Browsing by Author "Simske, Steven J., advisor"

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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 member
In 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.
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 member
The 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.
Open Access
Measuring disagreement in segments of the cybersecurity profession as a means of identifying vulnerabilities
(Colorado State University. Libraries, 2022) Scalco, Aleksandra, author; Simske, Steven J., advisor; Cale, James, committee member; Herber, Daniel, committee member; Dik, Bryan J., committee member
Disagreement exists among different groups of professionals about remediation of control system vulnerability due to discrepancies in engineering practice, paradigms, processes, and culture. Quantification of agreement among professionals is needed to increase understanding of areas where divergence arises. This need to quantify agreement is particularly among control system Operational Technology (OT) and business enterprise Information Technology (IT) professions. The control system OT workforce does not fully understand the relative vulnerability of each element of its system. Likewise, the business enterprise IT workforce does not widely understand control system assets that control critical infrastructure to achieve cybersecurity assurance. This disagreement among professionals leads to misalignment, which results in vulnerability. Similarly, known vulnerability can inform alignment and bring about agreement among professionals. The exposure induced by misalignment may be greater than innate system design vulnerability. This research introduces an analytical model and methodology for measuring multi-concern assurance among different groups of professions through the statistical uncertainty analysis of Likert and semantic differential scales used for interpreting the scores to identify specific areas of vulnerability.
Open Access
Optimizing text analytics and document automation with meta-algorithmic systems engineering
(Colorado State University. Libraries, 2023) Villanueva, Arturo N., Jr., author; Simske, Steven J., advisor; Hefner, Rick D., committee member; Krishnaswamy, Nikhil, committee member; Miller, Erika, committee member; Roberts, Nicholas, committee member
Natural language processing (NLP) has seen significant advances in recent years, but challenges remain in making algorithms both efficient and accurate. In this study, we examine three key areas of NLP and explore the potential of meta-algorithmics and functional analysis for improving analytic and machine learning performance and conclude with expansions for future research. The first area focuses on text classification for requirements engineering, where stakeholder requirements must be classified into appropriate categories for further processing. We investigate multiple combinations of algorithms and meta-algorithms to optimize the classification process, confirming the optimality of Naïve Bayes and highlighting a certain sensitivity to the Global Vectors (GloVe) word embeddings algorithm. The second area of focus is extractive summarization, which offers advantages to abstractive summarization due to its lossless nature. We propose a second-order meta-algorithm that uses existing algorithms and selects appropriate combinations to generate more effective summaries than any individual algorithm. The third area covers document ordering, where we propose techniques for generating an optimal reading order for use in learning, training, and content sequencing. We propose two main methods: one using document similarities and the other using entropy against topics generated through Latent Dirichlet Allocation (LDA).
Open Access
Situational strategic awareness monitoring surveillance system (SSAMSS)
(Colorado State University. Libraries, 2023) Maldonado, Kenly R., author; Simske, Steven J., advisor; Miller, Erika, committee member; Herber, Daniel, committee member; Dandy, David, committee member
This dissertation takes a Systems Engineering approach for the development of a cost-effective, deployable remote sensing materials safeguarding system. This co-called system-of-systems undergoes the major portions of the Systems Engineering development process to assure with confidence that a Situational Strategic Awareness Monitoring Surveillance System (SSAMSS) is a competitive product considered for actual development. The overall assessment takes a strategic approach by using selective tools to create, confirm and consider whether SSAMSS as a product idea ultimately should be developed into an actual prototypical Model (Engineering Model). Although the dissertation explores whether a prototype should be considerate with confidence and risk consideration it does not actually dive into the physical development of the model due to limited time and actual funding. Through the Systems Engineering V-Model, SSAMSS as a product and enterprise is vetted from the customer needs analyses through unit testing (the bottom of the V-model). This is the point where an actual customer would decide to continue the to incorporate SSAMSS into integration and testing, prototype to operations, maintenance, and retirement. Through simulations, assessment, and analysis it has been determined that SSAMSS as a product and enterprise is a viable option to supersede current material safeguarding systems that are competitive in the marketplace today.
