Matching Items (5)
Description
Shoulder injuries are the leading cause of shoulder discomfort or disabilities. Assessment of the glenohumeral joint functions through system identification technique approach is beneficial considering glenohumeral joint has major contributing factors associated with shoulder movement and stability. This function is identified by estimating a mathematical model by perturbing the glenohumeral joint and measuring the input angle and output torque. In this study, a shoulder exoskeleton robot was utilized, which makes use of a 4-bar spherical parallel manipulator (4B-SPM). The 4B-SPM exoskeleton has the advantage of high acceleration, fast enough to satisfy the speed requirement for the characterization of distinct neuromuscular properties of shoulder. Thirty-four healthy subjects (17 female, 17 male) were appointed with no history of shoulder impairment to characterize shoulder joint stiffness by providing filtered gaussianrandom perturbations with RMS value, frequency of 2 degrees and 3 Hz respectively. These perturbations arecaptured by 3-D Motion capture system by placing markers on arm brace which allows arm to be locked at a particular pose. Participants were instructed to maintain a relaxed state to avoid the interference of the muscle activation on the mechanical properties of the shoulder. Torque was measured using Force-Torque (FT) sensor at 15 different postures. These postures were divided among 3 flexion angles of the shoulder with a set of 5 horizontal extension postural configuration quantified for each flexion angle. The stiffness characterization was performed by utilizing Short Data Segment (SDS) method of time-varying system identification. It was observed that shoulder joint stiffness varied significantly depending on the arm's posture. The shoulder joint stiffness was observed to increase as the flexion angle decreases. Notably, a convex pattern emerged, wherein stiffness values increased as the arm deviated further from the mid-range of the shoulder joint's range of motion (ROM) in horizontal extension directions. These findings
suggest that maintaining the arm's posture near the mid-range of ROM decreases the stability of the shoulder joint. The shoulder joint stiffness was also observed to have significant difference on the basis of gender where in male
subjects were observed to have higher joint stiffness than female subjects.
ContributorsSaxena, Aditya (Author) / Lee, Hyunglae HL (Thesis advisor) / Marvi, Hamidreza HM (Committee member) / Xu, Zhe ZX (Committee member) / Arizona State University (Publisher)
Created2023
Description
Testing and verification is an essential procedure to assert a system adheres to some notion of safety. To validate such assertions, monitoring has provided an effective solution to verifying the conformance of complex systems against a set of properties describing what constitutes safe behavior. In authoring such properties, Temporal Logic (TL) has become a widely adopted specification language in many monitoring applications because of its ability to formally capture time-critical behaviors of reactive systems. This broad acceptance into the verification community and others, however, has naturally led to a lack of TL-based requirement elicitation standards as well as increased friction in tool interoperability. In this thesis, I propose a standardization of TL-based requirement languages through the development of a Formal Requirements Toolkit (FoRek): a modular, extensible, and maintainable collection of TL parsers, translators, and interfaces. To this end, six propositional TL languages are supported in addition to their appropriate past-time variants to provide a framework for a variety of applications using TL as a specification language. Furthermore, improvements to the Pythonic Formal Requirements Language (PyFoReL) tool are performed in addition to a formal definition on the structure of a PyFoReL program. And lastly, to demonstrate the results of this work, FoRek is integrated into an offline monitor to showcase its intended use and potential applications into other domains.
ContributorsAnderson, Jacob W (Author) / Fainekos, Georgios GF (Thesis advisor) / Pedrielli, Giulia GP (Thesis advisor) / Xu, Zhe ZX (Committee member) / Arizona State University (Publisher)
Created2023
Description
Aviation is a complicated field that involves a wide range of operations, from commercial airline flights to Unmanned Aerial Systems (UAS). Planning and scheduling are essential components in the aviation industry that play a significant role in ensuring safe and efficient operations. Reinforcement Learning (RL) has received increasing attention in recent years due to its capability to enable autonomous decision-making. To investigate the potential advantages and effectiveness of RL in aviation planning and scheduling, three topics are explored in-depth, including obstacle avoidance, task-oriented path planning, and maintenance scheduling. A dynamic and probabilistic airspace reservation concept, called Dynamic Anisotropic (DA) bound, is first developed for UAS, which can be added around the UAS as the separation requirement. A model based on Q-leaning is proposed to integrate DA bound with path planning for obstacle avoidance. Moreover, A deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed to guide the UAS to destinations while avoiding obstacles through continuous control. Results from case studies demonstrate that the proposed model can provide accurate and robust guidance and resolve conflict with a success rate of over 99%. Next, the single-UAS path planning problem is extended to a multi-agent system where agents aim to accomplish their own complex tasks. These tasks involve non-Markovian reward functions and can be specified using reward machines. Both cooperative and competitive environments are explored. Decentralized Graph-based reinforcement learning using Reward Machines (DGRM) is proposed to improve computational efficiency for maximizing the global reward in a graph-based Markov Decision Process (MDP). Q-learning with Reward Machines for Stochastic Games (QRM-SG) is developed to learn the best-response strategy for each agent in a competitive environment. Furthermore, maintenance scheduling is investigated. The purpose is to minimize the system maintenance cost while ensuring compliance with reliability requirements. Maintenance scheduling is formulated as an MDP and determines when and what maintenance operations to conduct. A Linear Programming-enhanced RollouT (LPRT) method is developed to solve both constrained deterministic and stochastic maintenance scheduling with an infinite horizon. LPRT categorizes components according to their health condition and makes decisions for each category.
