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At least 307 records · Page 17

Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis

Abstract Survival regression models can achieve longer warning times at similar receiver operating characteristic performance than previously investigated models. Survival regression models are also shown to predict the time until a disruption will occur with lower error than other predictors. Time-to-event predictions from time-series data can be obtained with a survival analysis statistical framework, and there have been many tools developed for this task which we aim to apply to disruption prediction. Using the open-source Auton-Survival package we have implemented disruption predictors with the survival regression models Cox Proportional Hazards, Deep Cox Proportional Hazards, and Deep Survival Machines. To compare with previous work, we also include predictors using a Random Forest binary classifier, and a conditional Kaplan-Meier formalism. We benchmarked the performance of these five predictors using experimental data from the Alcator C-Mod and DIII-D tokamaks by simulating alarms on each individual shot. We find that developing machine-relevant metrics to evaluate models is an important area for future work. While this study finds cases where disruptive conditions are not predicted, there are instances where the desired outcome is produced. Giving the plasma control system the expected time-to-disruption will allow it to determine the optimal actuator response in real time to minimize risk of damage to the device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Phonons in Copper Diphosphide (CuP 2 ): Raman Spectroscopy and Lattice Dynamics Calculations

Copper diphosphide (CuP 2 ) is an emerging binary semiconductor with promising properties for energy conversion and storage applications. While functionality and possible applications of CuP 2 have been studied, there is a curious gap in the investigation of its vibrational properties. In this work, we provide a reference Raman spectrum of CuP 2 , with a complete analysis of all Raman active modes from both experimental and theoretical perspectives. Raman measurements have been performed on polycrystalline CuP 2 thin films with close to stoichiometric composition. Detailed deconvolution of the Raman spectrum with Lorentzian curves has allowed identification of all theoretically predicted Raman active modes (9A g and 9B g ), including their positions and symmetry assignment. Furthermore, calculations of the phonon density of states (PDOS), as well as the phonon dispersions, provide a microscopic understanding of the experimentally observed phonon lines, in addition to the assignment to the specific lattice eigenmodes. We further provide the theoretically predicted positions of the infrared (IR) active modes, along with the simulated IR spectrum from density functional theory (DFT). Overall good agreement is found between the experimental and DFT-calculated Raman spectra of CuP 2 , providing a reference platform for future investigations on this material.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Abbreviated Report for 25-FS-011: Switch and Stitch Similar Subgraph Synthesizer

Government institutions utilize software from a wide array of development sources, including those written by large software companies, government contractors, and open-source repositories. Avoiding installation of malicious software components is an important national security endeavor. Automated analysis of previously unseen software is an active research area, and there is much research in the design of systems that compare new software artifacts to a large set of previously seen software records organized by various behaviors they contain. One attractive approach is to turn each compiled software binary into a graph representation and apply a graph similarity model that scores pairs of binaries by their relative similarity. When a pair is deemed similar, it is useful to know why, in the sense of providing explanations to security analysts regarding which portions of the software they should look into further.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Electrical and structural properties of binary Ga–Sb phase change memory alloys

Material properties of Ga–Sb binary alloy thin films deposited under ultra-high vacuum conditions were studied for analog phase change memory (PCM) applications. Crystallization of this alloy was shown to occur in the temperature range of 180–264 °C, with activation energy >2.5 eV depending on the composition. X-ray diffraction (XRD) studies showed phase separation upon crystallization into two phases, Ga-doped A7 antimony and cubic zinc-blende GaSb. Synchrotron in situ XRD analysis revealed that crystallization into the A7 phase is accompanied by Ga out-diffusion from the grains. X-ray absorption fine structure studies of the local structure of these alloys demonstrated a bond length decrease with a stable coordination number of 4 upon amorphous-to-crystalline phase transformation. Mushroom cell structures built with Ga–Sb alloys on ø110 nm TiN heater show a phase change material resistance switching behavior with resistance ratio >100 under electrical pulse measurements. TEM and Energy Dispersive Spectroscopy (EDS) studies of the Ga–Sb cells after ∼100 switching cycles revealed that partial SET or intermediate resistance states are attained by the variation of the grain size of the material as well as the Ga content in the A7 phase. A mechanism for a reversible composition control is proposed for analog cell performance. These results indicate that Te-free Ga–Sb binary alloys are potential candidates for analog PCM applications.

