Search for: All records

We present a study on the development of piezoelectric nanofibers for wearable hemodynamic sensing by incorporating nanoparticle doping of piezoelectric nanofibers. The composite material was characterized using various techniques, including scanning electron microscopy, X-ray diffraction, and tensile test. The material was also evaluated in a custom-built pressure chamber. Achieving optimal sensor performance, the study identified 20 wt% BTO composite materials as ideal, with a peak voltage output of 0.12V. Higher concentrations presented electrospinning difficulties, compromising material consistency. Quantitative analysis through fast Fourier transform (FFT) and digital bandpass filtering precisely isolated physiological signals, notably respiration and heartbeat, with the sensor demonstrating accurate detection capabilities at frequencies of 0.2, 1.35, and 2.65 Hz, indicative of the targeted physiological processes. The results demonstrate a promising potential for the use of these materials in future wearable hemodynamic sensing applications. 

We study zero-sum differential games with state constraints and one-sided information, where the informed player (Player 1) has a categorical payoff type unknown to the uninformed player (Player 2). The goal of Player 1 is to minimize his payoff without violating the constraints, while that of Player 2 is to either violate the state constraints, or otherwise, to maximize the payoff. One example of the game is a man-to-man matchup in football. Without state constraints, Cardaliaguet (2007) showed that the value of such a game exists and is convex to the common belief of players. Our theoretical contribution is an extension of this result to differential games with state constraints and the derivation of the primal and dual subdynamic principles necessary for computing the behavioral strategies. Compared with existing works on imperfect-information dynamic games that focus on scalability and generalization, our focus is instead on revealing the mechanism of belief manipulation behaviors resulted from information asymmetry and state constraints. We use a simplified football game to demonstrate the utility of this work, where we reveal player positions and belief states in which the attacker should (or should not) play specific random fake moves to take advantage of information asymmetry, and compute how the defender should respond. 

The values of two-player general-sum differential games are viscosity solutions to Hamilton-Jacobi-Isaacs (HJI) equations. Value and policy approximations for such games suffer from the curse of dimensionality (CoD). Alleviating CoD through physics-informed neural networks (PINN) encounters convergence issues when value discontinuity is present due to state constraints. On top of these challenges, it is often necessary to learn generalizable values and policies across a parametric space of games, eg, for game parameter inference when information is incomplete. To address these challenges, we propose in this paper a Pontryagin-mode neural operator that outperforms existing state-of-the-art (SOTA) on safety performance across games with parametric state constraints. Our key contribution is the introduction of a costate loss defined on the discrepancy between forward and backward costate rollouts, which are computationally cheap. We show that the discontinuity of costate dynamics (in the presence of state constraints) effectively enables the learning of discontinuous values, without requiring manually supervised data as suggested by the current SOTA. More importantly, we show that the close relationship between costates and policies makes the former critical in learning feedback control policies with generalizable safety performance.