Department of Systems Engineering
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Item Open Access A balance of design methodology for enterprise quality attribute consideration in System-of-Systems architecting(Colorado State University. Libraries, 2019) Nelson, Travis J., author; Borky, John M., advisor; Sega, Ronald M., advisor; Bradley, Thomas K., committee member; Roberts, Nicholas H., committee memberAn objective of System-of-Systems (SoS) engineering work in the Defense community is to ensure optimal delivery of operational capabilities to warfighters in the face of finite resources and constantly changing conditions. Assurance of enterprise-level capabilities for operational users in the Defense community presents a challenge for acquisitions in balancing multiple SoS architectures versus the more traditional system-based optimization. The problem is exacerbated by the complexity of SoS being realized by multiple, heterogeneous, independently-managed systems that interact to provide these capabilities. Furthermore, the comparison of candidate SoS architectures for selection of the design that satisfies the most enterprise-level objectives and how such decisions affect the future solution space lead to additional challenges in applying existing frameworks. As a result of the enormous challenge associated with enterprise capability development, this research proposes an enterprise architecting methodology leveraging SoS architecture data in the context of multiple enterprise-level objectives to enable the definition of candidate architectures for comparison and decision-making. In this context, architecture-based quality attributes of the enterprise (e.g., resilience, agility, changeability) must be considered. This research builds and extends previous SoS engineering work in the Department of Defense (DoD) to develop a process framework that can improve the analysis of architectural attributes within an enterprise. Certain system attributes of interest are quantified using selected Quality Attributes (QAts). The proposed process framework enables the identification of the quality attributes of interest as the desired characteristics to be balanced against performance measures. QAts are used to derive operational activities as well as design techniques for employment against an as-is SoS architecture. These activities and techniques are then mapped to metrics used to compare alternative architectures. These alternatives enable an SoS-based balance of design for performance and quality attribute optimization while employing a capability model to provide a comparison of available alternatives against overarching preferences. Approaches are then examined to analyze performance of the alternatives in meeting the enterprise capability objectives. These results are synthesized to enable an analysis of alternatives (AoA) to produce a "should-be" architecture vector based on a selected "to-be" architecture. A comparison of the vector trade space is discussed as a forward work in relation to the original enterprise level objectives for decision-making. The framework is illustrated using three case studies including a DoD Satellite Communications (SATCOM) case study; Position, Navigation, and Timing (PNT) case study; and a satellite operations "as-a-service" case study. For the SATCOM case study specifically, the question is considered of whether a certain QAt—resilience—can best be achieved through design alternatives of satellite disaggregation or diversification. The analysis shows that based on the metric mapping and design alternatives examined, diversification provides the greatest SATCOM capability improvement compared to the base architecture, while also enhancing resilience. These three separate cases studies show the framework can be extended to address multiple similar issues with system characteristics and SoS architecture questions for a wide range of enterprises.Item Open Access A combined classification and queuing system optimization approach for enhanced battery system maintainability(Colorado State University. Libraries, 2022) Pirani, Badruddin, author; Cale, James, advisor; Simske, Steven, committee member; Miller, Erika, committee member; Keller, Josh, committee memberBattery systems are used as critical power sources in a wide variety of advanced platforms (e.g., ships, submersibles, aircraft). These platforms undergo unique and extreme mission profiles that necessitate high reliability and maintainability. Battery system failures and non-optimal maintenance strategies have a significant impact on total fleet lifecycle costs and operational capability. Previous research has applied various approaches to improve battery system reliability and maintainability. Machine learning methodologies have applied data-driven and physics-based approaches to model battery decay and predict battery state-of-health, estimation of battery state-of-charge, and prediction of future performance. Queuing theory has been used to optimize battery charging resources ensure service and minimize cost. However, these approaches do not focus on pre-acceptance reliability improvements or platform operational requirements. This research introduces a two-faceted approach for enhancing the overall maintainability of platforms with battery systems as critical components. The first facet is the implementation of an advanced inspection and classification methodology for automating the acceptance/rejection decision for batteries prior to entering service. The purpose of this "pre-screening" step is to increase the reliability of batteries in service prior to deployment. The second facet of the proposed approach is the optimization of several critical maintenance plan design attributes for battery systems. Together, the approach seeks to simultaneously enhance both aspects of maintainability (inherent reliability and cost-effectiveness) for battery systems, with the goal of decreasing total lifecycle cost and increasing operational availability.Item Open Access A graph-based, systems approach for detecting violent extremist radicalization trajectories and other latent behaviors(Colorado State University. Libraries, 2017) Hung, Benjamin W. K., author; Jayasumana, Anura P., advisor; Chong, Edwin K. P., committee member; Ray, Indrajit, committee member; Sega, Ronald M., committee memberThe number and lethality of violent extremist plots motivated by the Salafi-jihadist ideology have been growing for nearly the last decade in both the U.S and Western Europe. While detecting the radicalization of violent extremists is a key component in preventing future terrorist attacks, it remains a significant challenge to law enforcement due to the issues of both scale and dynamics. Recent terrorist attack successes highlight the real possibility of missed signals from, or continued radicalization by, individuals whom the authorities had formerly investigated and even interviewed. Additionally, beyond considering just the behavioral dynamics of a person of interest is the need for investigators to consider the behaviors and activities of social ties vis-à-vis the person of interest. We undertake a fundamentally systems approach in addressing these challenges by investigating the need and feasibility of a radicalization detection system, a risk assessment assistance technology for law enforcement and intelligence agencies. The proposed system first mines public data and government databases for individuals who exhibit risk indicators for extremist violence, and then enables law enforcement to monitor those