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Interaction techniques are described. Six basic interaction tasks, requirements for each task, requirements related to interaction techniques, and a technique's hardware prerequisites affective device selection are discussed.
The organization of the RIACS auditing package is described along with how to installation instructions and how to interpret the output. How to set up both local and remote file system auditing is given. Logging is done on a time driven basis, and auditing in a passive mode.
Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.
In the past decade, automated systems on modern commercial flight decks have increased dramatically. Pilots now regularly interact and share tasks with these systems. This interaction has led human factors research to direct more attention to the pilot's cognitive processing and mental model of the information flow occurring on the flight deck. The experiment reported herein investigated how pilots mentally represent and process information typically available during flight. Fifty-two commercial pilots participated in tasks that required them to provide similarity ratings for pairs of flight-deck information and to prioritize this information under two contextual conditions. Pilots processed the information along three cognitive dimensions. These dimensions included the flight function and the flight action that the information supported and how frequently pilots refer to the information. Pilots classified the information as aviation, navigation, communications, or systems administration information. Prioritization results indicated a high degree of consensus among pilots, while scaling results revealed two dimensions along which information is prioritized. Pilot cognitive workload for flight-deck tasks and the potential for using these findings to operationalize cognitive metrics are evaluated. Such measures may be useful additions for flight-deck human performance evaluation.
Program analysis techniques and tools are essential to the development process because of the support they provide in detecting errors and deficiencies at different phases of development. The types of information rendered through analysis includes the following: statistical measurements of code, type checks, dataflow analysis, consistency checks, test data,verification of code, and debugging information. Analyzers can be broken into two major categories: dynamic and static. Static analyzers examine programs with respect to syntax errors and structural properties., This includes gathering statistical information on program content, such as the number of lines of executable code, source lines. and cyclomatic complexity. In addition, static analyzers provide the ability to check for the consistency of programs with respect to variables. Dynamic analyzers in contrast are dependent on input and the execution of a program providing the ability to find errors that cannot be detected through the use of static analysis alone. Dynamic analysis provides information on the behavior of a program rather than on the syntax. Both types of analysis detect errors in a program, but dynamic analyzers accomplish this through run-time behavior. This paper focuses on the following broad classification of dynamic analyzers: 1) Metrics; 2) Models; and 3) Monitors. Metrics are those analyzers that provide measurement. The next category, models, captures those analyzers that present the state of the program to the user at specified points in time. The last category, monitors, checks specified code based on some criteria. The paper discusses each classification and the techniques that are included under them. In addition, the role of each technique in the software life cycle is discussed. Familiarization with the tools that measure, model and monitor programs provides a framework for understanding the program's dynamic behavior from different, perspectives through analysis of the input/output data.
The National Airspace System s capacity will experience considerable growth in the next few decades. Weather adversely affects safe air travel. The FAA and NASA are working to develop new technologies that display weather information to support situation awareness and optimize pilot decision-making in avoiding hazardous weather. Understanding situation awareness and naturalistic decision-making is an important step in achieving this goal. Information representation and situation time stress greatly influence attentional resource allocation and working memory capacity, potentially obstructing accurate situation awareness assessments. Three naturalistic decision-making theories were integrated to provide an understanding of the levels of decision making incorporated in three operational situations and two conditions. The task characteristics associated with each phase of flight govern the level of situation awareness attained and the decision making processes utilized. Weather product s attributes and situation task characteristics combine to classify weather products according to the decision-making processes best supported. In addition, a graphical interface is described that affords intuitive selection of the appropriate weather product relative to the pilot s current flight situation.
