normalization Group (RNG) k-ε model. Shapes of airfoils were defined by modified PARSEC method with 10 parameters.Two optimization cases were conducted with maximum thickness limited to 10% and 20% of the chord length respectively. In both cases, Reynolds number was set at 2.0×106, which is the design condition of S809 airfoil. S809 airfoil is a well known airfoil used in wind turbines and many experimental data are available. The angle of attack for the optimization was set at 5.13 deg., the mount angle of S809. Non-dominated solutions obtained by this research were compared with the performance of S809 at several angles of attack. The results of optimization showed that 1) there is a strong influence of maximum thickness of airfoil to its performance, 2) non-dominated solutions constitute a gradual relationship which implies that there are many airfoil shapes that could be considered as an optimum. The resulting shape along this Pareto front showed higher performance than the existing blade section (i.e. NREL S809) in certain conditions.

Committee: James S. T'ien PhD (Committee Chair); Meng-Sing Liou PhD (Committee Member); J. Iwan D. Alexander PhD (Committee Member) Subjects: Aerospace Materials; Ecology; Energy; Engineering; Fluid Dynamics; Mechanical Engineering

Doctor of Philosophy, University of Toledo, 2016, Engineering

This research is aimed at developing efficient dim object tracking techniques in cluttered image sequences. In this dissertation, a number of new techniques are presented for image enhancement, super resolution (SR), dim object tracking, and multi-sensor object tracking. Cluttered images are impaired by noise. To deal with a mixed Gaussian and impulse noise in the image, a novel sparse coding super resolution is developed. The sparse coding has recently become a widely used tool in signal and image processing. The sparse linear combination of elements from an appropriately chosen over-complete dictionary can represent many signal patches. The proposed SR is composed of a Genetic Algorithm (GA) search step to find the optimum match from low resolution dictionary. By using GA, the proposed SR is capable of efficiently up-sampling the low resolution images while preserving the image details. Dim object tracking in a heavy clutter environment is a theoretical and technological challenge in the field of image processing. For a small dim object, conventional tracking methods fail for the lack of geometrical information. Multiple Hypotheses Testing (MHT) is one of the generally accepted methods in target tracking systems. However, processing a tree structure with a significant number of branches in MHT has been a challenging issue. Tracking high-speed objects with traditional MHT requires some presumptions which limit the capabilities of these methods. In this dissertation, a hierarchal tracking system in two levels is presented to solve this problem. For each point in the lower-level, a Multi Objective Particle Swarm Optimization (MOPSO) technique is applied to a group of consecutive frames in order to reduce the number of branches in each tracking tree. Thus, an optimum track for each moving object is obtained in a group of frames. In the upper-level, an iterative process is used to connect the matching optimum tracks of the consecutive frames based on the spatial infor (open full item for complete abstract)

Committee: Ezzatollah Salari (Committee Chair); Kim Junghwan (Committee Member); Jamali Mohsin (Committee Member); Carvalho Jackson (Committee Member); Eddie Yein Juin Chou (Committee Member) Subjects: Computer Engineering; Computer Science

Master of Science, The Ohio State University, 2024, Mechanical Engineering

Cooling indoor spaces is energy intensive but essential to ensure occupant comfort, regardless of environmental conditions. Therefore, increasing the energy efficiency of cooling systems can yield significant energy savings. This thesis presents data-enabled hybrid modeling approaches to optimize cooling systems in both commercial buildings and light-duty vehicles, aiming to enhance energy efficiency through both static and dynamic optimization strategies. First, a static optimization strategy is developed for the operation of individual chillers in a central chiller plant, with the goal of reducing power demand while meeting the cooling load. This is achieved by developing a hybrid model that combines energy-based and data-driven methods to describe the energy demand of the plant under varying cooling loads and environmental conditions. The model is calibrated and validated using operational data from The Ohio State University. The validated model is then integrated into a particle swarm optimization algorithm to determine the optimal load distribution for each chiller under different weather and operational conditions. Simulation results for a year of operation in Central Ohio show that the optimized strategy achieves, on average, a 4% reduction in daily peak power consumption during four mild weather months, with reductions reaching up to 12% in certain instances. Second, a dynamic optimization strategy is presented to improve the energy efficiency of a light-duty vehicle air conditioning system. By employing data-driven Koopman operator theory to characterize the non-linear dynamics of the system, a linear Model Predictive Control problem is formulated within the Koopman subspace. The computational efficiency of this quadratic programming problem is demonstrated by average computation times ranging from 2 to 50 milliseconds, depending on the lengths of the control and prediction horizons. When tested across four different driving routes, th (open full item for complete abstract)

