Results for “detect and avoid”

DOE OSTI2021

Adaptive Threshold-Based Zonal Isolation of Faults in a Multiterminal DC Using Local Measurements

Fast and accurate methods of fault detection and isolation are a pre-emptive measure in multiterminal dc (MTdc) systems. Zonal isolation of faults is necessary to prevent any misopertation of the dc breakers that can lead to a shutdown of the network. Existing techniques require fast-communication or data synchronization methods have their own disadvantages. This article proposes a method for efficient fault zone isolation without the need of a communication link that prevents any misoperation of the dc breakers in a radial MTdc. This method provides individual local measurement-based control to the hybrid dc circuit breakers (dcCB). Faults created outside the zone of protection for a breaker create a change in the rate of change of current or voltage leading to misoperation. To avoid detection using a fixed threshold, an adaptive threshold based approach is suggested that updates the threshold based on the present operating status. Sensitivity analysis by varying the current limiting inductance and fault location is performed. In conclusion, a three terminal radial model of an MTdc is used for zonal isolation using power system computer aided design/electromagnetic transients including dc.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Collaboration and Negotiation

Collaboration and Negotiation is a critical high-level function of an Autonomous Intelligent Cyber-Defense Agent (AICA) that enables communication among agents, central cyber C2, and human operators. Maintaining the Confidentiality, Integrity, and Availability (CIA) triad while achieving mission goals requires stealthy AICA agents to exercise: 1) minimal communication as needed for avoiding detection, 2) verification of information received with possibly limited resources, and 3) active learning during operations to address dynamic conditions. Moreover, negotiations to jointly identify and execute a Course of Action (COA) solution will require building consensus under distributed and/or decentralized multiagent settings with information uncertainties. This chapter presents algorithmic approaches for enabling the collaboration and negotiation function. Strengths and limitations of potential techniques are identified, and a representative example is illustrated. Recommendations for future development are also discussed.

Chatterjee, Samrat↗

DOE OSTI2023

Sensor and Actuator Attacks on Hierarchical Control Systems with Domain-Aware Operator Theory

Cyber-Physical Systems (CPSs) provide opportunities for cyber attacks to have physical impacts. Advanced Persistent Threats (APTs) are a subclass of cyber threats that act stealthily to avoid detection and enable long-term attacks. Here, we build on our past work in APT modelling to combine deception-based sensor bias attacks and direct actuator manipulations in attacks against a hierarchical control system. That past work used the Koopman operator to develop a data-driven, domain-aware, optimization-based attacker model. Using an expansion of this model, we compute several different attacks, including multiple simultaneous attacks, against a high-fidelity commercial building emulator and compare the impacts of those attacks to each other. One next step of interest is to construct a defender system, built on the same modelling approach, designed to detect and mitigate such attacks.

koopman operator, Cyber-Physical Security, machine↗

DOE OSTI2020

Relative reproductive phenology and synchrony affect neonate survival in a nonprecocial ungulate

Abstract Degree of reproductive synchronization in prey is hypothesized as a predator defense strategy reducing prey risk via predator satiation or predator avoidance. Species with precocial young, especially those exposed to specialist predators, should be highly synchronous to satiate predators (predator satiation hypothesis), while prey with nonprecocial (i.e. altricial) young, especially those exposed to generalist predators, should become relatively asynchronous to avoid predator detection (predator avoidance hypothesis). The white‐tailed deer Odocoileus virginianus in North America is an example of a nonprecocial ungulate that uses the hider strategy early in life; its primary predator (coyote; Canis latrans ) is a generalist, making white‐tailed deer a good model species to test the predator avoidance hypothesis. We used birth dates and known fates of white‐tailed deer neonates ( n = 1,032) across nine study sites varying in relative synchrony and predator assemblages to test the predator avoidance hypothesis. We predicted that relative birthing asynchrony of the population would increase relative survival at the population level; therefore, at the individual scale, neonate birth date nearer to mean birthing date in a respective population would not influence individual survival. Coyotes were responsible for the majority of predation events, and survival of those neonates increased the closer the individual was born to peak birthing season in each respective population. Also, at the population level, reproductive asynchronization negatively affected survival. Contrary to the predator avoidance hypothesis, our data indicate patterns in neonate survival for white‐tailed deer better support the predator satiation hypothesis at the individual and population level. Additionally, coyotes may present a selective force great enough to shift reproductive synchrony such that predator satiation may become a feasible defense strategy for neonates at local spatial scales. Our results indicate that synchronizing reproduction may still be the most effective strategy to reduce individual predation risk from generalist predators, particularly when the window of heightened resource availability to the prey is narrow. A free Plain Language Summary can be found within the Supporting Information of this article.

