Results for “Learning control” — Page 66

NASA NTRS2017

Weather Impact on Airport Arrival Meter Fix Throughput

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.

Machine Learning Model.↗

DOE OSTI2023

New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited]

The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.

Yoo, S. J. Ben (ORCID:0000000274201871)↗

DOE OSTI2020

Scalable Wind Turbine Generator Bearing Fault Prediction Using Machine Learning: A Case Study

Operation and maintenance (O&M) costs for wind turbines pose a risk to competitiveness and asset owners. With machine-learning technologies and digitalization rapidly maturing, the wind industry is actively investigating these new technologies to optimize O&M practices and reduce costs. This paper reviews recent work on machine-learning approaches to generator bearing failure prediction and presents a relevant real-world case study through a collaboration between the National Renewable Energy Laboratory and Envision Digital Corporation. In the case study, we evaluate the performance of representative machine-learning algorithms for predicting wind turbine generator bearing failures. Operational supervisory control and data acquisition data from one wind power plant was used to train and test the machine-learning models. The investigated data channels are chosen based on whether physically they reflect the failed generator bearing conditions and the component historical usage, including both environmental and operational conditions. Benefits and drawbacks of different methods are identified.

generator bearing failures↗

DOE OSTI2022

Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations (Abstract only)

This project directly addresses the primary goal of Area of Interest 2 in the CRADA call: to advance optimization-based integrated energy management systems in commercial and residential buildings. Pacific Northwest National Laboratory (PNNL) and its industry partner PassiveLogic aim to accomplish this by reaching three key objectives. First, to ensure a broad impact in the building controls industry, PNNL will extend its open-source library for predictive control synthesis by augmenting its capabilities with data-driven self-learning of building models and auto-calibration of predictive controllers. The effort will focus on building use cases selected in collaboration with PassiveLogic. The team will specifically address the development of methods for data-driven adaptation of building models, investigation of model architectures that best address specific building types, and automated synthesis of differentiable predictive controllers that optimize diverse objectives. Second, PNNL will collaborate with PassiveLogic to integrate the aforementioned methods with PasiveLogic’s advanced controls platform. The collaborative integration effort will inform the developments under the first objective by providing specific data on the attainable performance of model learning on resource-constrained edge computing platforms. This software integration effort will increase the technical maturity of the developed libraries by exploring the use of software integration tools and methods. Third, PNNL and PassiveLogic will work to improve the technology readiness of the developed predictive controllers by testing their performance in relevant test environments, such as high-fidelity simulation, hardware in the loop, and actual test buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Data-Driven Model Predictive Control for Temperature Management of Heat Pipe Microreactor

A data-driven model predictive control (MPC) was developed to enable the self-regulating capability of heat pipe (HP) nuclear microreactors. The MPC can proactively respond to potential disturbances of HP microreactors using three approaches for system identifications: linear state-space model, feedforward neural network, and recurrent neural networks with long short-term memory units. We present numerical results of data-driven MPCs to control the temperatures of selected HPs in a 37-HP test article. Our results show qualitatively that all data-driven MPCs produced similar control actions, while quantitatively, with artificial neural nets (especially feedforward neural nets), MPC can better follow drastic changes in setpoints with small errors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Assurance by Design for Cyber Physical Data-Driven Systems

Currently, Cyber Physical Data-Driven Systems (CPDDS) employ machine learning for the classification, data fusion, and control of our nation’s infrastructure, such as the power grid, transportation networks (e.g., fuel distribution, air traffic control), and DoD long-duration collaborative autonomous platforms including unmanned underwater, ground, surface, space, and aerial systems. Many CPDDSs are system-of-systems that should be designed to communicate over disadvantaged networks. It is important to assure that the CPDDSs are resilient against physical and cyber threats by design. Additionally, their design should tolerate misclassification errors resulting from natural and/or adversarial distribution shifts within their data driven components. The all-domain nature of the problem of assuring the design of CPDDSs requires a multi-disciplinary perspective as outlined in this chapter.