The fate of space-generated solid wastes, including trash, for future missions is under consideration by NASA. Several potential treatment options are under consideration and active technology development. Potential fates for space-generated solid wastes are: Storage without treatment; storage after treatment(s) including volume reduction, water recovery, sterilization, and recovery plus recycling of waste materials. Recycling might be important for partial or full closure scenarios because of the prohibitive costs associated with resupply of consumable materials. For this study, we determined the composition of trash returned from four recent STS missions. The trash material was 'Volume F' trash and other trash, in large zip-lock bags, that accompanied the Volume F trash. This is the first of two submitted papers on these wastes. This one will cover trash content, weight and water content. The other will report on the microbial Characterization of this trash. STS trash was usually made available within 2 days of landing at KSC. The Volume F bag was weighed, opened and the contents were catalogued and placed into one of the following categories: food waste (and containers), drink containers, personal hygiene items - including EVA maximum absorbent garments (MAGs)and Elbow packs (daily toilet wipes, etc), paper, and packaging materials - plastic firm and duct tape. Trash generation rates for the four STS missions: Total wet trash was 0.602 plus or minus 0.089 kg(sub wet) crew(sup -1) d(sup -1) containing about 25% water at 0.154 plus or minus 0.030 kg(sub water) crew(sup -1) d(sup -1) (avg plus or minus stdev). Cataloguing by category: personal hygiene wastes accounted for 50% of the total trash and 69% of the total water for the four missions; drink items were 16% of total weight and 16% water; food wastes were 22% of total weight and 15% of the water; office waste and plastic film were 2% and 11% of the total waste and did not contain any water. The results can be used by NASA to determine requirements and criteria for Waste Management Systems on future missions.
The project reported here provides microbial characterization support to the Waste Management Systems (WMS) element of NASA's Life Support and Habitation Systems (LSHS) program. Conventional microbiological methods were used to detect and enumerate microorganisms in STS Volume F Compartment trash for three shuttle missions: STS 133, 134, and 135. This trash was usually made available within 2 days of landing at KSC. The Volume F bag was weighed, opened and the contents were cataloged and placed into categories: personal hygiene items - inclUding EVA maximum absorbent garments (MAGs) and Elbow packs (daily toilet wipes, etc), drink containers, food waste (and containers), office waste (paper), and packaging materials - plastic film and duct tape. The average wet trash generation rate for the three STS missions was 0.362 % 0.157 kgwet crew 1 d-1 . This was considerably lower and more variable than the average rate for 4 STS missions reported for FY10. Trash subtotals by category: personal hygiene wastes, 56%; drink items, 11 %; food wastes, 18%; office waste, 3%; and plastic film, 12%. These wastes have an abundance of easily biodegraded compounds that can support the growth of microorganisms. Microbial characterization of trash showed that large numbers of bacteria and fungi have taken advantage of this readily available nutrient source to proliferate. Exterior and interior surfaces of plastic film bags containing trash were sampled and counts of cultivatable microbes were generally low and mostly occurred on trash bundles within the exterior trash bags. Personal hygiene wastes, drink containers, and food wastes and packaging all contained high levels of, mostly, aerobic heterotrophic bacteria and lower levels of yeasts and molds. Isolates from plate count media were obtained and identified .and were mostly aerobic heterotrophs with some facultative anaerobes. These are usually considered common environmental isolates on Earth. However, several pathogens were also isolated: Staphylococcus aureus and Escherichia coli.
In this paper two frequency domain techniques are applied to air traffic analysis. The Continuous Wavelet Transform (CWT), like the Fourier Transform, is shown to identify changes in historical traffic patterns caused by Traffic Management Initiatives (TMIs) and weather with the added benefit of detecting when in time those changes take place. Next, with the expectation that it could detect anomalies in the network and indicate the extent to which they affect traffic flows, the Spectral Graph Wavelet Transform (SGWT) is applied to a center based graph model of air traffic. When applied to simulations based on historical flight plans, it identified the traffic flows between centers that have the greatest impact on either neighboring flows, or flows between centers many centers away. Like the CWT, however, it can be difficult to interpret SGWT results and relate them to simulations where major TMIs are implemented, and more research may be warranted in this area. These frequency analysis techniques can detect off-nominal air traffic behavior, but due to the nature of air traffic time series data, so far they prove difficult to apply in a way that provides significant insight or specific identification of traffic patterns.
A method of analyzing National Air Space (NAS) air traffic that uses the Discrete Fourier Transform (DFT) is presented. The DFT is used to transform time domain traffic count data into the frequency domain where the sources of traffic in air spaces can be identified and characterized more easily. It is shown in simulation that individual traffic flows within Air Route Traffic Control Centers can be distinguished by their periodicity in the DFT plot. Next, three Traffic Management Initiatives (playbook rerouting, metered flows, and Ground Delay Programs) are implemented in simulations and their signature effects on the traffic are identified using the DFT. Finally, historical flight data is studied and the DFT is applied to sector traffic count data. It is found that in many cases, variations in traffic due to rerouting and convective weather disturbances are better highlighted in the frequency domain than in the original time domain data. Initial results of the DFT show it has potential as a tool for measuring and/or predicting NAS behavior for daily tactical planning and control purposes.
