Automatic Crane-Related Workflow Control for Nuclear Plant Outages through Computer Vision and Simulation Cheng Zhang, Pingbo Tang, Alper Yilmaz, Nancy Cooke, Verica Buchanan, Alan Chasey, Ronald Laurids Boring, Shawn W. St. Germain , Timothy Vaughn, and Samuel Jones Pages 889-897 (2016 Proceedings of the 33rd ISARC, Auburn, USA, ISBN 978-1-5108-2992-3, ISSN 2413-5844) Abstract: Nuclear power plant (NPP) outages involve a large number of maintenance activities with a tight schedule and zero-tolerance for accidents. Outage projects thus need real-time control to ensure safety and productivity. During outages, crane lifting is critical for outage control and risk management. An effective outage control method should monitor detailed interactions between human and workspaces, and streamline the workflows of cranes to control both productivity and risks. Unfortunately, current approaches of outage control rely heavily on tedious and error-prone manual inspection that can hardly achieve detailed spatiotemporal monitoring. This paper presents an automated outage control framework that enables detailed human behavior analysis, automatic comparison of as-planned and actual crane-related operations, and effective decision-making for crane-related workflow control. In this framework, a real-time human tracking algorithm uses 2D/3D imagery to automatically derive the status of workspaces (e.g., waiting, active). Then a change-analysis algorithm detects and diagnoses differences between as-is workflow information against as-planned schedules, and thus enables field managers to implement a close-loop outage control. Preliminary results indicate the potential of this integrated outage control in improving the safety, productivity, and quality of outages, as well as outage project planning. Keywords: Nuclear power plant, Outages, Crane, Workflow control, Computer vision, Simulation DOI: https://doi.org/10.22260/ISARC2016/0107 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Exploration and exploitation are commonly cited in search and rescue scenarios to explain the process by which individuals work in a team and gather information about their environment (exploration) and identify potential solutions and adaptations (exploitation) to pursue successful outcomes. In this paper, we discuss exploration and exploitation as critical design features and highlight the importance of balancing them when designing team-based search and rescue missions. To test the proposed design decisions, we developed a usability study that includes two missions wherein teams consisting of three participants are tasked to rescue victims within a Minecraft-based 3D testbed.
Pathfinder networks are evaluated in terms of their ability to predict Ss' response time in category and size judgment tasks. For both tasks, pairwise proximities derived from the network representation of relatedness estimates predicted response times independently of the original relatedness estimates. These results indicate that Pathfinder proximities represent psychological proximity as measured by judgment time better than Ss' own estimates of relatedness. Pathfinder's predictive ability is derived from a focus on relatedness estimates from the more sensitive related end of the rating scale. Evidence supporting this account is presented, and an explanation for sensitivity differences among ratings is proposed
Historically, domains such as business intelligence would require a single analyst to engage with data, develop a model, answer operational questions, and predict future behaviors. However, as the problems and domains become more complex, organizations are employing teams of analysts to explore and model data to generate knowledge. Furthermore, given the rapid increase in data collection, organizations are struggling to develop practices for intelligence analysis in the era of big data. Currently, a variety of machine learning and data mining techniques are available to model data and to generate insights and predictions, and developments in the field of visual analytics have focused on how to effectively link data mining algorithms with interactive visuals to enable analysts to explore, understand, and interact with data and data models. Although studies have explored the role of single analysts in the visual analytics pipeline, little work has explored the role of teamwork and visual analytics in the analysis of big data. In this article, we present an experiment integrating statistical models, visual analytics techniques, and user experiments to study the role of teamwork in predictive analytics. We frame our experiment around the analysis of social media data for box office prediction problems and compare the prediction performance of teams, groups, and individuals. Our results indicate that a team's performance is mediated by the team's characteristics such as openness of individual members to others' positions and the type of planning that goes into the team's analysis. These findings have important implications for how organizations should create teams in order to make effective use of information from their analytic models.
The continuing proliferation in the use of Unmanned Aerial Systems (UAS) in both civil and military operations has presented a multitude of human factors challenges from how to bridge the gap between the demand and availability of trained operators, to how to organize and present data in meaningful ways. Underlying many of these challenges is the issue of how automation capabilities can best be utilized to assist human operators manage increasing complexity and workload. The purpose of this discussion panel is to examine current research and perspectives on human automation interaction and how it relates to the future of UAS control. The panel is composed of five well-known researchers, all experts in the area of human-automation interaction. The range of topics that the panelists will discuss includes: how automation taxonomies can be applied to UAS design; opportunities to exploit automation capabilities in multi-vehicle contexts; current examples of automation research results, particularly in the area of multiple UAS control, and how they can be applied for future UAS; and how to design automation to maximize UAS mission effectiveness.
View Video Presentation: https://doi.org/10.2514/6.2021-2320.vid The present research defines a pattern-based measure for deviations from Closed Loop Communication Deviations (CLCD) that can be used to predict Loss of Separation (LOS). Six retired Air Traffic Controllers were recruited and tested in three conditions of varying workload in a TRACON arrival radar simulation. Communication transcripts from simulated trials were transcribed, and Closed Loop Communications (CLC) coding schemes were applied. Results of the study demonstrated a positive correlation between CLC and LOS, indicating that CLCD could be a variable used to predict LOS. However, more research is required to determine if CLCD can predict LOS with other dynamic measures, such as coordination of communication and heart rate variability.
Abstract : This report documents a 30-month effort sponsored by the Office of Naval Research that refined, applied and evaluated methods for analyzing the communication flow and content surrounding collaboration. The methods include four measures of communication content based on Latent Semantic Analysis and five methods that extract patterns in communication flow. Communication analysis methods were applied to the communication data from two studies in the context of a three-person Unmanned Aerial Vehicle ground control simulation. In the studies workload and geographic dispersion were manipulated and team performance, process, team situation awareness, and shared mental models were measured. Communication analysis methods were evaluated in terms of their ability to predict team performance in a consistent manner across studies. All methods, with the exception of the Process Surrogate flow-based method, were validated by these criteria. Barriers to full automation of the methods and generalization to different domains were identified with proposed solutions. Application of the communication analysis methods revealed that high performing teams developed stable, consistent patterns of communicating which could be contrasted to teams that were distributed, under high workload, or facing a communication malfunction which were characterized by variable, yet flexible and adaptive communication patterns. Findings led to an ecological perspective on team cognition, as well as new methods for assessing team situation awareness and team coordination that are inspired by this perspective. The methods can be applied to better understand collaboration or to assess collaboration in order to evaluate tools or techniques purported to enhance collaboration.
