Browsing by Author "Vans, Marie, committee member"

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Open Access
Systems engineering assessment and experimental evaluation of quality paradigms in high-mix low-volume manufacturing environments
(Colorado State University. Libraries, 2023) Normand, Amanda, author; Bradley, Thomas, advisor; Miller, Erika, committee member; Vans, Marie, committee member; Zhao, Jianguo, committee member; Sullivan, Shane, committee member
This research aimed to evaluate the effectiveness of applying industrial paradigm application in high-mix low-volume manufacturing (HMLV) environments using a Systems Engineering approach. An analysis of existing industrial paradigms was conducted and then compared to a needs analysis for a specific HMLV manufacturer. Several experiments were selected for experimental evaluation, inspired by the paradigms, in a real-world HMLV manufacturing setting. The results of this research showed that a holistic approach to paradigm application is essential for achieving optimal performance, based on cost advantage, throughput, and flexibility, in the HMLV manufacturing environment. The findings of this research study provide insights into the importance of considering the entire manufacturing system, including both technical and human factors, when evaluating the effectiveness of industrial paradigms. Additionally, this research highlights the importance of considering the unique characteristics of HMLV manufacturing environments, such as the high degree of variability and frequent changes in product mix in designing manufacturing systems. Overall, this research demonstrates the value of a systems engineering approach in evaluating and implementing industrial paradigms in HMLV manufacturing environments. The results of this research provide a foundation for future research in this field and can be used to guide organizations in making informed decisions about production management practices in HMLV manufacturing environments.
Open Access
The selective de-identification of ECGs
(Colorado State University. Libraries, 2022) Akhtar, Musamma, author; Simske, Steven, advisor; Wang, Zhijie, committee member; Vans, Marie, committee member
Biometrics are often used for immigration control, business applications, civil identity, and healthcare. Biometrics can also be used for authentication, monitoring (e.g., subtle changes in biometrics may have health implications), and personalized medical concerns. Increased use of biometrics creates identity vulnerability through the exposure of personal identifiable information (PII). Hence an increasing need to not only validate but secure a patient's biometric data and identity. The latter is achieved by anonymization, or de-identification, of the PII. Using Python in collaboration with the PTB-XL ECG database from Physionet, the goal of this thesis is to create "selective de-identification." When dealing with data and de-identification, clusters, or groupings, of data with similarity of content and location in feature space are created. Classes are groupings of data with content matching that of a class definition within a given tolerance and are assigned metadata. Clusters start without derived information, i.e., metadata, that is created by intelligent algorithms, and are thus considered unstructured. Clusters are then assigned to pre-defined classes based on the features they exhibit. The goal is to focus on features that identify pathology without compromising PII. Methods to classify different pathologies are explored, and the effect on PII classification is measured. The classification scheme with the highest "gain," or (improvement in pathology classification)/ (improvement in PII classification), is deemed the preferred approach. Importantly, the process outlined can be used in many other systems involving patient recordings and diagnostic-relevant data collection.
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.