Mountain Scholar :: Browsing by Author "Simske, Steve, advisor"

Browsing by Author "Simske, Steve, advisor"

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Open Access
Cost optimization in requirements management for space systems
(Colorado State University. Libraries, 2021) Katz, Tami E., author; Simske, Steve, advisor; Sega, Ron, committee member; Miller, Erika, committee member; Macdonald, John, committee member
When producing complex space systems, the transformation of customer needs into a realized system includes the development of product requirements. The ability to generate and manage the requirements can either enable the overall system development or drive significant cost and schedule impacts. Assessing practices in the industry and publications, it is observed that there is a substantial amount of documented approaches to address requirement development and product verification, but only a limited amount of documented approaches for requirements management. A complex system can have tens of thousands of requirements across multiple levels of development which, if not well managed, can lead to hidden costs associated with missed requirements and product rework. With current space system projects being developed at a rapid pace using more cost constrained approaches such as fixed budgets, an investigation into more efficient processes, such as requirements management, can yield methods to enable successful, cost effective system development. To address the optimal approach of managing requirements for complex space systems, this dissertation assesses current practices for requirements management, evaluates various contributing factors towards optimization of project costs associated with this activity, and proposes an optimized requirements management process to utilize during the development of space systems. Four key areas of process control are identified for requirements management optimization on a project, including utilization of a data focused requirements management approach, development (and review) of requirements using a collaborative software application, ensuring the requirement set is a consolidated with an appropriate amount of requirements for the project, and evaluating when to officially levy requirements on the product developers based on requirement maturation stability. Multiple case studies are presented to evaluate if the proposed requirements management process yields improvement over traditional approaches, including a simulation of the current state and proposed requirements management approaches. Ultimately, usage of the proposed optimized set of processes is demonstrated to be a cost effective approach when compared against traditional processes that may adversely impact the development of new space systems.
Open Access
Lifecycle assessment modeling and encouraging reuse in the corrugated packaging industry using persuasion and operant conditioning
(Colorado State University. Libraries, 2023) Ketkale, Harshwardhan, author; Simske, Steve, advisor; Miller, Erika, committee member; Conrad, Steve, committee member; Cleary, Anne, committee member
Greenhouse gas emission is a major contributor to climate change and global warming. Many sustainability efforts are aimed at reducing greenhouse gas emissions. These include recycling and the use of renewable energy. In the case of recycling, the general population is typically required to at least temporarily store, and possibly haul, the materials rather than simply throwing them away. This effort from the general population is a key aspect of recycling, and in order for recycling to work, some investment of time and effort is required by the public. In the case of corrugated cardboard boxes, it has been observed that there is less motivation for the general population to recycle them. Also, the manufacturing of a product such as a corrugated cardboard box (CCB) includes the extraction of a variety of raw materials in addition to supply chain efforts to get the raw materials to the industry. The extraction of raw material and its supply chain as well as the unproper end of lifecycle phase can significantly impact the carbon emission of a product over its lifecycle. This research explores different means of motivating people to reuse, and not just recycle, with different types of incentives. It addresses the use of persuasion techniques and operant conditioning techniques together to incentivize the general population to adopt sustainable efforts. Further, this study makes an attempt to segment the general population based on age, gender, persuasion preferences, operant condition preferences, personality types, awareness of environment/climate change as well as current recycling effort of the participants to use different forms of incentives and motivational work unlike any approaches found in the literature review. Four types of persuasion techniques and four types of operant conditioning are combined to give 16 different types of incentives. Three online surveys are conducted, and their data are analyzed (using entropy, Hamming distance, t-test, chi-square, and ANOVA). The results indicate that "positive reinforcement + ethos" is a cost-effective way to incentivize the general population. This study also conducts a Lifecycle Assessment (LCA) that gives the carbon emission of each phase of the product and a quantitative estimate of the overall product carbon footprint and its effect on the environment. This gives impetus to recommendations for improving the phases of the lifecycle to minimize carbon emissions. This research uses LCA to evaluate the carbon emission in each phase of the lifecycle of a typical 1 kg corrugated cardboard box in the United States. Carbon emission for the proposed "reuse" phase is also calculated, and the results are compared. To examine if the incremental cost of reusing the CCBs is less than the environmental and economic cost of reducing the extraction and supply chain of raw materials, this study explores the economic feasibility of the proposed "reuse" method that incentivizes the general population to reuse the CCBs instead of recycling or landfilling them. Economic tools such as willingness-to-pay vs. marginal cost curves and benefit-cost analyses are used to evaluate economic feasibility. The results indicate that the "reuse" method for CCBs is economically and environmentally feasible. It also supports the approach of using analytics, economics, and LCA to create a model that can be used for other products and processes as an evaluative process to determine if businesses can benefit from the reduction (or removal) of material extraction costs from the supply chain. The results of this study can be applied to a wide range of applications such as solar panels, incentives for vaccination, and other areas wherein sustainability-centric behavior is encouraged.
