Doctor of Philosophy

dissertationA safe and secure transportation system is critical to providing protection to those who employ it. Safety is being increasingly demanded within the transportation system and transportation facilities and services will need to adapt to change to provide it. This dissertation provides innovate methodologies to identify current shortcomings and provide theoretic frameworks for enhancing the safety and security of the transportation network. This dissertation is designed to provide multilevel enhanced safety and security within the transportation network by providing methodologies to identify, monitor, and control major hazards associated within the transportation network. The risks specifically addressed are: (1) enhancing nuclear materials sensor networks to better deter and interdict smugglers, (2) use game theory as an interdiction model to design better sensor networks and forensically track smugglers, (3) incorporate safety into regional transportation planning to provide decision-makers a basis for choosing safety design alternatives, and (4) use a simplified car-following model that can incorporate errors to predict situational-dependent safety effects of distracted driving in an ITS infrastructure to deploy live-saving countermeasures

The Importance of Quantum Information in the Stock Market and Financial Decision Making in Conditions of Radical Uncertainty

The Universe is a coin that’s already been flipped, heads or tails predetermined: all we’re doing is uncovering it the ‘paradox’ is only a conflict between reality and your feeling of what reality ‘ought to be’.Richard FeynmanThe aim of the research takes place through two parallel directions. The first is gaining an understanding of the applicability of quantum mechanics/quantum physics to human decision-making processes in the stock market with quantum information as a decision-making lever, and the second direction is neuroscience and artificial intelligence using postulates analogous to the postulates of quantum mechanics and radical uncertainty in conditions of radical uncertainty.The world of radical uncertainty (radical uncertainty is based on the knowledge of quantum mechanics from the claim that there is no causal certainty). it is everywhere in our world. "Radical uncertainty is characterized by vagueness, ignorance, indeterminacy, ambiguity and lack of information. He prefers to create 'mysteries' rather than 'puzzles' with defined solutions. Mysteries are ill-defined problems in which action is required, but the future is uncertain, the consequences unpredictable, and disagreement inevitable. "How should we make decisions in these circumstances?" (J. Kay and M. King, 2020), while "uncertainty and ambiguity are at the very core of the stock market. "Narratives are the currency of uncertainty" (N. Mangee, 2022)

The Importance of Quantum Information in the Stock Market and Financial Decision Making in Conditions of Radical Uncertainty

The Universe is a coin that’s already been flipped, heads or tails predetermined: all we’re doing is uncovering it the ‘paradox’ is only a conflict between reality and your feeling of what reality ‘ought to be’.Richard FeynmanThe aim of the research takes place through two parallel directions. The first is gaining an understanding of the applicability of quantum mechanics/quantum physics to human decision-making processes in the stock market with quantum information as a decision-making lever, and the second direction is neuroscience and artificial intelligence using postulates analogous to the postulates of quantum mechanics and radical uncertainty in conditions of radical uncertainty.The world of radical uncertainty (radical uncertainty is based on the knowledge of quantum mechanics from the claim that there is no causal certainty). it is everywhere in our world. "Radical uncertainty is characterized by vagueness, ignorance, indeterminacy, ambiguity and lack of information. He prefers to create 'mysteries' rather than 'puzzles' with defined solutions. Mysteries are ill-defined problems in which action is required, but the future is uncertain, the consequences unpredictable, and disagreement inevitable. "How should we make decisions in these circumstances?" (J. Kay and M. King, 2020), while "uncertainty and ambiguity are at the very core of the stock market. "Narratives are the currency of uncertainty" (N. Mangee, 2022)

Cyber Security of Traffic Signal Control Systems with Connected Vehicles

Our world is becoming increasingly connected through smart technologies. The same trend is emerging in transportation systems, wherein connected vehicles (CVs) and transportation infrastructure are being connected through advanced wireless communication technologies. CVs have great potential to improve a variety of mobility applications, including traffic signal control (TSC), a critical component in urban traffic operations. CV-based TSC (CV-TSC) systems use trajectory data to make more informed control decisions, therefore can accommodate real-time traffic fluctuations more efficiently. However, vehicle-infrastructure connectivity opens new doors to potential cyber attacks. Malicious attackers can potentially send falsified trajectory data to CV-TSC systems and influence signal control decisions. The benefit of CV-TSC systems can be realized only if the systems are secure in cyberspace. Although many CV-TSC systems have been developed within the past decade, few consider cyber security in their system design. It remains unclear exactly how vulnerable CV-TSC systems are, how cyber attacks may be perpetrated, and how engineers can mitigate cyber attacks and protect CV-TSC systems. Therefore, this dissertation aims to systematically understand the cyber security problems facing CV-TSC systems under falsified data attacks and provide a countermeasure to safeguard CV-TSC systems. These objectives are accomplished through four studies. The first study evaluates the effects of falsified data attacks on TSC systems. Two TSC systems are considered: a conventional actuated TSC system and an adaptive CV-TSC system. Falsified data attacks are assumed to change the input data to these systems and therefore influence control decisions. Numerical examples show that both systems are vulnerable to falsified data attacks. The second study investigates how falsified data attacks may be perpetrated in a realistic setting. Different from prior research, this study considers a more realistic but challenging black-box attack scenario, in which the signal control model is unavailable to the attacker. Under this constraint, the attacker has to learn the signal control model using a surrogate model. The surrogate model predicts signal timing plans based on critical traffic features extracted from CV data. The attacker can generate falsified CV data (i.e., falsified vehicle trajectories) to alter the values of critical traffic features and thus influence signal control decisions. In the third study, a data-driven method is proposed to protect CV-TSC systems from falsified data attacks. Falsified trajectories are behaviorally distinct from normal trajectories because they must accomplish a certain attack goal; thus, the problem of identifying falsified trajectories is considered an abnormal trajectory identification problem. A trajectory-embedding model is developed to generate vector representations of trajectory data. The similarity (distance) between each pair of trajectories can be computed based on these vector representations. Hierarchical clustering is then applied to identify abnormal (i.e., falsified) trajectories. In the final study, a testing platform is built upon a virtual traffic simulator and real-world transportation infrastructure in Mcity. The testing platform integrates the attack study and defense study in a unified framework and is used to evaluate the real-world impact of cyber attacks on CV-TSC systems and the effectiveness of defense strategies.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162931/1/edhuang_1.pd

Proceedings, MSVSCC 2017

Proceedings of the 11th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 20, 2017 at VMASC in Suffolk, Virginia. 211 pp

Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies

Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties. On the other hand, causality can exhibit emergence, meaning that new causal laws may arise as we increase the level of abstraction. Causal emergence theory aims to bridge these two concepts and even employs measures of causality to quantify emergence. This paper provides a comprehensive review of recent advancements in quantitative theories and applications of causal emergence. Two key problems are addressed: quantifying causal emergence and identifying it in data. Addressing the latter requires the use of machine learning techniques, thus establishing a connection between causal emergence and artificial intelligence. We highlighted that the architectures used for identifying causal emergence are shared by causal representation learning, causal model abstraction, and world model-based reinforcement learning. Consequently, progress in any of these areas can benefit the others. Potential applications and future perspectives are also discussed in the final section of the review.Comment: 57 pages, 17 figures, 1 tabl

Air Force Institute of Technology Research Report 2015

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Air Force Institute of Technology Research Report 2015

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Proceedings, MSVSCC 2013

Proceedings of the 7th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 11, 2013 at VMASC in Suffolk, Virginia