Results for “learning based systems” — Page 97

DOE OSTI2022

Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.)

Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Multi-Edge Graph Convolutional Networks for Power Systems

The exponential electrification of transportation has contributed to highly intermittent load variations in the distribution grid. This uncertainty has raised challenges for distribution system operation and control. Accurate nodal voltage estimation is highly essential for the safe and reliable operation of the grid. Graph convolutional networks have been used in machine-learning-based models for power grid applications like voltage estimation for their ability to capture the network topology of the grid. This paper presents a novel multi-edge graph convolutional layer that considers resistance and reactance as edge attributes. This layer is created by modifying the message-passing function within the graph convolutional network. The novel layer is then used to create a multi-edge graph convolutional network-based surrogate model for estimating voltage in the distribution network with highly uncertain electric vehicle loads. Results indicate improved performance of the multi-edge graph convolutional network model when compared to a standard graph convolutional network model.

Ravi, Abhijith↗

DOE OSTI2015

WE-G-303-00: Nanotechnology for Imaging and Therapy

Over the last decade, there has been a growing interest in applying nanotechnology to cancer detection, treatment, and treatment monitoring. Advances in nanotechnology have enabled the fabrication of nanoparticles from various materials with different shapes and sizes. Nanoparticles can be accumulated preferentially within tumors by either “passive targeting” through a phenomenon typically known as “enhanced permeability and retention” or “active targeting” in which nanoparticles are conjugated with antibodies or peptides directed against tumor and/or stromal markers. The tumor specificity of nanoparticles in conjunction with their unique physicochemical properties offers many novel strategies for cancer treatment and detection. For example, notable approaches in the radiation oncology setting include the use of gold nanoparticles for radiation response modulation of tumor or normal tissue and thermal ablation or hyperthermia treatment of tumors. Some of these approaches are currently being tested either on humans or on animals and, very likely, will become the clinical reality in the near future. Various computational and experimental techniques have also been applied to address unique research issues associated with nanoparticles and may become the standard tools for future investigations and clinical translations. Therefore, both clinicians and researchers may need to be properly educated about the basic principles as well as the promise of nanoparticle-based applications with regard to the future of cancer diagnostics and therapeutics. This symposium will familiarize the audience with the potential applications of nanoparticles in oncologic imaging and therapy using specific illustrative examples. The audience will be properly oriented by these illustrative examples to the multiple avenues for collaborative research amongst interdisciplinary teams of physicists, clinicians, engineers, chemists, and biologists in industry and academia. Learning Objectives: Understand the physical bases of gold nanoparticle applications for radiosensitization and x-ray fluorescence imaging Understand the parameters that define gold nanoparticle-mediated radiosensitization in biological systems Understand the potential of magnetic nanoparticle characterization of the microenvironment Understand the various strategies for radiolabeling of nanoparticles and their applications S.C. and S.K. acknowledge support from MD Anderson Cancer Center, NIH (R01CA155446 and P30CA16672) and DoD (W81XWH-12-1-0198); J.W. acknowledges support from NIH (U54CA151662-01); W.C. acknowledges support from the University of Wisconsin-Madison, NIH (R01CA169365, P30CA014520, and T32CA009206), DoD (W81XWH-11-1-0644 and W81XWH-11-1-0648), and ACS (125246-RSG-13-099-01-CCE)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-G-303-01: Physical Bases for Gold Nanoparticle Applications in Radiation Oncology and X-Ray Imaging