individuals at the scope and scale that is lawful, and account for the dynamic indicative behaviors of the individuals and their associates rigorously and automatically. In this thesis, we first identify the operational deficiencies of current law enforcement and intelligence agency efforts, investigate the environmental conditions and stakeholders most salient to the development and operation of the proposed system, and address both programmatic and technical risks with several initial mitigating strategies. We codify this large effort into a radicalization detection system framework. The main thrust of this effort is the investigation of the technological opportunities for the identification of individuals matching a radicalization pattern of behaviors in the proposed radicalization detection system. We frame our technical approach as a unique dynamic graph pattern matching problem, and develop a technology called INSiGHT (Investigative Search for Graph Trajectories) to help identify individuals or small groups with conforming subgraphs to a radicalization query pattern, and follow the match trajectories over time. INSiGHT is aimed at assisting law enforcement and intelligence agencies in monitoring and screening for those individuals whose behaviors indicate a significant risk for violence, and allow for the better prioritization of limited investigative resources. We demonstrated the performance of INSiGHT on a variety of datasets, to include small synthetic radicalization-specific data sets, a real behavioral dataset of time-stamped radicalization indicators of recent U.S. violent extremists, and a large, real-world BlogCatalog dataset serving as a proxy for the type of intelligence or law enforcement data networks that could be utilized to track the radicalization of violent extremists. We also extended INSiGHT by developing a non-combinatorial neighbor matching technique to enable analysts to maintain visibility of potential collective threats and conspiracies and account for the role close social ties have in an individual's radicalization. This enhancement was validated on small, synthetic radicalization-specific datasets as well as the large BlogCatalog dataset with real social network connections and tagging behaviors for over 80K accounts. The results showed that our algorithm returned whole and partial subgraph matches that enabled analysts to gain and maintain visibility on neighbors' activities. Overall, INSiGHT led to consistent, informed, and reliable assessments about those who pose a significant risk for some latent behavior in a variety of settings. Based upon these results, we maintain that INSiGHT is a feasible and useful supporting technology with the potential to optimize law enforcement investigative efforts and ultimately enable the prevention of individuals from carrying out extremist violence. Although the prime motivation of this research is the detection of violent extremist radicalization, we found that INSiGHT is applicable in detecting latent behaviors in other domains such as on-line student assessment and consumer analytics. This utility was demonstrated through experiments with real data. For on-line student assessment, we tested INSiGHT on a MOOC dataset of students and time-stamped on-line course activities to predict those students who persisted in the course. For consumer analytics, we tested the performance on a real, large proprietary consumer activities dataset from a home improvement retailer. Lastly, motivated by the desire to validate INSiGHT as a screening technology when ground truth is known, we developed a synthetic data generator of large population, time-stamped, individual-level consumer activities data consistent with an a priori project set designation (latent behavior). This contribution also sets the stage for future work in developing an analogous synthetic data generator for radicalization indicators to serve as a testbed for INSiGHT and other data mining algorithms.Item Open Access A modeling toolkit for comparing AC vs. DC electrical distribution efficiency in buildings(Colorado State University. Libraries, 2021) Othee, Avpreet, author; Cale, James, advisor; Young, Peter, committee member; Herber, Daniel, committee member; Jia, Gaofeng, committee memberAn increasing proportion of electrical devices in residential and commercial buildings operate from direct current (DC) power sources. In addition, distributed power generation systems such as solar photovoltaic (PV) and energy storage natively produce DC power. However, traditional power distribution is based on an alternating current (AC) model. Performing the necessary conversions between AC and DC power to make DC devices compatible with AC distribution results in energy losses. For these reasons, DC distribution may offer energy efficiency advantages in comparison to AC distribution. However, reasonably fast computation and comparison of electrical efficiencies of AC-only, DC-only, and hybrid AC/DC distributions systems is challenging because DC devices are typically (nonlinear) power-electronic converters that produce harmonic content. While detailed time-domain modeling can be used to simulate these harmonics, it is not computationally efficient or practical for many building designers. To address this need, this research describes a toolkit for computation of harmonic spectra and energy efficiency in mixed AC and DC electrical distribution systems, using a Harmonic Power Flow (HPF) methodology. The toolkit includes a library of two-port linear and nonlinear device models which can be used to construct and simulate an electrical distribution system. This dissertation includes a description of the mathematical theory and framework underlying the toolkit, development and fitting of linear and nonlinear device models, software implementation in Modelica, verification of the toolkit with laboratory measurements, and discussion of ongoing and future work to employ the toolkit to a variety of building designs.Item Open Access A systems engineering approach to community microgrid electrification and sustainable development in Papua New Guinea(Colorado State University. Libraries, 2019) Anderson, Alexander A., author; Suryanarayanan, Siddharth, advisor; Cale, James, committee member; Zimmerle, Dan, committee member; Chen, Suren, committee memberElectrification of remote communities worldwide represents a key necessity for sustainable development and advancement of the 17 United Nations Sustainable Development Goals (SDGs). With over 1 billion people still lacking access to electricity, finding new methods to provide safe, clean, reliable, and affordable energy to off-grid communities represents an increasingly dynamic area of research. However, traditional approaches to power system design focused exclusively on traditional metrics of cost and reliability do not provide a sufficiently broad view of the profound impact of electrification. Installation of a single microgrid is a life-changing experience for thousands of people, including both residents who receive direct electricity service and numerous others who benefit from better education, new economic opportunities, incidental job creation, and other critical infrastructure systems enabled by electricity. Moreover, an electrification microgrid must directly satisfy community needs, be sensitive to local environmental constraints, mitigate possible risks, and plan for at least a decade of sustainable operations and maintenance. These considerations extend beyond the technical and optimization problems typically