Master's Degree in Systems Engineering

About: Systems engineering is a transdisciplinary approach and means to enable the realization of successful systems by defining customer needs and required functionality early in the development cycle. Systems engineers are responsible for the design and management of complex systems guided by systems requirements.

There is a growing need for engineers who are concerned with the whole system and can take an interdisciplinary and top-down approach. Systems engineers need to be problem definers, not just problem solvers, and be involved with a system through its life cycle, from development through production, deployment, training support, operation, and disposal.

 

Term: Typically about 3 years

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  • Requirements
  • Course Information

Requirements

A Master of Science non-thesis program consists of:

  • At least 10 three-credit hour courses approved by the academic advisor.
  • The M.S. with thesis option requires 36 credit hours including the thesis.
  • All students are required to take the following:
    • SYS ENG 5101 Systems Engineering and Analysis
    • SYS ENG 6102 Information Based Design
    • SYS ENG 6103 Systems Life Cycling Costing
    • SYS ENG 6104 Systems Architecting
    • SYS ENG 6196 Systems Engineering Capstone
    • SYS ENG 6542 Model Based Systems Engineering
  • Specialization courses provide students with the ability to address his/her technology needs in the context of the overall Systems Engineering program. These graduate courses can be selected from engineering or the physical science department as long as they are approved by the program director.
  • One of the graduate certificates may be substituted for a specialization track with the permission of the program director.

Course Information

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Courses

Description

An introduction to cluster analysis and clustering algorithms rooted in computational intelligence, computer science, and statistics. Clustering in sequential data, massive data, and high dimensional data. Students will be evaluated by individual or group research projects and research presentations. Prerequisite: At least one graduate course in statistics, data mining, algorithms, computational intelligence, or neural networks, consistent with the student's degree program.

Learning Objective

  1. Understand the fundamental concepts of clustering in data analysis.
  2. Evaluate and compare various clustering algorithms.
  3. Apply clustering techniques to real-world systems engineering problems.
  4. Analyze and interpret clustering results.
  5. Implement clustering algorithms in Python or another programming language.

Course Content

  • Introduction to Clustering
  • Data Preprocessing
  • Distance Metrics
  • Partitional Clustering
  • Hierarchical Clustering
  • Density-Based Clustering
  • Advanced Topics in Clustering
  • Clustering in Systems Engineering

Course Evaluation Criteria

  • Research Assignments and Presentations
  • Final Project

Description

The concepts of Systems Engineering are introduced through a project. Students work in virtual teams. The topics covered are architecture development, basic system architectural design techniques, functional decomposition, design and technical review objectives, and initial specifications.

Learning Objective

  1. Provide the student with an introduction and basic understanding of the central concepts, tools, and processes of systems engineering and how they are applied to develop a well-defined system from an operational need. This includes customer needs, requirements development, system architecture and design, tradeoff analysis, risk management, and system evaluation in all stages of the system life cycle.

Course Content

  • Customer Introductions
  • System Definitions and SE Concepts
  • Operational Requirements
  • Functions, Analysis, Allocation
  • Labor Day Holiday
  • Trade Studies and QFD;
  • Detailed Requirements
  • Conceptual & Preliminary Design
  • Systems Engineering Management I
  • Architecture, Allocation & Evaluation
  • Manufacturability, Disposability, Supportability, Affordability & Maintainability
  • Reliability, Usability (HSI)
  • Test and Evaluation I
  • Trade studies - Physical synthesis
  • Risk Management
  • Requirements Analysis, Suppliers
  • Models for economic evaluation
  • Systems Engineering Management II
  • PDR – what is it and why do it
  • Modeling and Simulation II
  • Detail Design
  • Detail Design II
  • Modeling and Simulation III
  • Integration and Interfaces
  • Systems Engineering Management III
  • Optimization

Course Evaluation Criteria

  • Homework
  • Project Reviews
  • Final Project Paper
  • Participation/Teamwork/Evaluations

Description

Introduction to Computational Intelligence (CI), Biological and Artificial Neuron, Neural Networks, Evolutionary Computing, Swarm Intelligence, Artificial Immune Systems, Fuzzy Systems, and Hybrid Systems. CI application case studies covered include digital systems, control, power systems, forecasting, and time-series predictions.

Learning Objective

  1. Provide the student with a basic understanding of the main concepts, tools, and processes of computational intelligence and how the techniques can be applied to research areas.