Over the last decade, there has been a growing interest in applying nanotechnology to cancer detection, treatment, and treatment monitoring. Advances in nanotechnology have enabled the fabrication of nanoparticles from various materials with different shapes and sizes. Nanoparticles can be accumulated preferentially within tumors by either “passive targeting” through a phenomenon typically known as “enhanced permeability and retention” or “active targeting” in which nanoparticles are conjugated with antibodies or peptides directed against tumor and/or stromal markers. The tumor specificity of nanoparticles in conjunction with their unique physicochemical properties offers many novel strategies for cancer treatment and detection. For example, notable approaches in the radiation oncology setting include the use of gold nanoparticles for radiation response modulation of tumor or normal tissue and thermal ablation or hyperthermia treatment of tumors. Some of these approaches are currently being tested either on humans or on animals and, very likely, will become the clinical reality in the near future. Various computational and experimental techniques have also been applied to address unique research issues associated with nanoparticles and may become the standard tools for future investigations and clinical translations. Therefore, both clinicians and researchers may need to be properly educated about the basic principles as well as the promise of nanoparticle-based applications with regard to the future of cancer diagnostics and therapeutics. This symposium will familiarize the audience with the potential applications of nanoparticles in oncologic imaging and therapy using specific illustrative examples. The audience will be properly oriented by these illustrative examples to the multiple avenues for collaborative research amongst interdisciplinary teams of physicists, clinicians, engineers, chemists, and biologists in industry and academia. Learning Objectives: Understand the physical bases of gold nanoparticle applications for radiosensitization and x-ray fluorescence imaging Understand the parameters that define gold nanoparticle-mediated radiosensitization in biological systems Understand the potential of magnetic nanoparticle characterization of the microenvironment Understand the various strategies for radiolabeling of nanoparticles and their applications S.C. and S.K. acknowledge support from MD Anderson Cancer Center, NIH (R01CA155446 and P30CA16672) and DoD (W81XWH-12-1-0198); J.W. acknowledges support from NIH (U54CA151662-01); W.C. acknowledges support from the University of Wisconsin-Madison, NIH (R01CA169365, P30CA014520, and T32CA009206), DoD (W81XWH-11-1-0644 and W81XWH-11-1-0648), and ACS (125246-RSG-13-099-01-CCE)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-G-303-02: Gold Nanoparticles as Radiosensitizers - What Does It Take To Go From the Bench to the Bedside?

Over the last decade, there has been a growing interest in applying nanotechnology to cancer detection, treatment, and treatment monitoring. Advances in nanotechnology have enabled the fabrication of nanoparticles from various materials with different shapes and sizes. Nanoparticles can be accumulated preferentially within tumors by either “passive targeting” through a phenomenon typically known as “enhanced permeability and retention” or “active targeting” in which nanoparticles are conjugated with antibodies or peptides directed against tumor and/or stromal markers. The tumor specificity of nanoparticles in conjunction with their unique physicochemical properties offers many novel strategies for cancer treatment and detection. For example, notable approaches in the radiation oncology setting include the use of gold nanoparticles for radiation response modulation of tumor or normal tissue and thermal ablation or hyperthermia treatment of tumors. Some of these approaches are currently being tested either on humans or on animals and, very likely, will become the clinical reality in the near future. Various computational and experimental techniques have also been applied to address unique research issues associated with nanoparticles and may become the standard tools for future investigations and clinical translations. Therefore, both clinicians and researchers may need to be properly educated about the basic principles as well as the promise of nanoparticle-based applications with regard to the future of cancer diagnostics and therapeutics. This symposium will familiarize the audience with the potential applications of nanoparticles in oncologic imaging and therapy using specific illustrative examples. The audience will be properly oriented by these illustrative examples to the multiple avenues for collaborative research amongst interdisciplinary teams of physicists, clinicians, engineers, chemists, and biologists in industry and academia. Learning Objectives: Understand the physical bases of gold nanoparticle applications for radiosensitization and x-ray fluorescence imaging Understand the parameters that define gold nanoparticle-mediated radiosensitization in biological systems Understand the potential of magnetic nanoparticle characterization of the microenvironment Understand the various strategies for radiolabeling of nanoparticles and their applications S.C. and S.K. acknowledge support from MD Anderson Cancer Center, NIH (R01CA155446 and P30CA16672) and DoD (W81XWH-12-1-0198); J.W. acknowledges support from NIH (U54CA151662-01); W.C. acknowledges support from the University of Wisconsin-Madison, NIH (R01CA169365, P30CA014520, and T32CA009206), DoD (W81XWH-11-1-0644 and W81XWH-11-1-0648), and ACS (125246-RSG-13-099-01-CCE)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-G-303-03: Advances in in Vivo Magnetic NanoparticleSensing

