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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

The Second ARM Training and Science Application Event: Training the Next Generation of Atmospheric Scientists

The Atmospheric Radiation Measurement (ARM) user facility (Turner and Ellingson 2016) collects valuable observations of atmospheric variables affecting the Earth’s radiation budget. These include properties of aerosol, cloud, precipitation, dynamic, and thermodynamic fields. The ARM user facility also supports routine high-resolution modeling efforts at its Southern Great Plains (SGP) site, and over the years it has had significant impacts on cloud-resolving and Earth system model development (Krueger et al. 2016; Randall et al. 2016). In an effort to promote use of the ARM data by the next generation of atmospheric scientists, the second ARM Summer Training and Science Applications Event on observations and modeling of clouds and precipitation took place from 14 to 21 July 2018. The event was sponsored by the ARM User Facility and was catered toward graduate students and early career scientists interested in observations and modeling of aerosol, cloud, and precipitation processes. Theoretical and practical instructions on the application of ground- and aircraft-based observations from a wide array of active and passive sensors, and finescale-resolution model simulations were provided during the training. This informed the participants about innovative methods for using the ARM data and numerical models to address complex scientific questions. The event was locally organized by the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and was held at the National Weather Center in Norman, Oklahoma. The training was attended by 12 instructors and 24 participants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Robust metal-ceramic coaxial cable sensors for distributed temperature monitoring in fossil energy power systems

The project aims to develop a new type of low cost, robust metal-ceramic coaxial cable (MCCC) Fabry-Pérot interferometer (FPI) sensor and demonstrate the capability of cascading a series of FPI sensing points in a single MCCC for real-time distributed monitoring of temperature up to 1000°C. The technical accomplishments of this project include: (i) development of metal and ceramic materials for fabricating the MCCC suitable for use in high temperature (up to 1000oC) fossil fuel, (ii) design, construction and test of single point MCCC-FPI sensors for high temperature measurement at deviation within ±2.7%, (iv) demonstration of the MCCC-FPI sensors for real-time distributed temperature measurement with desired sensitivity, spatial resolution, stability, and response speed that are important to practical applications, and (v) development of a novel MCCC-FPI sensor platform for measuring high frequency dielectric constants for solids and liquids as function of temperature. Single point MCCC-FPI sensors are fabricated based on the FPI parameters, including the relationship between reflector width and reflectance as well as the inter-reflector distance for a FPI, estimated by model calculations. MCCC-FPI sensors have been made using insulators of packed ceramic powders, ceramic tubes, and air or inert gases; outer conductors of various metal tubes; inner conductors of wires of the same metal materials as the tubes used; and reflectors of dense ceramic discs, casted ceramic discs and air gaps. The insulator, conductor and reflector materials are investigated for their thermal, chemical, and structural stabilities under temperatures up to 1000°C in simulated coal-derived syngas. Based on the experimental findings, alumina tube and discs, and stainless steel tube and wire are selected for reflector, insulator and outer and inner conductors, respectively. Air or inert gases are found to be useful for reflectors (in the form of narrow