Browsing by Subject "Algorithm"

Now showing 1 - 6 of 6
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    Adversarial and Stochastic Search for Mobile Targets in Complex Environments
    (2016-02) Noori, Narges
    A new era of robotics has begun. In this era, robots are coming out of simple, structured environments (such as factory floors) into the real world. They are no longer performing simple, repetitive tasks. Instead, they will soon be operating autonomously in complex environments filled with uncertainties and dynamic interactions. Many applications have already emerged as a result of these potential advances. A few examples are precision agriculture, space exploration, and search-and-rescue operations. Most of the robotics applications involve a ``search'' component. In a search mission, the searcher is looking for a mobile target while the target is avoiding capture intentionally or obliviously. Some examples are environmental monitoring for population control and behavioral study of animal species, and searching for victims of a catastrophic event such as an earthquake. In order to design search strategies with provable performance guarantees, researchers have been focusing on two common motion models. The first one is the adversarial target model in which the target uses best possible strategy to avoid capture. The problem is then mathematically formulated as a pursuit-evasion game where the searcher is called the ``pursuer'' and the target is referred to as the ``evader''. In pursuit-evasion games, when a pursuit strategy exists, it guarantees capture against any possible target strategy and, for this reason, can be seen as the worst-case scenario. Considering the worst-case behavior can be too conservative in many practical situations where the target may not be an adversary. The second approach deals with non-adversarial targets by modeling the target's motion as a stochastic process. In this case, the problem is referred to as one-sided probabilistic search for a mobile target, where the target cannot observe the searcher and does not actively evade detection. In this dissertation, we study both adversarial and probabilistic search problems. In this regard, the dissertation is divided into two main parts. HASH(0x7f7fa33ea740) HASH(0x7f7fa33dadd8) In the first part, we focus on pursuit-evasion games, i.e., when the target is adversarial. We provide capture strategies that guarantee capture in finite time against any possible escape strategy. Our contributions are mainly in two areas whether the players have full knowledge of each other's location or not. First, we show that when the pursuer has line-of-sight vision, i.e., when the pursuer sees the evader only when there are no obstacles in the between them, it can guarantee capture in monotone polygons. Here, the pursuer must first ensure that it ``finds'' the evader when it is invisible by establishing line-of-sight visibility, and then it must guarantee capture by getting close to the evader within its capture distance. In our second set of results, we focus on pursuit-evasion games on the surface of polyhedrons assuming that the pursuers are aware of the location of the evader at all times and their goal is to get within the capture distance of the evader. HASH(0x7f7fa33f6a00) In the second part, we study search strategies for finding a random walking target. We investigate the search problem on linear graphs and also 2-D grids. Our goal here is to design strategies that maximize the detection probability subject to constraints on the time and energy, which is available to the searcher. We then provide field experiments to demonstrate the applicability of our proposed strategies in an environmental monitoring project where the goal is to find invasive common carp in Minnesota lakes using autonomous surface/ground vehicles.
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    Algorithm Optimization of non-DMSO Cryopreservation Protocols to Improve Mesenchymal Stem Cell Post-Thaw Function
    (2016-09) Pollock, Kathryn
    Mesenchymal stem cells (MSCs) are a common transfusion cell therapy that have been used in over 300 clinical trials to treat over 2000 patients with diseases ranging from Crohn’s disease to heart failure. These cells are frequently cryopreserved to better coordinate the timing of cell administration with patient care regimes and to accommodate transport of samples between different sites of collection, processing, and administration. However, cryopreservation with DMSO (the current gold standard) can result in poor cell function post-thaw and adverse reactions upon infusion. We hypothesize that non-DMSO cryopreservative molecules, including sugars, sugar alcohols, amino acids, and other small molecule additives, can be used in combination to protect cell viability and function post-thaw. This research demonstrates that some combinations of non-DMSO cryopreservatives preserve cell functionality better than others, and these effects are dependent not on osmotic or physical changes in solution, but on biological changes that affect the cell during the freezing process. We observe that there is likely a sweet spot concentration combination that produces maximum recovery for each combination of molecules, and demonstrate that an evolutionary algorithm can be used to identify optimized combinations of molecules that yield high cell recovery post-thaw. Additionally, we demonstrate that these novel solutions maintain MSC functionality when evaluated using surface markers, attachment, proliferation, actin alignment, RNA expression, and DNA hydroxymethylation. These advances in cryopreservation can improve cell therapy, and ultimately patient care.