Over the last decade, there has been a growing interest in applying nanotechnology to cancer detection, treatment, and treatment monitoring. Advances in nanotechnology have enabled the fabrication of nanoparticles from various materials with different shapes and sizes. Nanoparticles can be accumulated preferentially within tumors by either “passive targeting” through a phenomenon typically known as “enhanced permeability and retention” or “active targeting” in which nanoparticles are conjugated with antibodies or peptides directed against tumor and/or stromal markers. The tumor specificity of nanoparticles in conjunction with their unique physicochemical properties offers many novel strategies for cancer treatment and detection. For example, notable approaches in the radiation oncology setting include the use of gold nanoparticles for radiation response modulation of tumor or normal tissue and thermal ablation or hyperthermia treatment of tumors. Some of these approaches are currently being tested either on humans or on animals and, very likely, will become the clinical reality in the near future. Various computational and experimental techniques have also been applied to address unique research issues associated with nanoparticles and may become the standard tools for future investigations and clinical translations. Therefore, both clinicians and researchers may need to be properly educated about the basic principles as well as the promise of nanoparticle-based applications with regard to the future of cancer diagnostics and therapeutics. This symposium will familiarize the audience with the potential applications of nanoparticles in oncologic imaging and therapy using specific illustrative examples. The audience will be properly oriented by these illustrative examples to the multiple avenues for collaborative research amongst interdisciplinary teams of physicists, clinicians, engineers, chemists, and biologists in industry and academia. Learning Objectives: Understand the physical bases of gold nanoparticle applications for radiosensitization and x-ray fluorescence imaging Understand the parameters that define gold nanoparticle-mediated radiosensitization in biological systems Understand the potential of magnetic nanoparticle characterization of the microenvironment Understand the various strategies for radiolabeling of nanoparticles and their applications S.C. and S.K. acknowledge support from MD Anderson Cancer Center, NIH (R01CA155446 and P30CA16672) and DoD (W81XWH-12-1-0198); J.W. acknowledges support from NIH (U54CA151662-01); W.C. acknowledges support from the University of Wisconsin-Madison, NIH (R01CA169365, P30CA014520, and T32CA009206), DoD (W81XWH-11-1-0644 and W81XWH-11-1-0648), and ACS (125246-RSG-13-099-01-CCE)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-G-303-04: Intrinsically Radiolabeled Nanoparticles: An Emerging Paradigm

Over the last decade, there has been a growing interest in applying nanotechnology to cancer detection, treatment, and treatment monitoring. Advances in nanotechnology have enabled the fabrication of nanoparticles from various materials with different shapes and sizes. Nanoparticles can be accumulated preferentially within tumors by either “passive targeting” through a phenomenon typically known as “enhanced permeability and retention” or “active targeting” in which nanoparticles are conjugated with antibodies or peptides directed against tumor and/or stromal markers. The tumor specificity of nanoparticles in conjunction with their unique physicochemical properties offers many novel strategies for cancer treatment and detection. For example, notable approaches in the radiation oncology setting include the use of gold nanoparticles for radiation response modulation of tumor or normal tissue and thermal ablation or hyperthermia treatment of tumors. Some of these approaches are currently being tested either on humans or on animals and, very likely, will become the clinical reality in the near future. Various computational and experimental techniques have also been applied to address unique research issues associated with nanoparticles and may become the standard tools for future investigations and clinical translations. Therefore, both clinicians and researchers may need to be properly educated about the basic principles as well as the promise of nanoparticle-based applications with regard to the future of cancer diagnostics and therapeutics. This symposium will familiarize the audience with the potential applications of nanoparticles in oncologic imaging and therapy using specific illustrative examples. The audience will be properly oriented by these illustrative examples to the multiple avenues for collaborative research amongst interdisciplinary teams of physicists, clinicians, engineers, chemists, and biologists in industry and academia. Learning Objectives: Understand the physical bases of gold nanoparticle applications for radiosensitization and x-ray fluorescence imaging Understand the parameters that define gold nanoparticle-mediated radiosensitization in biological systems Understand the potential of magnetic nanoparticle characterization of the microenvironment Understand the various strategies for radiolabeling of nanoparticles and their applications S.C. and S.K. acknowledge support from MD Anderson Cancer Center, NIH (R01CA155446 and P30CA16672) and DoD (W81XWH-12-1-0198); J.W. acknowledges support from NIH (U54CA151662-01); W.C. acknowledges support from the University of Wisconsin-Madison, NIH (R01CA169365, P30CA014520, and T32CA009206), DoD (W81XWH-11-1-0644 and W81XWH-11-1-0648), and ACS (125246-RSG-13-099-01-CCE)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Insights from Optimizing HPL Performance on Exascale Systems: A Comparative Analysis of Panel Factorization