addressed in microgrid design. An enterprise system-of-systems framework for microgrid planning considering technical, economic, environmental, and social criteria is developed in response to the need for a comprehensive methodology for planning of community electrification projects. This framework spans the entire systems engineering discipline and incorporates elements from project management, risk management, enterprise architecture, numerical optimization, and multi-criteria decision-making, and sustainable development theory. To support the creation of the systems engineering framework, a comprehensive survey of multi-objective optimization formulations for planning and dispatch of islanded microgrids was conducted to form a baseline for further discussion. This survey identifies that all optimizations studies of islanded microgrids are based on formulations selecting a combination of 16 possible objective functions, 14 constraints, and 13 control variables. A sufficient group of decision-making elicitees are formed from the group of nearly 250 publications surveyed to create a comprehensive optimization framework based on technical, economic, environmental, and social attributes of islanded microgrids. This baseline enables the formulation of a flexible, computationally lightweight methodology for microgrid planning in consideration of multiple conflicting objectives using the simple multi-attribute ranking technique exploiting ranks (SMARTER). Simultaneously, the identified technical, economic, environmental, and social decision criteria form a network of functional, operational, and performance requirements in an enterprise system-of-systems structure that considers all stakeholders and actors in the development of community electrification microgrids. This framework considers community capacity building and sustainable development theory as a hierarchical structure, where each layer of the hierarchy is mapped both to a set of organizational, financial, and physical subsystems and to a corresponding subset of the 17 SDGs. The structure presents the opportunity not only to integrate classical project management and risk management tools, but also to create a new lifecycle for planning, funding, executing, and monitoring multi-phase community infrastructure projects. Throughout the research, a case study of the Madan Community in Jiwaka Province, Papua New Guinea is used to demonstrate the systems engineering concepts and tools developed by the research. The community is the center of multi-phase community capacity building project addressing critical needs of the deep rural community, including electricity, education, water, sanitation, healthcare, and economic opportunities. The researcher has been involved as a pro-bono consultant for the project since 2013 and helped raise over $1M USD in infrastructure materials, equipment, and consulting. The structure of the community-based organization and numerical optimization of a series of islanded microgrids are used to illustrate both the system-of-systems hierarchy and microgrid planning techniques based on both single-objective optimization using linear programming and the SMARTER methodology for consideration of multiple qualitative and quantitative decision criteria.Item Open Access Aircraft survivability modeling, evaluation, and optimization for multi-UAV operational scenarios(Colorado State University. Libraries, 2021) Lunsford, Ian, author; Bradley, Thomas, advisor; Borky, John, committee member; Shahroudi, Kamran, committee member; Arabi, Mazdak, committee memberThe unmanned aerial vehicle (UAV) has become a prominent aircraft design throughout aerospace applications including commercial, civilian, and military. A UAV is preferred in some missions and applications due to its unique abilities compared to manned aircraft. This dissertation aims to define an improved understanding of the concepts and modeling of aircraft survivability, as applied to UAVs. Traditionally, survivability as a field has defined and considered survivability primarily in the context of manned aircraft, and single aircraft. With UAV's increasing importance in multi-UAV operational scenarios, it has become increasingly important to understand aircraft survivability for singles and groups of UAVs. This research effort has been structured into three research questions defining contributions in survivability modeling, validation, and UAV aircraft design. Research Question 1 seeks to demonstrate the feasibility of a parametric model of UAV survivability. The result is a UAV survivability model and simulation which illustrates key tradeoffs within UAV survivability. The effects on survivability on UAV design characteristics (speed, wing area, drag and lift coefficients) is quantified specific to the detailed lethal envelope simulation method. Research Question 2 aims to verify and validate the UAV survivability simulation, providing evidence of the predictive capability of the survivability simulation results. Evidence is presented for verification and validation through comparison to previous modeling efforts, through solicitation of expert opinion, and through parameter variability and sensitivity analysis. Lastly, Research Question 3 seeks to apply the simulation results to multi-UAV tactical evaluation and single aircraft design. The results illustrate the level of improvement that can be realized through UAV design including armoring (a 25% survivability improvement through 1000kg of armoring), speed increases (a 100 mph increase in cruise speed realizes a 14% decrease in killability), and other relevant design variables. Results also demonstrate that multi-UAV tactics can improve the survivability of UAVs in combat. Loyal wingman tactics are simulated to increase the survivability of a C-130J (equivalent UAV) from 19.8% to 40.0%. Other single UAV tactics such as fuel dumping, afterburners are evaluated under the same framework for their relative effectiveness. This dissertation answers the described research questions by presenting an aircraft survivability evaluation approach that relates survivability with modern UAV applications, emerging threats, multi-UAV tactics, and UAV design. Aircraft survivability encounters with modern UAV countermeasures are considered and simulated. UAV metrics of performance are modeled and simulated to describe aircraft design parameters sensitive to improving aircraft survivability. By evaluating aircraft survivability with a modern multi-UAV tactical perspective, this study seeks to provide the UAV designer with more complete vision of survivability-derived design criteria.Item Open Access An enterprise system engineering analysis of KC-46A maintenance program decision-making(Colorado State University. Libraries, 2023) Blond, Kyle E., author; Bradley, Thomas, advisor; Ender, Tommer, committee member; Conrad, Steven, committee member; Herber, Daniel, committee member; Ozbek, Mehmet, committee memberThe KC-46A Pegasus is a United States Air Force (USAF) tanker, transport, and medical evacuation commercial derivative aircraft based on the Boeing 767. It is a top acquisition priority to modernize the USAF's refueling capabilities and is governed by a lifecycle sustainment strategy directed by USAF commercial variant policies aligned to Federal Aviation Administration (FAA) policy. While this strategy provides robust mechanisms to manage the KC-46A's performance during its operations and support phase, opportunity exists for the KC-46A sustainment enterprise to better achieve reliability, availability, maintainability, and