Committee: Marcello Canova (Committee Member); Stephanie Stockar (Advisor) Subjects: Automotive Engineering; Mechanical Engineering

Master of Science, Miami University, 2021, Computer Science and Software Engineering

Crowd modeling is an area of study of increasing importance for understanding the dynamics of a crowd in an environment. Evacuation modeling focuses on how humans evacuate these environments as well as the intricacies and outcomes of the evacuation. Previous evacuation models do not take into account how human populations are physically heterogeneous as well as how the presence of wheelchair users affects an evacuation. In order to address the aforementioned open challenges, in this research, we propose two novel models to study the effect of wheelchair users in evacuation modeling. While our first model is developed as an agent-based panic model, the second simulates a leader-follower evacuation strategy using a Particle-Swarm Optimization (PSO) technique. Both our models are built using the Unity gaming platform and include speed, mass, and radius as the agent attributes. From the agent-based panic model, it was observed that larger wheelchair populations increased evacuation times while a larger number of and width of doorways decreased the evacuation times. Of the agent attributes in the panic model, speed had a negative correlation while mass and radius both had positive correlations with evacuation time. The results of the PSO model require further future experimentation in order to fully understand the outcomes. The findings from this study suggest that future crowd models may benefit in accuracy by using heterogeneous populations.

Committee: Vaskar Raychoudhury (Advisor); Suman Bhunia (Committee Member); Alan Ferrenberg (Committee Member) Subjects: Computer Science

Master of Science, The Ohio State University, 2021, Mechanical Engineering

The EcoCAR Mobility Challenge is a student competition that tasks universities across North America with the hybridization and SAE Level 2 automation of a 2019 Chevy Blazer. In years 2 and 3 of the competition, the Ohio State EcoCAR team committed considerable effort to the development of an adaptive cruise control (ACC) feature. This paper provides a detailed discussion of what motivated the selection of a modified PID controller as the control method of choice for ACC. The state flow used by the team to achieve independent distance and velocity control is also reviewed. After designing the controller, the team performed particle swarm optimization to identify the ideal proportional, integral, and derivative gain values. In doing so, the team managed to greatly reduce maximum acceleration, RMS acceleration, and maximum jerk in simulation. While doing so, the efficiency of the vehicle was also improved by 8.45 percent. Then, in order to validate the real-world performance of the novel adaptive cruise controller, the team conducted a full range of anything-in-the-loop (XIL) testing. Across model, hardware, and vehicle closed-loop testing, Ohio State identified and resolved numerous potential issues in the controller and its implementation in the vehicle. Additionally, the safety and comfort of the ACC feature were verified across all testing environments, affirming the fidelity of the model and preparing the team for in-vehicle testing. Lastly, using a real target vehicle and live sensor data, Ohio State performed approach tests that demonstrate the functionality of its ACC in a real-world environment.

Committee: Shawn Midlam-Mohler (Advisor); Giorgio Rizzoni (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

Master of Sciences, Case Western Reserve University, 2021, EECS - Electrical Engineering

We elicit tactile sensation through peripheral nerve stimulation (PNS) for rehabilitation of people with limb loss. We use the Composite Flat Interface Nerve Electrode that has 15 contacts that can be used for stimulation individually or simultaneously. Current PNS paradigms designed for single-percept sensory restoration stimulate through a single contact. Existing biomimetic paradigms that reproduce aspects of natural afferent activity do not consider all four critical neural coding properties: firing rate, population size, type, and location. We present a new biomimetic PNS paradigm that approximates all four critical neural coding properties. Our paradigm provides multi-contact stimulation, where two contacts can be active simultaneously and contacts can be switched on a pulse-by-pulse basis throughout each stimulus. We show that switching between contacts outperforms using a single contact, as does using two-contact stimulation compared to single-contact stimulation. We hypothesize that our new paradigm can improve sensory feedback for those with limb loss.