Ume, Rubab (ORCID:0000000324766900)↗

Polymer Passivated All Inorganic Micro‐Structured CsPbI x Br y Perovskite Toward Highly Efficient Photodetectors

Abstract Solution‐processed inorganic perovskites cause chemical and structural defects unfavorable for photodetector application. Using a binary solvent, defects in CsPbI x Br y (CPIB) perovskite are passivated with poly 4‐vinylpyridine (PVP) and Poly methyl methacrylate (PMMA) polymers. X‐ray photoelectron spectroscopy and FTIR spectra reveal a Lewis base‐acid interaction between Pb 2+ and polymer, confirming the passivation of CPIB perovskite. Scanning electron microscopy analysis shows a dual‐surface morphology with microribbons and microcrystals in perovskites. After PMMA treatment, CPIB perovskite exhibits a blue shift in the bandgap (1.8 to 1.95 eV), while the PVP induced a redshift, reducing the bandgap to 1.7 eV. Blue shift in PL analysis indicates modification of grain boundaries. A higher lifetime obtained for CPIB/PVP confirms the restraint of non‐radiative recombinations. Photodetectors are fabricated with pristine CPIB, CPIB/PVP, and CPIB/PMMA perovskites. The passivated CPIB/PVP‐based photodetector exhibits a quick rise time of ≈23 ms and a decay time of ≈17 ms. It also demonstrates a remarkable photoresponsivity of 23 mA W −1 , an internal quantum efficiency of 4.9%, and a detectivity of 15.0 × 10 10 Jones at 10 mW cm −2 light intensity. This approach shows the potential for environmentally stable polymers to passivate inorganic perovskites for high photodetection performance.

Shah, Shruti↗

Accelerating cavity fault prediction using deep learning at Jefferson Laboratory

Abstract Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model’s ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.

43 PARTICLE ACCELERATORS↗

The potential role of organics in new particle formation and initial growth in the remote tropical upper troposphere

Abstract. Global observations and model studies indicate that new particle formation (NPF) in the upper troposphere (UT) and subsequent particles supply 40 %–60 % of cloud condensation nuclei (CCN) in the lower troposphere, thus affecting the Earth's radiative budget. There are several plausible nucleation mechanisms and precursor species in this atmospheric region, which, in the absence of observational constraints, lead to uncertainties in modeled aerosols. In particular, the type of nucleation mechanism and concentrations of nucleation precursors, in part, determine the spatial distribution of new particles and resulting spatial distribution of CCN from this source. Although substantial advances in understanding NPF have been made in recent years, NPF processes in the UT in pristine marine regions are still poorly understood and are inadequately represented in global models. Here, we evaluate commonly used and state-of-the-art NPF schemes in a Lagrangian box model to assess which schemes and precursor concentrations best reproduce detailed in situ observations. Using measurements of aerosol size distributions (0.003 < Dp < 4.8 µm) in the remote marine troposphere between ∼0.18 and 13 km altitude obtained during the NASA Atmospheric Tomography (ATom) mission, we show that high concentrations of newly formed particles in the tropical UT over both the Atlantic and Pacific oceans are associated with outflow regions of deep convective clouds. We focus analysis on observations over the remote Pacific Ocean, which is a region less perturbed by continental emissions than the Atlantic. Comparing aerosol size distribution measurements over the remote Pacific with box model simulations for 32 cases shows that none of the NPF schemes most commonly used in global models, including binary nucleation of sulfuric acid and water (neutral and ion-assisted) and ternary involving sulfuric acid, water, and ammonia, are consistent with observations, regardless of precursor concentrations. Through sensitivity studies, we find that the nucleation scheme among those tested that is able to explain most consistently (21 of 32 cases) the observed size distributions is that of Riccobono et al. (2014), which involves both organic species and sulfuric acid. The method of Dunne et al. (2016), involving charged sulfuric acid–water–ammonia nucleation, when coupled with organic growth of the nucleated particles, was most consistent with the observations for 5 of 32 cases. Similarly, the neutral sulfuric acid–water–ammonia method of Napari (2002), when scaled with a tuning factor and with organic growth added, was most consistent for 6 of 32 cases. We find that to best reproduce both nucleation and growth rates, the mixing ratios of gas-phase organic precursors generally need to be at least twice that of SO2, a proxy for dimethyl sulfide (DMS). Unfortunately, we have no information on the nature of oxidized organic species that participated in NPF in this region. Global models rarely include organic-driven nucleation and growth pathways in UT conditions where globally significant NPF takes place, which may result in poor estimates of NPF and CCN abundance and contribute to uncertainties in aerosol–cloud–radiation effects. Furthermore, our results indicate that the organic aerosol precursor vapors may be important in the tropical UT above marine regions, a finding that should guide future observational efforts.

