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At least 199 records · Page 11

A model of human decisionmaking in multiple process monitoring situations

It is proposed that human decisionmaking performance in multiple process monitoring situations can be modeled in terms of the detection of process related events and the allocation of attention among processes once events are felt to have occurred. An elementary pattern recognition technique, discriminant analysis, is used to generate estimates of event occurrence probability. A queueing theory framework is then utilized to incorporate these probabilities as well as other task characteristics into the solution of the attention allocation problem. The performance of the model is compared with that of subjects in two experiments.

Greenstein, J. S.↗

Assessing Cognitive Impacts of Errors from Machine Learning and Deep Learning Models: Final Report

Due to their recent increases in performance, machine learning and deep learning models are being increasingly adopted across many domains for visual processing tasks. One such domain is international nuclear safeguards, which seeks to verify the peaceful use of commercial nuclear energy across the globe. Despite recent impressive performance results from machine learning and deep learning algorithms, there is always at least some small level of error. Given the significant consequences of international nuclear safeguards conclusions, we sought to characterize how incorrect responses from a machine or deep learning-assisted visual search task would cognitively impact users. We found that not only do some types of model errors have larger negative impacts on human performance than other errors, the scale of those impacts change depending on the accuracy of the model with which they are presented and they persist in scenarios of evenly distributed errors and single-error presentations. Further, we found that experiments conducted using a common visual search dataset from the psychology community has similar implications to a safeguards- relevant dataset of images containing hyperboloid cooling towers when the cooling tower images are presented to expert participants. While novice performance was considerably different (and worse) on the cooling tower task, we saw increased novice reliance on the most challenging cooling tower images compared to experts. These findings are relevant not just to the cognitive science community, but also for developers of machine and deep learning that will be implemented in multiple domains. For safeguards, this research provides key insights into how machine and deep learning projects should be implemented considering their special requirements that information not be missed.

97 MATHEMATICS AND COMPUTING↗

Simulating Human Cognition in the Domain of Air Traffic Control

Experiments intended to assess performance in human-machine interactions are often prohibitively expensive, unethical or otherwise impractical to run. Approximations of experimental results can be obtained, in principle, by simulating the behavior of subjects using computer models of human mental behavior. Computer simulation technology has been developed for this purpose. Our goal is to produce a cognitive model suitable to guide the simulation machinery and enable it to closely approximate a human subject's performance in experimental conditions. The described model is designed to simulate a variety of cognitive behaviors involved in routine air traffic control. As the model is elaborated, our ability to predict the effects of novel circumstances on controller error rates and other performance characteristics should increase. This will enable the system to project the impact of proposed changes to air traffic control procedures and equipment on controller performance.

Freed, Michael↗

Using machine learning to identify extragalactic globular cluster candidates from ground-based photometric surveys of M87

Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87’s well-studied GC system to implement supervised machine learning (ML) classification algorithms – specifically random forest and neural networks – to identify GCs from foreground stars and background galaxies, using ground-based photometry from the Canada–France–Hawaii Telescope (CFHT). We compare these two ML classification methods to studies of ‘human-selected’ GCs and find that the best-performing random forest model can reselect 61.2 per cent ± 8.0 per cent of GCs selected from HST data (ACSVCS) and the best-performing neural network model reselects 95.0 per cent ± 3.4 per cent. When compared to human-classified GCs and contaminants selected from CFHT data – independent of our training data – the best-performing random forest model can correctly classify 91.0 per cent ± 1.2 per cent and the best-performing neural network model can correctly classify 57.3 per cent ± 1.1 per cent. ML methods in astronomy have been receiving much interest as Vera C. Rubin Observatory prepares for first light. The observables in this study are selected to be directly comparable to early Rubin Observatory data and the prospects for running ML algorithms on the upcoming data set yields promising results.