Michel, Eric S.↗

DOE OSTI2022

Assessing the Effectiveness of Generalized Likelihood Ratio Test Detector Schemes in Seismic Event Detection and the Avoidance of Nontarget Signals

Cross-correlation techniques have played a long-standing and pivotal role in seismic event monitoring. However, the performance of correlation-based detectors is challenged by nuisance seismicity, or nontarget signals. Such detections are a problem when the mission is to automatically map events to the correct source region. Using aftershocks of the 2014 $M_w$ 6.0 South Napa, California, earthquake, we demonstrate the effectiveness of utilizing a dynamic correlation processor framework in a generalized likelihood ratio test (GLRT) detector configuration to minimize nontarget detections. A GLRT maximizes a detection statistic with respect to one or more unknown parameters. In this case, the detection statistic is a template signal match against the waveform in a window sliding over a data stream, and the unknown parameter is an index variable indicating group membership of the template event or events. Detected events are assigned to the event group that yields the largest detection statistic. In this work, our results show that a GLRT detector will outperform a suite of independently operating correlation and subspace detectors in terms of having a lower nontarget detection rate at a given missed detection rate. We also show that a GLRT detector composed of a few high-rank subspace detectors has a slightly higher nontarget detection rate, but a significantly lower missed detection rate, than a GLRT detector composed of many low-rank subspace detectors. The high-rank GLRT configuration produced impressive results even with marginal data (single channel, single station, and very low time bandwidth product), which bodes well for the utility of building efficient aftershock classification systems and global monitoring systems at larger scales. However, future work is required to assess performance at the regional scale and to assess the performance of the system at detecting target events not used in the detector template creation.

58 GEOSCIENCES↗

DOE OSTI2021

Programmable intrusion detection for distributed energy resources in cyber–physical networked microgrids

We present a programmable intrusion detection method is presented to identify the malicious attacks to distributed energy resources (DERs) in the cyber-physical networked microgrids. The proposed method injects small programmable signals into the system and uses the response to identify abnormal conditions. Because of the low or even zero inertia induced by integrations of DER power-electronic-interfaces, microgrids have very limited resilience capability; and thus, being sensitive to attacks. One microgrid's malfunction caused by attacks can easily propagate to its neighboring systems when several microgrids are connected, leading to catastrophic electricity supply failures. Through the presented method, malicious intrusions can be effectively detected, located, and defended for securing microgrids. Theoretical derivations are provided to define the programmable detection rules. The detection rule is easy and flexible to update, making it difficult for attack actors to gain the knowledge of the detection rules, in order to avoid being detected. Numerical results on a cyber-physical networked microgrids system show that the proposed method is effective and efficient in precisely locating intrusion attacks to the microgrids system.

42 ENGINEERING↗

DOE OSTI2020

Window obscuration sensors for mobile gas and chemical imaging cameras

An infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including a focal plane array (FPA) unit behind an optical window. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. One or more of the optical channels may be used in detecting objects on or near the optical window, to avoid false detections of said target species.