cost (RAM C) objectives through enhancing KC-46A maintenance program decision making in the context of USAF and FAA policies. This research characterizes the KC-46A maintenance program as an industrial enterprise system governing the maintenance, repair, overhaul, and modification of KC-46A aircraft. Upon this basis, enterprise systems engineering (ESE) characterizes the KC-46A maintenance program and identifies decision making improvement opportunities in its management. Canonical ESE viewpoints are tailored to abstract the organizations, processes, and information composing KC-46A maintenance program decision making and model how decision support methods can better achieve KC-46A sustainment enterprise objectives. A decision making framework then evaluates the RAM C performance of KC-46A maintenance tasks as part of the KC-46A Continued Analysis and Surveillance System (CASS) program. The framework's heuristics classify the compliance, effectiveness, and optimality of a maintenance task to prescribe KC-46A CASS responses. A rule based expert system applies this framework and serves as the knowledge engine for the KC-46A CASS decision support system referred to as the "Pegasus Fleet Management Tool." A focus group of KC-46A sustainment experts evaluated the framework and produced consensus that it advances the state of the art in KC-46A maintenance program decision making. A business case analysis roadmaps the programmatic and technical activities required to implement the framework in PFMT and improve KC-46A sustainment.Item Open Access Application of systems engineering principles in the analysis, modeling, and development of a DoD data processing system(Colorado State University. Libraries, 2023) Fenton, Kevin P., author; Simske, Steven J., advisor; Bradley, Thomas, committee member; Carlson, Ken, committee member; Atadero, Rebecca, committee memberIn support of over 1000 military installations worldwide, the Department of Defense (DoD) has procured contracts with thousands of vendors that supply the military with hazardous materials constituting billions of dollars of defense expenses in support of facility and asset maintenance. These materials are used for a variety of purposes ranging from weapon system maintenance to industrial and facility operations. In order to comply with environmental, health, and safety (EHS) regulations, the vendors are contractually obligated to provide Safety Data Sheets (SDSs) listing EHS concerns compliant with the requirements set forth by the United Nations Globally Harmonized System of Classification and Labeling of Chemicals (GHS). Each year chemical vendors provide over 100 thousand SDSs in a PDF or hard copy format. These SDSs are then entered manually by data stewards into the DoD centralized SDS repository – the Hazardous Material Management Information System (HMIRS). In addition, the majority of these SDS are also loaded separately by separate data stewards into downstream environmental compliance systems that support specific military branches. The association between the vendor-provided SDSs and the materials themselves was then lost until the material reaches an installation at which point personnel must select the SDS associated to the hazardous material within the service-specific hazardous material tracking system. This research applied systems engineering principles in the analysis, modeling, and development of a DoD data processing system that could be used to increase efficiency, reduce costs, and provide an automated solution not only to data entry reduction but in transitioning and modernizing the hazard communication and data transfer towards a standardized approach. Research for the processing system covered a spectrum of modern analytics and data extraction techniques including optical character recognition, artificial neural networks, and meta-algorithmic processes. Additionally, the research covered potential integration into existing DoD framework and optimization to solve many long-standing chemical management problems. While the long-term focus was for chemical manufacturers to provide SDS data in a standardized machine-encoded format, this system is designed to act as a transitionary tool to reduce manual data entry and costs of over $3 million each year while also enhancing system features to address other major obstacles in the hazard communication process. Complexities involved with the data processing of SDSs included multi-lingual translation needs, image and text recognition, periodic use of tables, and while SDSs are structured with 16 distinct sections – a general lack of standardization on how these sections were formatted. These complexities have been addressed using a patent-pending meta-algorithmic approach to produce higher data extraction yields than what an artificial neural network can produce alone while also providing SDS-specific data validation and calculation of SDS-derived data points. As the research progressed, this system functionality was communicated throughout the DoD and became part of a larger conceptual digital hazard communication transformation effort currently underway by the Office of the Secretary of Defense and the Defense Logistics Agency. This research led to five publications, a pending patent, an award for $280,000 for prototype development, and a project for the development of this system to be used as one of the potential systems in a larger DoD effort for full chemical disclosure and proactive management of not only hazardous chemicals but potentially all DoD-procured products.Item Open Access Application of systems engineering to complex systems and system of systems(Colorado State University. Libraries, 2017) Sturdivant, Rick L., author; Chong, Edwin K. P., advisor; Sega, Ronald M., committee member; Jayasumana, Anura P., committee member; Atadero, Rebecca, committee memberThis dissertation is an investigation of system of systems (SoS). It begins with an analysis to define, with some rigor, the similarities and differences between complex systems and SoS. With this foundation, the baseline concept is development for several different types of systems and they are used as a practical approach to compare and contrast complex systems versus SoS. The method is to use a progression from simple to more complex systems. Specifically, a pico hydro electric power generation system, a hybrid renewable electric power generation system, a LEO satellites system, and Molniya orbit satellite system are investigated. In each of these examples, systems engineering methods are applied for the development of a baseline solution. While these examples are complex, they do not rise to the level of a SoS. In contrast, a multi-spectral drone detection system for protection of airports is investigated and a baseline concept for it is generated. The baseline is shown to meet the minimum requirements to be considered a SoS. The system combines multiple sensor types to distinguish drones as targets. The characteristics of the drone detection system which make it a SoS are discussed. Since emergence is considered by some to be a characteristic of a SoS, it is investigated. A solution to the problem of determining if system properties are emergent is presented and necessary and sufficient conditions for emergence are developed. Finally, this work concludes with a summary and suggestions for additional work.Item Open Access Applying model-based systems engineering in search of quality by design(Colorado State University. Libraries, 2022) Miller, Andrew R., author; Herber, Daniel