groves) or insulators (in the form of empty chamber cavity between ceramic discs) in construction of MCCC-FPI sensors. Single point MCCC-FPI sensors have been fabricated using stainless steel as conductors, dense α-alumina tubes as dielectric insulators, and two air gaps in the insulator as reflectors. The MCCC-FPI sensor with air gap reflectors has been demonstrated for measuring temperature between 200 and 500°C using microwave as sensing signal in a frequency range of 2 – 8 GHz. The temperature measurement is achieved by monitoring the frequency shift (Δƒ) of a selected resonant peak of the microwave interferogram reflected from the pair of air gap reflectors. The MCCC-FPI sensor exhibited excellent linear temperature-dependence for Δƒ, good accuracy (deviation ±2.7%), and reasonable response speed (<180 s for stabilization). Thermal drifting of the sensing signal was also rather minimal for the MCCC-FPI sensors after operating for more than one week at around 500oC. MCCC-FPI sensors with each containing 2 or 3 pairs of reflectors (i.e. 2 or 3 FPIs in a single cable) have been fabricated and demonstrated for distributed temperature measurement between 250 and 550°C in a microwave frequency range of 3-6 GHz. The measurement is achieved by monitoring the frequency shift of a selected resonant peak of the reflected microwave interferogram, which is obtained by the join-time-domain signal processing method. The results demonstrate that the FPI sensing points in both the three-point and two-point MCCC sensors can measure distributed high temperature with good reproducibility and consistency in multiple runs of measurement tests. The sensor signal displays excellent linear dependence between temperature and the corresponding frequency shift. The 2-point and 3-point MCCC-FPI sensors also exhibit fast response to environmental temperature changes. A three-point MCCC-FPI sensor has been further examined for temperature measurement up to 1000°C for multiple heating-cooling cycles and the results prove that the sensor is suitable for distributed temperature measurement up to 1000°C. MCCC-FPI sensors with more than 4 FPIs point in a single line are made in two structures: the first uses alumina tube as insulation and air gaps as reflectors; and the second uses air chamber as insulation and complete circular alumina discs (2 mm-thick) as reflectors. MCCC-FPIs in neither of the two structures are able to exceed four FPIs in a cable because the reflections from the FPIs are too strong for the microwave to transmit through >4 FPIs over a long distance. Specially shaped alumina discs made by removing parts of the circular discs are found to decrease the microwave reflectance because of reduced reflection areas. However, the smallest reflectance (in terms of percentage of the incident signal) obtained in this work is as high as 7.5%, which is still too large to construct more than 10 FPIs in a single MCCC. Nevertheless, the feasibility of the proposed multipoint MCCC-FPI has been demonstrated using air insulation and 2 mm-thick low-ε r Teflon discs as reflectors on a 2 m-long cable containing 10 FPIs (with reflectance <5% on each FPI). These results indicate that the contrast of dielectric constant between the insulation and reflector has a more significant impact on the reflectance. Thus, future research efforts should also be directed to developing ceramic materials (dense or porous) with low dielectric constant values which, when used as insulation or reflector, can realize large numbers of FPIs in a single cable over a long distance. To assist the development of high temperature dielectric materials, a novel MCCC-FPI sensor platform is developed and successfully demonstrated for measuring high frequency dielectric constants for fluids, packed dielectric solids, and ceramic tube materials and determining ε r dependencies on temperature and chemical composition, etc..