Open Access
The dual lens of sustainability: economic and environmental insights into novel carbon reduction technologies using systems modeling, data science, and multi-objective optimization
(Colorado State University. Libraries, 2024) Limb, Braden Jeffery, author; Quinn, Jason C., advisor; Simske, Steven J., advisor; Gallegos, Erika E., committee member; Ross, Matthew R. V., committee member
In an era marked by escalating climate change and increasing energy demands, the pursuit of sustainable solutions in energy production and environmental management is more critical than ever. This dissertation delves into this challenge, focusing on innovative technologies aimed at reducing carbon emissions in key sectors: power generation, wastewater treatment, and aviation. The first segment of the dissertation explores the integration of thermal energy storage with natural gas power plants using carbon capture, a crucial advancement given the dominant role of fossil fuel-based power plants in electricity generation. Addressing the economic and operational drawbacks of current carbon capture and storage (CCS) technologies, this study evaluates various thermal storage configurations. It seeks to enhance plant performance through energy arbitrage, a novel approach to offset the large heat loads required for carbon capture solvent regeneration. By optimizing these technologies for current and future grid pricing and comparing their feasibility with other production methods, this research aims to strike a balance between maintaining reliable power generation and adhering to stringent environmental targets. Results show that resistively charged thermal storage can both increase CCS flexibility and power plant profits through energy arbitrage when compared to power plants with CCS but without thermal storage. Beyond electrical systems, addressing climate change also necessitates improving the energy efficiency of water treatment technologies. Therefore, the dissertation investigates the potential of nature-based solutions as sustainable alternatives to traditional water treatment methods in the second section. This section probes into the efficacy of green technologies, such as constructed wetlands, in reducing costs and emissions compared to conventional gray infrastructure. By quantifying the impact of these technologies across the U.S. and evaluating the role of carbon financing, the research highlights a pathway towards more environmentally friendly and economically viable water treatment processes. Results show that nature-based water treatment technologies can treat up to 37% of future nutrient loading while both decreasing water treatment costs and emissions compared to traditional water treatment techniques. The transportation sector will play a key role in addressing climate change as it is the largest contributor to greenhouse gas emissions. While most of the transportation sector is expected to transition to electric vehicles to decrease its carbon footprint, aviation remains hard to decarbonize as electric passenger aviation is expected to be range limited. Therefore, the final segment of the dissertation addresses the challenge of meeting the U.S. Department of Energy's Sustainable Aviation Fuel (SAF) goals. It involves a comprehensive analysis of various bioenergy feedstocks for SAF production, using GIS modeling to assess their economic and environmental impacts across diverse land types. The study employs multi-objective optimization to strategize the deployment of these feedstocks, considering factors like minimum fuel selling price, greenhouse gas emissions, and breakeven carbon price. Furthermore, agent-based modeling is used to identify policy incentives that could encourage farmer adoption of bioenergy crops, a critical step towards meeting the SAF Grand Challenge goals. This dissertation offers a comprehensive analysis of novel carbon reduction technologies, emphasizing both economic viability and environmental sustainability. By developing integrated models across key sectors affected by climate change, it explores the benefits and trade-offs of various sustainability strategies. Incorporating geospatial and temporal dimensions, the research uses multi-objective optimization and systems thinking to provide targeted investment strategies for the greatest impact. The results provide important insights and actionable plans for policymakers and industry leaders, contributing to a sustainable and low-carbon future in essential areas of the global economy.
Open Access
Time-delta method for measuring software development contribution rates
(Colorado State University. Libraries, 2024) Bishop, Vincil Chapman, III, author; Simske, Steven J., advisor; Vans, Marie, committee member; Malaiya, Yashwant, committee member; Ray, Indrajit, committee member
The Time-Delta Method for estimating software development contribution rates provides insight into the efficiency and effectiveness of software developers. It proposes and evaluates a framework for assessing software development contribution and its rate (first derivative) based on Commit Time Delta (CTD) and software complexity metrics. The methodology relies on analyzing historical data from software repositories, employing statistical techniques to infer developer productivity and work patterns. The approach combines existing metrics like Cyclomatic Complexity with novel imputation techniques to estimate unobserved work durations, offering a practical tool for evaluating the engagement of software developers in a production setting. The findings suggest that this method can serve as a reliable estimator of development effort, with potential implications for optimizing software project management and resource allocation.