RSVP: Remote Sensing Visualization Platform for Data Fusion

Remote sensing involves the acquisition of data in terms of images, point clouds and so on. One of the major challenges with remote sensing datasets is managing and understanding the massive amounts of data that is collected. In many instances, scientists acquire data for the same region using varied sensing devices. Scientists would like to fuse and examine this data acquired from different sensing devices to further explore the region under investigation. Immersive visualization has emerged as an ideal solution for three-dimensional exploration of multimodal remote sensing data. The ability to manipulate data interactively in true 3D (using stereo) with interfaces designed specifically for the immersive environment can significantly speed up the exploration process. We have developed a visualization platform that facilitates the fusion of multiple modalities of remote sensing data and allows a scientist to learn more about the data obtained from different sensing devices. It is currently being used in research labs at Idaho State University and at the Idaho National Labs

A Multi-Methods Approach to HRA and Human Performance Modeling: A Field Assessment

The Advanced Test Reactor (ATR) is a research reactor at the Idaho National Laboratory is primarily designed and used to test materials to be used in other, larger-scale and prototype reactors. The reactor offers various specialized systems and allows certain experiments to be run at their own temperature and pressure. The ATR Canal temporarily stores completed experiments and used fuel. It also has facilities to conduct underwater operations such as experiment examination or removal. In reviewing the ATR safety basis, a number of concerns were identified involving the ATR canal. A brief study identified ergonomic issues involving the manual handling of fuel elements in the canal that may increase the probability of human error and possible unwanted acute physical outcomes to the operator. In response to this concern, that refined the previous HRA scoping analysis by determining the probability of the inadvertent exposure of a fuel element to the air during fuel movement and inspection was conducted. The HRA analysis employed the SPAR-H method and was supplemented by information gained from a detailed analysis of the fuel inspection and transfer tasks. This latter analysis included ergonomics, work cycles, task duration, and workload imposed by tool and workplace characteristics, personal protective clothing, and operational practices that have the potential to increase physical and mental workload. Part of this analysis consisted of NASA-TLX analyses, combined with operational sequence analysis, computational human performance analysis (CHPA), and 3D graphical modeling to determine task failures and precursors to such failures that have safety implications. Experience in applying multiple analysis techniques in support of HRA methods is discussed

Draft Function Allocation Framework and Preliminary Technical Basis for Advanced SMR Concepts of Operations

This report presents preliminary research results from the investigation in to the development of new models and guidance for concepts of operations (ConOps) in advanced small modular reactor (aSMR) designs. In support of this objective, three important research areas were included: operating principles of multi-modular plants, functional allocation models and strategies that would affect the development of new, non-traditional concept of operations, and the requiremetns for human performance, based upon work domain analysis and current regulatory requirements. As part of the approach for this report, we outline potential functions, including the theoretical and operational foundations for the development of a new functional allocation model and the identification of specific regulatory requirements that will influence the development of future concept of operations. The report also highlights changes in research strategy prompted by confirmationof the importance of applying the work domain analysis methodology to a reference aSMR design. It is described how this methodology will enrich the findings from this phase of the project in the subsequent phases and help in identification of metrics and focused studies for the determination of human performance criteria that can be used to support the design process

FRamework Assessing Notorious Contributing Influences for Error (FRANCIE): Perspective on Taxonomy Development to Support Error Reporting and Analysis

Beginning in the 1980s a primary focus of human reliability analysis was estimation of human error probabilities. However, detailed qualitative modeling with comprehensive representation of contextual variables often was lacking. This was likely due to the lack of comprehensive error and performance shaping factor taxonomies, and the limited data available on observed error rates and their relationship to specific contextual variables. In the mid 90s Boeing, America West Airlines, NASA Ames Research Center and INEEL partnered in a NASA sponsored Advanced Concepts grant to: assess the state of the art in human error analysis, identify future needs for human error analysis, and develop an approach addressing these needs. Identified needs included the need for a method to identify and prioritize task and contextual characteristics affecting human reliability. Other needs identified included developing comprehensive taxonomies to support detailed qualitative modeling and to structure meaningful data collection efforts across domains. A result was the development of the FRamework Assessing Notorious Contributing Influences for Error (FRANCIE) with a taxonomy for airline maintenance tasks. The assignment of performance shaping factors to generic errors by experts proved to be valuable to qualitative modeling. Performance shaping factors and error types from such detailed approaches can be used to structure error reporting schemes. In a recent NASA Advanced Human Support Technology grant FRANCIE was refined, and two new taxonomies for use on space missions were developed. The development, sharing, and use of error taxonomies, and the refinement of approaches for increased fidelity of qualitative modeling is offered as a means to help direct useful data collection strategies

Advancing Usability Evaluation through Human Reliability Analysis

This paper introduces a novel augmentation to the current heuristic usability evaluation methodology. The SPAR-H human reliability analysis method was developed for categorizing human performance in nuclear power plants. Despite the specialized use of SPAR-H for safety critical scenarios, the method also holds promise for use in commercial off-the-shelf software usability evaluations. The SPAR-H method shares task analysis underpinnings with human-computer interaction, and it can be easily adapted to incorporate usability heuristics as performance shaping factors. By assigning probabilistic modifiers to heuristics, it is possible to arrive at the usability error probability (UEP). This UEP is not a literal probability of error but nonetheless provides a quantitative basis to heuristic evaluation. When combined with a consequence matrix for usability errors, this method affords ready prioritization of usability issues

Human reliability analysis for computerized procedures, part two: Applicability of current methods

Computerized procedures (CPs) are an emerging technology within nuclear power plant control rooms. While CPs have been implemented internationally in advanced control rooms, to date no U.S. nuclear power plant has implemented CPs in its main control room. Yet, CPs are a reality of new plant builds and are an area of considerable interest to existing plants, which see advantages in terms of easier records management by omitting the need for updating hardcopy procedures. The overall intent of this paper is to provide a characterization of human reliability analysis (HRA) issues for computerized procedures. It is beyond the scope of this document to propose a new HRA approach or to recommend specific methods or refinements to those methods. Rather, this paper serves as a review of current HRA as it may be used for the analysis and review of computerized procedures

Culture Representation in Human Reliability Analysis

Understanding human-system response is critical to being able to plan and predict mission success in the modern battlespace. Commonly, human reliability analysis has been used to predict failures of human performance in complex, critical systems. However, most human reliability methods fail to take culture into account. This paper takes an easily understood state of the art human reliability analysis method and extends that method to account for the influence of culture, including acceptance of new technology, upon performance. The cultural parameters used to modify the human reliability analysis were determined from two standard industry approaches to cultural assessment: Hofstede’s (1991) cultural factors and Davis’ (1989) technology acceptance model (TAM). The result is called the Culture Adjustment Method (CAM). An example is presented that (1) reviews human reliability assessment with and without cultural attributes for a Supervisory Control and Data Acquisition (SCADA) system attack, (2) demonstrates how country specific information can be used to increase the realism of HRA modeling, and (3) discusses the differences in human error probability estimates arising from cultural differences