Open Access
Machine learning and artificial intelligence approaches to the analysis of physical activity from wearables and biosensors in clinical trials: applications of clustering and prediction of clinical outcomes
(Colorado State University. Libraries, 2022) Vlajnic, Vanja M., author; Simske, Steve, advisor; Miller, Erika, committee member; Cale, Jim, committee member; Reisfeld, Bradley, committee member
As human demographics continue to trend toward elderly, especially in advanced economies, the treatment of illness becomes more salient. Across many therapeutic areas, researchers examine potential treatments while incorporating novel technologies in an effort to prolong the years in which quality of life is achieved for patients around the world. In the area of cardiovascular disease, wearable and biosensor data is becoming increasingly used in order to compliment data traditionally collected from clinical trials. This work discusses a series of analytical approaches for the analysis of data from recent clinical trials in which accelerometry data from wearable devices were analyzed using clustering approaches (K-means and consensus clustering) and survival analyses (Cox proportional hazards and random survival forest) for the purposes of clustering patients and assessing their baseline clinical characteristics as well as for the prediction of clinical outcomes. Unique clinical phenotypes were identified within the patient aggregations as part of the clustering analyses. Furthermore, models were created with improved predictive accuracy for clinical outcomes of interest in the heart failure space. Taken collectively, the results from these analyses and the analytical approaches therein can be used to assess whether heterogeneous clinical subgroups of patients exist as well as further guide the clinical development programs.
Open Access
Material validation and part authentication process using hardness indentations with robotic arm implementation
(Colorado State University. Libraries, 2021) Weinmann, Katrina J., author; Simske, Steve, advisor; Chen, Thomas, committee member; Ma, Kaka, committee member; Zhao, Jianguo, committee member
In today's global economy, there are many levels of validation and authentication which must occur during manufacturing and distribution processes to ensure sufficient cyber-physical security of parts. This includes material inspection and validation during manufacturing, a method of track-and-trace for the entire supply chain, and individual forensic authentication of parts to prevent counterfeiting at any point in the manufacturing or distribution process. Traditionally, each level of validation or authentication is achieved through a separate step in the manufacturing or distribution process. In this work, a process is presented that uses hardness testing and the resulting indentations to simultaneously provide three critical functions for part validation and authentication: (i) material property validation and material property mapping achieved by administering multiple hardness tests over a given area on the part, (ii) part serialization that can be used for track-and-trace through administering hardness tests in a specific 'barcode' pattern, and (iii) the opportunity for forensic-level authentication through use of high-resolution images of the indents. Additionally, a fourth manufacturing advantage is gained in the provision of improved bonding potential for adhesive joints provided by the increase in surface area and surface roughness resulting from the addition of indents to the adherend surface. A methodology for implementing this process using a robotic arm with an end-effector-mounted portable hardness tester is presented. Implementation using a robotic arm allows a high degree of customizability of the process without changes in setup, making this process ideal for additive manufactured parts, which are often custom or low-batch and require a higher level of material validation. As a whole, this work presents a highly-customizable, single-step process that provides multi-level quality control, validation, authentication, and cyber-physical security of parts throughout the manufacturing and distribution processes
Open Access
Neural network security and optimization for single-person authentication using electroencephalogram data
(Colorado State University. Libraries, 2022) Andre, Naomi, author; Simske, Steve, advisor; Mueller, Jennifer, committee member; Lyons, Michael, committee member
Security is an important focus for devices that use biometric data, and as such security around authentication needs to be considered. This is true for brain-computer interfaces (BCIs), which often use electroencephalogram (EEG) data as inputs and neural network classification to determine their function. EEG data can also serve as a form of biometric authentication, which would contribute to the security of these devices. Neural networks have also used a method known as ablation to improve their efficiency. In light of this info, the goal of this research is to determine whether neural network ablation can also be used as a method to improve security by reducing a network's learning capabilities to include authenticating only a given target, and preventing adversaries from training new data to be authenticated. Data on the change in entropy of weight values of the networks after training was also