Large-Signal Stability Analysis for Inverter-Based Dynamic Microgrids Reconfiguration
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Engineering↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Transfer Learning-Based Independent Component Analysis
Understanding the underlying component structure is crucial for multivariate signal analysis. Among all the techniques that try to learn the latent structure, independent component analysis (ICA) is one of the most important and popular methods, which aims to extract independent components from multivariate signals and enables further analysis. For example, in electroencephalogram (EEG) analysis, artifacts filtering and disease detection are conducted based on the independent components of the signals. One critical challenge in existing ICA approaches is that the component extraction accuracy may degrade when the available data of a unit are limited. To address this issue, this paper proposes a transfer learning-based ICA method by innovatively transferring component distribution from a source domain, so that accurate component extraction results can be achieved even when only limited data are available in the target domain. To the best of our knowledge, this is the first work that leverages transfer learning to improve ICA accuracy with limited available data. In particular, we first extract all the independent components from the source domain by maximizing the log-likelihood function with a Newton-like method on a smooth manifold. Then for the target domain, the component with the largest negentropy is extracted in each round. To effectively leverage the knowledge from the source domain and to prevent the negative transfer, we try to find a component in the source domain that matches the component we are extracting. The probability density function of the matched component will then be used to improve the component extraction accuracy if such matched component can be found; otherwise, no knowledge will be transferred. Finally, numerical simulations and a case study with electrocardiogram (ECG) data are conducted, showing the effectiveness of the proposed method in transferring knowledge and reducing negative transfer.
42 ENGINEERING↗
A Stability Analysis Framework for Oscillation Analysis and Mitigation
This presentation showcases a framework for oscillation analysis and mitigation based on small-signal analysis.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Synthetic Infrasound Data for Machine Learning Detectors
Synthetic data is a powerful tool to generate large amounts of training data for machine learning models. The methods outlined in this report will be used to retrain the deep learning classifier for increased accuracy. Synthetic data will be useful to address the natural class imbalance between the different categories in the original ML work. Additionally, these tools will be applied for a variety of signal analysis methods that would use signals with a known signal-to-noise ratio for validation and testing.
58 GEOSCIENCES↗
Block Island Acoustic Data Analysis and Peak Detection Tools
This dataset contains acoustic signal analysis tools and processed datasets for pile driving noise characterization during Block Island Wind Farm construction (October 25, 2015), including peak detection algorithms, signal extraction methods, and visualization products for multi-channel hydrophone array data.
17 WIND ENERGY↗
Small signaling peptides, phylogenetic analysis
ML Phylogenetic analysis of small signaling peptides in Arabidopsis, Sorghum bicolor, Rice, Wheat, Maize, and Brachypodium. Sorghum only phylogenetic trees were used to name genes. All trees but RALF tree are rooted at midpoint.
Kurtz, Evan [Department of Biochemistry and Biophy↗
Beam Loss Assessment Through Use of Photomultiplier Tubes
Modern accelerators aim to deliver maximal beam current at stable energy with minimal beam loss. Environmental changes, among other factors, can result in increased beam loss and decreased beam throughput, prompting daily retuning of the accelerator. The compact nature of the oldest part of the linear accelerator limits the available beam instrumentation, making beam loss assessment and tuning difficult. Thus, additional devices for beam loss monitoring must be considered. Photomultiplier tube-based beam loss monitors (BLMs) were installed along the Fermilab drift tube Linac to assess beam loss. Due to noise, the data from the installed photomultiplier tubes was difficult to assess. After noise reduction and signal analysis, it was found that the signals produced by the photomultiplier tubes in response to beam loss were consistent for a given configuration and therefore a reasonable measure of beam loss. This project lays groundwork for future work in beam loss assessment using photomultiplier tubes, with the automation of the process developed in this project being the next step in this effort.
