Systems Engineering Commons^™

Imparting Systems Engineering Experience Via Interactive Fiction Serious Games, Thomas C. Ford, David Long, Echo Ford *

Faculty Publications

Serious games for education are becoming increasing popular. Interactive fiction games are some of the most popular in app stores and are also beginning to be heavily used in education to teach analysis and decision-making. Noting that it is difficult for systems engineers to experience all necessary situations which prepare them for the role of a chief engineer, in this paper, we explore the use of interactive fiction serious games to impart systems engineering experience and to teach systems engineering principles. The results of a cognitive viability, qualitative viability, and replayability analysis of 14 systems engineering serious games developed in …

Effects Of Individual Strategies For Resource Access On Collaboratively Maintained Irrigation Infrastructure, Jordan L. Stern, Afreen Siddiqi, Paul N. Grogan

Faculty Publications

Built infrastructure for water and energy supply, transportation, and other such services underpins human well-being and socioeconomic development. A fundamental understanding of how infrastructure design and user strategies interact can guide important design decisions as well as policy formulation for ensuring long-term infrastructure viability in conjunction with improved individual user benefits. In this work, an agent based model (ABM) is developed to study this issue for the specific case of irrigation canals. Cooperatively maintained irrigation canals serve essential roles in sustaining agriculture-based economies in many regions. Canal system design can strongly affect benefits derived by distributed users, regional agricultural output, …

Tank Level Controller Plc Lab, Siddhi Upadhyaya, Teghvir Grewal

Electrical Engineering

The California Polytechnic State University San Luis Obispo’s Electrical Engineering Department is currently developing lab experiments for the new EE435 (Industrial Power Control and Automation) class. In order to support these efforts, fourth year Cal Poly students are expected to develop laboratory experiments that will be conducted during this new class for the semester system. The lab experiment focused on in this project is called the Tank Level Controller. This experiment will introduce EE 435 students to Schneider Electric Programmable Logic Controller (PLC) hardware and software, which is prominent in the automation industry.
This experiment will require students to develop …

Cybersecurity In Industrial Automation Lab Design For Ee 435, Jules Khalil Emile Hajjar, Emily Zhou

Electrical Engineering

This project involves the creation of an instructional laboratory aimed at teaching cybersecurity for industrial automation applications. Specifically tailored for Electrical Engineering students at Cal Poly, the experiment focuses on configuring the Modicon M580, a PLC from Schneider Electric, and serves to introduce students to relevant cybersecurity protocols and techniques. This project will be implemented into the EE435 (Industrial Power Control and Automation) course curriculum upon Cal Poly’s transition to the semester system.

High-Level Requirements For Conceptual Design Of Bridge Deflection Measurement System Using Model-Based Systems Engineering, Mariana Villalabeitia Arenas

Theses and Dissertations

Bridges are essential in the infrastructure transportation system. Repairs and maintenance are the key activities to keep them safe for use. Over the years, the evolution of technology has been applied to improve the way bridges are designed, built, monitored, inspected, and repaired. Nevertheless, there is still a gap between the research efforts and the application of this knowledge in the practical field of bridge inspections. Model-based Systems Engineering is a formalized methodology in the system design that is centered around the model throughout all the life cycle stages of a system, supporting the requirements, design, analysis, verification, and validation …

Causal Modeling Framework For Nuclear Power Plant Licensing Process, Lauren Kimberly Kiser

Theses and Dissertations

Interests in clean energy revived the nuclear power industry. For the first time in decades, innovative technologies and plant designs are being considered by regulatory agencies. This dissertation explores a Bayesian Network and AHP approach to causal modeling of the Combined License review process for new nuclear power plants (NPP). Historically lengthy and expensive, NPP licensing is critical to ensuring safe operation of the plants. With this comes a high standard for applicants to reach that can result in multiple revision cycles and long review times. New plant designs and fluctuating public support lead to a complex and dynamic series …

Modeling The Impact Of Scheduling Risks On Multi-Team Agile Projects, Bria Marie Booth

Theses and Dissertations

Agile project management allows for a quick response to a changing project environment. This opens possible avenues for new opportunities, but also may expose ongoing projects to previously unknown or unexpected threats. Risks must be continuously monitored as a product is worked on to avoid lost potential. This paper will propose a discrete event simulation model that multi-team projects may use to predict the impact to the project’s schedule. Using discrete event simulation early in the project’s planning cycle offers a greater understanding of the possible or probable impact of risks on the schedule. This would help to prepare project …