collected for the purpose of determining patterns in weight distribution. Results from a set of ablated networks to a set of baseline (non-ablated) networks for five targets chosen randomly from a data set of 12 people were compared. The results found that ablated maintained accuracy through the ablation process, but that they did not perform as well as the baseline networks. Change in performance between single-target authentication and target-plus-invader authentication was also examined, but no significant results were found. Furthermore, the change in entropy differed between both baseline networks and ablated networks, as well as between single-target authentication and target-plus-invader authentication for all networks. Ablation was determined to have potential for security applications that need to be expanded on, and weight distribution was found to have some correlation with the complexity of an input to a network.
Open Access
On the integration of materials characterization into the product development lifecycle
(Colorado State University. Libraries, 2024) Dare, Matthew S., author; Simske, Steve, advisor; Yourdkhani, Mostafa, committee member; Herber, Daniel, committee member; Radford, Donald W., committee member
The document is broken down into four sections whereby a more complete integration of materials characterization into the product development lifecycle, when compared to traditional approaches, is researched and considered. The driving purpose behind this research is to demonstrate that an application of systems engineering principles to the characterization sciences mechanism within materials engineering and development will produce a more efficient and comprehensive understanding of complex material systems. This will allow for the mitigation of risk, enhancement of relevant data, and planning of characterization procedures proactively. The first section proposes a methodology for Characterization Systems Engineering (CSE) as an aid in the development life cycle of complex, material systems by combining activities traditionally associated with materials characterization, quality engineering, and systems engineering into an effective hybrid approach. The proposed benefits of CSE include shortened product development phases, faster and more complete problem solving throughout the full system life cycle, and a more adequate mechanism for integrating and accommodating novel materials into already complex systems. CSE also provides a platform for the organization and prioritization of comprehensive testing and targeted test planning strategies. Opportunities to further develop and apply the methodology are discussed. The second section focuses on the need for and design of a characterizability system attribute to assist in the development of systems that involve material components. While materials characterization efforts are typically treated as an afterthought during project planning, the argument is made here that leveraging the data generated via complete characterization efforts can enhance manufacturability, seed research efforts and intellectual property for next-generation projects, and generate more realistic and representative models. A characterizability metric is evaluated against a test scenario, within the domain of electromagnetic interference shielding, to demonstrate the utility and distinction of this system attribute. Follow-on research steps to improve the depth of the attribute application are proposed. In the third section, a test and evaluation planning protocol is developed with the specific intention of increasing the effectiveness of materials characterization within the system development lifecycle. Materials characterization is frequently not accounted for in the test planning phases of system developments, and a more proactive approach to streamlined verification and validation activities can be applied. By applying test engineering methods to materials characterization, systems engineers can produce more complete datasets and more adequately execute testing cycles. A process workflow is introduced to manage the complexity inherent to material systems development and their associated characterization sciences objectives. An example using queuing theory is used to demonstrate the potential efficacy of the technique. Topics for further test and evaluation planning for materials engineering applications are discussed. In the fourth section, a workflow is proposed to more appropriately address the risk generated by materials characterization activities within the development of complex material systems when compared to conventional engineering approaches. Quality engineering, risk mitigation efforts, and emergency response protocols are discussed with the intention of reshaping post-development phase activities to address in-service material failures. While root cause investigations are a critical component to stewardship of the full system lifecycle during a product's development, deployment and operation, a more tailored and proactive response to system defects and failures is required to meet the increasingly stringent technical performance requirements associated with modern, material-intensive systems. The analysis includes a Bayesian approach to risk assessment of materials characterization efforts through which uncertainty regarding scheduling and cost can be quantified.
