Matching Items (14)

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Perceptual Training for Sensor Operators: Scenario Development

Description

Improvised explosive devices (IEDs) have become a major threat to military personnel in recent years. In the United States Army, Mission Payload Operators (MPOs) operate cameras from unmanned aerial vehicles

Improvised explosive devices (IEDs) have become a major threat to military personnel in recent years. In the United States Army, Mission Payload Operators (MPOs) operate cameras from unmanned aerial vehicles (UAVs) to detect the threat of IEDs using real-time images received. Previous researchers obtained the expert knowledge of twelve MPOs at Fort Huachuca and learned that they rely on "behavioral signatures," the behavioral and environmental cues associated with IED threat rather than the IED itself (Cooke, Hosch, Banas, Hunn, Staszewski & Fensterer, 2010). To the best of our knowledge, no formal MPO training exists and all training is acquired on-the-job. The end goal is to create training systems for future MPOs using cognitive engineering based on expert skill (CEBES) that focus on detection of behavioral cues associated with IED threats. The complexity and dynamicity of cues associated with IED emplacement is to be noted, as such cues are influenced by sociocultural knowledge and often develop over significant periods of time. A dynamic full motion video simulation environment has been created, and embedded with cues elicited from expert MPOs. A three-part simulation has been created. The next step is verifying critical cues MPOs identify and focus on using eye tracking equipment.

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Date Created
  • 2014-12

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A Simulation Model of the Effect of Workplace Structure on Productivity

Description

Workplace productivity is a result of many factors, and among them is the setup of the office and its resultant noise level. The conversations and interruptions that come along with

Workplace productivity is a result of many factors, and among them is the setup of the office and its resultant noise level. The conversations and interruptions that come along with converting an office to an open plan can foster innovation and creativity, or they can be distracting and harm the performance of employees. Through simulation, the impact of different types of office noise was studied along with other changing conditions such as number of people in the office. When productivity per person, defined in terms of mood and focus, was measured, it was found that the effect of noise was positive in some scenarios and negative in others. In simulations where employees were performing very similar tasks, noise (and its correlates, such as number of employees), was beneficial. On the other hand, when employees were engaged in a variety of different types of tasks, noise had a negative overall effect. This indicates that workplaces that group their employees by common job functions may be more productive than workplaces where the problems and products that employees are working on are varied throughout the workspace.

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Date Created
  • 2017-05

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Perception of American Pit Bull Terriers

Description

Behavior traits were examined in an observed experiment with the presentation of an American Pit Bull Terrier. The experiment was conducted at two locations (Wal Mart, Pet Smart) with searching

Behavior traits were examined in an observed experiment with the presentation of an American Pit Bull Terrier. The experiment was conducted at two locations (Wal Mart, Pet Smart) with searching for behavior traits (positive, negative) with an American Pit Bull Terrier present. In contrast to the hypothesis, there was more positive behavior traits than negative behavior traits. Together, these findings suggest that the presentation of an American Pit Bull Terrier has a more positive outlook on the breed rather than negative. Similar studies should be conducted to change the legislation in regard of "Pit Bulls" that cause discrimination against the breed.

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Date Created
  • 2017-05

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Reunification Dynamics and Consensus Decisions in Temnothorax rugatulus

Description

Social insect colonies adeptly make consensus decisions that emerge from distributed interactions among colony members. How consensus is accomplished when a split decision requires resolution is poorly understood. I studied

Social insect colonies adeptly make consensus decisions that emerge from distributed interactions among colony members. How consensus is accomplished when a split decision requires resolution is poorly understood. I studied colony reunification during emigrations of the crevice-dwelling ant Temnothorax rugatulus. Colonies can choose the most preferred of several alternative nest cavities, but the colony sometimes initially splits between sites and achieves consensus later via secondary emigrations. I explored the decision rules and the individual-level dynamics that govern reunification using artificially split colonies. When monogynous colonies were evenly divided between identical sites, the location of the queen played a decisive role, with 14 of the 16 colonies reuniting at the site that held the queen. This suggests a group-level strategy for minimizing risk to the queen by avoiding unnecessary moves. When the queen was placed in the less preferred of two sites, all 14 colonies that reunited did so at preferred nest, despite having to move the queen. These results show that colonies balance multiple factors when reaching consensus, and that preferences for physical features of environment can outweigh the queen's influence. I also found that tandem recruitment during reunification is overwhelmingly directed from the preferred nest to the other nest. Furthermore, the followers of these tandem runs had a very low probability (5.7%) of also subsequently conducting transports.

