UNSCENTED GUIDANCE FOR POINT-TO-POINT REACTION WHEEL MANEUVERS

Attitude control system failures are often mission ending even when the mission payload remains operational. In this dissertation, the concept of unscented guidance is applied to reorient a reaction wheel satellite in the absence of feedback from star trackers or an inertial measurement unit (IMU). It is shown that an open-loop maneuver, properly designed using optimal control theory, can be used to achieve terminal attitude errors that are comparable with closed-loop control in the presence of uncertainty in the satellite inertia tensor. Typically, coarse closed-loop control is used to achieve < 1 degree pointing accuracy before more accurate pointing is done using fine guidance sensors to close the loop for science acquisition. It is shown that reaction wheel maneuvers designed using unscented guidance can also achieve sub-degree pointing accuracy of the spacecraft, making control hand-off to a functioning fine pointing control mode possible. The approach presented here enables large angle attitude control to be recovered so that mission operations may be continued despite IMU or star tracker failures.DoD Space, Chantilly, VA 20151Civilian, Department of the NavyApproved for public release. Distribution is unlimited

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Technical ReportThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Executive SummaryThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Project PosterThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Project PosterThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Entrepreneurial Values, Cognitive Attitude toward Business and Business Behavioural Intention of ABM Grade 12 and Fourth-Year Business Management Students: A Comparative Study

The study aimed to determine the difference in entrepreneurial values, cognitive attitude toward business, the business intention between ABM grade XII and the fourth-year college students of business management course. It also seeks to find out the effect of entrepreneurial values, cognitive attitude toward the business behavioral intention. The study found that entrepreneurial values, cognitive attitude toward business, and business intention of students are high and it further found that there is no significant difference between entrepreneurial values, cognitive attitude toward business, the business intention of ABM grade XII students, and the fourth – year college students of business management course. Thus, the hypothesis is rejected. It also found that there is a significant correlation between entrepreneurial values, cognitive attitude toward the business, and business behavioral intention. Therefore, the hypothesis is accepted

Are We Really Getting Into the Green Scene? Investigating the Effects of Kyoto Protocol Mechanisms on the Environment and Economy

The world has changed drastically in favor of the human race as the processes of production improve, leading to the stimulation of economic growth. However, we failed to consider how these changes affect the environment, causing pollution levels to rise due to it being an unavoidable by-product of production. Eventually, nations were driven to develop a strategy to combat climate change and increase greenhouse gas emissions in the form of the Kyoto Protocol. This research compares two of the Kyoto Protocol Mechanisms, specifically the Clean Development Mechanism (CDM) and the Emissions Trading Scheme (ETS), to investigate the effect of such mechanisms on the economic growth and the greenhouse gas emissions of developed and developing countries that have chosen to adopt their specific mechanism. The research utilizes a panel data regression model and the difference-in-difference (DID) model in quantifying the contribution rate of the country-specific economic indicators and the effect of the Kyoto protocol mechanisms on developed and developing countries. Findings from the study depict the ineffectiveness of the aforementioned mechanisms for most of the developed and developing countries, given that the drivers in pursuit of viable development vary in different areas. In assessing the findings, we have provided aspects to consider for the extension of the study on investigating emission reduction approaches. Policy insights were also devised for leaders and institutions that aim to mitigate emissions further

Investigating the Effects of Kyoto Protocol Mechanisms on the Environment and Economy

As production processes improve, the stimulation of economic growth has overlooked its externalities over time. In pursuit of sustainable development, nations were driven to combat climate change and increasing greenhouse gas emissions through the Kyoto Protocol. The policy brief is based on the investigation of the Kyoto Protocol Mechanisms (Clean Development and Emissions Trading Scheme) through difference-in-difference (DID) estimation, together with panel regression to evaluate the path towards viability using the metrics for externalities (total greenhouse gas emissions) and economic growth (GDP). For this reason, the DID takes into account the quantitative aspect in evaluating the effectiveness of the said mechanisms by investigating the pre- and post-treatment periods. The panel regression results then quantify the influence of the drivers of development towards the specified metrics. Results show that relying on the mechanisms aforementioned in promoting and reinforcing cleaner economic growth throughout the globe is inefficient. Such evidence will be fundamental in enhancing the said mechanisms for its continuity

A foundation for provitamin A biofortification of maize: genome-wide association and genomic prediction models of carotenoid levels.

Efforts are underway for development of crops with improved levels of provitamin A carotenoids to help combat dietary vitamin A deficiency. As a global staple crop with considerable variation in kernel carotenoid composition, maize (Zea mays L.) could have a widespread impact. We performed a genome-wide association study (GWAS) of quantified seed carotenoids across a panel of maize inbreds ranging from light yellow to dark orange in grain color to identify some of the key genes controlling maize grain carotenoid composition. Significant associations at the genome-wide level were detected within the coding regions of zep1 and lut1, carotenoid biosynthetic genes not previously shown to impact grain carotenoid composition in association studies, as well as within previously associated lcyE and crtRB1 genes. We leveraged existing biochemical and genomic information to identify 58 a priori candidate genes relevant to the biosynthesis and retention of carotenoids in maize to test in a pathway-level analysis. This revealed dxs2 and lut5, genes not previously associated with kernel carotenoids. In genomic prediction models, use of markers that targeted a small set of quantitative trait loci associated with carotenoid levels in prior linkage studies were as effective as genome-wide markers for predicting carotenoid traits. Based on GWAS, pathway-level analysis, and genomic prediction studies, we outline a flexible strategy involving use of a small number of genes that can be selected for rapid conversion of elite white grain germplasm, with minimal amounts of carotenoids, to orange grain versions containing high levels of provitamin A

Structured Resistance Training Improves Self-Esteem and Strength in Previously Untrained College Females

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