Open Access
Raw material optimization and CO₂ sensitivity-predictive analytics in cement manufacturing: a case study at Union Bridge Plant, Heidelberg Materials, Maryland
(Colorado State University. Libraries, 2024) Boakye, Kwaku, author; Simske, Steve, advisor; Bradley, Tom, committee member; Troxell, Wade, committee member; Goemans, Chris, committee member
Cement has been in use by humans throughout history, and its manufacturing process has undergone many changes. The high increase in economic growth around the world and the demand for rapid infrastructure development due to population growth are the underlying reasons for the globally high cement demand. Cement is produced by grinding clinker together with a mixture of ground gypsum. The clinker is produced using a rotary kiln which burns a mixture of limestone, clay, magnesium, silica, and iron with desired atomic percentages through the calcination process. The quarry serves as the main source of raw material for the rotary kiln in cement production. Over the years cement manufacturing has hurt environmental, social, and political aspects of society. This negative impact includes the overuse of raw material which is obtained by mining resulting in disturbed landmass, overproduction of rock waste material, and the emission of CO2 resulting from the calcination of limestone in the pyro process. The study looks at three cement manufacturing systems and uses different methodologies to achieve results that can be implemented in the cement industry. These three systems were (1) the quarry (2) the preheat tower and (3) the kiln. Ensuring the consistency of material feed chemistry, with the quarry playing a pivotal role, is essential for optimizing the performance of a rotary kiln. The optimization of the raw material also allows limited use of raw materials for cement manufacturing, cutting down waste. The study employed a six-step methodology, incorporating a modified 3D mining software modeling tool, a database computer loop prediction tool, and other resources to enhance mining sequencing, optimize raw material utilization, and ensure a consistent chemistry mix for the kiln. By using overburden as a raw material in the mix, the quarry nearly universally reduced the environmental impact of squandering unwanted material in the quarry. This has a significant environmental impact since it requires less space to manage the overburdened waste generated during mining. In addition, raw material usage was optimized for clinker production, causing a reduction of 4% in sand usage as raw material, a reduction in raw material purchase cost, a reduction of the variability of kiln feed chemistry, and the production of high-quality clinker. The standard deviation of kiln feed LSF experienced a 45 percent improvement, leading to a 65 percent reduction in the variability of kiln feed. The study also uses machine learning methods to model different stages of the calcination process in cement and to improve knowledge of the generation of CO2 during cement manufacturing. Calcination plays a crucial role in assessing clinker quality, energy requirements, and CO2 emissions within a cement-producing facility. However, due to the complexity of the calcination process, accurately predicting CO2 emissions has historically been challenging. The objective of this study is to establish a direct relationship between CO2 generation during the raw material manufacturing process and various process factors. In this paper, six machine-learning techniques are explored to analyze two output variables: (1) the apparent degree of oxidation, and (2) the apparent degree of calcination. Sensitivity analysis of CO2 molecular composition (on a dry basis) utilizes over 6000 historical manufacturing health data points as input variables, and the findings are utilized to train the algorithms. The Root Mean Squared Error (RMSE) of various regression models was examined, and the models were then run to ascertain which independent variables in cement manufacturing had the largest impact on the dependent variables. To establish which independent variable had the biggest impact on CO2 emissions, the significance of the other factors was also assessed.
