Scholarship & Research
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Item Development and applications of particle swarm optimization for constructing optimal experimental designs(Montana State University - Bozeman, College of Letters & Science, 2021) Walsh, Stephen Joseph; Chairperson, Graduate Committee: John J. BorkowskiThe primary objective motivating this dissertation was to illustrate the efficacy of particle swarm optimization (PSO) as the engine of an algorithm to generate optimal design of experiments (DoE). PSO is a wildly popular and successful metaheuristic and machine learning algorithm which makes no assumptions regarding the behavior of the function being optimized. Optimal DoE, in part thanks to modern computing, has become the current dominant paradigm for practitioners to generate a DoE with some desirable property. We bring together these concepts first by extending the PSO to optimizing functions that take matrix inputs. Julia software was developed for this purpose and validated against published results. A detailed benchmarking study of three PSO variants was conducted and a preferred version of the algorithm was identified for further research and application. Next, we implemented the approach to generating G-optimal designs--a difficult mini-max optimization problem. New heretofore unknown G-optimal designs have been produced and the efficacy of PSO in generating efficiently (w.r.t. computing time) highly G-optimal designs is compared to current published results. Next, a new algorithm for generating optimal designs with specified replication structure, and thereby guaranteed a degrees-of-freedom for estimating the pure error variance, is proposed, illustrated, benchmarked and validated. Last, we propose a new algorithm for generating optimal mixture experiment designs which implements a PSO type search using a non-Euclidean geometry (specifically the Aitchison geometry). In this setting the space of candidate matrices is the Cartesian product of standard (K - 1)-simplices. The algorithm is extended to mixture experiments with lower and upper constraints on the mixture proportions; in this setting, the space of candidate matrices is the Cartesian product of high-dimensional irregular convex polytopes. The algorithm is validated against very recent published results. In total, the work presented in this dissertation speaks very favorably to PSO as a tool for generating optimal DoEs. We believe this approach should become part of the standard machine learning and statistical tool box for generating optimal experimental designs.Item Optimization of error correcting codes in FPGA fabric onboard cube satellites(Montana State University - Bozeman, College of Engineering, 2019) Tamke, Skylar Anthony; Chairperson, Graduate Committee: Brock LaMeresThe harmful effects of radiation on electronics in space is a difficult problem for the aerospace industry. Radiation can cause faults in electronics systems like memory corruption or logic flips. One possible solution to combat these effects is to use FPGAs with radiation mitigation techniques. The following Masters of Science thesis details the design and testing of a radiation tolerant computing system at MSU. The computer is implemented on a field programmable gate array (FPGA), the reconfigurable nature of FPGAs allows for novel fault mitigation techniques on commercial devices. Some common fault mitigation techniques involve triple modular redundancy, memory scrubbing, and error correction codes which when paired with the partial reconfiguration. Our radiation tolerant computer has been in development for over a decade at MSU and is continuously being developed to expand its radiation mitigation techniques. This thesis will discuss the benefits of adding error correcting codes to the ever developing radiation tolerant computing system. Error correcting codes have been around since the late 1940's when Richard Hamming decided that the Bell computers he did his work on could automate their own error correcting capabilities. Since then a variety of error correcting codes have been developed for use in different situations. This thesis will cover several popular error correcting method for RF communication and look at using them in memory in our radiation tolerant computing system.Item On the usability of continuous time bayesian networks: improving scalability and expressiveness(Montana State University - Bozeman, College of Engineering, 2017) Perreault, Logan Jared; Chairperson, Graduate Committee: John SheppardThe Continuous Time Bayesian Network (CTBN) is a model capable of compactly representing the behavior of discrete state systems that evolve in continuous time. This is achieved by factoring a Continuous Time Markov Process using the structure of a directed graph. Although CTBNs have proven themselves useful in a variety of applications, adoption of the model for use in real-world problems can be difficult. We believe this is due in part to limitations relating to scalability as well as representational power and ease of use. This dissertation attempts to address these issues. First, we improve the expressiveness of CTBNs by providing procedures that support the representation of non-exponential parametric distributions. We also propose the Continuous Time Decision Network (CTDN) as a framework for representing decision problems using CTBNs. This new model supports optimization of a utility value as a function of a set of possible decisions. Next, we address the issue of scalability by providing two distinct methods for compactly representing CTBNs by taking advantage of similarities in the model parameters. These compact representations are able to mitigate the exponential growth in parameters that CTBNs exhibit, allowing for the representation of more complex processes. We then introduce another approach to managing CTBN model complexity by introducing the concept of disjunctive interaction for CTBNs. Disjunctive interaction has been used in Bayesian networks to provide significant reductions in the number of parameters, and we have adapted this concept to provide the same benefits within the CTBN framework. Finally, we demonstrate how CTBNs can be applied to the real-world task of system prognostics and diagnostics. We show how models can be built and parameterized directly using information that is readily available for diagnostic models. We then apply these model construction techniques to build a CTBN describing