36 MATERIALS SCIENCE↗
NASA NTRS1993

Improvements in ERBS attitude determination without gyros

Previous papers have described the modification of the Earth Radiation Budget Satellite (ERBS) Attitude Determination System (ADS) to overcome the impact of on board gyro degradation and failure on attitude ground support of the mission. Two approaches were taken: implementing a Kalman filter in place of the batch-least-squares attitude estimator to account for the propagation error produced by high-noise gyro data, and modeling the ERBS attitude dynamics to restore rate information in the case of gyro failure. Both of these methods had shortcomings. In practice, the filter attitude diverged without complete sensor observability, and accurate dynamics modeling required knowledge of disturbance torque parameters that had to be determined manually. These difficulties have been overcome by improved tuning of the filter and by incorporating dynamics parameter estimation into the ERBS ADS.

Chu, D.↗
DOE OSTI2022

Packaged SMIP Smart Connector for Ectron Computers

The project provides appropriate software modules, which allow to represent a physical oven as a virtual oven model. For that a virtual oven model is developed, which defines oven parameters like temperature, power consumption but also the door status and the humidity. To connect a real oven to the virtual model for monitoring and control multiple sensors are used to collect data. Using a MQTT based data connection the measured parameters are send to the CESMII environment for further processing and monitoring. Within this project the CESMII model for the general oven was developed and provided. Further the sensors were connected to the actual oven. The sensor data is collected via means of a MQTT based data transfer protocol to a gateway module. The gateway module then translates and transfers the collected data to the actual CESMII oven model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Radiation-Induced Noise Resilience of Neuromorphic Architectures

Neuromorphic event-based networks use asynchronous time-dependent information to extract features from input data that can allow for edge-based distributed applications such as object recognition. The noise resilience properties of such networks, especially in the context of space applications, are yet to be explored. In this paper, we use the hierarchy of time surfaces (HOTS) algorithm, which is one of the neuromorphic algorithms, to understand the least and most resilient modules in a neuromorphic network. The HOTS algorithm relies on the computing of time surfaces that maps the temporal delays between neighboring pixels into normalized features that involve many computations that are also found in other neuromorphic networks such as exponential decays, distance computations, etcetera. We implemented HOTS on a Digilent PYNQ board with a Xilinx Zynq 7020 system on a chip, and we subjected the boards running the HOTS network inference to neutron radiation at the Los Alamos Neutron Science Center. Furthermore, we used simulation models from our previous similar experiments on the event-based sensor to create a neutron induced noise model to quantify the effect of this noise on the overall performance of the network. This experiment provides the preliminary measurements of the reliability of the HOTS algorithm and proposes methods to create a more reliable HOTS architecture in future spacecraft missions.

Engineering↗
DOE OSTI2020

Simulation of Continental Shallow Cumulus Populations Using an Observation-Constrained Cloud-System Resolving Model

Continental shallow cumulus (ShCu) clouds observed on 30 August 2016 during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign are simulated by using an observation-constrained cloud-system resolving model. On this day, ShCu forms over Oklahoma and southern Kansas and some of these clouds transition to deeper, precipitating convection during the afternoon. We apply a four-dimensional ensemble-variational (4DEnVar) hybrid technique in the Community Gridpoint Statistical Interpolation (GSI) system to assimilate operational data sets and unique boundary layer measurements including a Raman lidar, radar wind profilers, radiosondes, and surface stations collected by the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory into the Weather Research and Forecasting (WRF) model to ascertain how improved environmental conditions can influence forecasts of ShCu populations and the transition to deeper convection. Independent observations from aircraft, satellite, as well as ARM's remote sensors are used to evaluate model performance in different aspects. Several model experiments are conducted to identify the impact of data assimilation (DA) on the prediction of clouds evolution. The analyses indicate that ShCu populations are more accurately reproduced after DA in terms of cloud initiation time and cloud base height, which can be attributed to an improved representation of the ambient meteorological conditions and the convective boundary layer. Extending the assimilation to 18 UTC (local noon) also improved the simulation of shallow-to-deep transitions of convective clouds.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Holodeck Testbed Project

