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Modeling probability of alert of Bluetooth low energy-based automatic exposure notifications
April 27, 2022
Project Report
Published in:
MIT Lincoln Laboratory Report ACTA-4
Summary
BLEMUR, or Bluetooth Low Energy Model of User Risk, is a model of the probability of alert at a given duration and distance of an index case for a specific configuration of settings for an Exposure Notification (EN) system.The Google-Apple EN framework operates in the duration and Bluetooth Low Energy (BLE) signal attenuation domains. However, many public health definitions of "exposure" to a disease are based upon the distance between an index case and another person. To bridge the conceptual gap for public health authorities (PHAs) from the familiar distance-and-duration space to the signal attenuation-and-duration space, BLEMUR uses BLE signal attenuation as a proxy for distance between people, albeit an imprecise one. This paper will discuss the EN settings that can be manipulated, the BLE data collected, how data support a model of the relationship between measured attenuation and distance between phones, and how BLEMUR calculates the probability of alert for a distance and duration based on the settings and data.
Summary
BLEMUR, or Bluetooth Low Energy Model of User Risk, is a model of the probability of alert at a given duration and distance of an index case for a specific configuration of settings for an Exposure Notification (EN) system.The Google-Apple EN framework operates in the duration and Bluetooth Low Energy...
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Utility of inter-subject transfer learning for wearable-sensor-based joint torque prediction models
October 31, 2021
Conference Paper
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.
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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Applicability and surrogacy of uncorrelated airspace encounter models at low altitudes
July 1, 2021
Journal Article
Published in:
J. Air Transport., Vol. 29, No. 3, July-September 2021, pp. 137-41.
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Summary
National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems are required to mitigate the risk of a midair collision between aircraft. Monte Carlo simulations have been a foundational capability for decades to develop, assess, and certify aircraft conflict avoidance systems. These are often validated through human-in-the-loop experiments and flight testing. For example, an update to the Traffic Collision Avoidance System (TCAS) mandated for manned aircraft was validated in part using this approach [1]. For many aviation safety studies, manned aircraft behavior is represented using the MIT Lincoln Laboratory statistical encounter models [2–5]. The original models [2–4] were developed from 2008 to 2013 to support safety simulations for altitudes above 500 feet above ground level (AGL). However, these models were not sufficient to assess the safety of smaller unmanned aerial systems (UAS) operations below 500 feet AGL and fully support the ASTM F38 and RTCA SC-147 standards efforts. In response, newer models [5–7] with altitude floors below 500 feet AGL have been in development since 2018. Many of the models assume that aircraft behavior is uncorrelated and not dependent on air traffic services or nearby aircraft. The models were trained using observations of cooperative aircraft equipped with transponders, but data sources and assumptions vary. The newer models are organized by aircraft types of fixed-wing multi-engine, fixed-wing single engine, and rotorcraft, whereas the original models do not consider aircraft type. Our research objective was to compare the various uncorrelated models of conventional aircraft and identify how the models differ. Particularly if models of rotorcraft were sufficiently different from models of fixed-wing aircraft to require type-specific models. The scope of this work was limited to altitudes below 5000 feet AGL, the expected altitude ceiling for many new airspace entrants. The scope was also informed by the Federal Aviation Administration (FAA) UAS Integration Office and Alliance for System Safety of UAS through Research Excellence (ASSURE). The primary contribution is guidance on which uncorrelated models to leverage when evaluating the performance of a collision avoidance system designed for low-altitude operations, such as prescribed by the ASTM F3442 detect and avoid standard for smaller UAS [8]. We also address which models can be surrogates for non-cooperative aircraft without transponders. All models and software used are publicly available under open source licenses [9].
Summary
National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems are required to mitigate the...
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Seasonal Inhomogeneous Nonconsecutive Arrival Process Search and Evaluation
January 15, 2021
Conference Paper
Published in:
25th International Conference on Pattern Recognition [submitted]
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Summary
Time series often exhibit seasonal patterns, and identification of these patterns is essential to understanding thedata and predicting future behavior. Most methods train onlarge datasets and can fail to predict far past the training data. This limitation becomes more pronounced when data is sparse. This paper presents a method to fit a model to seasonal time series data that maintains predictive power when data is limited. This method, called SINAPSE, combines statistical model fitting with an information criteria to search for disjoint, andpossibly nonconsecutive, regimes underlying the data, allowing for a sparse representation resistant to overfitting.