a vehicle system. The vehicle model makes use of some of the newly introduced algorithms and techniques, including the CTDN framework and disjunctive interaction. This extended application not only demonstrates the utility of the novel contributions presented in this work, but also serves as a template for applying CTBNs to other real-world problems.Item Factored evolutionary algorithms: cooperative coevolutionary optimization with overlap(Montana State University - Bozeman, College of Engineering, 2017) Strasser, Shane Tyler; Chairperson, Graduate Committee: John SheppardFactored Evolutionary Algorithms (FEA) define a relatively new class of evolutionary-based optimization algorithms that have been successfully applied to various problems, such as training neural networks and performing abductive inference in graphical models. FEA is unique in that it factors the function being optimized by creating subpopulations that optimize over a subset of dimensions of the function. However, unlike other optimization techniques that subdivide optimization problems, FEA encourages subpopulations to overlap with one another, allowing subpopulations to compete and share information. Although FEA has been shown to be very effective at function optimization, there is still little understanding with respect to its general characteristics. In this dissertation, we present seven results exploring the theoretical and empirical properties of FEA. First, we present a formal definition of FEA and demonstrate its relationships to other multiple population algorithms. Second, we demonstrate that FEA's success is independent of the underlying optimization algorithm by evaluating the performance of FEA using a wide variety of evolutionary- and swarm-based algorithms over single-population and non-overlapping versions. Third, we demonstrate that for a given problem, there is an optimal way to generate groups of overlapping subpopulations derived using the Markov blanket in Bayesian networks. Fourth, we establish that a class of optimization functions like NK landscapes can be mapped directly to probabilistic graphical models. Additionally, we demonstrate that factor architectures derived from Markov blankets maintain better diversity of individuals in their population. Fifth, we present a new discrete Particle Swarm Optimization (PSO) algorithm and compare its performance to competing approaches. In addition, we analyze the performance of FEA versions of discrete PSO and discover that FEA masks the poor performance of search algorithms. We show what conditions are necessary for FEA to converge and scenarios where FEA may become stuck in suboptimal regions in the search space. Finally, we explore the performance of FEA on unitation functions and discover several instances where FEA struggles to outperform single-population algorithms. These results allow us to determine which situations are appropriate for FEA when using solving real-world problems.Item New methods in computation of reaction fluxes from metabolomics data(Montana State University - Bozeman, College of Engineering, 2018) Salinas Duron, Daniel; Chairperson, Graduate Committee: Brendan MumeyChanges in cellular metabolism can be deduced from how they affect the measurable metabolites in cell samples. We provide methods to compute metabolic reaction rates from changes in measurable metabolites over time. The methods provided are intended to overcome technical challenges, such as the inapplicability of a steady state assumption, heterogeneity of samples from different donors, and the lack of targeted metabolomics data. Solutions to these challenges involve identifying metabolites constrained even under non-steady state, using components analysis to find the donor consensus, and using an integer linear program to solve a set cover variant designed to generate targeted data from untargeted data. The methods are applied on data derived from diseased articular cells. The results show that the reaction rates inferred from the incomplete data are biologically relevant, and that the minimal pathways captured ancillary processes that alternative approaches ignored. We conclude that, although the resulting rates and pathways are not conclusive, they provide useful guidance on experiments to pursue. On the experimental side, our findings have lead us to believe that osteoarthritic chondrocytes respond to compression by initiating protein synthesis, opening the possibility of physical therapy as a stimulus for cartilage regeneration.Item Constrained shortest paths in networks(Montana State University - Bozeman, College of Engineering, 1975) Shek, Chi-HungItem Constitutive laws of composite materials via multi-axial testing and numerical optimization(Montana State University - Bozeman, College of Engineering, 2003) Huang, Dongfang; Chairperson, Graduate Committee: Douglas S. CairnsItem Implementation of null steering algorithms in a compact analog array(Montana State University - Bozeman, College of Engineering, 2014) Condori Quispe, Hugo Orlando; Chairperson, Graduate Committee: Richard WolffIn this thesis, the implementation of null steering algorithms in a compact analog array is demonstrated and validated. The performance of the null steering algorithms is validated through extensive simulation and hardware implementation. The results of the techniques of null steering, including controlling the complex weights, usually have to rely on simulations to study system performances, design trade-offs, and system optimization, which by itself can be quite complex and a time-consuming task. Even after extensive simulations, it is not easy to get insights as to what parameters determine system performance in different system parameters, and the interactions on system parameters. Therefore, experimentation and deployment on a real system is required. Few studies have proposed null steering algorithms studies using real implementations. With this motivation, this work presents comprehensive performance comparison of some of the available null steering techniques using an analog array. The contributions of this thesis are: optimize the performance of null steering algorithms taking into account realistic considerations in the simulations and demonstrating the benefits through extensive simulations; and verify the performance of the null steering system through experimental implementation using a simple, compact, lightweight, low cost, high gain, high throughput analog antenna array.