Waggoner, Alexander↗
Machine Learning-Enabled Wearable Piezoelectric Acoustic Sensor for Real-Time Breast Abnormality Detection
In contemporary society, breast health has become a significant public health concern, particularly among women. According to statistics from the World Health Organization, both the incidence and mortality rates of breast tumors have steadily increased in recent years. Therefore, effective early-stage screening and postoperative monitoring are essential for maintaining breast health. However, conventional clinical diagnostic modalities are typically bulky, operationally complex, and unsuitable for continuous real-time monitoring, which limits their use in portable and everyday health management applications. To address these limitations, this study proposes a machine learning-integrated wearable piezoelectric sensing platform as an auxiliary tool for breast health assessment. The device consists of a PDMS matching layer embedded with flexible silver nanowires, a P(VDF-TrFE) piezoelectric layer, and a multi-channel low-noise signal acquisition circuit. It is capable of acquiring acoustic echo signals from tissue-mimicking environments and automatically evaluating signal validity using a convolutional neural network (CNN). By integrating piezoelectric sensing with deep learning-based signal analysis, the proposed system achieves a signal-to-noise ratio exceeding 70 dB and a real-time classification accuracy above 96% under controlled conditions. These results demonstrate that the platform provides a compact, portable, and intelligent approach for wearable sensing of mechanical heterogeneity and highlight its potential for future development in continuous biomedical monitoring technologies.
He, Shuaitong↗
Topological and Dynamical Representations for Radio Frequency Signal Classification
Radio Frequency (RF) signals are found throughout our world, carrying over-the-air information for both digital and analog uses with applications ranging from WiFi to the radio. One area of focus in RF signal analysis is determining the modulation schemes employed in these signals which is crucial in many RF signal processing domains from secure communication to spectrum monitoring. This work investigates the accuracy and noise robustness of novel Topological Data Analysis (TDA) and dynamic representation based approaches paired with a small convolution neural network for RF signal modulation classification with a comparison to state-of-the-art deep neural network approaches. We show that using TDA tools, like Vietoris-Rips and lower star filtrations, and the Takens' embedding in conjunction with a standard shallow neural network we can capture the intrinsic dynamical, geometric, and topological features of the underlying signal's manifold, offering informative representations of the RF signals. Our approach is effective in handling the modulation classification task and is notably noise robust, outperforming the commonly used deep neural network approaches in mode classification. Moreover, our fusion of dynamical and topological information is able to attain similar performance to deep neural network architectures with significantly smaller training datasets.
Myers, Audun D.↗
Investigation on Tuning Power–Frequency Droop for Improved Grid–Forming Inverter and Synchronous Generator Transient Load Sharing
Poor transient load sharing has been observed during the parallel operation of synchronous generators (SGs) and droop-controlled grid-forming (GFM) inverter-based resources (IBRs) in islanded microgrids. This can result in overcurrent protections, causing the inverters to trip, which leads to the collapse of voltage and frequency. In this article, small-signal analysis supported by electromagnetic transients (EMTs) simulation is performed on a detailed model of a microgrid containing IBRs and an SG to identify underdamped modes and their sensitivities to the power–frequency (P-F) droop parameter. Further EMT simulation of a more complex microgrid model with multiple GFM IBRs and SGs is performed to determine whether trends identified from the modal analysis extend to cases where the number of SGs in operation may vary. The objective is to investigate the effectiveness of tuning this parameter to reduce the likelihood of overcurrents in response to load and generation disturbances. The work uncovers both fast and slow modes of concern. Primarily, the findings show that increasing the P-F droop value improves the damping of the slower mode while degrading the damping of the faster mode, leading to an upper limit on the damping improvement of the GFM IBR output current. In EMT simulations of the more complex microgrid, the greatest damping improvement still exhibits significant maximum overshoot. Furthermore, the optimal value of the P-F droop parameter is sensitive to the number of SGs in operation. The conclusion is that tuning the P-F droop parameter is not an effective means to avoid the overcurrent tripping of GFM IBRs during response to large load or generation disturbances when operating in parallel to SGs in islanded microgrids.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Heterogeneous Integrated Failure Analysis Using Radio Frequency Signal Detection/Injection with Power Spectrum Analysis
Explore the source record for details and available documents.
Christiansen, Joel Timothy↗
Auto-Curation of Seismic Event Data for Signal Denoising
Denoising contaminated seismic signals for later processing is a fundamental problem in seismic signals analysis. Neural network approaches have shown success denoising local signals when trained on short-time Fourier transform spectrograms. One challenge of this approach is the onerous process of hand-labeling event signals for training. By leveraging the SCALODEEP seismic event detector, we develop an automated set of techniques for labeling event data. Despite region specific challenges, training the neural network denoiser on machine curated events shows comparable performance to the neural network trained on hand curated events. We showcase our technique with two experiments, one using Utah regional data and one using regional data from the Korean peninsula.