Summary
Time series often exhibit seasonal patterns, and identification of these patterns is essential to understanding thedata and predicting future behavior. Most methods train onlarge datasets and can fail to predict far past the training data. This limitation becomes more pronounced when data is sparse. This paper presents a method to...
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Seasonal Inhomogeneous Nonconsecutive Arrival Process Search and Evaluation
August 26, 2020
Conference Paper
Published in:
International Conference on Artificial Intelligence and Statistics, 26-28 August 2020 [submitted]
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Summary
Seasonal data may display different distributions throughout the period of seasonality. We fit this type of model by determiningthe appropriate change points of the distribution and fitting parameters to each interval. This offers the added benefit of searching for disjoint regimes, which may denote the samedistribution occurring nonconsecutively. Our algorithm outperforms SARIMA for prediction.
Summary
Seasonal data may display different distributions throughout the period of seasonality. We fit this type of model by determiningthe appropriate change points of the distribution and fitting parameters to each interval. This offers the added benefit of searching for disjoint regimes, which may denote the samedistribution occurring nonconsecutively. Our algorithm...
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Unified value-based feedback, optimization and risk management in complex electric energy systems
February 22, 2020
Journal Article
Published in:
Optim Eng 21, 427–483 (2020)
Summary
The ideas in this paper are motivated by an increased need for systematic data-enabled resource management of large-scale electric energy systems. The basic control objective is to manage uncertain disturbances, power imbalances in particular, by optimizing available power resources. To that end, we start with a centralized optimal control problem formulation of system-level performance objective subject to complex interconnection constraints and constraints representing highly heterogeneous internal dynamics of system components. To manage spatial complexity, an inherent multi-layered structure is utilized by modeling interconnection constraints in terms of unifed power variables and their dynamics. Similarly, the internal dynamics of components and sub-systems (modules), including their primary automated feedback control, is modeled so that their input–output characterization is also expressed in terms of power variables. This representation is shown to be key to managing the multi-spatial complexity of the problem. In this unifying energy/ power state space, the system constraints are all fundamentally convex, resulting in the convex dynamic optimization problem, for typically utilized quadratic cost functions. Based on this, an interactive multi-layered modeling and control method is introduced. While the approach is fundamentally based on the primal–dual decomposition of the centralized problem, this is formulated for the frst time for the couple real-reactive power problem. It is also is proposed for the frst time to utilize sensitivity functions of distributed agents for solving the primal distributed problem. Iterative communication complexity typically required for convergence of pointwise information exchange is replaced by the embedded distributed optimization by the modules when creating these functions. A theoretical proof of the convergence claim is given. Notably, the inherent multi-temporal complexity is managed by performing model predictive control (MPC)-based decision making when solving distributed primal problems. The formulation enables distributed decision-makers to value uncertainties and related risks according to their preferences. Ultimately, the distributed decision making results in creating a bid function to be used at the coordinating market-clearing level. The optimization approach in this paper provides a theoretical foundation for next-generation Supervisory Control and Data Acquisition (SCADA) in support of a Dynamic Monitoring and Decision Systems (DyMonDS) for a multi-layered interactive market implementation in which the grid users follow their sub-objectives and the higher layers coordinate interconnected sub-systems and the high-level system objectives. This forms a theoretically sound basis for designing IT-enabled protocols for secure operations, planning, and markets.
Summary
The ideas in this paper are motivated by an increased need for systematic data-enabled resource management of large-scale electric energy systems. The basic control objective is to manage uncertain disturbances, power imbalances in particular, by optimizing available power resources. To that end, we start with a centralized optimal control problem...
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Introducing DyMonDS-as-a-Service (DyMaaS) for Internet of Things
September 24, 2019
Conference Paper
Published in:
2019 IEEE High Performance Computing Conf., HPEC, 24-26 September 2019.
Summary
With recent trends in computation and communication architecture, it is becoming possible to simulate complex networked dynamical systems by employing high-fidelity models. The inherent spatial and temporal complexity of these systems, however, still acts as a roadblock. It is thus desirable to have adaptive platform design facilitating zooming-in and out of the models to emulate time-evolution of processes at a desired spatial and temporal granularity. In this paper, we propose new computing and networking abstractions, that can embrace physical dynamics and computations in a unified manner, by taking advantage of the inherent structure. We further design multi-rate numerical methods that can be implemented by computing architectures to facilitate adaptive zooming-in and out of the models spanning multiple spatial and temporal layers. These methods are all embedded in a platform called Dynamic Monitoring and Decision Systems (DyMonDS). We introduce a new service model of cloud computing called DyMonDS-as-a-Service (DyMaas), for use by operators at various spatial granularities to efficiently emulate the interconnection of IoT devices. The usage of this platform is described in the context of an electric microgrid system emulation.