79 ASTRONOMY AND ASTROPHYSICS↗

An architecture and model for cognitive engineering simulation analysis - Application to advanced aviation automation

The process of designing crew stations for large-scale, complex automated systems is made difficult because of the flexibility of roles that the crew can assume, and by the rapid rate at which system designs become fixed. Modern cockpit automation frequently involves multiple layers of control and display technology in which human operators must exercise equipment in augmented, supervisory, and fully automated control modes. In this context, we maintain that effective human-centered design is dependent on adequate models of human/system performance in which representations of the equipment, the human operator(s), and the mission tasks are available to designers for manipulation and modification. The joint Army-NASA Aircrew/Aircraft Integration (A3I) Program, with its attendant Man-machine Integration Design and Analysis System (MIDAS), was initiated to meet this challenge. MIDAS provides designers with a test bed for analyzing human-system integration in an environment in which both cognitive human function and 'intelligent' machine function are described in similar terms. This distributed object-oriented simulation system, its architecture and assumptions, and our experiences from its application in advanced aviation crew stations are described.

Corker, Kevin M.↗

Evaluation of Human and Anthropomorphic Test Device Finite Element Models under Spaceflight Loading Conditions

In an effort to develop occupant protection standards for future multipurpose crew vehicles, the National Aeronautics and Space Administration (NASA) has looked to evaluate the test device for human occupant restraint with the modification kit (THOR‐K) anthropomorphic test device (ATD) in relevant impact test scenarios. With the allowance and support of the National Highway Traffic Safety Administration, NASA has performed a series of sled impact tests on the latest developed THOR‐K ATD. These tests were performed to match test conditions from human volunteer data previously collected by the U.S. Air Force. The objective of this study was to evaluate the THOR‐K finite element (FE) model and the Total HUman Model for Safety (THUMS) FE model with respect to the tests performed. These models were evaluated in spinal and frontal impacts against kinematic and kinetic data recorded in ATD and human testing. Methods: The FE simulations were developed based on recorded pretest ATD/human position and sled acceleration pulses measured during testing. Predicted responses by both human and ATD models were compared to test data recorded under the same impact conditions. The kinematic responses of the models were quantitatively evaluated using the ISO‐metric curve rating system. In addition, ATD injury criteria and human stress/strain data were calculated to evaluate the risk of injury predicted by the ATD and human model, respectively. Results: Preliminary results show well‐correlated response between both FE models and their physical counterparts. In addition, predicted ATD injury criteria and human model stress/strain values are shown to positively relate. Kinematic comparison between human and ATD models indicates promising biofidelic response, although a slightly stiffer response is observed within the ATD. Conclusion: As a compliment to ATD testing, numerical simulation provides efficient means to assess vehicle safety throughout the design process and further improve the design of physical ATDs. The assessment of the THOR‐K and THUMS FE models in a spaceflight testing condition is an essential first step to implementing these models in the computational evaluation of spacecraft occupant safety. Promising results suggest future use of these models in the aerospace field.

Putnam, Jacob P.↗

Modeling human decision making behavior in supervisory control

An optimal decision control model was developed, which is based primarily on a dynamic programming algorithm which looks at all the available task possibilities, charts an optimal trajectory, and commits itself to do the first step (i.e., follow the optimal trajectory during the next time period), and then iterates the calculation. A Bayesian estimator was included which estimates the tasks which might occur in the immediate future and provides this information to the dynamic programming routine. Preliminary trials comparing the human subject's performance to that of the optimal model show a great similarity, but indicate that the human skips certain movements which require quick change in strategy.

Tulga, M. K.↗

Regulation of β-cell death by ADP-ribosylhydrolase ARH3 via lipid signaling in insulitis

Lipids are regulators of insulitis and β-cell death in type 1 diabetes development, but the underlying mechanisms are poorly understood. Here, we investigated how the islet lipid composition and downstream signaling regulate β-cell death. We performed lipidomics using three models of insulitis: human islets and EndoC-βH1 β cells treated with the pro-inflammatory cytokines interlukine-1β and interferon-γ, and islets from pre-diabetic non-obese mice. We also performed mass spectrometry and fluorescence imaging to determine the localization of lipids and enzyme in islets. RNAi, apoptotic assay, and qPCR were performed to determine the role of a specific factor in lipid-mediated cytokine signaling. Across all three models, lipidomic analyses showed a consistent increase of lysophosphatidylcholine species and phosphatidylcholines with polyunsaturated fatty acids and a reduction of triacylglycerol species. Imaging assays showed that phosphatidylcholines with polyunsaturated fatty acids and their hydrolyzing enzyme phospholipase PLA2G6 are enriched in islets. In downstream signaling, omega-3 fatty acids reduce cytokine-induced β-cell death by improving the expression of ADP-ribosylhydrolase ARH3. The mechanism involves omega-3 fatty acid-mediated reduction of the histone methylation polycomb complex PRC2 component Suz12, upregulating the expression of Arh3, which in turn decreases cell apoptosis. Our data provide insights into the change of lipidomics landscape in β cells during insulitis and identify a protective mechanism by omega-3 fatty acids.

