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CoSPA data product description

Published in:
MIT Lincoln Laboratory Report ATC-449

Summary

This document contains a description of Consolidated Storm Prediction for Aviation (CoSPA) data products that are packaged and distributed for external users. As described in Rappa and Troxel, 2013 [1] for Corridor Integrated Weather System (CIWS) data products, CoSPA products are categorized as gridded and non-gridded. Gridded products are typically expressed as rectangular arrays whose elements contain a data value coinciding with uniformly-spaced observations or computed results on a 2-D surface. Gridded data arrays map to the earth's surface through a map projection, for example, Lambert Conformal or Lambert Azimuthal Equal-Area. CoSPA generates only gridded products; there are no non-gridded data for CoSPA.
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Summary

This document contains a description of Consolidated Storm Prediction for Aviation (CoSPA) data products that are packaged and distributed for external users. As described in Rappa and Troxel, 2013 [1] for Corridor Integrated Weather System (CIWS) data products, CoSPA products are categorized as gridded and non-gridded. Gridded products are typically...

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A hybrid algorithm for parameter estimation (HAPE) for dynamic constant power loads

Published in:
IEEE Trans. Ind. Electron., Vol. 68, No. 11, November 2021, pp. 10326-35.
Topic:
R&D group:

Summary

Low-inertia microgrids may easily have a single load which can make up most of the total load, thereby greatly affecting stability and power quality. Instead of a static load model, a dynamic constant power load (DCPL) model is considered here. Next, a hybrid algorithm for parameter estimation (HAPE) is introduced. In order to verify the load model and the HAPE, two experiments are conducted with different DCPLs using a Power-Hardwarein-the-Loop (PHiL) testbed. The PHiL testbed consists of a real-time computer working with a programmable power amplifier in order to perturb the input voltage's amplitude and frequency. Each connected DCPL in two separate experiments serves as the device under test (DUT). Using the captured experimental data as a reference, the HAPE is then invoked. The resulting parameter estimates are used to define simulation models. Both resulting DCPL models are simulated to produce waveforms that closely resemble experimental waveforms. Finally, the HAPE's resulting parameter estimates are presented, and the performance of the HAPE is discussed.
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Summary

Low-inertia microgrids may easily have a single load which can make up most of the total load, thereby greatly affecting stability and power quality. Instead of a static load model, a dynamic constant power load (DCPL) model is considered here. Next, a hybrid algorithm for parameter estimation (HAPE) is introduced...

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Utility of inter-subject transfer learning for wearable-sensor-based joint torque prediction models

Published in:
43rd Annual Intl. Conf. of the IEEE Engineering in Medicine & Biology, 31 October 2021-4 November 2021.

Summary

Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensor-controlled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning techniques applied to machine learning models afford the ability to apply and investigate the viability and utility such knowledge-transfer scenarios. This paper investigates the utility of single- and multi-subject based parameter transfer on LSTM models trained for "sensor-to-joint torque" prediction tasks, with regards to task performance and computational resources required for network training. We find that parameter transfer between both single- and multi-subject models provide useful knowledge transfer, with varying results across specific "source" and "target" subject pairings. This could be leveraged to lower model training time or computational cost in compute-constrained environments or, with further study to understand causal factors of the observed variance in performance across source and target pairings, to minimize data collection and model retraining requirements to select and personalize a generic model for personalized wearable-sensor-based joint torque prediction technologies.
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Summary

Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensor-controlled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning...

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Application of complex split-activation feedforward networks to beamforming

Published in:
55th Asilomar Conf. on Signals, Systems and Computers, ACSSC, 31 October - 3 November 2021.

Summary

In increasingly congested RF environments and for jamming at closer ranges, amplifiers may introduce nonlinearities that linear adaptive beamforming techniques can't mitigate. Machine learning architectures are intended to solve such nonlinear least squares problems, but much of the current work and available software is limited to signals represented as real sequences. In this paper, neural networks using complex numbers to represent the complex baseband RF signals are considered. A complex backpropagation approach that calculates gradients and a Jacobian, allows for fast optimization of the neural networks. Through simulations, it is shown that complex neural networks require less training samples than their real counterparts and may generalize better in dynamic environments.
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Summary

In increasingly congested RF environments and for jamming at closer ranges, amplifiers may introduce nonlinearities that linear adaptive beamforming techniques can't mitigate. Machine learning architectures are intended to solve such nonlinear least squares problems, but much of the current work and available software is limited to signals represented as real...