Open Access
Sensing via signal analysis, analytics, and cyberbiometric patterns
(Colorado State University. Libraries, 2022) Anderson, Wesley, author; Simske, Steve, advisor; Lear, Kevin, committee member; Volckens, John, committee member; Carter, Ellison, committee member
Internet-connected, or Internet of Things (IoT), sensor technologies have been increasingly incorporated into everyday technology and processes. Their functions are situationally dependent and have been used for vital recordings such as electrocardiograms, gait analysis and step counting, fall detection, and environmental analysis. For instance, environmental sensors, which exist through various technologies, are used to monitor numerous domains, including but not limited to pollution, water quality, and the presence of biota, among others. Past research into IoT sensors has varied depending on the technology. For instance, previous environmental gas sensor IoT research has focused on (i) the development of these sensors for increased sensitivity and increased lifetimes, (ii) integration of these sensors into sensor arrays to combat cross-sensitivity and background interferences, and (iii) sensor network development, including communication between widely dispersed sensors in a large-scale environment. IoT inertial measurement units (IMU's), such as accelerometers and gyroscopes, have been previously researched for gait analysis, movement detection, and gesture recognition, which are often related to human-computer interface (HCI). Methods of IoT Device feature-based pattern recognition for machine learning (ML) and artificial intelligence (AI) are frequently investigated as well, including primitive classification methods and deep learning techniques. The result of this research gives insight into each of these topics individually, i.e., using a specific sensor technology to detect carbon monoxide in an indoor environment, or using accelerometer readings for gesture recognition. Less research has been performed on analyzing the systems aspects of the IoT sensors themselves. However, an important part of attaining overall situational awareness is authenticating the surroundings, which in the case of IoT means the individual sensors, humans interacting with the sensors, and other elements of the surroundings. There is a clear opportunity for the systematic evaluation of the identity and performance of an IoT sensor/sensor array within a system that is to be utilized for "full situational awareness". This awareness may include (i) non-invasive diagnostics (i.e., what is occurring inside the body), (ii) exposure analysis (i.e., what has gone into the body through both respiratory and eating/drinking pathways), and (iii) potential risk of exposure (i.e., what the body is exposed to environmentally). Simultaneously, the system has the capability to harbor security measures through the same situational assessment in the form of multiple levels of biometrics. Through the interconnective abilities of the IoT sensors, it is possible to integrate these capabilities into one portable, hand-held system. The system will exist within a "magic wand", which will be used to collect the various data needed to assess the environment of the user, both inside and outside of their bodies. The device can also be used to authenticate the user, as well as the system components, to discover potential deception within the system. This research introduces levels of biometrics for various scenarios through the investigation of challenge-based biometrics; that is, biometrics based upon how the sensor, user, or subject of study responds to a challenge. These will be applied to multiple facets surrounding "situational awareness" for living beings, non-human beings, and non-living items or objects (which we have termed "abiometrics"). Gesture recognition for intent of sensing was first investigated as a means of deliberate activation of sensors/sensor arrays for situational awareness while providing a level of user authentication through biometrics. Equine gait analysis was examined next, and the level of injury in the lame limbs of the horse was quantitatively measured and classified using data from IoT sensors. Finally, a method of evaluating the identity and health of a sensor/sensory array was examined through different challenges to their environments.
Open Access
The next generation space suit: a case study of the systems engineering challenges in space suit development
(Colorado State University. Libraries, 2023) Cabrera, Michael A., author; Simske, Steve, advisor; Marzolf, Greg, committee member; Miller, Erika, committee member; Delgado, Maria, committee member
The objective for a NASA contractor, the performing organization in this case study, is to develop and deliver the next generation space suit to NASA, the customer in this case study, against a radically different level of customer expectation from previous years. In 2019, the administration had proposed a return to the moon, thus transforming and changing the system context of the current, next generation space suit in addition to pushing schedule expectations forward two years. The purpose of this dissertation will serve as a case study in two specific areas with qualitative and quantitative analyses regarding a new process and approach to (i) project lifecycle development and (ii) requirements engineering with the intent that if utilized, these tools may have contributed to improvements across the project in terms of meeting cost, scope, budget and quality while appropriately accounting for risk management. The procedure entails a research method in which the current state of the project, current state of the art, and the identified systems engineering challenges are evaluated and iterative models are tempered through development by continual improvements by engineering evaluation of engineers on the project. The current results have produced (i) a prototype project lifecycle development method via agile, Lean and Scrum hybrid implementations into a Traditional Waterfall framework and (ii) a prototype requirements engineering scorecard with implementations of FMEA and quantitative analysis to determine root cause identification.

Browsing by Author "Simske, Steve, advisor"

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