D-side: A facility and workforce planning group multi-criteria decision support system for Johnson Space Center

“To understand and protect our home planet, to explore the universe and search for life, and to inspire the next generation of explorers” is NASA's mission. The Systems Management Office at Johnson Space Center (JSC) is searching for methods to effectively manage the Center's resources to meet NASA's mission. D-Side is a group multi-criteria decision support system (GMDSS) developed to support facility decisions at JSC. D-Side uses a series of sequential and structured processes to plot facilities in a three-dimensional (3-D) graph on the basis of each facility's alignment with NASA's mission and goals, the extent to which other facilities are dependent on the facility, and the dollar value of capital investments that have been postponed at the facility relative to the facility's replacement value. A similarity factor rank orders facilities based on their Euclidean distance from Ideal and Nadir points. These similarity factors are then used to allocate capital improvement resources across facilities. We also present a parallel model that can be used to support decisions concerning allocation of human resources investments across workforce units. Finally, we present results from a pilot study where 12 experienced facility managers from NASA used D-Side and the organization's current approach to rank order and allocate funds for capital improvement across 20 facilities. Users evaluated D-Side favorably in terms of ease of use, the quality of the decision-making process, decision quality, and overall value-added. Their evaluations of D-Side were significantly more favorable than their evaluations of the current approach.     

Investigating the validity of subjective workload rating (NASA TLX) and subjective situation awareness rating (SART) for cognitively complex human–machine work

Subjective workload and situation awareness measures, such as the NASA task load index (TLX) and the situational awareness rating technique (SART), are frequently used in human–system evaluation. However, the interpretation of these ratings is debated. In this study, empirical evidence for the measures' theoretical assumptions was investigated by comparing operators' ratings collected immediately after performing a scenario and ratings collected after operators' acquisition through a video review of the scenario, knowledge of actual system states. Eighteen licensed control room operators participated in the simulator study, running 12 relatively challenging scenarios. It was found that the interpretation of TLX items involving introspection remained stable after operators acquired factual scenario knowledge, while the interpretation of items involving the perception of external events, such as situation awareness and performance, depended on the operators' scenario knowledge. The result shows that operators’ ratings could discriminate between mental effort, performance, frustration, and situation awareness. No clear evidence for the SART index as a measure of situation awareness was found. Instead, a subjective situation awareness measure developed for this study was distinct from workload and related to operator performance, showing that this type of measure warrants future investigation of its validity. The study findings help in developing measurement procedures and interpreting subjective measures. Finally, the study reveals that informing operators about the scenario can provide useful subjective ratings of situation awareness and performance. Future research should include procedures for how to inform participants adequately and efficiently in subjective assessments.     

Teleoperation to robotics at Langley Research Center

A popular conception of the evolution of remote manipulation is a progression from teleoperation to telerobotics and then to robotics. This is logical because in going from teleoperation to robotics there would appear to be a continuous decrease in manned workload, an increase in system complexity, and an increase in the amount of intelligence in the automated system. During the past 10 years NASA Langley Research Center (LaRC) has been involved in all three areas. The decision on which system is most suitable for a task depends not only on the task, but on the allocation of responsibility for intelligence or high level control. The operator may be responsible for all intelligence and control functions, they may be shared between the operator and the automated system, or they may be performed by an autonomous system. This paper discusses some of the experiences in each area in applications related to possible space tasks and some of the lessons learned.     

