Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988

Middeck Active Control Experiment (MACE), phase A

A rationale to determine which structural experiments are sufficient to verify the design of structures employing Controlled Structures Technology was derived. A survey of proposed NASA missions was undertaken to identify candidate test articles for use in the Middeck Active Control Experiment (MACE). The survey revealed that potential test articles could be classified into one of three roles: development, demonstration, and qualification, depending on the maturity of the technology and the mission the structure must fulfill. A set of criteria was derived that allowed determination of which role a potential test article must fulfill. A review of the capabilities and limitations of the STS middeck was conducted. A reference design for the MACE test article was presented. Computing requirements for running typical closed-loop controllers was determined, and various computer configurations were studied. The various components required to manufacture the structure were identified. A management plan was established for the remainder of the program experiment development, flight and ground systems development, and integration to the carrier. Procedures for configuration control, fiscal control, and safety, reliabilty, and quality assurance were developed

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Predicting Deviations in Software Quality by Using Relative Critical Value Deviation Metrics

Abstract We develop a new metric, Relative Critical Value Deviation (RCVD

Crowdsourcing Real-Time Traveler Information Systems

In the last decade, the concept of collecting traffic data using location aware and data enabled smartphones in place of traditional sensor networks has received much attention. With a steady market growth for smartphones enabled with GPS chipsets, the potential of this technology is enormous. This combined with the pervasion of participatory paradigms such as crowdsourcing wherein individuals with portable sensors instead of physical networks serve as sensors providing information. Crowd sensed data overcome a number of issues with traditional physical sensor networks by providing wider coverage, real-time data, data redundancy and cost effectiveness to name a few. While there has been a lot of work on actual implementations of crowd sensed traffic monitoring programs, there is limited work on assessing the quality, and validity of crowd sensed data. A systematic analysis of quality and validity is needed before this paradigm can be more commonly adopted for traffic monitoring applications. To this end, research is underway to deploy a crowdsourced platform for monitoring and providing real-time transit information for shuttles that serve the University of Connecticut. The thesis develops a framework and an open-source prototype system that is able to produce real-time traveler information based on crowdsourced data. In order to build the prototype, first it implements a robust Hidden Markov Model based map-matching algorithm to position the crowdsourced data on the underlying road network and retrieve the likely path. The accuracy of the map-matching algorithm has been found satisfactory for the current usage even when the GPS points are sampled at low frequency. Next, to predict the travel condition across the network from the crowdsourced data, a travel time prediction algorithm, based on Regularized Least Square Regression, has been implemented as well. This travel time prediction algorithm, together with the map-matching algorithm, has been applied in a simulated crowdsourcing environment. The travel time prediction results of the simulation show that the prototype system is quite capable of predicting travel time even when the crowdsourced real-time data is sparse. The simulation tests the performance of the travel time prediction algorithm in different scenarios. From the demonstrated predictive performance of the implemented prototype system, this approach to providing real-time traveler information is found promising. It is also possible to apply the prototype to all regions and all modes of transportation, exploiting its generalized approach of providing real-time traveler information from crowdsourced data

NASA Space Human Factors Program

This booklet briefly and succinctly treats 23 topics of particular interest to the NASA Space Human Factors Program. Most articles are by different authors who are mainly NASA Johnson or NASA Ames personnel. Representative topics covered include mental workload and performance in space, light effects on Circadian rhythms, human sleep, human reasoning, microgravity effects and automation and crew performance

A systems approach to risk management through leading safety indicators

The goal of leading indicators for safety is to identify the potential for an accident before it occurs. Past efforts have focused on identifying general leading indicators, such as maintenance backlog, that apply widely in an industry or even across industries. Other recommendations produce more system-specific leading indicators, but start from system hazard analysis and thus are limited by the causes considered by the traditional hazard analysis techniques. Most rely on quantitative metrics, often based on probabilistic risk assessments. This paper describes a new and different approach to identifying system-specific leading indicators and provides guidance in designing a risk management structure to generate, monitor and use the results. The approach is based on the STAMP (System-Theoretic Accident Model and Processes) model of accident causation and tools that have been designed to build on that model. STAMP extends current accident causality to include more complex causes than simply component failures and chains of failure events or deviations from operational expectations. It incorporates basic principles of systems thinking and is based on systems theory rather than traditional reliability theory

The Legacy of Space Shuttle Flight Software

The initial goals of the Space Shuttle Program required that the avionics and software systems blaze new trails in advancing avionics system technology. Many of the requirements placed on avionics and software were accomplished for the first time on this program. Examples include comprehensive digital fly-by-wire technology, use of a digital databus for flight critical functions, fail operational/fail safe requirements, complex automated redundancy management, and the use of a high-order software language for flight software development. In order to meet the operational and safety goals of the program, the Space Shuttle software had to be extremely high quality, reliable, robust, reconfigurable and maintainable. To achieve this, the software development team evolved a software process focused on continuous process improvement and defect elimination that consistently produced highly predictable and top quality results, providing software managers the confidence needed to sign each Certificate of Flight Readiness (COFR). This process, which has been appraised at Capability Maturity Model (CMM)/Capability Maturity Model Integration (CMMI) Level 5, has resulted in one of the lowest software defect rates in the industry. This paper will present an overview of the evolution of the Primary Avionics Software System (PASS) project and processes over thirty years, an argument for strong statistical control of software processes with examples, an overview of the success story for identifying and driving out errors before flight, a case study of the few significant software issues and how they were either identified before flight or slipped through the process onto a flight vehicle, and identification of the valuable lessons learned over the life of the project