R., advisor; Bradley, Thomas, committee member; Miller, Erika, committee member; Simske, Steve, committee member; Yalin, Azer P., committee memberModel-Based System Engineering (MBSE) and Model-Based Engineering (MBE) techniques have been successfully introduced into the design process of many different types of systems. The application of these techniques can be reflected in the modeling of requirements, functions, behavior, and many other aspects. The modeled design provides a digital representation of a system and the supporting development data architecture and functional requirements associated with that architecture through modeling system aspects. Various levels of the system and the corresponding data architecture fidelity can be represented within MBSE environment tools. Typically, the level of fidelity is driven by crucial systems engineering constraints such as cost, schedule, performance, and quality. Systems engineering uses many methods to develop system and data architecture to provide a representative system that meets costs within schedule with sufficient quality while maintaining the customer performance needs. The most complex and elusive constraints on systems engineering are defining system requirements focusing on quality, given a certain set of system level requirements, which is the likelihood that those requirements will be correctly and accurately found in the final system design. The focus of this research will investigate specifically the Department of Defense Architecture Framework (DoDAF) in use today to establish and then assess the relationship between the system, data architecture, and requirements in terms of Quality By Design (QbD). QbD was first coined in 1992, Quality by Design: The New Steps for Planning Quality into Goods and Services [1]. This research investigates and proposes a means to: contextualize high-level quality terms within the MBSE functional area, provide an outline for a conceptual but functional quality framework as it pertains to the MBSE DoDAF, provides tailored quality metrics with improved definitions, and then tests this improved quality framework by assessing two corresponding case studies analysis evaluations within the MBSE functional area to interrogate model architectures and assess quality of system design. Developed in the early 2000s, the Department of Defense Architecture Framework (DoDAF) is still in use today, and its system description methodologies continue to impact subsequent system description approaches [2]. Two case studies were analyzed to show proposed QbD evaluation to analyze DoDAF CONOP architecture quality. The first case study addresses the analysis of DoDAF CONOP of the National Aeronautics and Space Administration (NASA) Joint Polar Satellite System (JPSS) ground system for National Oceanic and Atmospheric Administration (NOAA) satellite system with particular focus on the Stored Mission Data (SMD) mission thread. The second case study addresses the analysis of DoDAF CONOP of the Search and Rescue (SAR) navel rescue operation network System of Systems (SoS) with particular focus on the Command and Control signaling mission thread. The case studies help to demonstrate a new DoDAF Quality Conceptual Framework (DQCF) as a means to investigate quality of DoDAF architecture in depth to include the application of DoDAF standard, the UML/SysML standards, requirement architecture instantiation, as well as modularity to understand architecture reusability and complexity. By providing a renewed focus on a quality-based systems engineering process when applying the DoDAF, improved trust in the system and data architecture of the completed models can be achieved. The results of the case study analyses reveal how a quality-focused systems engineering process can be used during development to provide a product design that better meets the customer's intent and ultimately provides the potential for the best quality product.Item Open Access Applying model-based systems engineering to architecture optimization and selection during system acquisition(Colorado State University. Libraries, 2018) LaSorda, Michael, author; Sega, Ronald M., advisor; Borky, Mike, advisor; Bradley, Tom, committee member; Quinn, Jason, committee memberThe architecture selection process early in a major system acquisition is a critical step in determining the overall affordability and technical performance success of a program. There are recognized deficiencies that frequently occur in this step such as poor transparency into the final selection decision and excessive focus on lowest cost, which is not necessarily the best value for all of the stakeholders. This research investigates improvements to the architecture selection process by integrating Model-Based Systems Engineering (MBSE) techniques, enforcing rigorous, quantitative evaluation metrics with a corresponding understanding of uncertainties, and stakeholder feedback in order to generate an architecture that is more optimized and trusted to provide better value for the stakeholders. Three case studies were analyzed to demonstrate this proposed process. The first focused on a satellite communications System of Systems (SoS) acquisition to demonstrate the overall feasibility and applicability of the process. The second investigated an electro-optical remote sensing satellite system to compare this proposed process to a current architecture selection process typified by the United States Department of Defense (U.S. DoD) Analysis of Alternatives (AoA). The third case study analyzed the evaluation of a service-oriented architecture (SOA) providing satellite command and control with cyber security protections in order to demonstrate rigorous accounting of uncertainty through the architecture evaluation and selection. These case studies serve to define and demonstrate a new, more transparent and trusted architecture selection process that consistently provides better value for the stakeholders of a major system acquisition. While the examples in this research focused on U.S. DoD and other major acquisitions, the methodology developed is broadly applicable to other domains where this is a need for optimization of enterprise architectures as the basis for effective system acquisition. The results from the three case studies showed the new process outperformed the current methodology for conducting architecture evaluations in nearly all criteria considered and in particular selects architectures of better value, provides greater visibility into the actual decision making, and improves trust in the decision through a robust understanding of uncertainty. The primary contribution of this research then is improved information support to an architecture selection in the early phases of a system acquisition program. The proposed methodology presents a decision authority with an integrated assessment of each alternative, traceable to the concerns of the system's stakeholders, and thus enables a more informed and objective selection of the preferred alternative. It is recommended that the methodology proposed in this work is considered for future architecture evaluations.Item Open Access Artificial intelligence powered personalized agriculture(Colorado State University. Libraries, 2023) Tetala, Satya Surya Dattatreya Reddy, author; Simske, Steven, advisor; Conrad, Steve, committee member; Gaines, Todd, committee member; Nalam, Vamsi, committee memberThe integration of Artificial Intelligence (AI) in agriculture has shown the potential to improve crop selection and enhance sustainability