Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Detection”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Automated detection of part quality during two-photon lithography via deep learning

Two-photon lithography (TPL) is an additive manufacturing technique for fabricating three-dimensional objects with nanoscale features. A main challenge of TPL is the routine and labor-intensive task of finding suitable light dosage parameters, i.e. writing speed and laser intensity that induce photo-polymerization within a wide variety of candidate photo-curing polymers. Another challenge is the monitoring required during fabrication. In this work, we apply machine learning (ML) models to accelerate the process of identifying optimal light dosage parameters and automate the detection of part quality. We curate TPL videos of different parts fabricated under a range of light dosage parameters using different resins and train spatial-temporal ML models on this data. Our results show that ML models can detect TPL part quality with a 95.1% accuracy in milliseconds. We also evaluate classification failures and identify two operating modes: parameter optimization and part quality detection. Last but not least, we publicly release this labelled dataset so that it may serve as a useful benchmark to the community. Our approach to process optimization and part quality detection addresses important aspects of TPL industrialization, is applicable beyond TPL and should benefit other additive manufacturing techniques with similar barriers to operating at industrial scale.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effects of digital fringe projection operational parameters on detecting powder bed defects in additive manufacturing

Additive manufacturing is a technology transforming traditional production timelines. Specifically, metal additive manufacturing (MAM) has been increasingly adopted by a variety of industries, not only to prototype, but also to fulfill full production scale applications with much lower lead times. Like any maturing manufacturing technology, developments in verifying and validating processes are necessary to support continuous growth. Due to the complex nature of MAM, part quality and repeatability remain integral challenges that inhibit further adoption of MAM for critical component production. In this study, we present data taken from a developing in-process monitoring system designed to measure and detect powder bed defects (PBDs) in powder bed fusion MAM systems using surface height maps created with structured light illumination. We showcase the feasibility of the monitoring technique for in-process implementation by detecting streak PBDs with varying severities (height, width) created in a lab environment. We present results of powder bed measurements for varying experimental parameters of the structured light system such as illumination angle, illumination pattern, and number of illuminations. We also present an expression used to determine experimental height noise based on input parameters for PBD detection based on the instrument transfer function of the structured light monitoring system for arbitrary pixel intensity noise contributions. In conclusion, with the results of PBD detection across multiple experimental measurement parameters, we provide a best practices approach to in-process implementation of the monitoring system in powder bed fusion manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Effect of threshold parameters on infrared segmentation methods for porosity detection in electron beam powder bed fusion

In-situ process monitoring has seen significant interest in additive manufacturing to address qualification and certification goals. This is especially prevalent in metal powder bed fusion processes such as electron beam powder bed fusion (PBF-EB), with layer-wise infrared imaging being commonly used to detect defects. Here, this work compares two different segmentation methods (static thresholding and statistical thresholding) used for detecting porosity from in-situ infrared imaging data for PBF-EB. Samples were manufactured at a variety of focus offset values to induce porosity. Then, the segmented infrared images were compared to ex-situ X-ray computed tomography scans, which served as a ground-truth reference for objective evaluation. Through this analysis framework, the influential parameters, static threshold and N-value (number of standard deviations above the mean pixel value), respectively, for both image segmentation methods were analyzed and compared for their effects on porosity detection. With optimal parameter settings, the two methods had similar porosity detection performance, but the statistical method performed better under a larger variety of parameter settings.

Infrared imaging↗
DOE OSTI2022

Avoiding thermal runaway in lithium-ion batteries using ultrasound detection of early failure mechanisms

Thermal runaway leading to catastrophic failure has slowed the adoption of lithium-ion batteries, highlighting the need for early warning systems. In this work, ultrasound is used to detect physical changes in 0.950 Ah Li-ion battery cells by inducing conditions associated with thermal runaway. Ultrasound signal features are extracted as the batteries are cycled under baseline conditions and subsequently subjected to constant current and voltage overcharges. Two conditions identifying failure are defined: a warning to detect the start of overcharge and an emergency stop (E-stop), to immediately take the battery out of service. Using changes in ultrasound signal features, consistent warning and stopping classifications are applied to all failure experiments. On average, a warning notification was issued after 15% of the overcharge time and an E-stop was triggered after 35% of total overcharge duration before failure. Further testing suggests that it is possible to avoid thermal runaway by reacting to intermediate, warning notifications prior to an immediate E-stop. In other words, not only can detection derived by ultrasound identify battery failure before a catastrophic event, but can also provide early, actionable warnings so that overcharges can be detected and corrected quickly enough so the battery does not need to be decommissioned.

25 ENERGY STORAGE↗
DOE OSTI2022

Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis

The goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw formation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray computed tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objective, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.