58 GEOSCIENCES↗
Robust Infrasound Detection via Deep Learning (RIDDL)
RIDDL is a suite of software tools that enable AI/ML analysis of infrasound data. Methods and capabilities include construction, evaluation, and application of models for infrasound signal analysis, construction of synthetic data useful for construction and evaluation, as well as various other advanced data science tools enabling infrasound signal detection and categorization as well as downstream analysis methods such as localization and characterization of detected sources.
Blom, Philip↗
Evaluating Scalograms for Seismic Event Denoising
Denoising contaminated seismic signals for later processing is a fundamental problem in seismic signals analysis. The most straightforward denoising approach, using spectral filtering, is not effective when noise and seismic signal occupy the same frequency range. Neural network approaches have shown success denoising local signal when trained on short-time Fourier transform spectrograms (Zhu et al 2018; Tibi et al 2021). Scalograms, a wavelet-based transform, achieved ~15% better reconstruction as measured by dynamic time warping on a seismic waveform test set than spectrograms, suggesting their use as an alternative for denoising. We train a deep neural network on a scalogram dataset derived from waveforms recorded by the University of Utah Seismograph Stations network. We find that initial results are no better than a spectrogram approach, with additional overhead imposed by the significantly larger size of scalograms. A robust exploration of neural network hyperparameters and network architecture was not performed, which could be done in follow on work.
58 GEOSCIENCES↗
Multiplexing of frequency-modulation spectroscopy by spread-spectrum codes, demonstrated in continuous-wave LIDAR
Frequency-modulation spectroscopy (FMS) is generally suited to code-division multiplexing, and we demonstrate that capacity in a form of continuous-wave LIDAR, utilizing a sharp CO 2 absorption transition at 1.6 µm in simple ranging setups. The approach retains the advantages of FMS, including coherent detection and good rejection of broad absorption backgrounds. Extensions of this multiplexed approach to the continuous, simultaneous detection of several transitions would come by transmitting an encoded combination of frequency-modulated carriers, each tuned to detect a unique absorption transition. Signal analysis at the receiver involves a simple process of de-multiplexing that, in a general application, reveals targets at various distances and the absorption-related FMS signals in between.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
stochprop Documentation (Release 1.0)
Simulations of infrasonic propagation in the atmosphere typically utilize a single atmospheric specification describing the acoustic sound speed, ambient winds, and density as a function of altitude. Due to the dynamic and sparsely sampled nature of the atmosphere, there is a notable amount of uncertainty in the atmospheric state at a given location and time so that a more robust analysis of infrasonic propagation requires inclusion of this uncertainty. This Python library, stochprop, has been implemented using methods developed jointly by infrasound scientists at Los Alamos National Laboratory (LANL) and the University of Mississippi’s National Center for Physical Acoustics (NCPA). This software library includes methods to quantify variability in the atmospheric state, identify typical seasonal variability in the atmospheric state and generate suites of representative atmospheric states during a given season, as well as perform uncertainty analysis on a specified atmospheric state given some level of uncertainty. These methods have been designed to interface between propagation modeling capabilities such as InfraGA/GeoAc and NCPAprop and signal analysis methods in the LANL InfraPy tool.
58 GEOSCIENCES↗
The Utility of Infrasound in Global Monitoring of Extraterrestrial Impacts: A Case Study of the 2008 July 23 Tajikistan Bolide
Abstract Among the various observational techniques used for the detection of large bolides on a global scale is a low-frequency sound known as infrasound. Infrasound, which is also one of the four sensing modalities used by the International Monitoring System, offers continuous global monitoring and can be leveraged for planetary defense. Infrasonic records can provide an additional dimension for event characterization and a distinct perspective that might not be available through any other observational method. This paper describes the infrasonic detection and characterization of the bolide that disintegrated over Tajikistan on 2008 July 23. This event was detected by two infrasound stations at distances of 1530 and 2130 km. Propagation paths to one of the stations were not predicted by the model, despite being clearly detected. The presence of the signal is attributed to the acoustic energy being trapped in a weak but leaky stratospheric AtmoSOFAR channel. The infrasound signal analysis indicates that the shock originated at the point of the main breakup at an altitude of 35 km. The primary mode of the shock production of the signal detected at the two stations was a spherical blast resulting from the main gross fragmentation episode. The energy estimate, based on the signal period, is 0.17–0.51 kt of TNT equivalent, suggesting a mass of 6.6–23.5 tons. The corresponding object radius, assuming the chondritic origin, was 0.78–1.18 m.
79 ASTRONOMY AND ASTROPHYSICS↗