practices. In this study, we aim to investigate the benefits and feasibility of using AI-powered personalized recommendations for crop selection and sustainability practices in the context of agroecology. We propose to lay the foundation for an agricultural recommendation engine that considers several parameters that influence yield and presents the best crop(s) to sow based on the model's output. We aim to examine this recommendation engine's impact on agriculture's sustainability and to evaluate its effectiveness and accuracy. Our ultimate goal is to provide a comprehensive understanding of the potential benefits and challenges of using AI-powered recommendations in agriculture and to lay the foundation for the development of a practical, effective, and user-friendly recommendation engine that can help farmers make informed decisions about their crops and improve the long-term sustainability of agriculture.Item Open Access Autonomous UAV control and testing methods utilizing partially observable Markov decision processes(Colorado State University. Libraries, 2018) Eaton, Christopher M., author; Chong, Edwin K. P., advisor; Maciejewski, Anthony A., advisor; Bradley, Thomas, committee member; Young, Peter, committee memberThe explosion of Unmanned Aerial Vehicles (UAVs) and the rapid development of algorithms to support autonomous flight operations of UAVs has resulted in a diverse and complex set of requirements and capabilities. This dissertation provides an approach to effectively manage these autonomous UAVs, effectively and efficiently command these vehicles through their mission, and to verify and validate that the system meets requirements. A high level system architecture is proposed for implementation on any UAV. A Partially Observable Markov Decision Process algorithm for tracking moving targets is developed for fixed field of view sensors while providing an approach for more fuel efficient operations. Finally, an approach for testing autonomous algorithms and systems is proposed to enable efficient and effective test and evaluation to support verification and validation of autonomous system requirements.Item Open Access Avoiding technical bankruptcy in system development: a process to reduce the risk of accumulating technical debt(Colorado State University. Libraries, 2023) Kleinwaks, Howard, author; Bradley, Thomas, advisor; Batchelor, Ann, advisor; Marzolf, Gregory, committee member; Wise, Daniel, committee member; Turner, John F., committee memberThe decisions made early in system development can have profound impacts on later capabilities of the system. In iterative systems development, decisions made in each iteration produce impacts on every future iteration. Decisions that have benefits in the short-term may damage the long-term health of the system. This phenomenon is known as technical debt. If not carefully managed, the buildup of technical debt within a system can lead to technical bankruptcy: the state where the system development can no longer proceed with its lifecycle without first paying back some of the technical debt. Within the schedule constrained development paradigm of iteratively and incrementally developed systems, it is especially important to proactively manage technical debt and to understand the potential long-term implications of decisions made to achieve short-term delivery goals. To enable proactive management of technical debt within systems engineering, it is first necessary to understand the state of the art with respect to the application of technical debt methods and terminology within the field. While the technical debt metaphor is well-known within the software engineering community, it is not as well known within the systems engineering community. Therefore, this research first characterizes the state of technical debt research within systems engineering through a literature review. Next, the prevalence of the technical debt metaphor among practicing systems engineers is established through an empirical survey. Finally, a common ontology for technical debt within systems engineering is proposed to enable clear and concise communication about the common problems faced in different systems engineering development programs. Using the research on technical debt in systems engineering and the ontology, this research develops a proactive approach to managing technical debt in iterative systems development by creating a decision support system called List, Evaluate, Achieve, Procure (LEAP). The LEAP process, when used in conjunction with release planning methods, can identify the potential for technical debt accumulation and eventually technical bankruptcy. The LEAP process is developed in two phases: a qualitative approach to provide initial assessments of the state of the system and a quantitative approach that models the effects of technical debt on system development schedules and the potential for technical bankruptcy based on release planning schedules. Example applications of the LEAP process are provided, consisting of the development of a conceptual problem and real applications of the process at the Space Development Agency. The LEAP process provides a novel and mathematical linkage of the temporal and functional dependencies of system development with the stakeholder needs, enabling proactive assessments of the ability of the system to satisfy those stakeholder needs. These assessments enable early identification of potential technical debt, reducing the risk of negative long-term impacts on the system health.Item Open Access Big Data decision support system(Colorado State University. Libraries, 2022) Ma, Tian J., author; Chong, Edwin, advisor; Simske, Steve, committee member; Herber, Daniel, committee member; Pezeshki, Ali, committee memberEach day, the amount of data produced by sensors, social and digital media, and Internet of Things is rapidly increasing. The volume of digital data is expected to be doubled within the next three years. At some point, it might not be financially feasible to store all the data that is received. Hence, if data is not analyzed as it is received, the information collected could be lost forever. Actionable Intelligence is the next level of Big Data analysis where data is being used for decision making. This thesis document describes my scientific contribution to Big Data Actionable Intelligence generations. Chapter 1 consists of my colleagues and I's contribution in Big Data Actionable Intelligence Architecture. The proven architecture has demonstrated to support real-time actionable intelligence generation using disparate data sources (e.g., social media, satellite, newsfeeds). This work has been published in the Journal of Big Data. Chapter 2 shows my original method to perform real-time detection of moving targets using Remote Sensing Big Data. This work has also been published in the Journal of Big Data and it has received an issuance of a U.S. patent. As the Field-of-View (FOV) in remote sensing continues to expand, the number of targets observed by each sensor continues to increase. The ability to track large quantities of targets in real-time poses a significant challenge. Chapter 3 describes my colleague and I's contribution to the multi-target tracking domain. We have demonstrated that we can overcome real-time tracking challenges when there are large number of targets. Our work was published in the Journal of Sensors.Item Open Access Cislunar system of systems architecture evaluation and optimization(Colorado State University. Libraries, 2023) Duffy, Laura, author; Adams, Jim, advisor; Sega, Ronald