42 ENGINEERING↗
DOE OSTI2023

K-region tetrasubstituted deep-blue pyrene-based luminogens: Visual detection of trace nitroaniline

In recent years, fluorescence sensors have been widely used in explosive detection due to their portability and high efficiency. Here, in the present work, two K-region tetrasubstituted deep-blue pyrene-based luminogens were designed and synthesized by introducing electron-donating groups. The optical properties were investigated by experimental and density functional theory (DFT) methods. Reasonable luminescence efficiency and electron rich characteristic allowed for the possible detection of nitro-explosives. Both luminogens, especially TFPy, exhibit a sensitive response towards nitroanilines (NA), with a limit of detection (LOD) for p-NA as low as 2.75 × 10 –8 M, which is mainly attributed to the electron-donating substituents on the periphery of the pyrene core and the extended π-conjugated structure. These research results establish a low-cost and simple strategy for the detection of trace nitroaniline.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Reliable statistics-based detection and investigation of anomalies in a SMART valve system

Reliable anomaly detection and diagnosis are critical for the safe operation of complex engineered systems. This study presents a unified framework that integrates statistical, model-based, and data-driven techniques for anomaly detection and investigation, demonstrated on SMART valve systems in hybrid energy applications. Four detection methods—mean deviation, seasonal extreme studentized deviate, ARIMA forecasting, and matrix profiling—were implemented and compared. Matrix profiling was particularly effective in revealing subtle deviations and hidden relationships among variables. Anomaly investigation was performed by analyzing variable-level and grouped signal profiles, with system topology incorporated to distinguish primary faults from propagated effects. Grouping signals by type enhanced interpretability, enabling accurate localization of anomalies across multi-dimensional datasets. Experimental results confirmed the framework's capability to consistently detect and isolate anomalies while providing actionable insights into system interdependencies. The proposed methodology offers a robust, interpretable, and scalable solution for condition monitoring, with potential applications in safety-critical domains such as nuclear energy, aerospace, and process industries.

ARIMA models↗
DOE OSTI2022

Reduction of detection limit and quantification uncertainty due to interferent by neural classification with abstention

Many measurements in the physical sciences can be cast as counting experiments, where the number of occurrences of a physical phenomenon informs the prevalence of the phenomenon's source. Often, detection of the physical phenomenon (termed signal) is difficult to distinguish from naturally occurring phenomena (termed background). In this case, the discrimination of signal events from background can be performed using classifiers, and they may range from simple, threshold-based classifiers to sophisticated neural networks. These classifiers are often trained and validated to obtain optimal accuracy, however we show that the optimal accuracy classifier does not generally coincide with a classifier that provides the lowest detection limit, nor the lowest quantification uncertainty. Here, we present a derivation of the detection limit and quantification uncertainty in the classifier-based counting experiment case. We also present a novel abstention mechanism to minimize the detection limit or quantification uncertainty a posteriori. We illustrate the method on two data sets from the physical sciences, discriminating Ar-37 and Ar-39 radioactive decay from non-radioactive events in a gas proportional counter, and discriminating neutrons from gammas in an inorganic scintillator and report results therefrom.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Automating the detection of hydrological barriers and fragmentation in wetlands using deep learning and InSAR

The loss of hydrological connectivity and fragmentation of natural wetlands is a widespread driver of wetland degradation. Understanding where and how natural connectivity is impaired is essential for managing, protecting and remediating these ecosystems. Wetland Interferometric Synthetic Aperture Radar (Wetland InSAR) can provide information on surface flow orientation in wetlands at a high spatial resolution, which can be used for barrier detection. However, the broad application of this approach is constrained by the labour-intensive manual delineation of barriers based on mapped water levels. This study presents the first deep learning-based methodology for the automated detection of hydrological barriers. We trained a deep convolutional network to segment edge features of hydrological barriers in 25 image pairs captured by ALOS PALSAR-1 L-Band InSAR between 2006 and 2011. The training dataset consists of manually labelled and delineated barriers showing abrupt changes in water surface elevation and wrapped interferograms with high coherence. We tested this method across three wetland sites: the Everglades and southern Louisiana wetlands (United States) and the Cienaga de Zapata (Cuba). Across these sites, the convolutional network detected hydrological barriers with up to 84% accuracy. The model performed particularly well for linear hydrological barriers such as roads, dikes, and channels. Notably, some barriers impede flow only seasonally, appearing during low water levels and disappearing when water levels rise. Our automated approach to detecting and assessing wetland hydrologic connectivity can be applied more broadly to support the effective management of fragmented wetland ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Brain-implantable multifunctional probe for simultaneous detection of glutamate and GABA neurotransmitters