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    Algorithms, Machine Learning, and Speech: The Future of the First Amendment in a Digital World
    (2017-06) Wiley, Sarah
    We increasingly depend on algorithms to mediate information and thanks to the advance of computation power and big data, they do so more autonomously than ever before. At the same time, courts have been deferential to First Amendment defenses made in light of new technology. Computer code, algorithmic outputs, and arguably, the dissemination of data have all been determined as constituting “speech” entitled to constitutional protection. However, continuing to use the First Amendment as a barrier to regulation may have extreme consequences as our information ecosystem evolves. This paper focuses on developing a new approach to determining what should be considered “speech” if the First Amendment is to continue to protect the marketplace of ideas, individual autonomy, and democracy.
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    A Comparison of the Genetic Algorithm and the Mixing Genetic Algorithm
    (2020-07) Gulfam, Muhammad
    Genetic Algorithms (GAs) are optimization techniques inspired by the idea of evolution. They can sometimes take a long time to find the solution to a problem, but it is not always obvious when, or how to configure their various parameters. Recently, a new GA was introduced [8] that has a lot of potential for parallelization. This algorithm, called the Mixing Genetic Algorithm, has shown promising results on the well-known Traveling Salesman Problem. In this work, we have compared the effectiveness of the Mixing GA over a traditional GA on three discrete optimization problems: the OneMax problem and two topologies of the Ising Model (Ising Model on Tree and Ising Model on Ring). The comparison has been done for the success rate at the given time, for the given problem size and size of population. The comparison has been done for, both, serial and parallel implementations. Overall, the success rate for the Mixing GA is better than the traditional GA. We have also compared two population selection methods, namely, tournament selection and generational population selection. The tournament selection outperformed generational population selection for all the problems and problem sizes that we experimented with.
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    Evaluation Of Selective Dry Cow Therapy For Controlling Mastitis And Improving Antibiotic Stewardship In U.S. Dairy Herds
    (2020-03) Rowe, Samuel
    The objective of this research was to identify strategies that reduce antibiotic use at dry-off (dry cow therapy; DCT) without having negative effects on cow health and productivity. Chapter 2 reports findings from a cross-sectional study of 2,889 late lactation cows from 80 herds in the US. Herds were purposively selected to achieve near-equal representation of four bedding materials of interest. Each herd was visited twice. At each visit, aseptic quarter-milk samples were collected (n = 10,448), along with bedding samples (n = 158). Milk and bedding samples were cultured under aerobic conditions. Quarter-level prevalence of IMI was 21.1%, indicating that selective DCT (SDCT) could result in a more efficient use of antibiotics than blanket DCT (BDCT) in some U.S. herds. Counts in bacteria were positively associated with IMI, indicating that antibiotic use at dry-off could potentially be reduced by preventing IMI during lactation through improved bedding management. Chapters 3-5 report findings from a multi-site, randomized, controlled, clinical trial. Cows (n=1275) from 7 herds at 4 sites were randomized to either BDCT, rapid culture-guided SDCT or algorithm-guided SDCT. Health and productivity were monitored during the dry period and the first 120 days of lactation. Both SDCT approaches reduced antibiotic use at dry-off by 55%. Both SDCT approaches performed similarly to BDCT for dry period IMI dynamics (IMI cure, new IMI and post-calving IMI risk; Chapter 3) and post-calving health and production (clinical mastitis and culling/death rates, somatic cell counts and milk yield; Chapter 4). The agreement (Cohen’s Kappa; κ) and negative predictive values (NPV) for detection of IMI, as determined by the reference test, laboratory-based aerobic culture were rapid-culture (κ = 0.28 , NPV = 0.87) and algorithm (κ = 0.09, NPV = 0.80), indicating that some infected quarters escaped antibiotic treatment at dry-off (Chapter 5). Culture- and algorithm-guided SDCT can be used in commercial dairy farms for reduction of antibiotic use.
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    Using an empirical model of human turning motion to aid heading estimation in a personal navigation system
    (2013-12) Jakel, Tom Bryan
    With the adoption of Global Navigation Satellite Systems in smart phones, soldier equipment, and emergency responder navigation systems users have realized the usefulness of low cost Personal Navigation Systems. The state-of-the-art Personal Navigation System is a unit that fuses information based on external references with a low cost IMU. Due to the size, weight, power, and cost constraints imposed on a pedestrian navigation systems as well as current IMU performance limitations, the gyroscopes used to determine heading exhibit significant drift limiting the performance of the navigation system. In this thesis biomechanical signals are used to predict the onset of pedestrian turning motion. Experimental data from eight subjects captured in a gait laboratory using a Vicon motion tracking unit is used for validation. The analysis of experimental data shows the heading computed by turn prediction augmented integration is more accurate than open loop gyro integration alone.