Course Content

  • Evolutionary Computation
  • Fuzzy Systems
  • Neural Networks 
  • Supervised learning 
  • Unsupervised learning 
  • Computational Intelligence and Agents 
  • Computational Intelligence and Agents 
  • New areas in Computational Intelligence 

Course Evaluation Criteria

  • Assignments
  • Final Project 
  • Professionalism  

Description

The concepts of Systems Engineering are introduced through a project. Students work in virtual teams. The topics covered are architecture development, basic system architectural design techniques, functional decomposition, design and technical review objectives, and initial specifications.

Learning Objective

  1. Creating insights from data
  2. Improving our ability to forecast planning more accurately
  3. Help to quantify risk
  4. Yield better alternatives for systems and programs through analysis and optimization.  
  5. Enhance the ability to use descriptive, predictive, and prescriptive analytical methods and models that can aid decision-makers. 

Course Content

  • Information-Based Design with Data and Analytical Tools 
  • Probability and Introduction to Modelling 
  • Statistical Inference
  • Linear Regression
  • Data Visualization
  • Descriptive Statistics
  • Time Series and Forecasting
  • Predictive Data Mining
  • Spread Sheet Models
  • Monte Carlo Simulation
  • Linear Optimization Models
  • Integer Optimization Models
  • Non-Linear Optimization Models
  • Decision Analysis

Course Evaluation Criteria

  • Homework 
  • Project Reviews                
  • Final Project Paper                
  • Participation/Teamwork/Evaluations

Description

Methods of economic evaluation for engineering projects involving complex systems. Economic impacts on choosing system alternatives, life cycle costing, economic decisions involving risk and uncertainty, and engineering cost estimation for projects in government, defense, and commercial industries.

Learning Objective

  1. Understand the importance of Systems Life Cycle Costing (LCC) in systems engineering.
  2. Explain the fundamental concepts and principles of LCC.
  3. Apply LCC methodologies to estimate and analyze the total cost of ownership for complex systems.
  4. Evaluate the trade-offs between initial costs, operating costs, and maintenance costs.
  5. Develop LCC models and conduct sensitivity analyses.
  6. Make informed decisions based on LCC data and analysis.

Course Content

  • LCC Fundamentals
  • LCC Models and Sensitivity Analysis
  • Trade-offs and Optimization
  • Decision Support Tools for LCC
  • LCC in Systems Engineering Projects

Course Evaluation Criteria

  • Midterm Exam
  • Final Exam
  • Final Project

Description

Optimization in the presence of model uncertainty or system stochasticity is discussed. The course covers the fundamentals of stochastic programming, robust optimization, and dynamic programming.

Learning Objective

  1. Understand the challenges and significance of optimization under uncertainty.
  2. Analyze and model uncertainty in decision-making processes.
  3. Apply stochastic programming techniques to solve optimization problems with probabilistic data.
  4. Utilize robust optimization methods to make decisions that are resilient to uncertainty.
  5. Develop dynamic programming models to optimize decisions over time.
  6. Evaluate the trade-offs between risk and reward in uncertain optimization scenarios.

Course Content

  • Stochastic Programming
  • Uncertainty Modeling
  • Stochastic Optimization Methods
  • Dynamic Programming
  • Decision-Making under Risk and Uncertainty

Course Evaluation Criteria

  • Assignments
  • Final Exam
  • Term Project

Description

The topics covered are the Systems Engineering Management Plan (SEMP), Systems Engineering processes, process re-engineering, standards, and systems engineering case studies.  Students will apply the skills and theory that they mastered in the previous five core courses to the analysis of assigned cases.

Learning Objective

  1. Create a Systems Engineering Management Plan for a project
  2. Describe methods for continuous organizational process improvement
  3. Analyze a complex system development activity and define and implement an improvement activity using DMAIC 6 Sigma process improvement practices.

Course Content

  • Introduction to the Systems Engineering Management Plan – SEMP 1 & 2 – 
  • Technical Processes
  • Technical Planning and Assessment
  • Schedule, Decision Analysis, Risk, Issue, Opportunity Management
  • Configuration, Interface, and Information Management
  • Organizational Investment
  • Project Reengineering – Value Methodology
  • Enterprise Considerations – Introduction to Process Reengineering
  • Process Re-engineering: Define
  • Process Re-engineering: Measure
  • Process Re-engineering: Analyze
  • Process Reengineering: Improve
  • Process Reengineering: Control
  • Organizational Learning and Leadership

Course Evaluation Criteria

  • Assignments
  • Midterm Project 
  • Final Project

Description

Review of Neurocontrol and Optimization, Introduction to Approximate Dynamic Programming (ADP), Reinforcement Learning (RL), Combined Concepts of ADP and RL - Heuristic Dynamic Programming (HDP), Dual Heuristic Programming (DHP), Global Dual Heuristic Programming (GDHP), and Case Studies.