54 ENVIRONMENTAL SCIENCES↗

Observations with the Differential Speckle Survey Instrument. X. Preliminary Orbits of K-dwarf Binaries and Other Stars

This paper details speckle observations of binary stars taken at the Lowell Discovery Telescope, the WIYN telescope, and the Gemini telescopes between 2016 January and 2019 September. The observations taken at Gemini and Lowell were done with the Differential Speckle Survey Instrument (DSSI), and those done at WIYN were taken with the successor instrument to DSSI at that site, the NN-EXPLORE Exoplanet Star and Speckle Imager (NESSI). In total, we present 378 observations of 178 systems, and we show that the uncertainty in the measurement precision for the combined data set is ∼2 mas in separation, ∼1°–2° in position angle depending on the separation, and ∼0.1 mag in magnitude difference. Together with data already in the literature, these new results permit 25 visual orbits and one spectroscopic-visual orbit to be calculated for the first time. In the case of the spectroscopic-visual analysis, which is done on the ternary star HD 173093, we calculate masses with a precision of better than 1% for all three stars in that system. Twenty-one of the visual orbits calculated have a K dwarf as the primary star; we add these to the known orbits of K-dwarf primary stars and discuss the basic orbital properties of these stars at this stage. Although incomplete, the data that exist so far indicate that binaries with K-dwarf primaries tend not to have low-eccentricity orbits at separations of one to a few tens of astronomical units, that is, on solar system scales.

47 OTHER INSTRUMENTATION↗

From Bulk to Interface: Solvent Exchange Dynamics and Their Role in Ion Transport and the Interfacial Model of Rechargeable Magnesium Batteries

Multivalent battery chemistries have been explored in response to the increasing demand for high-energy rechargeable batteries utilizing sustainable resources. Solvation structures of working cations have been recognized as a key component in the design of electrolytes; however, most structure–property correlations of metal ions in organic electrolytes usually build upon favorable static solvation structures, often overlooking solvent exchange dynamics. We here report the ion solvation structures and solvent exchange rates of magnesium electrolytes in various solvents by using multimodal nuclear magnetic resonance (NMR) analysis and molecular dynamics/density functional theory (MD/DFT) calculations. These magnesium solvation structures and solvent exchange dynamics are correlated to the combined effects of several physicochemical properties of the solvents. Moreover, Mg 2+ transport and interfacial charge transfer efficiency are found to be closely correlated to the solvent exchange rate in the binary electrolytes where the solvent exchange is tunable by the fraction of diluent solvents. Our primary findings are (1) most battery-related solvents undergo ultraslow solvent exchange coordinating to Mg 2+ (with time scales ranging from 0.5 μs to 5 ms), (2) the cation transport mechanism is a mixture of vehicular and structural diffusion even at the ultraslow exchange limit (with faster solvent exchange leading to faster cation transport), and (3) an interfacial model wherein organic-rich regions facilitate desolvation and inorganic regions promote Mg 2+ transport is consistent with our NMR, electrochemistry, and cryogenic X-ray photoelectron spectroscopy (cryo-XPS) results. In conclusion, this observed ultraslow solvent exchange and its importance for ion transport and interfacial properties necessitate the judicious selection of solvents and informed design of electrolyte blends for multivalent electrolytes.