Committee: Kenneth Loparo (Committee Chair); Emily Graczyk (Advisor); Dustin Tyler (Committee Member) Subjects: Biomedical Engineering; Biomedical Research; Electrical Engineering; Neurosciences; Rehabilitation

Master of Science, Miami University, 2020, Computer Science and Software Engineering

With the rapid growth of on-demand taxi services, like Uber, Lyft, etc., urban public transportation scenario is shifting towards a personalized transportation choice for most commuters. While taxi rides are comfortable and time efficient, they often lead to higher cost and road congestion due to lower overall occupancy than bigger vehicles. A possible solution to improve taxi occupancy is to adopt ride sharing. Existing ride sharing solutions are mostly centralized and proprietary. How- ever, given the wide spatio-temporal variation of incoming ride requests designing a dynamic and distributed shared-ride scheduling system is NP-hard. In this thesis, we have proposed a publisher (passengers) and subscriber (taxis) based ride sharing system that provides effective real-time ride scheduling for multiple passengers willing to share rides in part or in full. A particle swarm based route optimization strategy has been applied to determine the most preferable route for passengers. Empirical analysis using large scale single-user taxi ride records from Chicago Transit Authority, show that, our proposed system, ensures a maximum of 91.74% and 63.29% overall success rates during peak and non-peak hours, respectively.

Committee: Vaskar Raychoudhury Dr. (Advisor); Karen Davis Dr. (Committee Member); Md Osman Gani Dr. (Committee Member) Subjects: Computer Science

Master of Science, The Ohio State University, 2020, Mechanical Engineering

Lithium ion batteries are enabling vehicle electrification due to their high energy and power density compared to other electrochemical energy storage technologies. However, a potential limitation for automotive applications relates to the degradation mechanisms that can age the cell by causing a continual loss of capacity and increase in internal resistance. This is particularly a concern while fast charging, as higher current rates and temperatures can accelerate the degradation. To ensure battery systems meet longevity requirements, the state of health (SOH) evolution can be predicted in durability studies by developing models that are calibrated with experimental aging campaigns. As these experiments are not comprehensive of all possible operating conditions, degradation models based on first principles are critical, especially when considering time-varying conditions or extrapolation. Further, the ability of physics-based models to predict the incremental degradation can allow one to determine the optimal control to decrease the charging time while managing the aging. Although, the large number of partial differential equations (PDEs) and algebraic constraints necessary to describe these processes can lead to an insurmountable computational burden when performing long time horizon SOH estimation. In addition, the complexity of the models can lead to challenges in the fast charging optimization process. In this thesis, a physics-based reduced-order aging model is developed that predicts the incremental capacity loss due to solid-electrolyte interface (SEI) layer growth and loss of active material. This model stems from physically based principles but relies on several simplifying assumptions to improve the computational efficiency. These assumptions are validated by considering experimental aging campaigns for multiple cell chemistries. In addition, a condition is integrated to predict the onset of lithium plating while fast charging. The (open full item for complete abstract)

Committee: Marcello Canova (Advisor); Yann Guezennec (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2019, Mechanical Engineering