We report Glutamate (GLU) and gamma-aminobutyric acid (GABA) are neurotransmitters (NTs) with an essential role in signal transmission in the brain. Brain disorders, such as epilepsy, Alzheimer's and Parkinson's diseases, and traumatic brain injury can be linked to imbalances in the GLU-GABA homeostasis that occurs in sub-second to seconds time frames. Current measurement techniques can detect these two NT concentrations simultaneously only in vitro. The present work reports on the fabrication of a silicon multifunctional biosensor microarray probe for sub-second simultaneous GLU-GABA detection in real-time, with excellent analyte sensitivity and selectivity and in vivo capabilities. The novel Si probes feature four surface-functionalized platinum ultramicroelectrodes (UMEs) for simultaneous amperometric detection of GLU and GABA with a sentinel, and a built-in microfluidic channel for the introduction of neurochemicals in the proximity of the UMEs. The microchannel also allows functioning of an On-Demand In-situ Calibrator that runs in-situ biosensor calibration. The probe exhibited excellent robustness at insertion in agarose-gel brain-tissue-mimicking test, and remarkably high hydrogen peroxide sensitivity (a by-product of GLU-GABA enzyme biosensor) with values on the order of 5000 nA μM -1 cm -2 and maximum sensitivities of 204 ± 15 nA μM -1 cm -2 and 37 ± 7 nA μM -1 cm -2 for GLU and GABA, respectively. Furthermore, the limit of detection of the biosensors reached as low as 7 nM, 165 nM and 750 nM for H 2 O 2 , GLU and GABA, respectively and a temporal resolution of hundreds of milliseconds during in vivo studies using freely moving rats.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A polarimetry-based field-deployable non-interruptive mirror soiling detection method

The soiling level of heliostat mirrors in Concentrated Solar Power (CSP) fields is one of the key factors that significantly influences optical efficiency. State-of-the-art methods of monitoring heliostats soiling levels still face various challenges, including slow speed, labor-intensive operations, resolution and accuracy constraints or interruptions to solar field operations. Here, we present a rapid, cost-effective, and non-intrusive method for mirror soiling detection based on polarimetric imaging, referred to as Polarimetric Imaging-based Mirror Soiling (PIMS). The compact PIMS device is designed for integration with unmanned aerial vehicles (UAVs), enabling rapid, large-area assessments of heliostat mirrors for efficient soiling detection. Our method utilizes the correlation between the Degree of Linear Polarization (DoLP) and surface soiling level based on Mie scattering theory and Monte Carlo simulations. Field deployment of the PIMS method requires minimal device installation, and its UAV-based operation allows for soiling detection without interrupting plant activities. The PIMS method holds the potential for mirror soiling detection across various concentrated solar power (CSP) plants and can be further adapted for other types of solar fields, such as parabolic trough systems.

CSP Field↗
DOE OSTI2023

RadioGalaxyNET: Dataset and novel computer vision algorithms for the detection of extended radio galaxies and infrared hosts

Abstract Creating radio galaxy catalogues from next-generation deep surveys requires automated identification of associated components of extended sources and their corresponding infrared hosts. In this paper, we introduce RadioGalaxyNET, a multimodal dataset, and a suite of novel computer vision algorithms designed to automate the detection and localization of multi-component extended radio galaxies and their corresponding infrared hosts. The dataset comprises 4 155 instances of galaxies in 2 800 images with both radio and infrared channels. Each instance provides information about the extended radio galaxy class, its corresponding bounding box encompassing all components, the pixel-level segmentation mask, and the keypoint position of its corresponding infrared host galaxy. RadioGalaxyNET is the first dataset to include images from the highly sensitive Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope, corresponding infrared images, and instance-level annotations for galaxy detection. We benchmark several object detection algorithms on the dataset and propose a novel multimodal approach to simultaneously detect radio galaxies and the positions of infrared hosts.

Astronomy & Astrophysics↗
DOE OSTI2020

A Size-Selectively Biomolecule-Immobilized Nanoprobe-Based Chemiluminescent Lateral Flow Immunoassay for Detection of Avian-Origin Viruses

