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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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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Event Classifications on DNE2 Main Experiment Data using a Convolutional Neural Network Ensemble

The Dynamic Networks (DN) Experiment for FY24 (DNE2) is an experiment within DN with the goal of quantitatively evaluating the effectiveness of solutions developed so far by various researchers under the Low Yield Nuclear Monitoring (LYNM) program using a shared set of metrics and datasets. A key component of this experiment is the mimicking of a signature processing pipeline, and comparing currently accepted and standard-use processing methods to more state-of-the-art processes developed under DN. In this work, we focus specifically on the Event Characterization (EC) Focus Area (FA) of the pipeline, where a seismic event’s magnitude, yield and class are identified. We use Deep Learning (DL) to classify the type of events being processed as either earthquakes (EQs) or explosions (EXs) for three iterations of experiment datasets. The model is noticeably more confident and accurate in classifying explosions than earthquakes, reflecting a known shortcoming of the model, that being of a bias towards predicting explosions over earthquakes in the west coast due to training data biases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

36 MATERIALS SCIENCE↗
NASA NTRS1972

Convoluted fabric for full-pressure gloves

Fabric, made of nylon ripstop coated with Neoprene, provides expansive and contractive mobility along posterior surface of glove fingers allowing maximum digital dexterity and tactility.

Elkins, W.↗
NASA NTRS1971

Performance of convolution coding concatenated with MFSK modulation in a Gaussian channel

The improvement in db due to concatenation over conventional M-ary coding is studied to reduce the probability of a bit error and to increase the available bit rate for the same system parameters of error rate, transmitter power, and range. The results of calculations for orthogonal modulation with noncoherent detection and Q-level correlator quantization are presented. It is shown that the correlator outputs are quantized to one of the Q levels, and the receiver output is a vector consisting of a list of the M correlator quantum levels. The channel has Q(M) possible outputs and M possible inputs. Optimum output is approached by increasing fine quantization

Choudhury, A. K.↗
NASA NTRS1972

Recent results in convolution feedback systems.

Survey of recent results obtained by the authors concerning certain types of multiinput, multioutput feedback systems. The discrete-time case as well as the continuous-time case are considered. In each case three theorems are shown. These give insight into the nature of the relationship between the open-loop operator and the closed-loop operator of the system, as well as necessary and sufficient conditions for stability of the closed-loop system when 'unstable' poles are present in their open-loop transfer function.

Desoer, C. A.↗
NASA NTRS1972

Convolution feedback systems.

Linear time-invariant feedback systems with multiple inputs and multiple outputs are examined. It is demonstrated that no loss of generality takes place considering the feedback to be unity. Necessary and sufficient conditions are derived for the closed-loop impulse response to be stable in a prescribed sense.

Desoer, C. A.↗
NASA NTRS1973

Enhancement of spectra by digital convolution.

A method is presented for convolving digitally scanned spectra by means of computerized filtering. This method is an effective tool for improving the quality of noisy spectra and for deconvolving high-quality spectra by removing instrument signature.

Lorre, J. J.↗
NASA NTRS1974

Convolutional codes. II - Maximum-likelihood decoding. III - Sequential decoding

Maximum-likelihood decoding is characterized as the determination of the shortest path through a topological structure called a trellis. Aspects of code structure are discussed along with questions regarding maximum-likelihood decoding on memoryless channels. A general bounding technique is introduced. The technique is used to obtain asymptotic bounds on the probability of error for maximum-likelihood decoding and list-of-2 decoding. The basic features of sequential algorithms are discussed along with a stack algorithm, questions of computational distribution, and the martingale approach to computational bounds.

Forney, G. D., Jr.↗