This thesis investigates a metaheuristic optimization method to solve a mission planning problem which requires the solar-powered unmanned ground vehicle (UGV) to frequently visit multiple assigned points in outdoor environment with a desired performance index. The mission planning problem can be decoupled into two parts: a) a decision-making problem of the visiting sequence, which can be formulated as a traveling salesman problem; b) a motion planning problem for the entire mission with complex constraints. The major difficulty of solving this problem comes from the time-varying outdoor environment, the energy constraint, multiple terrain types and mixed-type decision variables. In order to solve the problem effectively and efficiently, a hybrid cascaded heuristic optimization algorithm is developed to generate an optimized motion plan such that the objective function is minimized under constraints. To obtain the environmental information, a solar irradiance map of the operational area is constructed from satellite images at the beginning and an initial path is generated using the proposed algorithm. In the following repeated routes through the operational area, the updated solar irradiance map built from on-board camera images will be applied to provide more accurate environmental information for the re-planned path, such that the UGV could have better performance of the assigned mission. Experiments in outdoor environment were conducted to validate the methods presented in a structured, highly-explored region to achieve energy sustainable mission.

Committee: Ran Dai (Advisor); Wei Zhang (Committee Member) Subjects: Robotics

PhD, University of Cincinnati, 2018, Engineering and Applied Science: Mechanical Engineering

Cable-stayed bridges have received significant attention in recent years due to ease of construction, reduced bending moments applied on the deck, being stiffer in comparison with other types of long span bridges, and their aesthetic value. In a cable-stayed bridge, the dead load (weight of the bridge) and the live load (the traffic load) are directly transferred to the towers through stay cables. Wind/rain induced vibrations, fatigue, and galvanic corrosion can cause cable deterioration. Deterioration of stay cables can cause the reduction of cable load capacity; thus, continuous health monitoring of stay cables is strongly suggested. There are different condition assessment methods proposed to monitor the stay cables in cable structures such as traditional visual inspection methods, dissection of stay cables, ultrasonic testing, thermography, impulse radar, and radiography. Consistency of cable tension over time is also considered as a health indicator for both cables and super structure of cable structures. Cable tension can be measured directly (using load sensors) or it can be estimated by measuring different parameters of the cable such as stress, strain, or natural frequencies. The methods that use cable natural frequencies to estimate the cable tension are called vibration-based tension estimation methods. The main objective of this dissertation is to propose a general framework for vibration-based cable tension estimation so that it can be used along with various cable models and system identification methods to estimate the cable tension in cable structures. System identification methods will be used to identify the natural frequencies of the stay cables and cable models will be employed to create an error function representative of the difference between experimentally measured cable natural frequencies and analytical cable natural frequencies. Employing different cable models, the proposed framework will be evaluated using the experimental data measured (open full item for complete abstract)

Committee: Randall Allemang Ph.D. (Committee Chair); David Brown Ph.D. (Committee Member); Arthur Helmicki Ph.D. (Committee Member); Victor Hunt Ph.D. (Committee Member); Allyn Phillips Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2018, Aero/Astro Engineering

Unmanned vehicles research and application has become a major industry that is at the forefront of innovation. Commercial and hobbyist users alike have begun to harness the ability for these systems to be used in a range of applications such as, environmental monitoring, search and rescue, and more recently package delivery. However, these systems are limited in their ability to complete these missions as they typically require prolonged operational times that are currently infeasible. Instead of taking a more traditional approach of optimizing each component, which has its own limitations, a more novel approach is extended in this work, incorporating solar harvesting capabilities into the unmanned vehicles. When taking this approach, understanding the mission and environment is fundamental for successful mission planning and operation. This manuscript explores multiple mission planning problems, such as information gathering and persistent traveling vehicle problem with unmanned ground vehicles. In each of these problems, the mission, hardware, and environmental constraints are modeled as conventional optimal controls problems and heuristic methodologies are presented that can handle the nonlinearity and discontinuities of the problems. While the proposed methodologies can effectively handle the individual problems, in order to develop more reliable, effective and efficient outdoor, solar-robotics a new real-time mission planning framework is presented to handle the difficulty of environmental analysis and efficient path planning of nonlinear problems. The resulting simulations and experimental tests of all the developed methods are presented and discussed.