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Effect of a wet spherical radome on the reflected power for an S-band planar phased array radar antenna

Published in:
2021 Antenna Measurement Techniques Association Symp., AMTA, 24-29 October 2021.

Summary

An active S-band dual-polarized multifunction phased array radar (MPAR), the Advanced Technology Demonstrator (ATD), has recently been developed for weather sensing and aircraft surveillance. The ATD is an active electronically scanned array (AESA) with 4864 transmit/receive (T/R) modules and was installed in a spherical radome. Simulations and a novel phased array measurement technique have been explored to assess the impact of high reflectivity from a wet radome during rain that can potentially induce voltages exceeding the transmit amplifier breakdown voltage. The measurement technique uses array elements radiating one at a time to illuminate the radome, and uses superposition to quantify the received signal power in a reference antenna on the face of the array. It is shown that when the radome surface is wet and highly reflective, certain electronic steering angles sum to a large reflected signal focused on the array face. This measurement technique can be used prior to high-power phased array radar operation to monitor the magnitude of reflections and help avoid element transmit amplifier failures.
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Summary

An active S-band dual-polarized multifunction phased array radar (MPAR), the Advanced Technology Demonstrator (ATD), has recently been developed for weather sensing and aircraft surveillance. The ATD is an active electronically scanned array (AESA) with 4864 transmit/receive (T/R) modules and was installed in a spherical radome. Simulations and a novel phased...

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Metrics for quantifying cognitive factors that may underlie individual variation in exoskeleton use

Published in:
Proc. of the Human Factors and Ergonomics Society Annual Meeting, Vol. 65, No. 1, 2021, pp. 216-20.

Summary

Individual differences in adaptation to exoskeletons have been observed, but are not well understood. Kinematic, kinetic, and physiologic factors are commonly used to assess these systems. Parameters from experimental psychology and gait literature wereadapted to probe the lower extremity perception-cognition-action loop using measures of reaction times, gait task performance, and gait strategy. Parameters were measured in 15 subjects via two tasks: (1) a modified Simon task and (2) a speed-achievement task with secondary go/no-go cues on a self-paced treadmill. Outcome metrics were assessed for significantly different intra- versus inter-subject variability. Reaction time measures from the modified Simon task, as well two speed-achievement metrics and one gait-strategy characteristic were found to show significant differences in intra- versus inter-subject variability. These results suggest that select cognitive factors may differentiate between individuals and be potential predictors for individual variation during exoskeleton system operation.
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Summary

Individual differences in adaptation to exoskeletons have been observed, but are not well understood. Kinematic, kinetic, and physiologic factors are commonly used to assess these systems. Parameters from experimental psychology and gait literature wereadapted to probe the lower extremity perception-cognition-action loop using measures of reaction times, gait task performance, and...

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Benchmarking the processing of aircraft tracks with triples mode and self-scheduling

Published in:
2021 IEEE High Performance Extreme Computing Conf., HPEC, 20-24 September 2021.

Summary

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Developing and certifying collision avoidance systems often rely on the extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. To train these models, high performance computing resources are required. We've prototyped a high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process billions of observations of aircraft. However, the prototype has various computational and storage bottlenecks that limited rapid or more comprehensive analyses and models. In response, we've developed a novel workflow to take advantage of various job launch and task distribution technologies to improve performance. The workflow was benchmarked using two datasets of observations of aircraft, including a new dataset focused on the environment around aerodromes. Optimizing how the workflow was parallelized drastically reduced the execution time from weeks to days.
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Summary

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Developing and certifying collision avoidance systems often rely on the extensive use of...

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Benefits of realist ontologies to systems engineering

Published in:
Joint Ontology Workshops 2021 Episode VII: The Bolzano Summer of Knowledge, JOWO 2021, 11-18 September 2021.