Chikkagoudar, Satish↗

DOE OSTI2022

Feedback Control Approaches for Restoration of Power Grids from Blackouts

The automated restoration of power systems with variable energy resources is a timely problem to tackle. Automated restoration advice can support operators in deciding on strategic actions to restore power grids from a blackout with a mix of conventional and renewable generation resources. To this end, this paper frames the restoration process of power grids with solar resources as a nonlinear dynamic model with algebraic constraints in discrete time which is steered by feedback control loops. We discuss two feedback-control strategies based on greedy and reinforcement learning algorithms, and contrast their performance with restoration plans generated by a mixed-integer linear program. We found that the reinforcement learning algorithm infers restoration actions faster than the greedy one. However, the tuning process of the reinforcement learning parameters is slower than for the greedy one.

machine learning↗

DOE OSTI2024

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F 1 -score of 0.846 ± 0.012, surpassing process-driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Summary of the IAEA technical meeting on plasma disruptions and their mitigation

Here we summarize the contributions presented at the IAEA technical meeting on plasma disruptions and their mitigation, held virtually, 20–23 July 2020. The meeting brought together more than 120 experts from nuclear fusion research sites worldwide to discuss experimental, theoretical and modelling work in the field of plasma disruptions with special emphasis on developing a solid basis for possible disruption mitigation strategies in ITER and next generation fusion devices. The main topics of the meeting were: (i) disruption consequences, including electromagnetic loads, heat loads, and runaway electrons; (ii) disruption prediction and avoidance, including machine learning and physics-based approaches, and control aspects; and (iii) disruption mitigation, including shattered pellet injection, alternative techniques and general aspects of disruption mitigation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

SU-E-T-589: Optimization of Patient Head Angle Position to Spare Hippocampus During the Brain Radiation Therapy

Purpose: Hippocampus is one of the important organs which controls emotions, behaviors, movements the memorizing and learning ability. In the conventional head & neck therapy position, it is difficult to perform the hippocampal-sparing brain radiation therapy. The purpose of this study is to investigate optimal head angle which can save the hippocampal-sparing and organ at risk (OAR) in conformal radiation therapy (CRT), Intensity modulation radiation therapy (IMRT) and helical tomotherapy (HT). Methods: Three types of radiation treatment plans, CRT, IMRT and Tomotherapy plans, were performed for 10 brain tumor patients. The image fusion between CT and MRI data were used in the contour due to the limited delineation of the target and OAR in the CT scan. The optimal condition plan was determined by comparing the dosimetric performance of the each plan with the use of various parameters which include three different techniques (CRT, IMRT, HT) and 4 angle (0, 15, 30, 40 degree). The each treatment plans of three different techniques were compared with the following parameters: conformity index (CI), homogeneity index (HI), target coverage, dose in the OARs, monitor units (MU), beam on time and the normal tissue complication probability (NTCP). Results: HI, CI and target coverage was most excellent in head angle 30 degree among all angle. When compared by modality, target coverage and CI showed good results in IMRT and TOMO than compared to the CRT. HI at the head angle 0 degrees is 1.137±0.17 (CRT), 1.085±0.09 (IMRT) and 1.077±0.06 (HT). HI at the head angle 30 degrees is 1.056±0.08 (CRT), 1.020±0.05 (IMRT) and 1.022±0.07 (HT). Conclusion: The results of our study show that when head angle tilted at 30 degree, target coverage, HI, CI were improved, and the dose delivered to OAR was reduced compared with conventional supine position in brain radiation therapy. This work was supported by the Radiation Technology R&D program (No. 2013M2A2A7043498) and the Mid-career Researcher Program (2014R1A2A1A10050270) through the National Research Foundation of Korea funded by the Ministry of Science, ICT&Future Planning.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

WE-DE-209-04: 3D Surface Image-Guided

Breast radiation therapy is associated with some risk of lung toxicity as well as cardiac toxicity for left-sided cases. Radiation doses to the lung and heart can be reduced by using the deep inspiration breath hold (DIBH) technique, in which the patient is simulated and treated during the deep inspiration phase of the breathing cycle. During DIBH, the heart is usually displaced posteriorly, inferiorly, and to the right, effectively expanding the distance between the heart and the breast/chest wall. As a result, the distance between the medial treatment field border and heart/lung is increased. Also, in a majority of DIBH patients, the air drawn into the thoracic cavity increases the total lung volume. The DIBH was discussed by an AAPM Task Group 10 years ago in the AAPM TG 76 report. However, DIBH is still not the standard of care in many clinics, which may be partially due to challenges associated with its implementation. Therefore, this seccion will focus primarily on how to clinically implement four different DIBH techniques: (1) Active Breathing Control, (2) Spirometric Motion Management, (3) 3D Surface Image-Guided, and (4) Self-held Breath Control with Respiratory Monitoring and Feedback Guidance. Learning Objectives: Describe the physical displacement of the heart and the change in lung volume during DIBH and discuss dosimetric consequences of those changes. Provide an overview of the technical aspects. Describe work flow for patient simulation and treatment. Give an overview of commissioning and routine. Provide practical tips for clinical implementation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-DE-209-05: Self-Held Breath Control with Respiratory Monitoring and Feedback Guidance