Committee: Dai Ran Dr. (Advisor); Mrinal Kumar Dr. (Committee Member); David Hoelzle Dr. (Committee Member) Subjects: Aerospace Engineering; Electrical Engineering; Experiments; Robotics

Master of Science, University of Toledo, 2017, Engineering (Computer Science)

In this thesis, three bio-inspired algorithms viz. genetic algorithm, particle swarm optimizer (PSO) and grey wolf optimizer (GWO) are used to optimally determine the architecture of a convolutional neural network (CNN) that is used to classify handwritten numbers. The CNN is a class of deep feed-forward network, which have seen major success in the field of visual image analysis. During training, a good CNN architecture is capable of extracting complex features from the given training data; however, at present, there is no standard way to determine the architecture of a CNN. Domain knowledge and human expertise are required in order to design a CNN architecture. Typically architectures are created by experimenting and modifying a few existing networks.The bio-inspired algorithms determine the exact architecture of a CNN by evolving the various hyperparameters of the architecture for a given application. The proposed method was tested on the MNIST dataset, which is a large database of handwritten digits that is commonly used in many machine-learning models. The experiment was carried out on an Amazon Web Services (AWS) GPU instance, which helped to speed up the experiment time. The performance of all three algorithms was comparatively studied. The results show that the bio-inspired algorithms are capable of generating successful CNN architectures. The proposed method performs the entire process of architecture generation without any human intervention.

Committee: Devinder Kaur (Advisor); Kevin Xu (Committee Member); Ahmad Javaid (Committee Member) Subjects: Computer Science

Doctor of Philosophy, University of Toledo, 2016, Electrical Engineering

Hybrid power generation system and distributed generation technology are attracting more investments due to the growing demand for energy nowadays and the increasing awareness regarding emissions and their environmental impacts such as global warming and pollution. The price fluctuation of crude oil is an additional reason for the leading oil producing countries to consider renewable resources as an alternative. Saudi Arabia as the top oil exporter country in the word announced the "Saudi Arabia Vision 2030" which is targeting to generate 9.5 GW of electricity from renewable resources. Two of the most promising renewable technologies are wind turbines (WT) and photovoltaic cells (PV). The integration or hybridization of photovoltaics and wind turbines with battery storage leads to higher adequacy and redundancy for both autonomous and grid connected systems. This study presents a method for optimal generation unit planning by installing a proper number of solar cells, wind turbines, and batteries in such a way that the net present value (NPV) is minimized while the overall system redundancy and adequacy is maximized. A new renewable fraction technique (RFT) is used to perform the generation unit planning. RFT was tested and validated with particle swarm optimization and HOMER Pro under the same conditions and environment. Renewable resources and load randomness and uncertainties are considered. Both autonomous and grid-connected system designs were adopted in the optimal generation units planning process. An uncertainty factor was designed and incorporated in both autonomous and grid connected system designs. In the autonomous hybrid system design model, the strategy including an additional amount of operation reserve as a percent of the hourly load was considered to deal with resource uncertainty since the battery storage system is the only backup. While in the grid-connected hybrid system design model, demand response was incorporated to overcome the impact o (open full item for complete abstract)

Committee: Lingfeng Wang (Committee Chair); Hong Wang (Committee Member); Jackson Carvalho (Committee Member); Richard Molyet (Committee Member); Weiqing Sun (Committee Member) Subjects: Electrical Engineering; Energy

Master of Science (MS), Bowling Green State University, 2016, Computer Science

Forest fires are a dangerous and devastating phenomenon. Being able to accurately predict the burned area of a forest fire could potentially limit biological damage as well as better prepare for ensuing economical and ecological damage. A data set from the Montesinho Natural Park in Portugal provides a difficult regression task regarding the prediction of forest fire burn area due to the limited amount of data entries and the imbalanced nature of the data set. This thesis focuses on improving these results through the use of a Backpropagation trained Artificial Neural Network which is systematically evaluated over a variety of configurations, activation functions, and input methodologies, resulting in approximately 30% improvements to regression error rates. A Particle Swarm Optimization (PSO) trained Artificial Neural Network is also evaluated in a variety of configurations providing approximately 75% improvement of regression error rates. Going further, the data is also clustered on both inputs and outputs using k-Means and Spectral algorithms in order to pursue the task of classification where near perfect classification is achieved when clustering on inputs is considered and an accuracy of roughly 60% is achieved when clustering on output values.