Here, a signal-amplifiable nanoprobe-based chemiluminescent lateral flow immunoassay (CL-LFA) was developed to detect avian influenza viruses (AIV) and other contagious and fatal viral avian-origin diseases worldwide. Signal-amplifiable nanoprobes are capable of size-selective immobilization of antibodies (binding receptors) and enzymes (signal transducers) on sensitive paper-based sensor platforms. Particle structure designs and conjugation pathways conducive for antigen accessibility to maximum amounts of immobilized enzymes and antibodies have advanced. The detection limit of the CL-LFA using the signal-amplifiable nanoprobe for the nucleoprotein of the H3N2 virus was 5 pM. Sensitivity tests for low pathogenicity avian influenza H9N2, H1N1, and high pathogenicity avian influenza H5N9 viruses were conducted, and the detection limits of CL-LFA were found to be 10 3.5 50% egg infective dose (EID 50 )/mL, 10 2.5 EID 50 /mL, and 10 4 EID 50 /mL, respectively, which is 20 to 100 times lower than that of a commercial AIV rapid test kit. Moreover, CL-LFA demonstrated high sensitivity and specificity against 37 clinical samples. The signal-amplifiable probe designed in this study is a potential diagnostic probe with ultrahigh sensitivity for applications in the field of clinical diagnosis, which requires sensitive antigen detection as evidenced by enhanced signaling capacity and sensitivity of the LFAs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Growth of High-Purity CsPbBr 3 Crystals for Enhanced Gamma-Ray Detection

High-quality CsPbBr 3 crystals hold significant potential for gamma-ray detection due to their remarkable optoelectronic properties. This study details an optimized production process using the Bridgman method to achieve highly pure CsPbBr 3 crystals. By implementing rigorous synthesis and purification techniques, we successfully reduced the total impurity levels to 9 ppm, as confirmed by glow discharge mass spectroscopy (GDMS). The resulting CsPbBr 3 crystals demonstrate exceptional performance, including high transparency, intense photoemission, and prolonged photoluminescence decay times. These properties facilitate superior gamma-ray detection with an energy resolution of 1.4% for the 137 Cs 662 keV gamma-rays, comparable to commercial Cd 1-x Zn x Te (CZT) detectors. Our findings underscore the critical relationship between material purity and detector performance, highlighting the potential of CsPbBr 3 as a cost-effective alternative in radiation detection applications. Further studies on defect origins and electronic states are necessary to fully leverage the capabilities of CsPbBr 3 crystals in practical high-energy radiation detection systems.

Bridgman↗
DOE OSTI2024

Readout of Oriented Triplet Excitons in Linear Acenes via Room-Temperature Electrically Detected Magnetic Resonance

In this study, optically generated molecular spin centers offer an attractive platform for room-temperature spintronic and quantum applications. The linear acene family of molecules are especially good candidates due to their efficient generation of highly polarized triplet excitons via singlet fission. However, the direct detection and manipulation of these spin centers in thin films via the electrical means desirable for ultimate microelectronic devices has proven challenging. In particular, highly oriented triplet features have previously been detected in crystalline anthracene but longer acenes reveal only doublet features in Electrically-Detected Magnetic Resonance (EDMR). In this work we present EDMR spectra of highly oriented triplet excitons in pentacene for the first time, using a host-guest style device made of tetracene and pentacene. The guest acts as an energetic trap site, permitting the isolation and detection of molecular triplets at room temperature. Modeling of these results shows that the observed resonance features correspond to triplet sublevel transitions on isolated pentacene guest molecules. Rotation of the applied field confirms the tendency of the linear acenes to self-orient with the longest molecular axis perpendicular to the device substrate. Lastly, we find the disappearance of resonant triplet features in the neat acenes is not primarily due to the effects of exciton delocalization, but a broader mechanism of spin relaxation primarily influenced by exciton diffusivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Rapid, Sensitive, Label-Free Electrical Detection of SARS-CoV-2 in Nasal Swab Samples

Rapid diagnosis of coronavirus disease 2019 (COVID-19) is key for the long-term control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) amid renewed threats of mutated SARS-CoV-2 around the world. Here, we report on an electrical label-free detection of SARS-CoV-2 in nasopharyngeal swab samples directly collected from outpatients or in saliva-relevant conditions by using a remote floating-gate field-effect transistor (RFGFET) with a 2-dimensional reduced graphene oxide (rGO) sensing membrane. RFGFET sensors demonstrate rapid detection (<5 min), a 90.6% accuracy from 8 nasal swab samples measured by 4 different devices for each sample, and a coefficient of variation (CV) < 6%. Also, RFGFET sensors display a limit of detection (LOD) of pseudo-SARS-CoV-2 that is 10 000-fold lower than enzyme-linked immunosorbent assays, with a comparable LOD to that of reverse transcriptionpolymerase chain reaction (RT-PCR) for patient samples. To achieve this, comprehensive systematic studies were performed regarding interactions between SARS-CoV-2 and spike proteins, neutralizing antibodies, and angiotensin-converting enzyme 2, as either a biomarker (detection target) or a sensing probe (receptor) functionalized on the rGO sensing membrane. Finally, taken together, this work may have an immense effect on positioning FET bioelectronics for rapid SARS-CoV-2 diagnostics.

60 APPLIED LIFE SCIENCES↗