High performance LINPACK (HPL) remains the primary benchmark for evaluating supercomputing performance. It includes many parts with substantial internal complexity, and its performance is affected by a large number of parameters that interact in ways that are difficult to predict on large-scale heterogeneous supercomputer systems. We present a comprehensive performance analysis of HPL on Frontier, the world’s first exascale supercomputer, which achieved HPL performance of 1.35 exaflops. Through empirical parameter tuning, detailed modeling, and comparative evaluation, we uncover critical performance insights, share lessons learned, and outline best practices for effective parameter tuning on exascale systems. We introduce and evaluate two novel PDFACT strategies: a dedicated-thread (DT) variant and a GPU-based variant (GPUPDFACT) implementation using HIP cooperative groups, demonstrating that GPU-based factorization outperforms conventional CPU-based PDFACT on Frontier’s architecture. Our findings establish key performance factors for HPL on exascale systems and offer valuable guidance for future high-performance computing and benchmarking efforts.

Lu, Hao [ORNL] (ORCID:000000018941870X)↗

DOE OSTI2025

ACTIVE

The Automated Control Testbed for Integration, Verification, and Emulation (ACTIVE) framework is a software platform designed to support the optimized operation and management of a wide range of building types. It enables the development, testing, and validation of diverse control strategies, including AI-based, rule-based, and model-based approaches. The platform facilitates a seamless transition from simulation-based evaluation of control strategies to real-world field validation and deployment. ACTIVE supports the full building management lifecycle, encompassing data acquisition and management, system monitoring, optimized control, adaptive learning services, device dispatch and coordination, as well as advanced analytics and visualization. Together, these capabilities provide an integrated environment for improving building performance, operational efficiency, reducing energy cost, and reliability.

Smith, Robert [Oak Ridge National Laboratory (ORNL↗

DOE OSTI2021

Cyber State Awareness For Resilience

Characterization of cyber-physical attacks requires a holistic understanding of cyber and physical behavior in a system. Machine Learning Anomaly Detection provides a compelling solution for continuously identifying suspicious behavior within these complex systems. In our software, we present an approach for holistic characterization of cyber-physical systems based on cyber and physical anomaly correlation. The approach consists of three main components: 1) an architecture for real-time data acquisition, management, and analysis of both cyber and physical data; 2) cyber and physical data driven anomaly detection systems (ADSs), 3) a metric that combines cyber and physical ADSs to provide a holistic characterization of the system..

Rieger, CraigG.↗

NASA NTRS1992

Repetitive control in the presence of multiple periodic disturbances

In this paper, the authors introduce several methods for implementing repetitive control of systems subjected to noninteger multiple periodic disturbances. One method uses a single repetitive controller to learn at the common period of the disturbances. The second employs multiple p-integrator based repetitive controllers operating in parallel. The third method introduces the concept of sequenced repetitive controllers. Cross-talk elimination algorithms are proposed to improve the repetitive control performance in the presence of transients. A novel averaging algorithm is introduced for on-line and off-line control strategies.

Solcz, Edward J.↗

NASA NTRS1992

Indirect decentralized learning control

The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper develops improved indirect learning control algorithms, and studies the use of such controllers in decentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The basic result of the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample time in the digital learning controller is sufficiently short.

Longman, Richard W.↗

NASA NTRS2005

ISS ECLSS Technology Evolution for Exploration

The baseline environmental control and life support systems (ECLSS) currently deployed on the International Space Station (ISS) and the regenerative oxygen generation and water reclamation systems to be added in 2008 are based on technologies selected during the early 1990's. While they are generally meeting, or exceeding requirements for supporting the ISS crew, lessons learned from hardware development and on orbit experience, together with advances in technology state of the art, and the unique requirements for future manned exploration missions prompt consideration of the next steps to be taken to evolve these technologies to improve robustness and reliability, enhance performance, and reduce resource requirements such as power and logistics upmass. This paper discusses the current state of ISS ECLSS technology and identifies possible areas for evolutionary enhancement or improvement.

Carrasquillo, Robyn L.↗

DOE OSTI2018

A simplified regression building thermal modelling method for detached two-floor house in U.S.

