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Results for “unlabeled event”

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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Data-Driven Framework for Power System Event Type Identification via Safe Semi-Supervised Techniques

Herein this paper investigates the use of phasor measurement unit (PMU) data with deep learning techniques to construct real-time event identification models for transmission networks. Increasing penetration of distributed energy resources represents a great opportunity to achieve decarbonization, as well as challenges in systematic situational awareness. When high-resolution PMU data and sufficient manually recorded event labels are available, the power event identification problem is defined as a statistical classification problem that can be solved by numerous cutting-edge classifiers. However, in real grids, collecting tremendous high-quality event labels is quite expensive. Utilities frequently have a large number of event records without in-depth details (i.e., unlabeled events). To bridge this gap, we propose a novel semi-supervised learning-based method to improve the performance of event classifiers trained with a limited number of labeled events by exploiting the information from massive unlabeled events. In other words, compared to existing data-driven methods, our method requires only a small portion of labeled data to achieve a similar level of accuracy. Meanwhile, this work discusses and addresses the performance degradation caused by class distribution mismatch between the training set and the real applications. Based on the proposed safe learning mechanism, our model does not directly use all unlabeled events during model training, but selectively uses them through a comprehensive evaluation procedure. Numerical studies on a sizable PMU dataset have been used to validate the performance of the proposed method.

42 ENGINEERING↗
DOE OSTI2025

Grid Event Signature Library's Signature Matching Tool

The Signature Matching Tool (SMT) is a tool that assists users in labeling any unlabeled signatures, according to the hierarchical, event tags taxonomy developed for the Department of Energy's (DOE) Grid Event Signature Library (GESL).

Joo, Jhi Young [Lawrence Livermore National Labora↗
NASA NTRS2003

Furrow microtubules and localized exocytosis in cleaving Xenopus laevis embryos

In dividing Xenopus eggs, furrowing is accompanied by expansion of a new domain of plasma membrane in the cleavage plane. The source of the new membrane is known to include a store of oogenetically produced exocytotic vesicles, but the site where their exocytosis occurs has not been described. Previous work revealed a V-shaped array of microtubule bundles at the base of advancing furrows. Cold shock or exposure to nocodazole halted expansion of the new membrane domain, which suggests that these microtubules are involved in the localized exocytosis. In the present report, scanning electron microscopy revealed collections of pits or craters, up to approximately 1.5 micro m in diameter. These pits are evidently fusion pores at sites of recent exocytosis, clustered in the immediate vicinity of the deepening furrow base and therefore near the furrow microtubules. Confocal microscopy near the furrow base of live embryos labeled with the membrane dye FM1-43 captured time-lapse sequences of individual exocytotic events in which irregular patches of approximately 20 micro m(2) of unlabeled membrane abruptly displaced pre-existing FM1-43-labeled surface. In some cases, stable fusion pores, approximately 2 micro m in diameter, were seen at the surface for up to several minutes before suddenly delivering patches of unlabeled membrane. To test whether the presence of furrow microtubule bundles near the surface plays a role in directing or concentrating this localized exocytosis, membrane expansion was examined in embryos exposed to D(2)O to induce formation of microtubule monasters randomly under the surface. D(2)O treatment resulted in a rapid, uniform expansion of the egg surface via random, ectopic exocytosis of vesicles. This D(2)O-induced membrane expansion was completely blocked with nocodazole, indicating that the ectopic exocytosis was microtubule-dependent. Results indicate that exocytotic vesicles are present throughout the egg subcortex, and that the presence of microtubules near the surface is sufficient to mobilize them for exocytosis at the end of the cell cycle.

Non-NASA Center↗
DOE OSTI2025

Self-Supervised T-GCN for Detection of Disturbance and Propagation in Power Grid

Urban power systems increasingly rely on dense sensing to monitor grid reliability, yet disturbance labels are scarce and events are rare. We present a self-supervised spatio-temporal method that detects, localizes, and characterizes grid frequency disturbances across urban areas using only unlabeled data. Our approach trains a tiny Temporal Graph Convolutional Network (T-GCN) to forecast per-site frequency residuals (deviation from 60 Hz). The sensor graph is constructed directly from signals using pre-event Pearson correlation with a cross-correlation lag penalty without geocoding. At inference, node-level anomalies are the model's forecast errors; region-level alarms arise from connected components of high-score nodes. We estimate disturbance propagation by computing per-node arrival times (first persistent exceedance), then fit a planar or time-of-arrival model to obtain direction, speed, and an epicenter proxy. With only three real events collected at decisecond resolution across U.S. cities, we evaluate the T-GCN and report time-to-detect, footprint size, and propagation consistency. We further show that short-window embeddings from the T-GCN's hidden states enable few-shot event-vs-background recognition via a simple prototypical classifier. Despite minimal data and no labels, our system yields fast, spatially coherent detection and interpretable propagation maps, offering a practical, lightweight pathway to city-scale grid resilience analytics.

Niu, Haoran [ORNL] (ORCID:0000000155228297)↗
NASA NTRS2019

Ask-The-Expert: Minimizing Human Review for Big Data Analytics Through Active Learning

In this CIF project, we worked toward semi-automating knowledge discovery from anomaly detection algorithms through the use of active learning. Active learning is an area of research within machine learning that uses an "expert in the loop" to learn from large data sets that have very few annotations or labels available, and where providing such labels is expensive. In our case, the task can be defined as the identification of safety events from flight operational data. Since traditional anomaly detection algorithms cannot differentiate between operationally relevant and irrelevant statistical anomalies, Subject Matter Experts (SMEs) have a lengthy and expensive burden of investigating every example identified by the detection algorithm, classifying and labeling them as relevant or irrelevant. Active learningidentifies the unlabeled example for which a label would most improve the classifier, asks the domain expert for a label, and repeats this process until there are no more resources (time, budget) available for labeling or a minimum required performance is reached. A positive label indicates an operationally significant safety event whereas a negative label indicates otherwise. Based on these few labels we propose to build an active learning system that utilizes the SME's time in the most effective manner by iteratively asking for labels for as few informative instances as possible. Our work was proposed to be a stepping stone toward implementation and deployment of the system with user interface to be pursued by the Aviation Operations and Safety Program (AOSP) given its interest in safety monitoring and discovery of safety incidents.

aviation safety↗