25 ENERGY STORAGE↗

Dissecting neurofilament tail sequence-phosphorylation-structure relationships with multicomponent reconstituted protein brushes

Neurofilaments (NFs) are multisubunit, bottlebrush-shaped intermediate filaments abundant in the axonal cytoskeleton. Each NF subunit contains a long intrinsically disordered tail domain, which protrudes from the NF core to form a “brush” surrounding each NF. Precisely how the tails’ variable charge patterns and repetitive phosphorylation sites mediate their conformation within the brush remains an open question in axonal biology. We address this problem by grafting recombinant NF tail protein constructs NF-Light, -Medium, and -Heavy (NFL, NFM, and NFH) to surfaces, yielding protein brushes of defined stoichiometry that can be phosphorylated in vitro. Atomic force microscopy measurements reveal that brush height depends on composition monotonically but not always linearly for binary NFL:NFM or NFL:NFH systems, and that NFM-based brushes are highly extended, while brushes incorporating the much larger NFH are surprisingly compact even after multisite phosphorylation. Complementary self-consistent field theory (SCFT) predicts multilayer brush morphologies for NFM and phosphorylated NFH brushes. Further experiments and SCFT analysis with designed mutants reveal that N-terminal negative charges in the NFH tail repel phosphorylated residues to generate the multilayer morphology, while the C-terminal charge-neutral region contributes to multilayer brush morphology but not total brush height. Charge-shuffled NFM variants show that charge segregation promotes brush collapse near physiological ionic strengths. Collectively, this study supports a role for NFM in establishing a dynamic range for NF brush conformation, lending insight into previous in vitro and in vivo findings. More broadly, this work establishes a platform for dissecting contributions of disordered protein sequence to conformation at interfaces.

Science & Technology - Other Topics↗

Scalability Analysis of Quantum Models for Stress and Emotion Detection

Stress and emotion detection from high-dimensional physiological signals is a challenging task, particularly when aiming for accurate classification across diverse behavioral states. Quantum machine learning (QML) is promising for modeling such high-dimensional data, but scalability is limited by qubit resources and the exponential cost of classical statevector simulation. This work studies the scalability of quantum support vector machines (QSVMs) for binary stress detection and three-class emotion recognition (Negative/Neutral/Positive) under varying qubit counts and angle-encoding strategies. We also present a comparison study with one-feature-per-qubit (1:1) and two-features-per-qubit (2:1) mappings. Experiments are executed on HPC infrastructure using NVIDIA CUDA-Q to evaluate performance, variance, and class-dependent separability at higher-qubit setups. Results show that larger Hilbert spaces can improve peak accuracy but may increase instability. At the same time, dense 2:1 encoding yields more consistent stress detection performance. For emotion recognition, scaling improves discrimination for classes like Negative and Positive more than Neutral. We find that effective QML scaling is task-dependent and benefits more from encoding design than simply increasing qubit count.

Onim, Md. Saif Hassan [University of Tennessee, Kn↗

Five percent measurement of the gravitational constant in the Large Magellanic Cloud

We perform a novel test of General Relativity by measuring the gravitational constant in the Large Magellanic Cloud (LMC). The LMC contains six well-studied Cepheid variable stars in detached eclipsing binaries. Radial velocity and photometric observations enable a complete orbital solution, and precise measurements of the Cepheids’ periods permit detailed stellar modelling. Both are sensitive to the strength of gravity, the former via Kepler’s third law and the latter through the gravitational free-fall time. We jointly fit the observables for stellar parameters and the gravitational constant. Performing a full Markov Chain Monte Carlo analysis of the parameter space including all relevant nuisance parameters, we constrain the gravitational constant in the Large Magellanic Cloud relative to the Solar System to be G LMC /G SS = $0.93{^{+0.05}_{-0.04}}$. We discuss the implications of this 5% measurement of Newton’s constant in another galaxy for dark energy and modified gravity theories. This result excludes one Cepheid, CEP-1812, which is an outlier and needs further study: it is either a highly unusual system to which our model does not apply, or it prefers G LMC < G SS at 2.6σ. We also obtain new bounds on critical parameters that appear in semi-analytic descriptions of stellar processes. In particular, we measure the mixing length parameter to be $α = 0.90{^{+0.36}_{-0.26}}$ (when assumed to be constant across our sample), and obtain constraints on the parameters describing turbulent dissipation and convective flux.