Mallery, Ryan↗

DOE OSTI2022

Window obscuration sensors for mobile gas and chemical imaging cameras

An infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including a focal plane array (FPA) unit behind an optical window. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. One or more of the optical channels may be used in detecting objects on or near the optical window, to avoid false detections of said target species.

Mallery, Ryan↗

DOE OSTI2023

Window obscuration sensors for mobile gas and chemical imaging cameras

An infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including a focal plane array (FPA) unit behind an optical window. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. One or more of the optical channels may be used in detecting objects on or near the optical window, to avoid false detections of said target species.

Mallery, Ryan↗

DOE OSTI2023

Window obscuration sensors for mobile gas and chemical imaging cameras

An infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including a focal plane array (FPA) unit behind an optical window. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. One or more of the optical channels may be used in detecting objects on or near the optical window, to avoid false detections of said target species.

Mallery, Ryan↗

DOE OSTI2024

Window obscuration sensors for mobile gas and chemical imaging cameras

An infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including a focal plane array (FPA) unit behind an optical window. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. One or more of the optical channels may be used in detecting objects on or near the optical window, to avoid false detections of said target species.

Mallery, Ryan↗

DOE OSTI2024

Window obscuration sensors for mobile gas and chemical imaging cameras

An infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including a focal plane array (FPA) unit behind an optical window. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. One or more of the optical channels may be used in detecting objects on or near the optical window, to avoid false detections of said target species.

Mallery, Ryan↗

DOE OSTI2019

Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks

Here this paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%.

42 ENGINEERING↗

DOE OSTI2023

Perception Testing in Fog for Autonomous Flight

As the path towards Urban Air Mobility (UAM) continues to take shape, there are outstanding technical challenges to achieving safe and effective air transportation operations under this new paradigm. To inform and guide technology development for UAM, NASA is investigating the current state-of-the-art in key technology areas including traffic management, detect-and-avoid, and autonomy. In support of this effort, a new perception testbed was developed at NASA Ames Research Center to collect data from an array of sensing systems representative of those that could be found on a future UAM vehicle. This testbed, featuring a Light-Detection-and-Ranging (LIDAR) instrument, a long-wave infrared sensor, and a visible spectrum camera was deployed for a multiday test campaign in the Fog Chamber at Sandia National Laboratories (SNL), in Albuquerque, New Mexico. During the test campaign, fog conditions were created for tests with targets including a human, a resolution chart, and a small unmanned aerial vehicle (sUAV). Here, this paper describes in detail, the developed perception testbed, the experimental setup in the fog chamber, the resulting data, and presents an initial result from analysis of the data with the evaluation of methods to increase contrast through filtering techniques.

aeronautics↗

DOE OSTI2020

Comparison and Discussion on Shortcircuit Protections for Silicon-Carbide MOSFET Modules: Desaturation Versus Rogowski Switch-Current Sensor

Survivability of silicon-carbide (SiC) mosfet modules during short circuit (SC) is essential for modern power electronics systems due to large economic implications. SiC mosfet modules exhibit narrow SC withstand times and generally much lower SC robustness than silicon (Si) insulated-gate bipolar transistors (IGBTs). This puts a critical concern on their utilization, further stressing the importance of reliable protection. This article presents a comprehensive analysis and discussion on the SiC mosfet module SC protection and performance comparison between conventional desaturation detection, previously used for Si IGBT protection, and Rogowski switch-current sensor (RSCS) detection. In addition, this article presents the experimental design for soft turn-off as a reaction to the SC detection to avoid excessive overshoots during the abrupt turn-off of the SC current. To perform different types of SC with various fault inductances on different temperatures, and for the sake of fair comparison under the same conditions, a gate-driver unit for 1.2 kV SiC mosfet half-bridge modules is developed containing both detection methods. The results demonstrate that both methods are successful in protecting the module in all SC types, with superiority of the RSCS detection and concerns for desaturation detection in high-inductance and high dc-bus voltage SC events.