The main objective of the Holodeck Testbed is to create a cost effective, realistic, and highly immersive environment that can be used to train astronauts, carry out engineering analysis, develop procedures, and support various operations tasks. Currently, the Holodeck testbed allows to step into a simulated ISS (International Space Station) and interact with objects; as well as, perform Extra Vehicular Activities (EVA) on the surface of the Moon or Mars. The Holodeck Testbed is using the products being developed in the Hybrid Reality Lab (HRL). The HRL is combining technologies related to merging physical models with photo-realistic visuals to create a realistic and highly immersive environment. The lab also investigates technologies and concepts that are needed to allow it to be integrated with other testbeds; such as, the gravity offload capability provided by the Active Response Gravity Offload System (ARGOS). My main two duties were to develop and animate models for use in the HRL environments and work on a new way to interface with computers using Brain Computer Interface (BCI) technology. On my first task, I was able to create precise computer virtual tool models (accurate down to the thousandths or hundredths of an inch). To make these tools even more realistic, I produced animations for these tools so they would have the same mechanical features as the tools in real life. The computer models were also used to create 3D printed replicas that will be outfitted with tracking sensors. The sensor will allow the 3D printed models to align precisely with the computer models in the physical world and provide people with haptic/tactile feedback while wearing a VR (Virtual Reality) headset and interacting with the tools. Getting close to the end of my internship the lab bought a professional grade 3D Scanner. With this, I was able to replicate more intricate tools at a much more time-effective rate. The second task was to investigate the use of BCI to control objects inside the hybrid reality ISS environment. This task looked at using an Electroencephalogram (EEG) headset to collect brain state data that could be mapped to commands that a computer could execute. On this Task, I had a setback with the hardware, which stopped working and was returned to the vendor for repair. However, I was still able to collect some data, was able to process it, and started to create correlation algorithms between the electrical patterns in the brain and the commands we wanted the computer to carry out. I also carried out a test to investigate the comfort of the headset if it is worn for a long time. The knowledge gained will benefit me in my future career. I learned how to use various modeling and programming tools that included Blender, Maya, Substance Painter, Artec Studio, Github, and Unreal Engine 4. I learned how to use a professional grade 3D scanner and 3D printer. On the BCI Project I learned about data mining and how to create correlation algorithms. I also supported various demos including a live demo of the hybrid reality lab capabilities at ComicPalooza. This internship has given me a good look into engineering at NASA. I developed a more thorough understanding of engineering and my overall confidence has grown. I have also realized that any problem can be fixed, if you try hard enough, and as an engineer it is your job to not only fix problems but to embrace coming up with solutions to those problems.

Arias, Adriel↗
NASA NTRS1990

COBE attitude as seen from the FDF

The goal of the Flight Dynamics Facility (FDF) attitude support is twofold: to determine spacecraft attitude and to explain deviations from nominal attitude behavior. Attitude determination often requires resolving contradictions in the sensor observations. This may be accomplished by applying calibration corrections or by revising the observation models. After accounting for all known sources of error, solution accuracy should be limited only by observation and propagation noise. The second half of the goal is to explain why the attitude may not be as originally intended. Reasons for such deviations include sensor or actuator misalignments and control system performance. In these cases, the ability to explain the behavior should, in principle, be limited only by knowledge of the sensor and actuator data and external torques. Documented here are some results obtained to date in support of the Cosmic Background Explorer (COBE). Advantages and shortcomings of the integrated attitude determination/sensor calibration software are discussed. Some preliminary attitude solutions using data from the Diffuse Infrared Background Experiment (DIRBE) instrument are presented and compared to solutions using Sun and Earth sensors. A dynamical model is constructed to illustrate the relative importance of the various sensor imprefections. This model also shows the connection between the high- and low-frequency attitude oscillations.

Sedlak, J.↗
NASA NTRS2003

Concepts and Development of Bio-Inspired Distributed Embedded Wired/Wireless Sensor Array Architectures for Acoustic Wave Sensing in Integrated Aerospace Vehicles

This paper discusses the modeling of acoustic emissions in plate structures and their sensing by embedded or surface bonded piezoelectric sensor arrays. Three different modeling efforts for acoustic emission (AE) wave generation and propagation are discussed briefly along with their advantages and disadvantages. Continuous sensors placed at right angles on a plate are being discussed as a new approach to measure and locate the source of acoustic waves. Evolutionary novel signal processing algorithms and bio-inspired distributed sensor array systems are used on large structures and integrated aerospace vehicles for AE source localization and preliminary results are presented. These systems allow for a great reduction in the amount of data that needs to be processed and also reduce the chances of false alarms from ambient noises. It is envisioned that these biomimetic sensor arrays and signal processing techniques will be useful for both wireless and wired sensor arrays for real time health monitoring of large integrated aerospace vehicles and earth fixed civil structures. The sensor array architectures can also be used with other types of sensors and for other applications.