79 ASTRONOMY AND ASTROPHYSICS↗

59 Co(p,X) spallation reaction cross sections for 250 MeV to 2 GeV protons

Cobalt is an advantageous target for probing the physics of nuclear spallation, because it is a naturally mono-isotopic element ( 59 Co), and its per-nucleon binding energy (BE/A = 8.768 MeV) is near the maximum value for all nuclei. We measured nuclear spallation cross sections for the 59 Co(p,X) reaction at five kinetic energies ranging from 250 MeV to 2 GeV. Cross sections for the production of 58 Co, 57 Co, 57 Mn, 56 Co, 56 Mn, 56 Cr, 55 Fe, 53 Fe, 54 Mn, 52 Mn, 51 Cr, 49 Cr, 48 V, 47 Sc, 46 Sc, 44 Sc, and 44 Scm are reported. Where comparable data exist in the EXFOR reaction database, we find that our measured cross sections generally agree. In many cases, we provide data for reactions or energies not currently reported in EXFOR. Our cross sections also provide evidence for the presence of α-clusters within the 59 Co nucleus, a surprising result given the asymmetry in Z (27) and N (32) for this nucleus. Finally, we use our measurements to evaluate the accuracy of spallation cross section simulations from GEANT4-based radiation transport toolkit, performed with the INCLXX-, Bertini-, and Binary-ion-cascade (BIC) based physics lists. This benchmarking activity revealed that the simulations overestimated the cross sections by a factor of ∼2–4 on average, and that the INCL-XX physics list provides the most reliable results. This evaluation informs the selection of the GEANT4 physics lists used for the analysis of data from NASA’s Psyche mission, which will measure γ rays and neutrons resulting from spallation reactions occurring on the surface of an asteroid whose surface is thought to be rich in iron-nickel metal.

43 PARTICLE ACCELERATORS↗

Isoplethal study of phase formation and morphology in uranium-304L steel via scanning electron microscopy

Understanding the formation of uranium alloys with steel is important to advance nuclear technologies involving U metal fuels and machining U metal, and for nuclear forensics applications. No known phase diagram for the quaternary U-(M = Fe, Ni, Cr) system exists. For this work, we synthesize samples of U-304 L steel (nominal composition 70.1:18.3:10.4 at% Fe:Cr:Ni) across the U composition range 4.45—63.35 at%U by arc melting under inert conditions. Using the binary UFe phase diagram as a reference, we identify four U-steel alloy phases. We find the known U-Fe analogue phases UM 2 and U 6 M, and two low-U composition phases with nominal compositions UM 10 and U 2 M 7 . We apply a correlation length analysis to backscatter scanning electron microscopy images of sectioned and polished cross sections to quantify the domain formation length scale. We demonstrate that these depend heavily on the initial composition and range from 30 nm to 1.5 µm. This result, in particular, could be applicable to theoretical predictions of transport properties. Furthering our understanding of U alloy phase formation with important structural elements such as steel primaries is foundational in developing future nuclear technology.

36 MATERIALS SCIENCE↗

Strategies for preparing and analyzing thin passive films with atom probe tomography

Atom probe tomography provides a unique, three-dimensional map of elemental and isotopic distributions over a wide range of materials with near-atomic scale resolution and is particularly strong at analyzing buried interfaces within materials. However, it is much more difficult to apply atom probe to the analysis of nanoscale surface films, such as those formed during alloy passivation, where unique challenges persist for sample preparation and data collection. Here, we present sample preparation strategies involving the deposition of a < 100 nm capping layer that enables reliable characterization of thin passive films approximately 2–5 nm thick formed on binary and multi-principal element alloys via atom probe tomography. Several capping layer materials (Pt, Ti, Ni/Cr bi-layer) and deposition methods are contrasted. Our results indicate a sputtered Ni/Cr bi-layer enables the characterization of the entire passive film and concentration profiles that can easily be interpreted to clearly distinguish base alloy/passive film/capping layer interfaces. Lastly, we highlight ongoing challenges and opportunities for this experimental approach.