Evaluation of hydraulic excavator Human–Machine Interface concepts using NASA TLX

This study evaluated newly proposed Human–Machine Interface (HMI) design concepts for improving the ergonomics of hydraulic excavators. The design concepts were based on an augmented interaction technique which involved the use of heads-up display (HUD) and coordinated control as HMI elements. Two alternative HMI designs were elaborated in order to separately evaluate the ergonomic impacts of the head-up display and the coordinated control by comparing them to the standard HMI design. The effectiveness of these three HMI designs in terms of the reduction of the operators' mental and physical workload were assessed by conducting experiments utilizing human subjects, ages 23–35 years. The National Aeronautics and Space Administration's Task Load Index (NASA TLX) method was used for collecting subjective workload scores based on a weighted average of ratings of six factors: Mental Demand, Physical Demand, Temporal Demand, Own Performance, Effort, and Frustration Level. The results showed that the type of HMI design affects different aspects of the operator's workload. Indeed, it showed how the proposed augmented interaction is an effective solution for reducing the ergonomic gaps in terms of mental workload, and to a lesser extent the physical workload, subjected by the standard HMI design.     

????????????????????????????

针对国内外尚无对霾的遥感监测,霾在NASA的MODIS产品中作为薄云处理的情况,基于MODIS数据,采用.NET分布式架构,结合MatlabCOM, ArcEngine等组件,建立一个区域霾监测系统,论述系统的设计思想及改进的霾反演算法,对华北地区部分霾天进行监测,结果显示该系统能够准确地模拟大气污染状况。     

从空间对地观测到预测地球未来的变化(一)-NASA地球科学事业(ESE)战略计划述评

分析介绍了NASA的ESE战略计划,该计划以提高人类对地球系统的科学认识、改进对天气、气候和自然灾害的预测和预报为目的,通过观测、模拟等手段认识控制地球系统变化的过程,回答一系列地球系统科学问题;计划制定了未来十年NASA的科学、应用和技术目标,针对不同的科学问题确定了相应的卫星任务并且展望了预期成果。最后对ESE战略计划的若干特点进行了概括和分析。     

Development of an experimentally validated semi-empirical fully-coupled performance model of a PEM electrolysis cell with a 3-D structured porous transport layer

A semi-empirical non-isothermal model incorporating coupled momentum, heat and mass transport phenomena for predicting the performance of a proton exchange membrane (PEM) water electrolysis cell operating without flow channels is presented. Model input parameters such as electro-kinetics properties and mean pore size of the porous transport layer (PTL) were determined by rotating disc electrode and capillary flow porometry, respectively. This is the first report of a semi-empirical fully coupled model which allows one to quantify and investigate the effect of the gas phase and bubble coverage on PEM cell performance up to very high current densities of about 5 A/cm². The mass transport effects are discussed in terms of the operating conditions, design parameters and the microstructure of the PTL. The results show that, the operating temperature and pressure, and the inlet water flowrate and thickness of the PTL are the critical parameters for mitigating mass transport limitation at high current densities. The model presented here can serve as a tool for further development and scale-up effort in the area of PEM water electrolysis, and provide insight during the design stage.     

美国航空航天局的航天机器人Robonaut

在美国国家航空航天局 (NASA)的德克萨斯州休斯顿约翰逊太空中心 ,有一个机器人小组正在罗伯特·阿莫布鲁斯博士的领导下开发一种新型的命名为Robonaut的航天机器人。该机器人的外形设计尽可能接近人形 ,遥控操作 ,目的是代替宇航员在太空船外活动     

Automatic Generation of Test Oracles—From Pilot Studies to Application

We describe a progression from pilot studies to development and use of domain-specific verification and validation (V&V) automation. Our domain is the testing of an AI planning system that forms a key component of an autonomous spacecraft. We used pilot studies to ascertain opportunities for, and suitability of, automating various analyses whose results would contribute to V&V in our domain. These studies culminated in development of an automatic generator of automated test oracles. This was then applied and extended in the course of testing the spacecraft's AI planning system.Richardson et al. (1992, In Proceedings of the 14th International Conference on Software Engineering, Melbourne, Australia, pp. 105–118), presents motivation for automatic test oracles, and considered the issues and approaches particular to test oracles derived from specifications. Our work, carried through from conception to application, confirms many of their insights. Generalizing from our specific domain, we present some additional insights and recommendations concerning the use of test oracles for V&V of knowledge-based systems.