Ghoshal, Anindya↗
DOE OSTI2022

A guideline to document occupant behavior models for advanced building controls

The availability of computational power, and a wealth of data from sensors have boosted the development of model-based predictive control for smart and effective control of advanced buildings in the last decade. More recently occupant-behavior models have been developed for including people in the building control loops. However, while important objectives of scientific research are reproducibility and replicability of results, not all information is available from published documents. Therefore, the aim of this paper is to propose a guideline for a thorough and standardized occupant-behavior model documentation. For that purpose, the literature screening for the existing occupant behavior models in building control was conducted, and the occupant behavior modeling processes were studied to extract practices and gaps for each of the following phases: problem statement, data collection, and preprocessing, model development, model evaluation, and model implementation. Here, the literature screening pointed out that the current state-of-the-art on model documentation shows little unification, which poses a particular burden for the model application and replication in field studies. In addition to the standardized model documentation, this work presented a model-evaluation schema that enabled benchmarking of different models in field settings as well as the recommendations on how OB models are integrated with the building system.

Building control↗
DOE OSTI2024

Soft X-ray quantum efficiency of a commercial CMOS imaging sensor

The demonstrated performance and cost-effectiveness of complementary metal-oxide-semiconductor (CMOS) sensors make them a potentially attractive option for low-cost space-based X-ray observatories. We have previously reported on the performance of a commercially available backside-illuminated Sony IMX290LLR-C CMOS sensor and found it to offer X-ray spectral resolutions comparable to the charged coupled devices (CCDs) aboard Suzaku and Chandra and to have a sufficient radiation hardness for use in low Earth orbit. Here, in this work, we report on the quantum efficiency (QE) of this sensor, an essential metric for modeling the sensitivity of an instrument as an X-ray detector. Using the Advanced Photon Source at Argonne National Laboratory, we measure the soft X-ray QE of this CMOS sensor to be 0.28 ± 0.02 at a photon energy of 490.5 eV. This energy was chosen for its proximity to the astrophysically important O VII triplet emission lines (~574 eV) studied by the HaloSat mission. Although not surpassing that of the back-illuminated CCDs aboard Suzaku and Chandra, this QE compares favorably to that of the front-illuminated CCDs aboard the same observatories and is competitive with that of the silicon drift detectors used aboard HaloSat, making it a strong candidate for use on future X-ray small satellite (SmallSat) missions.

47 OTHER INSTRUMENTATION↗
NASA NTRS1995

Sensor Fusion in Telerobotic Task Controls

Model-based telerobotic task control and automation is typically limited to motion control of robot arms in free space, and contact events under predictable conditions. A display and control method- ology is described for combining different multidimensional sensor data to guide the performance of telerobotic contact or near-contact tasks successfully in manual or supervised automatic modes of control.

telerobotics robotic task controllers sensor fusio↗
DOE OSTI2021

Impact of Thermal Stress on Attachment and Stability of High Temperature Strain Sensors

Static and dynamic strain sensing is required in high-temperature (HT), harsh-environments (HE) for industrial, aerospace, and energy sector applications to ensure equipment and process safety, reduce the cost of operation and maintenance, and increase process efficiency. Challenges that arise in HT HE sensing applications include device mounting, packaging, integrity, and stability to HT HE conditions. In previously reported work, static and dynamic strain surface acoustic wave resonator (SAWR) sensors were fabricated on langasite (LGS) and mounted on Inconel 625 strain beams for wireless testing up to 400°C. In this work, it has been identified that after subjecting the mounted SAWR strain sensor to thermal cycling between 100°C and 425°C, the measured sensitivity to dynamic strain decreased by 73% due to cracking at the adhesive/LGS interface, further deteriorating after additional thermal cycles. Strain modeling of the mounted LGS sensor chip up to 400°C revealed the existence of concentrated strain at the borders of the LGS chip. Microcracks caused by dicing make the chip boarders the most susceptible location for cracks to initiate when the sensor is subjected to thermal stress. In an attempt to mitigate the high strain at the LGS chip borders due to heating, adhesive shaping is proposed in this work. Simulations indicate a strain reduction of 50% at the border is achieved using both circular and triangular adhesive shapes, while also reducing the maximum strain over the entire adhesive/LGS interface by around 30%. Furthermore, the technique is thus promising for improving the integrity, reliability, and stability of static and dynamic strain sensors, particularly while operating under HT HE with several hundreddegree Celsius temperature excursions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A machine learning approach for particle accelerator errant beam prediction using spatial phase deviation