Geometric Inference In Machine Learning: Applications Of Fisher Information For Model Selection And Other Statistical Applications, Trevor Herntier

Theses and Dissertations

We consider the problem of model selection using the Minimum Description Length (MDL) criterion for distributions with parameters on the hypersphere. Model selection algorithms aim to find a compromise between goodness of fit and model complexity. Variables often considered for complexity penalties involve number of parameters, sample size and shape of the parameter space, with the penalty term often referred to as stochastic complexity. Because Laplace approximation techniques yield inaccurate results for curved spaces, existing criteria incorrectly penalize complexity. We demonstrate how the use of a constrained Laplace approximation on the hypersphere yields a novel complexity measure that more accurately …

Towards Developing A Digital Twin Implementation Framework For Manufacturing Systems, Jonatan H. Loaiza

Theses and Dissertations

This research studies the implementation of digital twins in manufacturing systems. Digital transformation is relevant due to changing manufacturing techniques and user demands. It brings new business opportunities, changes organizations, and allows factories to compete in the digital era. Nevertheless, digital transformation presents many uncertainties that could bring problems to a manufacturing system. Some potential problems are loss of data, cybersecurity threats, unpredictable behavior, and so on. For instance, there are doubts about how to integrate the physical and virtual spaces. Digital twin (DT) is a modern technology that can enable the digital transformation of manufacturing companies. DT works by …

A Framework For The Verification And Validation Of Artificial Intelligence Machine Learning Systems, Swala B. Burns

Theses and Dissertations

An effective verification and validation (V&V) process framework for the white-box and black-box testing of artificial intelligence (AI) machine learning (ML) systems is not readily available. This research uses grounded theory to develop a framework that leads to the most effective and informative white-box and black-box methods for the V&V of AI ML systems. Verification of the system ensures that the system adheres to the requirements and specifications developed and given by the major stakeholders, while validation confirms that the system properly performs with representative users in the intended environment and does not perform in an unexpected manner. Beginning with …

Smart Warehousing Implementaion And Education Of Supply Chain Management Leaders, David Quintanilla

Doctoral Dissertations

Paper #1 Overview

Businesses have to adapt to new challenges and technologies in the marketplace which influence warehousing. In order to support this growth, Industry 4.0 technologies have been implemented along the value chain to optimize their organizations and production processes; however, there are still gaps for warehousing research for Industry 4.0. We present four pillars¾location strategy, infrastructure/design, data management, and advanced planning and control¾ as a framework for businesses to use for their adaptation into smart warehousing. In particular, this framework will guide companies in their logistics journey into Industry 4.0. Industry experts and senior logistics professionals were interviewed …

Sealion Cubesat Mission Architecture Using Model Based Systems Engineering With A Docs As Code Approach, Kevin Yi-Tzu Chiu

Mechanical & Aerospace Engineering Theses & Dissertations

CubeSats are a growing population within the space industry. Every year, universities launch numerous amounts of CubeSats due to their inexpensive cost of development, launch, and deployment. However, this comes with numerous challenges. As the number of university-CubeSats grow, so too do the numbers that fail. With development teams consisting mainly of students with little to no training, proper and yet easy to use tools or methods should be implemented to help ensure mission success. Especially in the critical stages of planning before and during development, a technical approach to quickly track life cycle development of a CubeSat is needed. …

Ads-B Classification Using Multivariate Long Short-Term Memory–Fully Convolutional Networks And Data Reduction Techniques, Sarah Bolton, Richard Dill, Michael R. Grimaila, Douglas Hodson

Faculty Publications

Researchers typically increase training data to improve neural net predictive capabilities, but this method is infeasible when data or compute resources are limited. This paper extends previous research that used long short-term memory–fully convolutional networks to identify aircraft engine types from publicly available automatic dependent surveillance-broadcast (ADS-B) data. This research designs two experiments that vary the amount of training data samples and input features to determine the impact on the predictive power of the ADS-B classification model. The first experiment varies the number of training data observations from a limited feature set and results in 83.9% accuracy (within 10% of …