59 BASIC BIOLOGICAL SCIENCES↗

Fleet Algorithm Design for Pooled Rideshare: Integrating Human Factors, Simulation, and Optimization

This dissertation explores the study the integration of human factors modeling and rideshare fleet control algorithms. Pooled rideshare is a unique transportation mode offering that allows riders increased flexibility and accessibility over public transportation, and decreased cost relative to personal vehicles or traditional rideshare. Additionally, relative to personal vehicles, pooled rideshare offers reduced costs and options for those with difficulty obtaining transportation. Prior research in the space typically focused on modeling human behavior, or optimizing system performance, but a lack of integration of the concepts leads to unrealistic or underutilized outcomes. To tackle this problem, novel rideshare assignment, and repositioning strategies were designed and implemented in a simulation environment. Through a series of successive studies, improvements to current rideshare processes were identified, and beneficial outcomes for profitability, accessibility, and traffic were explored. Further, improved metrics to assess rideshare performance were designed and analyzed in the context of improved rideshare offerings. This research contributes to the field of transportation by tackling novel but pragmatic approaches to challenges facing the rideshare industry. Through the course of this dissertation, rideshares impacts on users, operators, and even regulators will be explored in detail. The justification behind the use of a simulation environment, a set of simulated regional models for testing, and the focus on realism and deployability is illustrated. The research identifies holes in potential markets for the use of both private, and public rideshare systems.

Paul, Joseph↗

Human heading estimation during visually simulated curvilinear motion

Recent studies have suggested that humans cannot estimate their direction of forward translation (heading) from the resulting retinal motion (flow field) alone when rotation rates are higher than approximately 1 deg/sec. It has been argued that either oculomotor or static depth cues are necessary to disambiguate the rotational and translational components of the flow field and, thus, to support accurate heading estimation. We have re-examined this issue using visually simulated motion along a curved path towards a layout of random points as the stimulus. Our data show that, in this curvilinear motion paradigm, five of six observers could estimate their heading relatively accurately and precisely (error and uncertainty < approximately 4 deg), even for rotation rates as high as 16 deg/sec, without the benefit of either oculomotor or static depth cues signaling rotation rate. Such performance is inconsistent with models of human self-motion estimation that require rotation information from sources other than the flow field to cancel the rotational flow.

NASA Center ARC↗

Generative AI for Grid Operations [Slides]

In the last few years, the development and use of generative artificial intelligence (AI) and large-language models (LLMs) have changed the landscape of how AI and machine learning (ML) are being used in power systems. LLMs are built on foundational models based on large data sets that can be trained to provide information rapidly and through simple natural language prompts. Generative AI can then perform human-like tasks using ML models to identify and mimic pattens in the data sets. This presentation explores how generative AI can enhance grid operations by improving forecasts, enabling rapid contingency analyses, and offering real-time operational suggestions. By providing grid operators with valuable insights, generative AI will empower them to manage power systems more effectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Models of remote manipulation in space

Robots involved in high value manipulation must be effectively coupled to a human operator either at the work-site or remotely connected via communication links. In order to make use of experimental performance evaluation data, models must be developed. Powerful models of remote manipulation by humans can be used to predict manipulation performance in future systems based on today's laboratory systems. In this paradigm, the models are developed from experimental data, and then used to predict performance in slightly different situations. Second, accurate telemanipulation will allow design of manipulation systems which extend manipulation capability beyond its current bounds.