M., committee member; Herber, Daniel R., committee member; Fankell, Douglas, committee memberCislunar space is the next frontier of space exploration, but a sustainable architecture is lacking. Cislunar space is considered a complex system of systems because it consists of multiple independent systems that work together to deliver unique capabilities. The independent systems of the cislunar system of systems include the communications, navigation, and domain awareness systems. Additionally, the methodology to design, evaluate and optimize a complex system of systems has not been published. To close the gap, a comprehensive needs analysis is performed for cislunar space. Next, model-based systems engineering is used to design the cislunar system of systems. The cislunar architectures are designed in terms of constellations and payloads. The architectures are each evaluated in terms of cost and performance. An appropriate optimization algorithm is found for the system of systems, and the results of the optimization are evaluated using multiple techniques for comparison. A literature review is included on the topics of cislunar architectures, system of systems, model-based systems engineering, system architecture evaluation, and system architecture optimization. During the research of cislunar architectures, a needs analysis is completed which identifies the three primary missions planned for cislunar space and eight supporting functions to provide the infrastructure for the primary missions. The primary missions identified include science, commerce, and defense. The eight supporting functions identified include transportation, communication, domain awareness, service, energy, shelter, and control. Technologies and programs are identified for each supporting function, included gaps in needed technology or programs. For the evaluation and optimization of the system of systems, the supporting functions are down-selected to include only the three necessary supporting functions for any operations in cislunar space: communications, navigation, and domain awareness. A system architecture is developed using Systems Modeling Language in Cameo Systems ModelerTM. The model is designed using the Model-based Systems Architecture Process which includes the design of the Operational Viewpoint, Logical/Functional Viewpoint, and Physical Viewpoint. The Operational Viewpoint includes structural, behavioral, data, and contextual perspectives. The Logical/Functional Viewpoint includes structural, behavioral, data, and contextual perspectives. The Physical Viewpoint includes design, standards, data, and contextual perspectives. Each of these perspectives are represented in the form of Cameo Systems ModelerTM diagrams or tables. Diagrams include block definition diagrams, internal block diagrams, use case diagrams, activity diagrams, and sequence diagrams. Additional modeling concepts beyond the Model-based Systems Architecture Process are included in the Cameo Systems ModelerTM model and analysis of the model. These topics include allocating requirements, stereotypes, patterns in architecture decisions, architecture optimization, verification, validation, complexity, and open systems architecture. Cislunar constellations and payloads are designed which account for the cislunar physical environment. Six constellations are designed to be included in the optimization algorithm. These constellations include Lagrange light, Lagrange medium, Lagrange heavy, Earth-based, Earth plus Moon, and Earth plus Lagrange. These constellations essentially represent the location of the bus while the payloads provide the functionality of the system. Payloads are designed for the supporting functions deemed essential for a basic cislunar infrastructure, which are communications, navigation, and domain awareness. The optimization algorithm runs through each possible combination of payload and bus, including any opportunities to integrate multiple payloads on a single bus. The total number of possible architecture combinations for the optimization algorithm is 288. The payload sensors are modeled in Systems Tool Kit and evaluated for physical performance. Additionally, each payload and bus possibility are evaluated for cost using the Unmanned Space Vehicle Cost Model and professional estimates. The performance and cost metrics are used in the optimization algorithm. The optimization algorithm uses multi-objective optimization with an integer linear program. The result of the optimization algorithm is a pareto front of the highest-performance, lowest-cost architectures. The architectures along the pareto front are evaluated using multi-criteria decision making with and without evidential reasoning to find the "best" architecture. A Kiviat chart assessment is also performed, though this method is shown to not be practical for the cislunar application. The model and conclusions of the dissertation are validated using a variety of industry-accepted techniques. The cislunar architectures are validated via peer-review. The performance evaluations are validated via a validated physics model. The cost evaluations are validated by a validated cost-model when possible and by peer-review. The optimization algorithm is validated by comparison to a manual optimization method. The Cameo Systems ModelerTM model is validated using validation techniques internal to the tool. Suggestions for future work are presented. Future work could include fully integrating the Cameo Systems ModelerTM model with the Systems Tool Kit model, providing improved cost estimates, using alternative optimization parameters, adding supporting functions as they are identified, evaluating the architectures using additional metrics, evaluating additional constellations, applying integration at the functional level, or assessing non-homogenous requirements.Item Open Access Cloud Computing cost and energy optimization through Federated Cloud SoS(Colorado State University. Libraries, 2017) Biran, Yahav, author; Collins, George J., advisor; Pasricha, Sudeep, advisor; Young, Peter, committee member; Borky, John M., committee member; Zimmerle, Daniel J., committee memberThe two most significant differentiators amongst contemporary Cloud Computing service providers have increased green energy use and datacenter resource utilization. This work addresses these two issues from a system's architectural optimization viewpoint. The proposed approach herein, allows multiple cloud providers to utilize their individual computing resources in three ways by: (1) cutting the number of datacenters needed, (2) scheduling available datacenter grid energy via aggregators to reduce costs and power outages, and lastly by (3) utilizing, where appropriate, more renewable and carbon-free energy sources. Altogether our proposed approach creates an alternative paradigm for a Federated Cloud SoS approach. The proposed paradigm employs a novel control methodology that is tuned to obtain both financial and environmental advantages. It also supports dynamic expansion and contraction of computing capabilities for handling sudden variations in service demand as well as for maximizing usage of time varying green energy supplies. Herein we analyze the core SoS requirements, concept synthesis, and functional architecture with an eye on avoiding inadvertent cascading conditions. We suggest a physical architecture that diminishes unwanted outcomes while encouraging desirable results. Finally, in our approach, the constituent cloud services retain their independent