Breast radiation therapy is associated with some risk of lung toxicity as well as cardiac toxicity for left-sided cases. Radiation doses to the lung and heart can be reduced by using the deep inspiration breath hold (DIBH) technique, in which the patient is simulated and treated during the deep inspiration phase of the breathing cycle. During DIBH, the heart is usually displaced posteriorly, inferiorly, and to the right, effectively expanding the distance between the heart and the breast/chest wall. As a result, the distance between the medial treatment field border and heart/lung is increased. Also, in a majority of DIBH patients, the air drawn into the thoracic cavity increases the total lung volume. The DIBH was discussed by an AAPM Task Group 10 years ago in the AAPM TG 76 report. However, DIBH is still not the standard of care in many clinics, which may be partially due to challenges associated with its implementation. Therefore, this seccion will focus primarily on how to clinically implement four different DIBH techniques: (1) Active Breathing Control, (2) Spirometric Motion Management, (3) 3D Surface Image-Guided, and (4) Self-held Breath Control with Respiratory Monitoring and Feedback Guidance. Learning Objectives: Describe the physical displacement of the heart and the change in lung volume during DIBH and discuss dosimetric consequences of those changes. Provide an overview of the technical aspects. Describe work flow for patient simulation and treatment. Give an overview of commissioning and routine. Provide practical tips for clinical implementation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-DE-209-00: Practical Implementation of Deep Inspiration Breath Hold Techniques for Breast Radiation Therapy

Breast radiation therapy is associated with some risk of lung toxicity as well as cardiac toxicity for left-sided cases. Radiation doses to the lung and heart can be reduced by using the deep inspiration breath hold (DIBH) technique, in which the patient is simulated and treated during the deep inspiration phase of the breathing cycle. During DIBH, the heart is usually displaced posteriorly, inferiorly, and to the right, effectively expanding the distance between the heart and the breast/chest wall. As a result, the distance between the medial treatment field border and heart/lung is increased. Also, in a majority of DIBH patients, the air drawn into the thoracic cavity increases the total lung volume. The DIBH was discussed by an AAPM Task Group 10 years ago in the AAPM TG 76 report. However, DIBH is still not the standard of care in many clinics, which may be partially due to challenges associated with its implementation. Therefore, this seccion will focus primarily on how to clinically implement four different DIBH techniques: (1) Active Breathing Control, (2) Spirometric Motion Management, (3) 3D Surface Image-Guided, and (4) Self-held Breath Control with Respiratory Monitoring and Feedback Guidance. Learning Objectives: Describe the physical displacement of the heart and the change in lung volume during DIBH and discuss dosimetric consequences of those changes. Provide an overview of the technical aspects. Describe work flow for patient simulation and treatment. Give an overview of commissioning and routine. Provide practical tips for clinical implementation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-DE-209-01: Dosimetric Benefits of DIBH

Breast radiation therapy is associated with some risk of lung toxicity as well as cardiac toxicity for left-sided cases. Radiation doses to the lung and heart can be reduced by using the deep inspiration breath hold (DIBH) technique, in which the patient is simulated and treated during the deep inspiration phase of the breathing cycle. During DIBH, the heart is usually displaced posteriorly, inferiorly, and to the right, effectively expanding the distance between the heart and the breast/chest wall. As a result, the distance between the medial treatment field border and heart/lung is increased. Also, in a majority of DIBH patients, the air drawn into the thoracic cavity increases the total lung volume. The DIBH was discussed by an AAPM Task Group 10 years ago in the AAPM TG 76 report. However, DIBH is still not the standard of care in many clinics, which may be partially due to challenges associated with its implementation. Therefore, this seccion will focus primarily on how to clinically implement four different DIBH techniques: (1) Active Breathing Control, (2) Spirometric Motion Management, (3) 3D Surface Image-Guided, and (4) Self-held Breath Control with Respiratory Monitoring and Feedback Guidance. Learning Objectives: Describe the physical displacement of the heart and the change in lung volume during DIBH and discuss dosimetric consequences of those changes. Provide an overview of the technical aspects. Describe work flow for patient simulation and treatment. Give an overview of commissioning and routine. Provide practical tips for clinical implementation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-DE-209-02: Active Breathing Control