Particle accelerators are extremely complex systems that are expected to operate on high availability. Predicting impending failures only by utilizing data collected from diagnostic equipment already on board can help operators to avoid installing expensive sensors, unscheduled downtime and associated costs. For this purpose we explore the predictive power of Machine Learning algorithms to detect faulty beams prior to the failure. In this study, we propose a Machine Learning approach to model mapping from a pair of sensors located across the accelerator. While the model is trained to represent normal operation, we evaluate the predictive performance on known faulty beam pulses. We also investigate the model performance on unseen data through k-fold cross-validation. Then we recap the analysis with a neural architecture search and hyperparameter optimization study to fine tune our initial model. In conclusion, this paper will also introduce a sustainable framework that can standardize Machine Learning workflow applied to particle accelerators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1987

Optical methods for model angle of attack and transition measurement

A novel laser-based sensor for the in situ measurement of wind tunnel model angle of attack is described and the results of a successful demonstration in the NASA Ames 9 x 7-ft. Supersonic wind tunnel are presented. The design concepts of a scanning laser-based turbulence detector, which will enable nonintrusive, microscopic studies of the onset and extent of transition on wind tunnel test models, is also described.

Owen, F. K.↗
DOE OSTI2018

Closed- and open-state models of human skeletal muscle sodium channel

Highlights: • Closed- and open-state models of hNav1.4 are shown. • The incomplete outward movement of S4 of domain III holds the channel closed. • The presence of intermediate state in S4 outward movement is revealed. • Movement of ∼6.5 elementary charges is required to activate the channel from hyperpolarized state. • Beta-scorpion toxin Ts1 binds to the closed-state model. Voltage-gated sodium channels play important roles in human physiology. However, their complexity hinders the understanding of their physiology and pathology at atomic level. We took advantage of the structural reports of similar channels obtained by cryo-EM (EeNav1.4, and NavPaS), and constructed models of human Nav1.4 channels at closed and open states. The open-state model is very similar to the recently published cryo-EM structure of hNav1.4. The comparison of both models shows shifts of the voltage sensors (VS) of DIII and DIV. The activated position of VS-DII in the closed model was demonstrated by Ts1 docking, thereby confirming the requirement that VS-DI, VS-DII and VS-DIII must be activated for the channel to open. The interactions observed with VS-DIII suggest a stepwise, yet fast, transition from resting to activated state. These models provide structural insights on the closed-open transition of the channel.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2015

Low Order Wavefront Sensing and Control for WFIRST-AFTA Coronagraph

To maintain the required WFIRST Coronagraph performance in a realistic space environment, a low order wavefront sensing and control (LOWFS/C) subsystem is necessary. The LOWFS/C use s the rejected stellar light from coronagraph to sense and suppress the telescope pointing drift and jitter as well as the low order wavefront errors due to changes in thermal loading of the telescope and the rest of the observatory. In this paper we will present an overview of the low order wavefront sensing and control subsystem for the WFIRST -AFTA Coronagraph. We will describe LOWFS/C's Zernike wavefront sensor concept and WFIRST LOWFS/C control design. We will present an overview of our analysis and modeling results on the Zernike wavefront sensor, the line -of-sight jitter suppression loop performance, as well as the low order wavefront error correction with the coronagraph's deformable mirror. In this paper we will also report the LOWFS/C testbed design and the preliminary in-air test results, which show a very promising performance of the Zernike wavefront sensor and FSM feedback loop.

exoplanets↗
DOE OSTI2022

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.

Gaikwad, Aniruddha (ORCID:0000000285642621)↗