Model Based Systems Engineering Approaches To Chemicals And Materials Manufacturing, Quang Le

Theses and Dissertations

Model-based systems engineering (MBSE) is part of a long-term trend toward model-centric approaches adopted by many engineering disciplines. This work establishes the need for an MBSE approach by reviewing the importance, complexity, and vulnerability of the U.S. chemical supply chains. The origins, work processes, modeling approaches, and supporting tools of the systems engineering discipline (SE) are discussed, along with the limitations of the current Process Systems Engineering (PSE) framework. The case is made for MBSE as a more generalizable and robust approach. Systems modeling strategies for MBSE are introduced, as well as a novel MBSE method that supports the automation …

Fugacity-Based Lattice Boltzmann Method For Multicomponent Multiphase Systems, Muzammil Soomro, Luis F. Ayala, Cheng Peng, Orlando M. Ayala

Engineering Technology Faculty Publications

The free-energy model can extend the lattice Boltzmann method to multiphase systems. However, there is a lack of models capable of simulating multicomponent multiphase fluids with partial miscibility. In addition, existing models cannot be generalized to honor thermodynamic information provided by any multicomponent equation of state of choice. In this paper, we introduce a free-energy lattice Boltzmann model where the forcing term is determined by the fugacity of the species, the thermodynamic property that connects species partial pressure to chemical potential calculations. By doing so, we are able to carry out multicomponent multiphase simulations of partially miscible fluids and generalize …

Like You Search, Gipson Bachman

LMU/LLS Theses and Dissertations

As consumers continue to make their purchases solely online without visiting brick-and-mortar stores, they increasingly look for ways to “touch, feel and try on” products without actually doing so. We believe social media influencers provide a way to do this, with the influencers themselves acting as “stand-ins” for consumers in their place. Finding influencers that look like you in 2022 sounds like it would be easy, but it isn’t. Until now.

LIKE YOU aims to answer this gap in the market by creating a proprietary search engine that, through answering a few questions, recommends a list of influencers that can …

Development Of Directed Randomization For Discussing A Minimal Security Architecture, Henrique Oyama, Dominic Messina, Keshav Kasturi Rangan, Akkarakaran Francis Leonard, Kip Nieman, Helen Durand, Katie Tyrrell, Katrina Hinzman, Michael Williamson

Chemical Engineering and Materials Science Faculty Research Publications

Strategies for mitigating the impacts of cyberattacks on control systems using a control-oriented perspective have become of greater interest in recent years. Our group has contributed to this trend by developing several methods for detecting cyberattacks on process sensors, actuators, or both sensors and actuators simultaneously using an advanced optimization-based control strategy known as Lyapunov-based economic model predictive control (LEMPC). However, each technique comes with benefits and limitations, both with respect to one another and with respect to traditional information technology and computer science-type approaches to cybersecurity. An important question to ask, therefore, is what the goal should be of …

Investigating Applications Of Deep Learning For Diagnosis Of Post Traumatic Elbow Disease, Hugh James

McKelvey School of Engineering Theses & Dissertations

Traumatic events such as dislocation, breaks, and arthritis of musculoskeletal joints can cause the development of post-traumatic joint contracture (PTJC). Clinically, noninvasive techniques such as Magnetic Resonance Imaging (MRI) scans are used to analyze the disease. Such procedures require a patient to sit sedentary for long periods of time and can be expensive as well. Additionally, years of practice and experience are required for clinicians to accurately recognize the diseased anterior capsule region and make an accurate diagnosis. Manual tracing of the anterior capsule is done to help with diagnosis but is subjective and timely. As a result, there is …

Practical Requirements Elicitation In Modern Product Development: A Multi-Case Study In Discontinuous Innovation, Michael King

Theses and Dissertations

Practical modern product development, specifically rapid, lean efforts to create new disrupting or specialized products, face constraints that require modified requirements elicitation (RE) techniques. Requirements elicitation conventions have not been updated to address the challenges of these approaches, and industry practitioners lack the tools to select the most efficient techniques. This study examines the RE approaches performed by three resource-limited teams conducting discontinuous new product development through a multi-case study to identify gaps between the literature and practice, with suggestions to fill them. Our findings suggest modern RE practices and challenges closely reflect those found by studies on RE in …

Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal Veeravalli

ACI Journal Articles

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features …