Committee: Robert Green PhD. (Advisor); Jong Kwan Lee PhD. (Committee Member); Robert Dyer PhD. (Committee Member) Subjects: Computer Science

Master of Science, University of Toledo, 2015, Mechanical Engineering

Over the last two decades inverse problems have become increasingly popular due to their widespread applications. This popularity continuously demands designers to find alternative methods, to solve the inverse problems, which are efficient and accurate. Using effective techniques that are both highly accurate and of low computational cost is of highest priority. This thesis presents a method for solving inverse problems through Artificial Neural Network (ANN) theory. This thesis also presents a method to apply Grey Wolf Optimizer (GWO) algorithm to solve inverse problems. GWO is a recent optimization method demonstrating great results. Both of the methods are then compared to traditional methods such as Particle Swarm Optimization (PSO) and Markov Chain Monte Carlo (MCMC). Four classical engineering design problems are used to compare the four methods' performance. The results from the engineering design problems show that the GWO outperforms other methods in terms of efficiency and accuracy. The error is comparable among the proposed ANN method and PSO method, while the latter has better computational efficiency.

Committee: Efstratios Nikolaidis PhD (Committee Chair); Vijay Devabhaktuni PhD (Committee Co-Chair); Matthew Franchetti PhD (Committee Member); Mehdi Pourazady PhD (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2015, Mechanical Engineering

The automotive industry has been pressured to improve fuel economy of average lightduty vehicle [1]. To combat the need for improved fuel efficiency, vehicle manufactures are researching technologies that can improve the efficiency of the internal combustion engine. While many scientific fields are being investigated, exhaust waste heat recovery is now a viable option with today's technology. In order to produce the best possible waste heat recovery system, a design and optimization methodology needed to be produced for multivariable systems. This thesis discusses a proposed design and optimization methodology that can assist in the application of advanced systems. The original purpose of developing the design method was for an Organic Rankine Cycle exhaust gas heat recovery system for automotive applications. Several examples of the utilizing this design methodology are included in this thesis.

Committee: Marcello Canova PhD (Advisor); Shawn Midlam-Mohler PhD (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2014, Engineering and Applied Science: Mechanical Engineering

Feedback, such as an inspection of a part, is a key step in the design and manufacture of complex products. It determines where a product or manufacturing process should be re-evaluated to conform to design specifications. The inspection of a part is characteristically accomplished by comparing the CAD model to the measurements of a manufactured part. This is simple for parts that contain a commonality: central axis, plane on a flat side, center of a sphere, etc. When a part does not share a commonality—like free-form surfaces—the comparison analysis becomes complex. This complexity occurs when determining the process for correspondence of every point on a manufactured part to every point on a design model. Whenever one coordinate system is shifted, the comparison can be lost and, then, has to be reevaluated, creating an iteration. The demand for substantial iterations protracts the process and thwarts optimization. It is, also, challenging to mathematically determine which points should be compared to another. Is the selected point optimal for comparison? Is a higher resolution of points needed? This problem of how the coordinate systems of the CAD model and the measured part can be aligned is termed as localization and is extensively researched [1]. Currently, most algorithms use a line or surface fitting technique that minimizes the sum of the square of the errors, drawing upon Gunnarsson and Prinz's original idea [2]. Such nonlinear approaches may result in local minima when minimized, resulting in false solutions. Additionally, a solution achieved may not be optimal due to averaging of outliers in the data. This thesis proposes a methodology that automatically aligns the coordinate systems of free-form CAD models to collected manufactured measurements, with resiliency to outliers of the fit and false solutions given by local minima, by maximizing the shared extent depending on dimension. To perform this, data from the manufactured surface an (open full item for complete abstract)

Committee: Sundararaman Anand Ph.D. (Committee Chair); Thomas Richard Huston Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering

Master of Science, University of Toledo, 2014, Engineering (Computer Science)

Optimization algorithms are truly complex procedures that consider many elements when optimizing a specific problem. Cloud computing (CCom) and Wireless sensor networks (WSNs) are full of optimization problems that need to be solved. One of the main problems of using the clouds is the underutilization of the reserved resources, which causes longer makespans and higher usage costs. Also, the optimization of sensor nodes' power consumption, in WSNs, is very critical due to the fact that sensor nodes are small in size and have constrained resources in terms of power/energy, connectivity, and computational power. This thesis formulates the concern on how CCom systems and WSNs can take advantage of the computational intelligent techniques using single- or multi-objective particle swarm optimization (SOPSO or MOPSO), with an overall aim of concurrently minimizing makespans, localization time, energy consumption during localization, and maximizing the number of nodes fully localized. The cloudlet scheduling method is implemented inside CloudSim advancing the work of the broker, which was able to maximize the resource utilization and minimize the makespan demonstrating improvements of 58\% in some cases. Additionally, the localization method optimized the power consumption during a Trilateration-based localization (TBL) procedure, through the adjustment of sensor nodes' output power levels. Finally, a parameter-study of the applied PSO variants for WSN localization is performed, leading to results that show algorithmic improvements of up to 32\% better than the baseline results in the evaluated objectives.

Committee: Mansoor Alam (Committee Chair); Robert Green II (Committee Co-Chair); Weiqing Sun (Committee Member); Vijay Devabhaktuni (Committee Member) Subjects: Artificial Intelligence; Computer Science; Engineering

Master of Science (M.S.), University of Dayton, 2013, Electrical Engineering

Optimizing a multidimensional function -- uni-modal or multi-modal -- is a problem that regularly comes about in engineering and science. Evolutionary Computation techniques, including Evolutionary Algorithm and Swarm Intelligence (SI), are biological systems inspired search methods often used to solve optimization problems. In this thesis, the SI technique Particle Swarm Optimization (PSO) is studied. Convergence and stability of swarm optimizers have been subject of PSO research. Here, using discrete-time adaptive control tools found in literature, an adaptive particle swarm optimizer is developed. An error system is devised and a controller is designed to adaptively drive the error to zero. The controller features a function approximator, used here as a predictor to estimate future signals. Through Lyapunov's direct method, it is shown that the devised error system is ultimately uniformly bounded and the adaptive optimizer is stable. Moreover, through LaSalle-Yoshizawa theorem, it is also shown that the error system goes to zero as time evolves. Experiments are performed on a variety of benchmark functions and results for comparison purposes between the adaptive optimizer and other algorithms found in literature are provided.

Committee: Raúl Ordóñez Ph.D. (Advisor); Russell Hardie Ph.D. (Committee Member); Malcolm Daniels Ph.D. (Committee Member) Subjects: Electrical Engineering

Master of Science in Electrical Engineering, University of Toledo, 2013, College of Engineering

Processing large graph datasets represents an increasingly important area in computing research and applications. The size of many graph datasets has increased well beyond the processing capacity of a single computing node, thereby necessitating distributed approaches. As these datasets are processed over a distributed system of nodes, this leads to an inter-node communication cost problem (also known as inter-partition communication), negatively affecting the system performance. This research proposes new graph partitioning algorithms to minimize the inter-node communication by achieving a sufficiently balanced partition. Initially, an intuitive graph partitioning algorithm using Random Selection method coupled with Breadth First Search is developed for reducing inter-node communication by achieving a sufficiently balanced partition. Second, another graph partitioning algorithm is developed using Particle Swarm Optimization with Breadth First Search to reduce inter-node communication further. Simulation results demonstrate that the inter-node communication using PSO with BFS gives better results (reduction of approximately 6% to 10% more) compared to the RS method with BFS. However, both the algorithms minimize the inter-node communication efficiently in order to improve the performance of a distributed system.

Committee: Robert Green (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); William Acosta (Committee Member); Mansoor Alam (Committee Member) Subjects: Computer Engineering; Computer Science