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Yoo, S.

Publications and source records attributed to Yoo, S..

12 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events

Uncertainty quantification (UQ) methods play an important role in reducing errors in weather forecasting. Conventional approaches in UQ for weather forecasting rely on generating an ensemble of forecasts from physics-based simulations to estimate the uncertainty. However, it is computationally expensive to generate many forecasts to predict real-time extreme weather events. Evidential Deep Learning (EDL) is an uncertainty-aware deep learning approach designed to provide confidence about its predictions using only one forecast. It treats learning as an evidence acquisition process where more evidence is interpreted as increased predictive confidence. We apply EDL to storm forecasting using real-world weather datasets and compare its performance with traditional methods. Our findings indicate that EDL not only reduces computational overhead but also enhances predictive uncertainty. This method opens up novel opportunities in research areas such as climate risk assessment, where quantifying the uncertainty about future climate is crucial.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Waveform processing using neural network algorithms on the front-end electronics

In a multi-channel radiation detector readout system, waveform sampling, digitization, and raw data transmission to the data acquisition system constitute a conventional processing chain. The deposited energy on the sensor is estimated by extracting peak amplitudes, area under pulse envelopes from the raw data, and starting times of signals or time of arrivals. However, such quantities can be estimated using machine learning algorithms on the front-end Application-Specific Integrated Circuits (ASICs), often termed as “edge computing”. Edge computation offers enormous benefits, especially when the analytical forms are not fully known or the registered waveform suffers from noise and imperfections of practical implementations. In this work, we aim to predict peak amplitude from a single waveform snippet whose rising and falling edges containing only 3 to 4 samples. We thoroughly studied two well-accepted neural network algorithms, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) by varying their model sizes. Further, to better fit front-end electronics, neural network model reduction techniques, such as network pruning methods and variable-bit quantization approaches, were also studied. By combining pruning and quantization, our best performing model has the size of 1.5 KB, reduced from 16.6 KB of its full model counterpart. It can reach mean absolute error of 0.034 comparing to that of a naive baseline of 0.135. Such parameter-efficient and predictive neural network models established feasibility and practicality of their deployment on front-end ASICs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Deep Learning Systems for Increased Safeguards Surveillance Review Productivity

Nuclear safeguards inspectors expend significant time and maintain intense focus in reviewing video surveillance for safeguards relevant events. To increase efficiency and reduce the time burden of safeguards inspectors performing surveillance data review, this paper presents a novel deep learning (DL) systems concept to integrate generalized DL models into the safeguards surveillance review workflow. The Agency is investigating several DL algorithms for object recognition, localization, tracking, and flagging relevant activities. The project team is working closely with nuclear safeguards inspectors to identify review use cases (based on specific safeguards objectives) and collect their associated requirements. We focused on CANDU and LWR Nuclear Power Plants (NPPs) and their associated dry storage areas as these present a particularly heavy burden on the inspector surveillance review process due to the number of these facilities under safeguards worldwide. Initial DL algorithm results on safeguards data are promising. Using a convolutional neural network, the team attained a mean average precision (mAP) of 92.9% identifying and localizing spent fuel (SF) casks from a 475 surveillance image dataset. Further, the team had initial success in training a recurrent neural network to identify reactor area activities in video clips, successfully indicating when SF casks enter or exit a pool. We discuss how such DL algorithms would be integrated into the Next Generation Surveillance Review (NGSR) software application. Another issue impacting review productivity is the long time inspectors may have to wait when running these algorithms in NGSR. We present a concept to pre-process remotely collected surveillance data with DL models as the data arrives to IAEA headquarters so that results are already available when starting a new review in NGSR. The proposed DL system concept shows a pathway and workflow for increasing an inspector’s surveillance review productivity by quickly and accurately identifying declared and undeclared safeguards relevant objects and activities in large quantities of surveillance imagery data.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

YOLO Test Software v1.4

This is a user manual of the software being developed at Brookhaven National Laboratory to test deep learnings algorithms. (YOLO) You Only Look Once. V1.4 added new features, including Display metric average recall, early termination option and display filename/image.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE): National Geospatial-Intelligence Agency Calibration Target Placements Field Campaign Report

The purpose of our research has been to develop a capability of simulating image collection from any sensor so as to account for sensor configuration, ground spectral radiance, and atmospheric contributions. Atmosphere is arguably the most difficult aspect of the image pipeline to accurately characterize and integrate into an image simulation. Some of the reasons for this include the highly variable spatial content of different absorbing and scattering species in the atmosphere. Additionally, the interaction of land and atmosphere can significantly impact the total radiation budget, especially in the southern Great Plains.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Time-delayed transition of normal-to-abnormal glow in pin-to-water discharge

Time-delayed transition of normal-to-abnormal glow was investigated in discharge between spoke-like pins and ultrapure water by applying AC-driven power at a frequency of 14.3 kHz at atmospheric pressure. The normal-to-abnormal transition can be recognized from the slope changes of current density, gas temperature, electrode temperature, and OH density. The slope changes took place in tens of minutes rather than just after discharge, in other words, the transition was delayed. The time-delay of the transition was caused by the interaction between the plasma and water. The plasma affected water properties, and then the water affected plasma properties.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Sensor network based solar forecasting using a local vector autoregressive ridge framework

The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

A zonal CFD method for three-dimensional wing simulations

The primary objective of this work is to demonstrate the feasibility of a 3D potential/viscous flow coupling procedure for reducing computational effort while maintaining solution accuracy. The closed-loop, overlapped, velocity-coupling concept has been developed in a new code, ZAP3D, that couples a potential flow panel code with a Navier-Stokes method. The current ZAP3D calculation for an aspect ratio 5 wing with an outer domain radius of about 1.2 chords represents a speed-up in CPU time over the ARC3D large domain calculation by about a factor of 2.5. This improvement is achieved for less than a 0.5 percent deviation in C(L), 10 counts change in C(D), and 0.0015 variation in C(My). Additional reductions in the required computational domain for ZAP3D are expected as the method is further developed and refined.

Summa, J. M.↗