SEARCH · Engineering Papers

Results for “human inattention”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

5 records

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Reinforcement Learning for feedback-enabled cyber resilience

The rapid growth in the number of devices and their connectivity has enlarged the attack surface and made cyber systems more vulnerable. As attackers become increasingly sophisticated and resourceful, mere reliance on traditional cyber protection, such as intrusion detection, firewalls, and encryption, is insufficient to secure the cyber systems. Cyber resilience provides a new security paradigm that complements inadequate protection with resilience mechanisms. A Cyber-Resilient Mechanism (CRM) adapts to the known or zero-day threats and uncertainties in real-time and strategically responds to them to maintain the critical functions of the cyber systems in the event of successful attacks. Feedback architectures play a pivotal role in enabling the online sensing, reasoning, and actuation process of the CRM. Reinforcement Learning (RL) is an important gathering of algorithms that epitomize the feedback architectures for cyber resilience. It allows the CRM to provide dynamic and sequential responses to attacks with limited or without prior knowledge of the environment and the attacker. In this work, we review the literature on RL for cyber resilience and discuss the cyber-resilient defenses against three major types of vulnerabilities, i.e., posture-related, information-related, and human-related vulnerabilities. Here we introduce moving target defense, defensive cyber deception, and assistive human security technologies as three application domains of CRMs to elaborate on their designs. The RL algorithms also have vulnerabilities themselves. We explain the major vulnerabilities of RL and present develop several attack models where the attacker target the information exchanged between the environment and the agent: the rewards, the state observations, and the action commands. We show that the attacker can trick the RL agent into learning a nefarious policy with minimum attacking effort. The paper introduces several defense methods to secure the RL-enabled systems from these attacks. However, there is still a lack of works that focuses on the defensive mechanisms for RL-enabled systems. Last but not least, we discuss the future challenges of RL for cyber security and resilience and emerging applications of RL-based CRMs.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2017

Repeated Induction of Inattentional Blindness in a Simulated Aviation Environment

The study reported herein is a subset of a larger investigation on the role of automation in the context of the flight deck and used a fixed-based, human-in-the-loop simulator. This paper explored the relationship between automation and inattentional blindness (IB) occurrences in a repeated induction paradigm using two types of runway incursions. The critical stimuli for both runway incursions were directly relevant to primary task performance. Sixty non-pilot participants performed the final five minutes of a landing scenario twice in one of three automation conditions: full automation (FA), partial automation (PA), and no automation (NA). The first induction resulted in a 70 percent (42 of 60) detection failure rate with those in the PA condition significantly more likely to detect the incursion compared to the FA condition or the NA condition. The second induction yielded a 50 percent detection failure rate. Although detection improved (detection failure rates declined) in all conditions, those in the FA condition demonstrated the greatest improvement with doubled detection rates. The detection behavior in the first trial did not preclude a failed detection in the second induction. Group membership (IB vs. Detection) in the FA condition showed a greater improvement than those in the NA condition and rated the Mental Demand and Effort subscales of the NASA-TLX (NASA Task Load Index) significantly higher for Time 2 compared Time 1. Participants in the FA condition used the experience of IB exposure to improve task performance whereas those in the NA condition did not, indicating the availability and reallocation of attentional resources in the FA condition. These findings support the role of engagement in operational attention detriment and the consideration of attentional failure causation to determine appropriate mitigation strategies.

Kennedy, Kellie D.↗

NASA NTRS2013

HRA Aerospace Challenges

Compared to equipment designed to perform the same function over and over, humans are just not as reliable. Computers and machines perform the same action in the same way repeatedly getting the same result, unless equipment fails or a human interferes. Humans who are supposed to perform the same actions repeatedly often perform them incorrectly due to a variety of issues including: stress, fatigue, illness, lack of training, distraction, acting at the wrong time, not acting when they should, not following procedures, misinterpreting information or inattention to detail. Why not use robots and automatic controls exclusively if human error is so common? In an emergency or off normal situation that the computer, robotic element, or automatic control system is not designed to respond to, the result is failure unless a human can intervene. The human in the loop may be more likely to cause an error, but is also more likely to catch the error and correct it. When it comes to unexpected situations, or performing multiple tasks outside the defined mission parameters, humans are the only viable alternative. Human Reliability Assessments (HRA) identifies ways to improve human performance and reliability and can lead to improvements in systems designed to interact with humans. Understanding the context of the situation that can lead to human errors, which include taking the wrong action, no action or making bad decisions provides additional information to mitigate risks. With improved human reliability comes reduced risk for the overall operation or project.

DeMott, Diana↗

NASA NTRS1997

Speed of mental processing in the middle of the night

This study aimed to determine whether human mental processing actually slows down during the night hours, separately from the previously documented microsleeps, lapses in attention, and general slowing of motor responses. Eighteen healthy young adults were studied during 36 hours of constant wakeful bedrest. Every 2 hours, they performed a logical reasoning task. Items phrased in the negative voice took reliably longer to respond to than items phrased in the positive voice, indicating the need for more mental processing in those items. By subtracting "negative" from "positive" reaction times at each time of day, we were able to plot a circadian rhythm in the time taken for this extra mental processing to be done separately from microsleeps, psychomotor slowing, and inattention. The extra mental processing took longer at night and on the day following sleep loss than it did during the day before the sleep loss, suggesting that human mental processing slows down during the night under sleep deprivation.

Non-NASA Center↗

NASA NTRS2005

Large Liquid Rocket Testing: Strategies and Challenges

Rocket propulsion development is enabled by rigorous ground testing in order to mitigate the propulsion systems risks that are inherent in space flight. This is true for virtually all propulsive devices of a space vehicle including liquid and solid rocket propulsion, chemical and non-chemical propulsion, boost stage and in-space propulsion and so forth. In particular, large liquid rocket propulsion development and testing over the past five decades of human and robotic space flight has involved a combination of component-level testing and engine-level testing to first demonstrate that the propulsion devices were designed to meet the specified requirements for the Earth to Orbit launchers that they powered. This was followed by a vigorous test campaign to demonstrate the designed propulsion articles over the required operational envelope, and over robust margins, such that a sufficiently reliable propulsion system is delivered prior to first flight. It is possible that hundreds of tests, and on the order of a hundred thousand test seconds, are needed to achieve a high-reliability, flight-ready, liquid rocket engine system. This paper overviews aspects of earlier and recent experience of liquid rocket propulsion testing at NASA Stennis Space Center, where full scale flight engines and flight stages, as well as a significant amount of development testing has taken place in the past decade. The liquid rocket testing experience discussed includes testing of engine components (gas generators, preburners, thrust chambers, pumps, powerheads), as well as engine systems and complete stages. The number of tests, accumulated test seconds, and years of test stand occupancy needed to meet varying test objectives, will be selectively discussed and compared for the wide variety of ground test work that has been conducted at Stennis for subscale and full scale liquid rocket devices. Since rocket propulsion is a crucial long-lead element of any space system acquisition or development, the appropriate plan and strategy must be put in place at the outset of the development effort. A deferment of this test planning, or inattention to strategy, will compromise the ability of the development program to achieve its systems reliability requirements and/or its development milestones. It is important for the government leadership and support team, as well as the vehicle and propulsion development team, to give early consideration to this aspect of space propulsion and space transportation work.

Rahman, Shamim A.↗