Summary

Applied ontologies have been used more and more frequently to enhance systems engineering. In this paper, we argue that adopting principles of ontological realism can increase the benefits that ontologies have already been shown to provide to the systems engineering process. Moreover, adopting Basic Formal Ontology (BFO), an ISO standard for top-level ontologies from which more domain specific ontologies are constructed, can lead to benefits in four distinct areas of systems engineering: (1) interoperability, (2) standardization, (3) testing, and (4) data exploitation. Reaping these benefits in a model-based systems engineering (MBSE) context requires utilizing an ontology's vocabulary when modeling systems and entities within those systems. If the chosen ontology abides by the principles of ontological realism, a semantic standard capable of uniting distinct domains, using BFO as a hub, can be leveraged to promote greater interoperability among systems. As interoperability and standardization increase, so does the ability to collect data during the testing and implementation of systems. These data can then be reasoned over by computational reasoners using the logical axioms within the ontology. This, in turn, generates new data that would have been impossible or too inefficient to generate without the aid of computational reasoners.
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Summary

Applied ontologies have been used more and more frequently to enhance systems engineering. In this paper, we argue that adopting principles of ontological realism can increase the benefits that ontologies have already been shown to provide to the systems engineering process. Moreover, adopting Basic Formal Ontology (BFO), an ISO standard...

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Detecting pathogen exposure during the non-symptomatic incubation period using physiological data: proof of concept in non-human primates

Summary

Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First, we seek to determine the upper limits of early warning detection through physiological measurements. Second, we investigate whether the detected physiological response is specific to the pathogen. Third, we explore the feasibility of extending early warning detection with wearable devices. Research Methods: For the first objective, we developed a supervised random forest algorithm to detect pathogen exposure in the asymptomatic period prior to overt symptoms (fever). We used high-resolution physiological telemetry data (aortic blood pressure, intrathoracic pressure, electrocardiograms, and core temperature) from non-human primate animal models exposed to two viral pathogens: Ebola and Marburg (N = 20). Second, to determine reusability across different pathogens, we evaluated our algorithm against three independent physiological datasets from non-human primate models (N = 13) exposed to three different pathogens: Lassa and Nipah viruses and Y. pestis. For the third objective, we evaluated performance degradation when the algorithm was restricted to features derived from electrocardiogram (ECG) waveforms to emulate data from a non-invasive wearable device. Results: First, our cross-validated random forest classifier provides a mean early warning of 51 ± 12 h, with an area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.01. Second, our algorithm achieved comparable performance when applied to datasets from different pathogen exposures – a mean early warning of 51 ± 14 h and AUC of 0.95 ± 0.01. Last, with a degraded feature set derived solely from ECG, we observed minimal degradation – a mean early warning of 46 ± 14 h and AUC of 0.91 ± 0.001. Conclusion: Under controlled experimental conditions, physiological measurements can provide over 2 days of early warning with high AUC. Deviations in physiological signals following exposure to a pathogen are due to the underlying host’s immunological response and are not specific to the pathogen. Pre-symptomatic detection is strong even when features are limited to ECG-derivatives, suggesting that this approach may translate to non-invasive wearable devices.
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Summary

Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First...

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Unsupervised Bayesian adaptation of PLDA for speaker verification

Published in:
Interspeech, 30 August - 3 September 2021.

Summary

This paper presents a Bayesian framework for unsupervised domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA). By interpreting class labels as latent random variables, Variational Bayes (VB) is used to derive a maximum a posterior (MAP) solution of the adapted PLDA model when labels are missing, referred to as VB-MAP. The VB solution iteratively infers class labels and updates PLDA hyperparameters, offering a systematic framework for dealing with unlabeled data. While presented as a general solution, this paper includes experimental results for domain adaptation in speaker verification. VBMAP estimation is applied to the 2016 and 2018 NIST Speaker Recognition Evaluations (SREs), both of which included small and unlabeled in-domain data sets, and is shown to provide performance improvements over a variety of state-of-the-art domain adaptation methods. Additionally, VB-MAP estimation is used to train a fully unsupervised PLDA model, suffering only minor performance degradation relative to conventional supervised training, offering promise for training PLDA models when no relevant labeled data exists.
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Summary

This paper presents a Bayesian framework for unsupervised domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA). By interpreting class labels as latent random variables, Variational Bayes (VB) is used to derive a maximum a posterior (MAP) solution of the adapted PLDA model when labels are missing, referred to as VB-MAP...

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