Breast radiation therapy is associated with some risk of lung toxicity as well as cardiac toxicity for left-sided cases. Radiation doses to the lung and heart can be reduced by using the deep inspiration breath hold (DIBH) technique, in which the patient is simulated and treated during the deep inspiration phase of the breathing cycle. During DIBH, the heart is usually displaced posteriorly, inferiorly, and to the right, effectively expanding the distance between the heart and the breast/chest wall. As a result, the distance between the medial treatment field border and heart/lung is increased. Also, in a majority of DIBH patients, the air drawn into the thoracic cavity increases the total lung volume. The DIBH was discussed by an AAPM Task Group 10 years ago in the AAPM TG 76 report. However, DIBH is still not the standard of care in many clinics, which may be partially due to challenges associated with its implementation. Therefore, this seccion will focus primarily on how to clinically implement four different DIBH techniques: (1) Active Breathing Control, (2) Spirometric Motion Management, (3) 3D Surface Image-Guided, and (4) Self-held Breath Control with Respiratory Monitoring and Feedback Guidance. Learning Objectives: Describe the physical displacement of the heart and the change in lung volume during DIBH and discuss dosimetric consequences of those changes. Provide an overview of the technical aspects. Describe work flow for patient simulation and treatment. Give an overview of commissioning and routine. Provide practical tips for clinical implementation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-DE-209-03: Spirometric Motion Management System

Breast radiation therapy is associated with some risk of lung toxicity as well as cardiac toxicity for left-sided cases. Radiation doses to the lung and heart can be reduced by using the deep inspiration breath hold (DIBH) technique, in which the patient is simulated and treated during the deep inspiration phase of the breathing cycle. During DIBH, the heart is usually displaced posteriorly, inferiorly, and to the right, effectively expanding the distance between the heart and the breast/chest wall. As a result, the distance between the medial treatment field border and heart/lung is increased. Also, in a majority of DIBH patients, the air drawn into the thoracic cavity increases the total lung volume. The DIBH was discussed by an AAPM Task Group 10 years ago in the AAPM TG 76 report. However, DIBH is still not the standard of care in many clinics, which may be partially due to challenges associated with its implementation. Therefore, this seccion will focus primarily on how to clinically implement four different DIBH techniques: (1) Active Breathing Control, (2) Spirometric Motion Management, (3) 3D Surface Image-Guided, and (4) Self-held Breath Control with Respiratory Monitoring and Feedback Guidance. Learning Objectives: Describe the physical displacement of the heart and the change in lung volume during DIBH and discuss dosimetric consequences of those changes. Provide an overview of the technical aspects. Describe work flow for patient simulation and treatment. Give an overview of commissioning and routine. Provide practical tips for clinical implementation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Training reinforcement learning models via an adversarial evolutionary algorithm

When training for control problems, more episodes used in training usually leads to better generalizability, but more episodes also requires significantly more training time. There are a variety of approaches for selecting the way that training episodes are chosen, including fixed episodes, uniform sampling, and stochastic sampling, but they can all leave gaps in the training landscape. In this work, we describe an approach that leverages an adversarial evolutionary algorithm to identify the worst performing states for a given model. We then use information about these states in the next cycle of training; this process can be repeated until the desired level of model performance is met. We demonstrate this approach with the OpenAI Gym cart-pole problem. With this problem, we show that the adversarial evolutionary algorithm did not reduce the number of episodes required in training needed to attain model generalizability when compared with stochastic sampling, and actually performed slightly worse.

Coletti, Mark↗

DOE OSTI2021

pnnl/FlexyAirDeepMPC

This is a trained machine learning model written in Pytorch for automated control of the FlexyAir (https://ocl.sk/hardware/flexyair/) dynamical system control educational device. This software is to be released to support our demonstration of our deepMPC methods on embedded hardware to control a real (albeit tutorial) physical system with control actuators.

Tuor, Aaron↗