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1. Detection of Internal Hemorrhage via Sequential Inference: An In Silico Feasibility Study

   https://doi.org/10.3390/diagnostics14171970  

   Chalumuri, Yekanth Ram; Jin, Xin; Tivay, Ali; Hahn, Jin-Oh (September 2024, Diagnostics)

   This paper investigates the feasibility of detecting and estimating the rate of internal hemorrhage based on continuous noninvasive hematocrit measurement. A unique challenge in hematocrit-based hemorrhage detection is that hematocrit decreases in response to hemorrhage and resuscitation with fluids, which makes hemorrhage detection during resuscitation challenging. We developed two sequential inference algorithms for detection of internal hemorrhage based on the Luenberger observer and the extended Kalman filter. The sequential inference algorithms use fluid resuscitation dose and hematocrit measurement as inputs to generate signatures to enable detection of internal hemorrhage. In the case of the extended Kalman filter, the signature is nothing but inferred hemorrhage rate, which allows it to also estimate internal hemorrhage rate. We evaluated the proof-of-concept of these algorithms based on in silico evaluation in 100 virtual patients subject to diverse hemorrhage and resuscitation rates. The results showed that the sequential inference algorithms outperformed naïve internal hemorrhage detection based on the decrease in hematocrit when hematocrit noise level was 1% (average F1 score: Luenberger observer 0.80; extended Kalman filter 0.76; hematocrit 0.59). Relative to the Luenberger observer, the extended Kalman filter demonstrated comparable internal hemorrhage detection performance and superior accuracy in estimating the hemorrhage rate. The analysis of the dependence of the sequential inference algorithms on measurement noise and plant parametric uncertainty showed that small (≤1%) hematocrit noise level and personalization of sequential inference algorithms may enable continuous noninvasive detection of internal hemorrhage and estimation of its rate. 

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2. A Generative Approach to Testing the Performance of Physiological Control Algorithms

   https://doi.org/10.1115/1.4065934  

   Tivay, Ali; Bighamian, Ramin; Hahn, Jin-Oh; Scully, Christopher G (July 2024, ASME Letters in Dynamic Systems and Control)

   Abstract Physiological closed-loop control algorithms play an important role in the development of autonomous medical care systems, a promising area of research that has the potential to deliver healthcare therapies meeting each patient's specific needs. Computational approaches can support the evaluation of physiological closed-loop control algorithms considering various sources of patient variability that they may be presented with. In this article, we present a generative approach to testing the performance of physiological closed-loop control algorithms. This approach exploits a generative physiological model (which consists of stochastic and dynamic components that represent diverse physiological behaviors across a patient population) to generate a select group of virtual subjects. By testing a physiological closed-loop control algorithm against this select group, the approach estimates the distribution of relevant performance metrics in the represented population. We illustrate the promise of this approach by applying it to a practical case study on testing a closed-loop fluid resuscitation control algorithm designed for hemodynamic management. In this context, we show that the proposed approach can test the algorithm against virtual subjects equipped with a wide range of plausible physiological characteristics and behavior and that the test results can be used to estimate the distribution of relevant performance metrics in the represented population. In sum, the generative testing approach may offer a practical, efficient solution for conducting preclinical tests on physiological closed-loop control algorithms. 

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3. Mathematical Modeling of Hemodynamic Responses to Burn Injury and Resuscitation

   Ghazal Arabi Darreh Dor, Ali Tivay (September 2018, Carnegie Mellon Forum on Biomedical Engineering)

   Fluid resuscitation is an integral part of critical care for burn injury patients where the necessary infusion rate is determined based on patient’s urinary output (UO). Motivated by an increasing interest in model-based development and in silico testing of automated burn resuscitation algorithms, we are investigating mathematical modeling of hemodynamic responses to burn injury and resuscitation. The model consists of 3 main components: (1) multi-compartmental volume kinetics including vascular and interstitial fluids and the associated flow interactions, (2) burn-induced hemodynamic perturbation including alterations in tissue permeability and compliance as well as denaturation with protein release, and (3) renal regulatory function including glomerular filtration rate as a function of intravascular volume state and reabsorption function representing the UO dependence on vasopressin. Preliminary evaluation of the initial model with data collected from animals show that the model can reproduce general trend of hemodynamic responses anticipated from burn injury and resuscitation. 

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4. Distributed model predictive control for ink-jet 3D printing

   https://doi.org/10.1109/AIM.2017.8014056  

   Guo, Yijie; Peters, Joost; Oomen, Tom; Mishra, Sandipan (July 2017, IEEE Advanced Intelligent Mechatronics)

   This paper develops a closed-loop approach for ink-jet 3D printing. The control design is based on a distributed model predictive control scheme, which can handle constraints (such as droplet volume) as well as the large-scale nature of the problem. The high resolution of ink-jet 3D printing make centralized methods extremely time-consuming, thus a distributed implementation of the controller is developed. First a graph-based height evolution model that can capture the liquid flow dynamics is proposed. Then, a scalable closed-loop control algorithm is designed based on the model using Distributed MPC, that reduces computation time significantly. The performance and efficiency of the algorithm are shown to outperform open-loop printing and closed-loop printing with existing Centralized MPC methods through simulation results. 

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5. Integration of Hardware and Software for Battery Hardware-In-the-Loop Towards Battery Artificial Intelligence

   https://doi.org/10.1109/TTE.2023.3270870  

   Park, Saehong; Moura, Scott; Lee, Kyoungtae (April 2023, IEEE Transactions on Transportation Electrification)

   This paper demonstrates a novel, compact-sized hardware-in-the-loop system, and its verification using machine learning and artificial intelligence features in battery controls. Conventionally, a battery management system involves algorithm development for battery modeling, estimation, and control. These tasks are typically validated by running the battery tester open-loop, i.e., the tester equipment executes the pre-defined experimental protocols line by line. Additional equipment is required to make the testing closed-loop, but the integration is typically not straightforward. To improve flexibility and accessibility for battery management, this work proposes a low-cost highly reliable closed-loop charger and discharger. We first focus on the electronic circuit design for battery testing systems to maximize the applied current accuracy and precision. After functional verification, we further investigate applications for closed-loop battery management systems. In particular, we extend the proposed architecture into the learning-based control design, which is a feedback controller. We utilize reinforcement learning techniques to highlight the benefits of closed-loop controls. As an example, we compare this learning-based control strategy with a conventional battery charging control. The experimental results demonstrate that the proposed experimental design is able to handle the learning-based controller and achieve a more reliable and safer charging protocol driven by artificial intelligence. 

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