Learning Objective

  1. Understand the fundamentals and significance of Adaptive Dynamic Programming (ADP) in systems engineering.
  2. Explain the principles of reinforcement learning and optimization in ADP.
  3. Apply ADP algorithms to solve complex decision-making problems.
  4. Develop adaptive control systems using ADP techniques.
  5. Evaluate the trade-offs between exploration and exploitation in ADP.

Course Content

  • Reinforcement Learning Basics
  • Dynamic Programming in ADP
  • Reinforcement Learning Algorithms
  • Heuristic Dynamic Programming
  • Exploration and Exploitation Strategies

Course Evaluation Criteria

  • Assignments
  • Final Exam
  • Term Project

Description

This course discusses issues related to distributed systems architecting, modeling, analysis, and representation, with a specific focus on the digital system engineering domain. Distributed modeling techniques and other model decomposition methods using simulation modeling and scalability issues will also be addressed.

Learning Objective

  1. Understand the fundamentals of distributed systems architecting and modeling.
  2. Analyze the unique challenges and complexities of digital system engineering in a distributed context.
  3. Apply distributed modeling techniques to represent and analyze complex distributed systems.
  4. Evaluate scalability issues and design strategies for distributed systems.
  5. Develop skills in simulation modeling for distributed system analysis.
  6. Utilize decomposition methods for large-scale distributed system representation.

Course Content

  • Fundamentals of Distributed Systems
  • Distributed Systems Modeling
  • Decomposition Methods in Distributed Systems
  • Distributed System Analysis

Course Evaluation Criteria

  • Assignments
  • Final Exam
  • Term Project

Description

Provides the student with an understanding of the use of models to represent systems and validate system architectures. The student will gain proficiency in using a systems modeling language and shifting systems engineering from a document-centric to a model-centric paradigm.

Learning Objective

  1. Understand the significance of using models in systems engineering.
  2. Explain the principles of system modeling and its role in system architecture validation.
  3. Gain proficiency in using a systems modeling language.
  4. Apply model-centric approaches to systems engineering processes.
  5. Create, analyze, and validate system models.
  6. Evaluate the benefits of model-centric systems engineering.

Course Content

  • Systems Modeling Language (e.g., SysML)
  • Model-Centric System Engineering Processes
  • System Modeling and Validation
  • Model Transformation and Code Generation
  • Collaborative Modeling and Teamwork

Course Evaluation Criteria

  • Assignments
  • Final Exam
  • Term Project

Description

Tools and concepts of architecting complex engineering systems. Ambiguity in Systems Architecting and Fuzzy Systems; Search as an Architecting Process; Architecting Heuristics; Systems Scoping and Attribute Selection; Assessing Architectures; Systems Aggregation, Partitioning; Systems Behavior Generation; System Science and Thinking, Cyber Physical Systems.

Learning Objective

  1. To understand the fundamental concepts and principles of systems architecting.
  2. To develop skills in defining system requirements and objectives.
  3. To explore various architectural frameworks and methodologies.
  4. To learn techniques for system trade-off analysis and optimization.
  5. To gain proficiency in system integration and testing.
  6. To apply systems architecting principles to practical case studies.
  7. To foster critical thinking and problem-solving abilities in the context of complex systems.

Course Content

  • Introduction to Systems Architecting
  • Requirements Analysis and Specification in Systems Architecture
  • System Decomposition and Integration
  • Trade-off Analysis and Decision Making 
  • Validation and Verification of System Architectures

Course Evaluation Criteria

  • Minute Paper 
  • Project
  • Midterm Exam
  • Final Exam 
  • Final Paper

Description

Use of deep learning and advanced neural networks in the design of cyber-physical complex adaptive systems. Machine learning basics, deep feedforward networks, regularization for deep learning, optimization for training deep models, convolutional networks, recurrent and recursive nets, practical, vision, and natural language processing applications.

Learning Objective

  1. Understand the fundamental concepts of deep learning and advanced neural networks.
  2. Apply various deep learning architectures to solve complex engineering problems.
  3. Analyze and interpret the results of deep learning models in the context of systems engineering.
  4. Implement state-of-the-art deep learning techniques for pattern recognition, image analysis, and natural language processing.
  5. Evaluate the ethical and societal implications of advanced neural networks in engineering systems.
  6. Collaborate effectively on projects that involve deep learning solutions.

Course Content

  • Introduction to Deep Learning
  • Feedforward Neural Networks
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Natural Language Processing (NLP)
  • Deep Learning in Systems Engineering

Course Evaluation Criteria

  • Assignments
  • Final Exam 
  • Final Project