42 ENGINEERING↗

DOE OSTI2023

The MeerKAT Fornax Survey: II. The rapid removal of H I from dwarf galaxies in the Fornax cluster

We present MeerKAT Fornax Survey atomic hydrogen (H I) observations of the dwarf galaxies located in the central ~2.5 × 4 deg 2 of the Fornax galaxy cluster (R vir ~2°). The H I images presented in this work have a 3σ column density sensitivity between 2.7 and 50 × 10 18 cm –2 over 25 km s –1 for spatial resolution between 4 and 1 kpc. We are able to detect an impressive M HI = 5 × 10 5 M ⊙ 3σ point source with a line width of 50 km s –1 at a distance of 20 Mpc. We detected H I in 17 out of the 304 dwarfs in our field, with 14 out of the 36 late-type dwarfs (LTDs) and three out of the 268 early-type dwarfs (ETDs). The H I-detected LTDs have likely just joined the cluster and are on their first infall as they are located at large clustocentric radii, with comparable MHI and mean stellar surface brightness at fixed luminosity as blue, star-forming LTDs in the field. By contrast, the H I-detected ETDs have likely been in the cluster longer than the LTDs and acquired their H I through a recent merger or accretion from nearby H I. Eight of the H I-detected LTDs host irregular or asymmetric H I emission and disturbed or lopsided stellar emission. There are two clear cases of ram pressure shaping the H I, with the LTDs displaying compressed H I on the side closest to the cluster centre and a one-sided, starless tail pointing away from the cluster centre. The H I-detected dwarfs avoid the most massive potentials (i.e. cluster centre and massive galaxies), consistent with massive galaxies playing an active role in the removal of H I. We created a simple toy model to quantify the timescale of H I stripping in the cluster by reproducing the observed M r' –M HI relation. We find that a M HI = 10 8 M ⊙ dwarf is stripped in ~240 Myr. The model is consistent with our observations, where low-mass LTDs are directly stripped of their H I from a single encounter and more massive LTDs can harbour a disturbed H I morphology due to longer times or multiple encounters being required to fully strip their H I. This is the first time dwarf galaxies with M HI ≲ 1 × 10 6 M ⊙ have been detected and resolved beyond the local group and in a galaxy cluster.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

A taxonomy of automatic differentiation pitfalls

Automatic differentiation is a popular technique for computing derivatives of computer programs. While automatic differentiation has been successfully used in countless engineering, science, and machine learning applications, it can sometimes nevertheless produce surprising results. In this paper, we categorize problematic usages of automatic differentiation, and illustrate each category with examples such as chaos, time-averages, discretizations, fixed-point loops, lookup tables, linear solvers, and probabilistic programs, in the hope that readers may more easily avoid or detect such pitfalls. We also review debugging techniques and their effectiveness in these situations.

Autodiff↗

DOE OSTI2022

Evaluation of possible reactivation of undetected faults during CO 2 injection

Geologic storage of carbon dioxide can efficiently contribute to reduce greenhouse gas emissions to the atmosphere. Two major hazards of this technology are leakage towards the ground surface and fluid-induced seismicity. While major faults may be detected and avoided during site characterization, undetected subseismic faults could be encountered once injection has started. This paper investigates leakage and reactivation of undetected faults through coupled thermal–hydraulic–mechanical modeling. The simulations are performed using a recently developed sequential simulator, TOUGH-Pylith, that allows to accurately account for the thermodynamics of brine-CO 2 injection mixtures, and to model faults as surfaces of discontinuity using state-of-the-art fault friction laws. The simulator is benchmarked against well-known analytical solutions and subsequently applied to investigate two cases of CO 2 injection injection close to undetected faults under normal and strike-slip faulting regimes. Furthermore, although the scenarios are generic and represent unfavorable conditions, they suggest that leakage could occur and that undetected faults could trigger minor seismic events. Therefore, careful site characterization and continuous monitoring during operations should be always performed.

58 GEOSCIENCES↗