ownership, objectives, funding, and sustainability means. This work analyzes the core SoS requirements, concept synthesis, and functional architecture. It suggests a physical structure that simulates the primary SoS emergent behavior to diminish unwanted outcomes while encouraging desirable results. The report will analyze optimal computing generation methods, optimal energy utilization for computing generation as well as a procedure for building optimal datacenters using a unique hardware computing system design based on the openCompute community as an illustrative collaboration platform. Finally, the research concludes with security features cloud federation requires to support to protect its constituents, its constituents tenants and itself from security risks.Item Open Access Colorado Greenhouse Gas Emission Tool (CO-GT) associated with article “A Colorado-Specific Life Cycle Assessment Model to Support Evaluation of Low-Carbon Transportation Fuels and Policy”(Colorado State University. Libraries, 2021) Somers, Michael; Batan, Liaw; Al-Alawi, Baha; Bradley, Thomas H.The transportation sector accounts for over 20 percent of greenhouse gas (GHG) emissions in Colorado which without intervention will grow to over 30 million metric tons (MMT) of GHG emissions per year. This study seeks to develop a specific characterization of the Colorado fuel and transportation system using a customized life cycle assessment (LCA) model. The model (CO-GT) was developed as an analytical tool to define Colorado's 2020 baseline life cycle GHG emissions for the transportation sector, and to examine Colorado-specific pathways for GHG reductions through fuel types and volumes changes that might be associated with a state clean fuel standard (CFS). By developing a life cycle assessment of transportation fuels that is specific to the state of Colorado's geography, fleet makeup, policies, energy sector and more, these tools can evaluate various proposals for the transition towards a more sustainable state transportation system. The results of this study include a quantification of the Colorado-specific roles of clean fuels, electricity, extant policies, and fleet transition in projections of the state's 2030 transportation sector GHG emissions. Relative to a 2020 baseline, electrification of the vehicle fleet is found to reduce state-wide lifecycle GHG emissions by 7.7 MMT CO2e by 2030, and a model CFS policy able to achieve similar reductions in the carbon intensity of clean fuels as was achieved by California in the first 10 years of its CFS policies is found to only reduce state-wide lifecycle GHG emissions by 0.2 MMT CO2e by 2030. These results illustrate the insensitivity of Colorado's transportation fleet GHG emissions reductions to the presence of CFS policies, as proposed to date.Item Open Access Comparative analysis of model-based systems engineering and traditional systems engineering approaches for architecting robotic space systems through knowledge categorization, automatic information transfer, and automatic knowledge processing measures(Colorado State University. Libraries, 2021) Younse, Paulo, author; Bradley, Thomas, advisor; Borky, John, committee member; Sega, Ron, committee member; Reising, Steven, committee memberRobotic space systems have enabled us to explore the far reaches of our solar system. However, these missions are high-cost, high-risk, and prone to accidents due to their complex nature. As these systems continue to grow even more capable and complex, spacecraft costs and mission success risk are also expected to grow. Current systems engineering approaches are finding it challenging to manage this growth in system complexity. Model-Based Systems Engineering (MBSE) offers techniques to aid in the development of complex systems, aiming to reduce design errors, reduce cost through prevention of costly rework, and improve system quality and project performance over traditional systems engineering techniques. Robotic space systems have much to benefit from an MBSE approach due to their intrinsic complexity, particularly if MBSE is implemented during the early architecting phase of the project. Case studies from the literature assert that there are benefits to using MBSE when applied to developing complex systems. However, none of these studies perform in-depth quantitative comparative analysis of applying MBSE vs. non-MBSE approaches, and there currently is a lack of substantial and compelling evidence to establish broad adoption of MBSE within the systems engineering community. This research measures the benefits of MBSE approaches over traditional, non-MBSE approaches for architecting robotic space systems though comparative analysis, focusing on quantitative evidence supporting how MBSE better describes, develops, and evaluates the system architecture, all which can aid in the adoption of MBSE within the robotics space systems domain. These advantages will be investigated through studying 1) how an MBSE approach better captures the information content for describing a robotic space system architecture relative to a non-MBSE approach, 2) how an MBSE approach reduces the implementation effort required to developing a robotic space system architecture relative to a non-MBSE approach, and 3) how an MBSE approach more efficiently evaluates a robotic space system architecture relative to a non-MBSE approach. A Mars orbiting sample Capture and Orient Module (COM) system for a Capture, Contain, and Return System (CCRS) payload concept for the notional Mars Sample Return (MSR) campaign develop at the NASA Jet Propulsion Laboratory was used as a case study to investigate the advantages of MBSE. The MBSE approach provided measurable advantages to architecting the COM robotic space system in terms of a higher fraction of formally captured architecture content in the appropriate knowledge category, a higher quantity of automatic information transfer between architecting tasks, and a higher quantity of automatic knowledge processing during modeling and simulation activities.Item Open Access Continuity of object tracking(Colorado State University. Libraries, 2022) Williams, Haney W., author; Simske, Steven J., advisor; Azimi-Sadjadi, Mahmood R., committee member; Chong, Edwin K. P., committee member; Beveridge, J. Ross, committee memberThe demand for object tracking (OT) applications has been increasing for the past few decades in many areas of interest: security, surveillance, intelligence gathering, and reconnaissance. Lately, newly-defined requirements for unmanned vehicles have enhanced the interest in OT. Advancements in machine learning, data analytics, and deep learning have facilitated the recognition and tracking of objects of interest; however, continuous tracking is currently a problem of interest to many research projects. This dissertation presents a system implementing a means to continuously track an object and predict its trajectory based on its previous pathway, even when the object is partially or fully concealed for a period of time. The system is divided into two phases: The first phase exploits a single fixed camera system and the second phase is composed of a mesh of multiple fixed cameras. The first phase system is composed of six main subsystems: Image Processing, Detection Algorithm, Image Subtractor, Image Tracking, Tracking Predictor, and the Feedback Analyzer. The second phase of the system adds two main subsystems: Coordination Manager and Camera Controller Manager. Combined, these systems allow for reasonable object continuity in the face of object concealment.