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Date Created
  • 2016-12

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Investigating Team Coordination in Baseball Using a Novel Joint Decision Making Paradigm

Description

A novel joint decision making paradigm for assessing team coordination was developed and tested using baseball infielders. Balls launched onto an infield at different trajectories were filmed using four video

A novel joint decision making paradigm for assessing team coordination was developed and tested using baseball infielders. Balls launched onto an infield at different trajectories were filmed using four video cameras that were each placed at one of the typical positions of the four infielders. Each participant viewed temporally occluded videos for one of the four positions and were asked to say either “ball” if they would attempt to field it or the name of the bag that they would cover. The evaluation of two experienced coaches was used to assign a group coordination score for each trajectory and group decision times were calculated. Thirty groups of 4 current college baseball players were: (i) teammates (players from same team/view from own position), (ii) non-teammates (players from different teams/view from own position), or (iii) scrambled teammates (players from same team/view not from own position). Teammates performed significantly better (i.e., faster and more coordinated decisions) than the other two groups, whereas scrambled teammates performed significantly better than non-teammates. These findings suggest that team coordination is achieved through both experience with one’s teammates’ responses to particular events (e.g., a ball hit up the middle) and one’s own general action capabilities (e.g., running speed). The sensitivity of our joint decision making paradigm to group makeup provides support for its use as a method for studying team coordination.

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Date Created
  • 2017-06-07

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Attitudes Towards Autonomous Vehicles (AVs): Insights Gained through Surveys and Proposed Experiments on a Small-Scale Traffic Testbed

Description

In the next decade or so, there will be a shift in the industry of transportation across the world. Already today we have autonomous vehicles (AVs) tested in the Greater

In the next decade or so, there will be a shift in the industry of transportation across the world. Already today we have autonomous vehicles (AVs) tested in the Greater Phoenix area showing that the technology has improved to a level available to the public eye. Although this technology is not yet released commercially (for the most part), it is being used and will continue to be used to develop a safer future. With a high incidence of human error causing accidents, many expect that autonomous vehicles will be safer than human drivers. They do still require driver attention and sometimes intervention to ensure safety, but for the most part are much safer. In just the United States alone, there were 40,000 deaths due to car accidents last year [1]. If traffic fatalities were considered a disease, this would be an epidemic. The technology behind autonomous vehicles will allow for a much safer environment and increased mobility and independence for people who cannot drive and struggle with public transport. There are many opportunities for autonomous vehicles in the transportation industry. Companies can save a lot more money on shipping by cutting the costs of human drivers and trucks on the road, even allowing for simpler drop shipments should the necessary AI be developed.Research is even being done by several labs at Arizona State University. For example, Dr. Spring Berman’s Autonomous Collective Systems Lab has been collaborating with Dr. Nancy Cooke of Human Systems Engineering to develop a traffic testbed, CHARTopolis, to study the risks of driver-AV interactions and the psychological effects of AVs on human drivers on a small scale. This testbed will be used by researchers from their labs and others to develop testing on reaction, trust, and user experience with AVs in a safe environment that simulates conditions similar to those experienced by full-size AVs. Using a new type of small robot that emulates an AV, developed in Dr. Berman’s lab, participants will be able to remotely drive around a model city environment and interact with other AV-like robots using the cameras and LiDAR sensors on the remotely driven robot to guide them.
Although these commercial and research systems are still in testing, it is important to understand how AVs are being marketed to the general public and how they are perceived, so that one day they may be effectively adopted into everyday life. People do not want to see a car they do not trust on the same roads as them, so the questions are: why don’t people trust them, and how can companies and researchers improve the trustworthiness of the vehicles?

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Created

Date Created
  • 2019-05

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Teamwork in Orchestras

Description

The knowledge of cognitive processes of teams and how they work as a system, has drastically broadened in recent years. However, few researchers have applied their findings to an orchestral

The knowledge of cognitive processes of teams and how they work as a system, has drastically broadened in recent years. However, few researchers have applied their findings to an orchestral setting. In the current study, team cognition was observed and analyzed based off an 8th grade orchestra, in addition to the middle and highest-level orchestras at a junior high and high school in the Arizona Public School system. It was found, that in the 8th grade orchestra, most communication is either given or received in the form of auditory cues both verbal and musical. Regardless of skill level, groups that have higher interactions during practices have better performances.

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Date Created
  • 2018-05

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An Investigation of Morality in Driving Situations as a Basis for Determining Autonomous Vehicle Ethics

Description

As urban populations increase, so does the demand for innovative transportation solutions which reduce traffic congestion, reduce pollution, and reduce inequalities by providing mobility for all kinds of people. One