Hannaford, Blake↗

An Investigation of the Effects of the Time Lag Due to Long Transmission Distance upon Remote Control. Phase II - Vehicle Experiments. Phase III - Conclusions

An experimental program is undertaken to define the effects upon remote control of long transmission delays. Investigation centers around remote control of a ground vehicle, which is considered to be a representative remote control task. A series of pursuit tracking tests is performed with transport lags ranging from 0 to 6 seconds between the control and the controlled quantity. Various target speeds are tracked with both velocity and acceleration controls. Two types of tracking are performed in an attempt to bracket the actual vehicle situation. In the first the operator attempts to follow the target with his controlled quantity in real time, using the delayed position and rates as feedback. In the second he attempts to follow the target with the delayed controlled quantity. In addition, tests are performed substituting simple electronic models for the human in an attempt to gain an understanding of human response with time delays in the control loop. A series of tests with an actual vehicle are performed with the intent of relating the tracking tests to the actual situation of interest. Time delays of from 0 to 3 seconds are included in the control loop. Performance is scored at various speeds over both continuous and obstacle courses. Both two and four-wheel steering are investigated. In the experiments the effects of all variables except delay magnitude and target complexity are minimized. Curves are obtained showing performance as a function of time delay and target complexity. These curves are discussed. Correlation of the tracking and vehicle experiments is discussed. Comments are made concerning the design of a minimum control system for a remotely controlled lunar roving vehicle.

REMOTE CONTROL↗

USSR Space Life Sciences Digest, issue 21

This is the twenty-first issue of NASA's USSR Space Life Sciences Digest. It contains abstracts of 37 papers published in Russian language periodicals or books or presented at conferences and of a Soviet monograph on animal ontogeny in weightlessness. Selected abstracts are illustrated with figures and tables from the original. A book review of a work on adaptation to stress is also included. The abstracts in this issue have been identified as relevant to 25 areas of space biology and medicine. These areas are: adaptation, biological rhythms, body fluids, botany, cardiovascular and respiratory systems, cytology, developmental biology, endocrinology, enzymology, equipment and instrumentation, exobiology, gravitational biology, habitability and environmental effects, hematology, human performance, life support systems, mathematical modeling, metabolism, microbiology, musculoskeletal system, neurophysiology, operational medicine, perception, psychology, and reproductive system.

Hooke, Lydia Razran↗

Improving the human factors of software with CLIPS

The use of CLIPS has transformed a conventional graduate course on the human factors of software. Previously, the class centered on lectures and discussions of a mix of ideas for improving the user-friendliness of software. By using CLIPS, the course can focus instead on teaching students to build three rule-based projects in CLIPS for improving the human factors of software. For the first project, students construct a friendly CLIPS front-end to existing software. For the second project students build a CLIPS expert system to help comply with user-interface guidelines. Alternatively, students may build an expert system to assist in detecting discrepancies between user-interfaces and guidelines. For the third project, students use CLIPS to implement a GOMS Model Methodology to assess the human performance impacts of given user-interfaces. Feedback on the projects from the students' colleagues and superiors in the workplace confirm the effectiveness of this CLIPS project-oriented approach to teaching the human factors of user-computer systems. Future refinements are described. Suggestions for those wishing to try this approach are outlined.

Nagy, Thomas J.↗

Electrostatic Discharge Effects on Thin Film Resistors

Recently, open circuit failures of individual elements in thin film resistor networks have been attributed to electrostatic discharge (ESD) effects. This paper will discuss the investigation that came to this conclusion and subsequent experimentation intended to characterize design factors that affect the sensitivity of resistor elements to ESD. The ESD testing was performed using the standard human body model simulation. Some of the design elements to be evaluated were: trace width, trace length (and thus width to length ratio), specific resistivity of the trace (ohms per square) and resistance value. However, once the experiments were in progress, it was realized that the ESD sensitivity of most of the complex patterns under evaluation was determined by other design and process factors such as trace shape and termination pad spacing. This paper includes pictorial examples of representative ESD failure sites, and provides some options for designing thin film resistors that are ESD resistant. The risks of ESD damage are assessed and handling precautions suggested.

Sampson, Michael J.↗

A model of human decisionmaking in a fault diagnosis task

Utilizing elementary concepts from the theory of fuzzy sets as well as several nonfuzzy heuristics, a model is presented of human decisionmaking in the task of troubleshooting graphically displayed networks. The performance of the model is compared to the results of two previously reported experimental studies. The ability of the model to represent human decisionmaking as a function of network size, forced-pacing, and computer aiding is considered.

Rouse, W. B.↗