The residential building sector accounts for approximately 37% of total U.S. electricity consumption. Within the residential building sector, heating and cooling is the main target for peak load shifting/reduction since it is the largest contributor to peak demand. In fact, the flexibility of residential HVAC loads can provide continuous variation of demand to provide grid services by varying their demand over a baseline. The performance of HVAC load control to provide grid services relies heavily on the accuracy of indoor air temperature or cooling/heating demand predictions and therefore the quality of building model. Besides forward models, popular building models are data-driven models which can be broken down into two categories: gray-box model, e.g. Resistance-Capacitance (RC) model and black-box model. RC model, also called lumped capacitance or network model, which is constituted with electrical analogue pattern with resistance (R) and capacitance (C). In general, RC models require considerable computation burden and long periods of data to train limited number of model coefficients. Black-box models have gained increasing interest due to their capability in analyzing large-scale data and flexibility in practical applications. But, the data-mining based (machine learning algorithms/techniques based) models tend to have invisible model structures which poses a problem when trying to use the model for optimal control or model predictive control of the HVAC system. Hence, there is a continuing need for efficient online system identification techniques, which can provide explicit parameters for the model. Traditional regression models fit well for this specific purpose. This paper presents an innovative way to predict average indoor temperature in separate floors of typical detached residential house. A rolling horizon linear regression model, which includes online adaptive correction component, is proposed to predict the temperature difference between downstairs and upstairs. A RC model is used to predict the overall mean indoor air temperature. Since the adaptive algorithm needs to be implemented online, a less computation-demanding polynomial fitting algorithm is adopted. This kind of fitting problem can be cast as linear regression problem with multiple variables, parameters of which can be efficiently obtained by well-known gradient descent method.The validation is conducted by comparing the predicted results with the results from data-mining based models as well as measured data from a real typical detached two-floor house. The results show that the developed method has satisfactory performance in predicting the building indoor temperature in 1st and 2nd floors.

Cui, Borui↗

DOE OSTI2022

A Single-Feature Machine Learning Method for Detecting Multiple Types of Events from PMU Data

This paper describes simple and efficient machine learning (ML) methods for efficiently detecting multiple types of power system events captured by PMUs scarcely placed in a large power grid. It uses a single feature from each PMU based on a rectangle area enclosing the event in a given data window. This single feature is sufficient to enable commonly used ML models to detect different types of events quickly and accurately. The feature is used by five ML models on four different data-window sizes. The results indicated a tradeoff between the execution speed and detection accuracy in variety of data-window size choices. The proposed method is insensitive to most data quality issues typical for data from field PMUs, and thus it does not require major data cleansing efforts prior to feature extraction.

Dokic, Tatjana↗

DOE OSTI2024

Blockchain-based decentralized computing

An exemplary blockchain-based decentralized computing system and method are disclosed for industrial analytics applications. The exemplary system and method leverage blockchain technology to deliver and execute privacy-preserving decentralized predictive analytics, machine learning, and optimization operations for various industrial applications using a set of self-contained analytics block smart contracts that can be readily utilized and in analytics applications to deploy across multiple sites.

Ramanan, Paritosh P.↗

DOE OSTI2026

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Artificial neural networks↗

DOE OSTI2025

Energy storage planning for enhanced resilience of power systems against wildfires and heatwaves

Extreme weather events pose significant risks to power grid stability due to their severe consequences and potential for widespread failures. Energy storage systems hold great potential for enhancing grid resilience against such events by providing reliable power during peak demand periods. However, accurately quantifying the size, location, and investment costs of new energy storage assets is a complex task, as energy storage planning decisions depend on the investment choices of other generation technologies and the integration of new transmission projects. Here, this paper presents a novel capacity expansion planning framework that simultaneously optimizes investments in energy storage, generation, and transmission, determining their optimal size, location, and type, while incorporating extreme weather events into long-term planning. More specifically, our stress-event-informed planning framework integrates the impact of heatwaves and wildfires into the planning process, identifying least-cost investment solutions that comply with policy goals and enhance grid resilience. The proposed framework employs machine-learning-based modeling to project heatwave-induced loads and performance-based risk assessment to evaluate wildfire-driven transmission line derates. Using industry-standard datasets to accurately represent the transmission topology of the Western Interconnection (WI) system, the proposed framework is applied to the WI 40-zone system, with investment decisions reported for the years 2030, 2035, and 2040. Simulation results reveal that with just a 10% increase in investment costs, resilience against extreme events can be significantly improved, with investment decisions heavily favoring energy storage, particularly 4-hour energy storage systems.

25 ENERGY STORAGE↗