As urban populations increase, so does the demand for innovative transportation solutions which reduce traffic congestion, reduce pollution, and reduce inequalities by providing mobility for all kinds of people. One emerging solution is self-driving vehicles, which have been coined as a safer driving method by reducing fatalities due to driving accidents. While completely automated vehicles are still in the testing and development phase, the United Nations predict their full debut by 2030 [1]. While many resources are focusing their time on creating the technology to execute decisions such as the controls, communications, and sensing, engineers often leave ethics as an afterthought. The truth is autonomous vehicles are imperfect systems that will still experience possible crash scenarios even if all systems are working perfectly. Because of this, ethical machine learning must be considered and implemented to avoid an ethical catastrophe which could delay or completely halt future autonomous vehicle development. This paper presents an experiment for determining a more complete view of human morality and how this translates into ideal driving behaviors.
This paper analyzes responses to deviated Trolley Problem scenarios [5] in a simulated driving environment and still images from MIT’s moral machine website [8] to better understand how humans respond to various crashes. Also included is participants driving habits and personal values, however the bulk of that analysis is not included here. The results of the simulation prove that for the most part in driving scenarios, people would rather sacrifice themselves over people outside of the vehicle. The moral machine scenarios prove that self-sacrifice changes as the trend to harm one’s own vehicle was not so strong when passengers were introduced. Further defending this idea is the importance placed on Family Security over any other value.
Suggestions for implementing ethics into autonomous vehicle crashes stem from the results of this experiment but are dependent on more research and greater sample sizes. Once enough data is collected and analyzed, a moral baseline for human’s moral domain may be agreed upon, quantified, and turned into hard rules governing how self-driving cars should act in different scenarios. With these hard rules as boundary conditions, artificial intelligence should provide training and incremental learning for scenarios which cannot be determined by the rules. Finally, the neural networks which make decisions in artificial intelligence must move from their current “black box” state to something more traceable. This will allow researchers to understand why an autonomous vehicle made a certain decision and allow tweaks as needed.

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Created

Date Created
  • 2019-05

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Team Cognition and Outage Management: Improving Nuclear Power Plant Resilience

Description

Nuclear Power Plants (NPP) have complex and dynamic work environments. Nuclear safety and organizational management rely largely on human performance and teamwork. Multi-disciplinary teams work interdependently to complete cognitively demanding

Nuclear Power Plants (NPP) have complex and dynamic work environments. Nuclear safety and organizational management rely largely on human performance and teamwork. Multi-disciplinary teams work interdependently to complete cognitively demanding tasks such as outage control. The outage control period has the highest risk of core damage and radiation exposure. Thus, team coordination and communication are critically important during this period. The purpose of this thesis is to review and synthesize teamwork studies in NPPs, outage management studies, official Licensee Event Reports (LER), and Inspection Reports (IRs) to characterize team brittleness in NPP systems. Focusing on team brittleness can provide critical insights about how to increase NPP robustness and to create a resilient NPP system. For this reason, more than 900 official LERs and IRs reports were analyzed to understand human and team errors in the United States (US) nuclear power plants. The findings were evaluated by subject matter experts to create a better understanding of team cognition in US nuclear power plants. The results of analysis indicated that human errors could be caused by individual human errors, team errors, procedural errors, design errors, or organizational errors. In addition to these, some of the findings showed that number of reactors, operation year and operation mode could affect the number of reported incidents.

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Date Created
  • 2020

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System Identification, State Estimation, And Control Approaches to Gestational Weight Gain Interventions

Description

Excessive weight gain during pregnancy is a significant public health concern and has been the recent focus of novel, control systems-based interventions. Healthy Mom Zone (HMZ) is an intervention study

Excessive weight gain during pregnancy is a significant public health concern and has been the recent focus of novel, control systems-based interventions. Healthy Mom Zone (HMZ) is an intervention study that aims to develop and validate an individually tailored and intensively adaptive intervention to manage weight gain for overweight or obese pregnant women using control engineering approaches. Motivated by the needs of the HMZ, this dissertation presents how to use system identification and state estimation techniques to assist in dynamical systems modeling and further enhance the performance of the closed-loop control system for interventions.

Underreporting of energy intake (EI) has been found to be an important consideration that interferes with accurate weight control assessment and the effective use of energy balance (EB) models in an intervention setting. To better understand underreporting, a variety of estimation approaches are developed; these include back-calculating energy intake from a closed-form of the EB model, a Kalman-filter based algorithm for recursive estimation from randomly intermittent measurements in real time, and two semi-physical identification approaches that can parameterize the extent of systematic underreporting with global/local modeling techniques. Each approach is analyzed with intervention participant data and demonstrates potential of promoting the success of weight control.

In addition, substantial efforts have been devoted to develop participant-validated models and incorporate into the Hybrid Model Predictive Control (HMPC) framework for closed-loop interventions. System identification analyses from Phase I led to modifications of the measurement protocols for Phase II, from which longer and more informative data sets were collected. Participant-validated models obtained from Phase II data significantly increase predictive ability for individual behaviors and provide reliable open-loop dynamic information for HMPC implementation. The HMPC algorithm that assigns optimized dosages in response to participant real time intervention outcomes relies on a Mixed Logical Dynamical framework which can address the categorical nature of dosage components, and translates sequential decision rules and other clinical considerations into mixed-integer linear constraints. The performance of the HMPC decision algorithm was tested with participant-validated models, with the results indicating that HMPC is superior to "IF-THEN" decision rules.

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Created

Date Created
  • 2018