Summary
With recent trends in computation and communication architecture, it is becoming possible to simulate complex networked dynamical systems by employing high-fidelity models. The inherent spatial and temporal complexity of these systems, however, still acts as a roadblock. It is thus desirable to have adaptive platform design facilitating zooming-in and out...
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Monetized weather radar network benefits for tornado cost reduction
August 29, 2019
Project Report
Published in:
MIT Lincoln Laboratory Report NOAA-35
Summary
A monetized tornado benefit model is developed for arbitrary weather radar network configurations. Geospatial regression analyses indicate that improvement in two key radar coverage parameters--fraction of vertical space observed and cross-range horizontal resolution--lead to better tornado warning performance as characterized by tornado detection probability and false alarm ratio. Previous experimental results showing faster volume scan rates yielding greater warning performance, including increased lead times, are also incorporated into the model. Enhanced tornado warning performance, in turn, reduces casualty rates. In combination, then, it is clearly established that better and faster radar observations reduce tornado casualty rates. Furthermore, lower false alarm ratios save costs by cutting down on people's time lost when taking shelter.
Summary
A monetized tornado benefit model is developed for arbitrary weather radar network configurations. Geospatial regression analyses indicate that improvement in two key radar coverage parameters--fraction of vertical space observed and cross-range horizontal resolution--lead to better tornado warning performance as characterized by tornado detection probability and false alarm ratio. Previous experimental...
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Shining light on thermophysical Near-Earth Asteroid modeling efforts
January 22, 2019
Conference Paper
Published in:
1st NEO and Debris Detection Conf., 22-24 January 2019.
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Summary
Comprehensive thermophysical analyses of Near-Earth Asteroids (NEAs) provide important information about their physical properties, including visible albedo, diameter, composition, and thermal inertia. These details are integral to defining asteroid taxonomy and understanding how these objects interact with the solar system. Since infrared (IR) asteroid observations are not widely available, thermophysical modeling techniques have become valuable in simulating properties of different asteroid types. Several basic models that assume a spherical asteroid shape have been used extensively within the research community. As part of a program focused on developing a simulation of space-based IR sensors for asteroid search, the Near-Earth Asteroid Model (NEATM) developed by Harris, A. in 1998, was selected. This review provides a full derivation of the formulae behind NEATM, including the spectral flux density equation, consideration of the solar phase angle, and the geometry of the asteroid, Earth, and Sun system. It describes how to implement the model in software and explores the use of an ellipsoidal asteroid shape. It also applies the model to several asteroids observed by NASA's Near-Earth Object Wide-field Survey Explorer (NEOWISE) and compares the performance of the model to the observations.
Summary
Comprehensive thermophysical analyses of Near-Earth Asteroids (NEAs) provide important information about their physical properties, including visible albedo, diameter, composition, and thermal inertia. These details are integral to defining asteroid taxonomy and understanding how these objects interact with the solar system. Since infrared (IR) asteroid observations are not widely available, thermophysical...
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Modeling and validation of a mm-wave shaped dielectric lens antenna
July 29, 2018
Conference Paper
Published in:
2018 Int. Applied Computational Electromagnetics Society Symp., ACES, 29 July - 1 August 2018.
Summary
The modeling and validation of a 33 GHz shaped dielectric antenna design is investigated. The electromagnetic modeling was performed in both WIPL-D and FEKO, and was used to validate the antenna design prior to fabrication of the lens. It is shown that both WIPL-D and FEKO yield similarly accurate results as compared to measured far-field gain radiation patterns.
Summary
The modeling and validation of a 33 GHz shaped dielectric antenna design is investigated. The electromagnetic modeling was performed in both WIPL-D and FEKO, and was used to validate the antenna design prior to fabrication of the lens. It is shown that both WIPL-D and FEKO yield similarly accurate results...
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