ContributorsHu, Jueming (Author) / Liu, Yongming YL (Thesis advisor) / Yan, Hao HY (Committee member) / Lee, Hyunglae HL (Committee member) / Zhang, Wenlong WZ (Committee member) / Xu, Zhe ZX (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation focuses on a comprehensive exploration of machine learning (ML) and topological data analysis (TDA) with applications for engineering and clinical diagnostics and prognostics. The interface of TDA and ML is called topological machine learning (TML). The key focus and benefit of the proposed TML are on the automated, consistent, and robust handling of high-dimensional data, specifically for the complexities inherent in spatial-temporal datasets. TML's unique ability to capture and quantify high-dimensional geometric and topological features (such as homology) facilitates a deep understanding of the underlying structures of data. The associated dimension reduction capabilities significantly enhance diagnostics and prognostics accuracy and interpretability. TML is first demonstrated using an unsupervised learning setting, where the label information is not required for machine learning. Spatial-temporal data from resting-state functional magnetic resonance imaging (rs-fMRI) are collected and analyzed for Parkinson's disease. Fractal analysis is used to extract topological characteristics of the signal, and extracted features are used in a manifold embedding and projection model for low-dimensional space visualization. The low-dimensional data is integrated with a neural network-based classifier for disease diagnosis. A similar methodology is extended to structural health monitoring problems in engineering. Following this, the TML is developed for a supervised learning setting, where the major application is regression and prediction. Euler characteristics using filtration are used as the topological feature extraction method and extracted features are used in Gaussian Process (GP) modeling for regression analysis. The methodology is first demonstrated with a toy random field problem where a time-dependent field is characterized by varying topological features. The developed method is then demonstrated with crack growth problems with numerical and experimental data. Finally, the topological data analysis is Reflecting on the significant strides made in pushing the envelope of theoretical knowledge while showcasing tangible applications, this work not only charts a course for future progress in the field but also enriches our understanding of machine learning, structural health monitoring, predictive modeling, and beyond. The exploration initiated in this dissertation is just the beginning, with each chapter paving the way for new realms of exploration, innovation, and discovery.
ContributorsXu, Nan (Author) / Liu, Yongming YL (Thesis advisor) / Hong, Qijun QH (Committee member) / Li, Lin LL (Committee member) / Xu, Zhe ZX (Committee member) / Yan, Hao HY (Committee member) / Zhuang, Houlong HZ (Committee member) / Arizona State University (Publisher)
Created2024
Description
Multi-agent reinforcement learning (MARL) plays a pivotal role in artificial intelligence by facilitating the learning process in complex environments inhabited by multiple entities. This thesis explores the integration of learning high-level knowledge through reward machines (RMs) with MARL to effectively manage non-Markovian reward functions in non-cooperative stochastic games. Reward machines offer a sophisticated way to model the temporal structure of rewards, thereby providing an enhanced representation of agent decision-making processes. A novel algorithm JIRP-SG is introduced, enabling agents to concurrently learn RMs and optimize their best response policies while navigating the intricate temporal dependencies present in non-cooperative settings. This approach employs automata learning to iteratively acquire RMs and utilizes the Lemke-Howson method to update the Q-functions, aiming for a Nash equilibrium. It is demonstrated that the method introduced reliably converges to accurately encode the reward functions and achieve the optimal best response policy for each agent over time. The effectiveness of the proposed approach is validated through case studies, including a Pacman Game scenario and a Factory Assembly scenario, illustrating its superior performance compared to baseline methods. Additionally, the impact of batch size on learning performance is examined, revealing that a diligent agent employing smaller batches can surpass the performance of an agent using larger batches, which fails to summarize experiences as effectively.
ContributorsKim, Hyohun (Author) / Xu, Zhe ZX (Thesis advisor) / Lee, Hyunglae HL (Committee member) / Berman, Spring SB (Committee member) / Arizona State University (Publisher)
Created2024