- Motivation - Very little literature exists characterizing software errors in real-time avionic systems - How, where, and why is software most likely to fail? - Purpose - Raise awareness of how software fails through historical study - Recommend improvements to software fault tolerant design based on historical study - Outline - Discuss Software Failures - Common Cause, Failure Classes, Mitigation strategies - Review NASA requirements for Software Fault Tolerance - Review Historical Software Failures - Analyze failures and provide statistics - Erroneous vs. fail-Silent - Reboot recoverability likelihood - Code Location - Missing or unknown code?
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Retrievals of the isotopic composition of water vapor from the Aura Tropospheric Emission Spectrometer (TES) have unique value in constraining moist processes in climate models. Accurate comparison between simulated and retrieved values requires that model profiles that would be poorly retrieved are excluded, and that an instrument operator be applied to the remaining profiles. Typically, this is done by sampling model output at satellite measurement points and using the quality flags and averaging kernels from individual retrievals at specific places and times. This approach is not reliable when the model meteorological conditions influencing retrieval sensitivity are different from those observed by the instrument at short time scales, which will be the case for free-running climate simulations. In this study, we describe an alternative, categorical approach to applying the instrument operator, implemented within the NASA GISS ModelE general circulation model. Retrieval quality and averaging kernel structure are predicted empirically from model conditions, rather than obtained from collocated satellite observations. This approach can be used for arbitrary model configurations, and requires no agreement between satellite-retrieved and model meteorology at short time scales. To test this approach, nudged simUlations were conducted using both the retrieval-based and categorical operators. Cloud cover, surface temperature and free-tropospheric moisture content were the most important predictors of retrieval quality and averaging kernel structure. There was good agreement between the D fields after applying the retrieval-based and more detailed categorical operators, with increases of up to 30 over the ocean and decreases of up to 40 over land relative to the raw model fields. The categorical operator performed better over the ocean than over land, and requires further refinement for use outside of the tropics. After applying the TES operator, ModelE had D biases of 8 over ocean and 34 over land compared to TES D, which were less than the biases using raw model D fields.
Terrestrial applications of solar cells and design systems are considered for those applications that show the most promise for becoming practical and accepted by users within the next five years. The study includes the definition, categorization, evaluation and screening of the most attractive potential terrestrial applications for solar cells. Potential markets are initially grouped and categorized in a general sense and are weighted in priority by their business volume, present and future. From a categorized list including marine, transportation, security, communication, meteorological and others, 66 potential solar cell applications have been cataloged. A methodology was formulated to include the criteria for evaluation and screening. The evaluation process covers all parts and components of the complete system required for each application and gives consideration to all factors, such as engineering, economic, production, marketing and other factors that may have an influence on the acceptance of the system.
The author has identified the following significant results. By use of distilled water samples in the laboratory, and very clear lakes in the field, a technique was developed where the atmosphere and surface noise effects on LANDSAT signals from water bodies can be removed. The residual signal dependent only on the material in water was used as a basis for computer categorization of lakes by type and concentration of suspended material. Several hundred lakes in the Madison and Spooner, Wisconsin area were categorized by computer techniques for tannin or nontannin waters and for the degree of algae, silt, weeds, and bottom effects present. When the lakes are categorized as having living algae or weeds, their concentration is related to the enrichment or eutrophication of the lake.
The author has identified the following significant results. A technique for training automated analysis of satellite multispectral data based on in situ measurements of target reflectance was tested and applied in delineating cover communities in Delaware's tidal wetlands. Land cover categorization of data from the same overpass in four test wetland areas was carried out using a four category classification system. The tests indicate that training data based on in situ reflectance measurements and atmospheric correction of LANDSAT data can produce comparable accuracy of categorization to that achieved using more conventional relative radiance training. Analysis of the four wetlands cover categories (salt marsh cordgrass, salt hay, water, and unvegetated tidal flat) produced average categorization accuracies of 82.1% by conventional relative radiance training and 81.4% by use of in situ reflectance measurements.