Kautz, Elizabeth J. [University of Florida]↗

Irradiation of UC1+x kernels using the MiniFuel vehicle: Microstructure, phase analysis, and initial post-irradiation examination

Uranium carbide is a candidate fuel form for a wide range of advanced reactors, including larger Generation IV reactors as well as small modular reactors and microreactors. However, its commercial deployment timeline faces challenges via traditional qualification approaches. To address this issue, an accelerated fission rate irradiation test was performed to collect basic fuel performance data to inform fuel performance models and potential future integral tests. Hyperstoichiometric UC (UC1+x) kernels were irradiated in the High Flux Isotope Reactor using the MiniFuel irradiation vehicle. The test matrix spanned two temperature regimes (700 °C and 800 °C) and burnup levels (1.8 % FIMA and 2.8 % FIMA). Between 21 and 63 kernels were tested at each unique temperature and burnup condition. As-fabricated microstructural analysis revealed a multiphase composition with UC, UC2, UC2−y, and U-C-O bearing phases for the irradiated kernels. Following irradiation, fission gas release, swelling, and microstructures were analyzed. Measured fission gas release was below 5 % for all irradiation conditions, reaching a maximum at the highest temperature and burnup condition. A binary swelling response was observed; the lower burnup and temperature conditions resulted in negligible swelling, but the higher burnup and temperature conditions produced significant anisotropic swelling and densification in a subset of kernels. The basic microstructural exams of kernels following irradiation were not capable of showing a correlation between kernels that exhibited excessive swelling and those that did not. Characterization of a subset of samples using the Advanced Photon Source and more detailed microstructural examination of unirradiated kernels revealed that a subset of kernels contained very high UC2 phase fractions. The anomalous swelling response is hypothesized to have been driven by this chemical variation. The results of this irradiation highlight the potential of accelerated fission rate irradiation testing to explore such behaviors and inform the development of fuel specifications.

Adorno Lopes, Denise [ORNL] (ORCID:000900023705987↗

Effects of thermophoresis on high-pressure binary-species boundary layers with uniform and non-uniform compositions

Direct numerical simulations of high-pressure binary-species temporal boundary layers are performed to investigate the flow physics for three situations: (1) uniform and equal composition, (2) uniform but unequal compositions and (3) non-uniform composition. Both colder- and hotter-wall situations compared with the free stream are simulated. The working fluid is a nitrogen/methane mixture. The analysis is performed at a case-specific self-similar state. Even when the initial composition is uniform, the methane mean mass fraction decreases near the colder wall, whereas it increases near the hotter wall and the mass fraction fluctuates in the entire boundary layer. Analysis of the species-mass diffusion balance and flow structures reveal that both mass-fraction variation and fluctuations are induced by the Soret effect. When the initial composition is non-uniform and the wall is colder, the methane mean mass fraction monotonically increases from the wall akin to its initial profile. However, when the wall is hotter the mean mass fraction decreases near the wall in contrast to its initial profile, a fact traced through the species-mass diffusion balance to the Soret effect being large and enriching methane near the wall. In contrast, the direction of the Soret flux is opposite for the colder wall, thus keeping the methane concentration small. Although the initial magnitude of the difference between the wall and free-stream temperature is the same in all cases, the situation is not symmetric between colder- and hotter-wall cases; the flow structure exhibits much smaller scales when the wall is hotter than when the wall is colder.

Mechanics↗

3DBFSVBF (3D BatFinder Smart Video BioFilter and Multi-class BatFinder Smart Video BioFilter) [SWR-22-88]

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source 3D computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The machine learning has been trained on 3D data and allows for automated real-time data collection, identification and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. This machine learning model is an added feature to the previous BatFinder Smart Video BioFilter and increases the accuracy of that systems classification by increasing the accuracy of identifying bats (90% accuracy) and insects (69% accuracy) to a 97% accuracy. There are two object classifier machine learning models, Binary and multi-classification. Binary object classifier labeled BatFinder_Smart_Video_BioFilter.h5 distinguishes between biological objects and non-biological objects. The main goal of this object classifier is to ignore the turbine blades while detecting biological object flying withing the rotor swept area of the turbine. Non-biological objects have a probability of 0 and biological objects have a probability of 1. Multi-classifier labeled Multiclass_BatFinder_Smart_Video_BioFilter.h5 distinguishes between bats, birds, insects and non-biological.

Yarbrough, John↗