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Estimation model considers learning curve quantities, and time of break to forecast losses due to break in production schedule. Major parameters capable of predicting costs are number of units made prior to production sequence, length of production break, and slope of learning curve produced prior to break.
There has been growing interest in utilizing natural language processing (NLP) algorithms in Aviation Safety. This interest has extended to leveraging the decades of records publicly available on the Aviation Safety Reporting System (ASRS). While related literature has given more emphasis in lessons learned from the narratives, our prior work has focused on using NLP to support narrative search in the ASRS. Specifically, we evaluated if the use of alternative search mechanisms to keyword search, such as the retrieval of related narratives even without matching keywords could improve narrative discovery. A difficulty in experimenting alternative search mechanisms in any information retrieval task is the lack of ground truth. To address this limitation, we propose Kaona, a lightweight interface which enables the prototyping of alternative search retrieval tasks, by tracking user experience both explicitly (user-specified feedback), or implicitly (user navigation through interface affordances). Differently from distracting requests for feedback during user navigation, Kaona collects explicit feedback from users by mapping them to affordances which support the user workflow, while obtaining ground truth information for learning tasks.
Mars Pathfinder will place a single lander on the surface of Mars on July 4, 1997, following a December 1996 launch. As a result of the very successful first Mars Pathfinder Landing Site Workshop, the project has selected the Ares Vallis outflow channel in Chryse Planitia as the landing site. This location is where a large catastrophic outflow channel debouches into the northern lowlands. A second workshop and series of field trips, entitled Mars Pathfinder Landing Site Workshop 2: Characteristics of the Ares Vallis Region and Field Trips in the Channeled Scabland, Washington, were held in Spokane and Moses Lake, Washington. The purpose of the workshop was to provide a focus for learning as much as possible about the Ares Vallis region on Mars before landing there. The rationale is that the more that can be learned about the general area prior to landing, the better scientists will be able interpret the observations made by the lander and rover and place them in the proper geologic context. The field trip included overflights and surface investigations of the Channeled Scabland (an Earth analog for the martian catastrophic outflow channels), focusing on areas particularly analogous to Ares Vallis and the landing site. The overflights were essential for placing the enormous erosional and depositional features of the Channeled Scabland into proper three-dimensional context. The field trips were a joint educational outreach activity involving K-12 science educators, Mars Pathfinder scientists and engineers, and interested scientists from the Mars scientific community. Part 1 of the technical report on this workshop includes a description of the Mars Pathfinder mission, abstracts accepted for presentation at the workshop, an introduction to the Channeled Scabland, and field trip guides for the overflight and two field trips. This part, Part 2, includes the program for the workshop, summaries of the workshop technical sessions, a summary of the field trips and ensuing discussions, late abstracts of workshop presentations, reports on the education and public outreach activities carried out by the educators, and a list of the workshop and field trip participants.
An important objective of the Semi-Span Super-Sonic Transport (S4T) wind tunnel model program was the demonstration of Flutter Suppression (FS), Gust Load Alleviation (GLA), and Ride Quality Enhancement (RQE). It was critical to evaluate the stability and robustness of these control laws analytically before testing them and experimentally while testing them to ensure safety of the model and the wind tunnel. MATLAB based software was applied to evaluate the performance of closed-loop systems in terms of stability and robustness. Existing software tools were extended to use analytical representations of the S4T and the control laws to analyze and evaluate the control laws prior to testing. Lessons were learned about the complex windtunnel model and experimental testing. The open-loop flutter boundary was determined from the closed-loop systems. A MATLAB/Simulink Simulation developed under the program is available for future work to improve the CPE process. This paper is one of a series of that comprise a special session, which summarizes the S4T wind-tunnel program.
This presentation presents a look at the space program's background prior to lunar exploration and highlights the Apollo program and lessons learned from lunar exploration. The possibilities of exposures and difficulties attributed to lunar dust are described, including obscured vision, clogged equipment, coated surfaces, and inhalation, among others. A lunar dust simulant is proposed to support preliminary studies. Lunar dust is constantly activated by meteorite lunar dust, UV radiation and elements of solar wind - this active dust could produce reactive species. Methods of deactivation must be determined before new lunar missions, but first we must understand how to reactivate dust on Earth. Activation methods tested and described here include crushing/grinding or UV activation. Grinding time has a direct effect on amount of hydroxyl radicals produced upon addition of ground quartz to a solution. An increase in hydroxyl production was also seen for a lunar simulant with increased grinding.
A method is being developed that provides for an artificial-intelligence system to learn a user's preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics. The method does not require the user to perform the often-difficult task of quantitatively specifying preferences; instead, the user provides examples of preferred sets of objects. This method goes beyond related prior artificial-intelligence methods for learning which individual items are preferred by the user: this method supports a concept of setbased preferences, which include not only preferences for individual items but also preferences regarding types and degrees of diversity of items in a set. Consideration of diversity in this method involves recognition that members of a set may interact with each other in the sense that when considered together, they may be regarded as being complementary, redundant, or incompatible to various degrees. The effects of such interactions are loosely summarized in the term portfolio effect. The learning method relies on a preference representation language, denoted DD-PREF, to express set-based preferences. In DD-PREF, a preference is represented by a tuple that includes quality (depth) functions to estimate how desired a specific value is, weights for each feature preference, the desired diversity of feature values, and the relative importance of diversity versus depth. The system applies statistical concepts to estimate quantitative measures of the user s preferences from training examples (preferred subsets) specified by the user. Once preferences have been learned, the system uses those preferences to select preferred subsets from new sets. The method was found to be viable when tested in computational experiments on menus, music playlists, and rover images. Contemplated future development efforts include further tests on more diverse sets and development of a sub-method for (a) estimating the parameter that represents the relative importance of diversity versus depth, and (b) incorporating background knowledge about the nature of quality functions, which are special functions that specify depth preferences for features.
The search for evidence of life on Mars is a highly interdisciplinary enterprise which extends beyond the traditional life sciences. Mars conceivably had a pervasive ancient biosphere which may have persisted even to the present, but only in subsurface environments. Understanding the history of Mars' global environment, including its inventory of volatile elements, is a crucial part of the search strategy. Those deposits (minerals, sediments, etc.) which could have and retained a record of earlier biological activity must be identified and examined. While the importance of. seeking another biosphere has not diminished during the years since the Viking mission, the strategy for Mars exploration certainly has been modified by later discoveries. The Viking mission itself demonstrated that the present day surface environment of Mars is hostile to life as we know it. Thus, to search effectively for life on Mars, be it extant or extinct, we now must greatly improve our understanding of Mars the planet. Such an understanding will help us broaden our search beyond the Viking lander sites, both back in time to earlier epochs and elsewhere to other sites and beneath the surface. Exobiology involves much more than simply a search for extant life beyond Earth. It addresses the prospect of long-extinct biospheres and also the chemistry, organic and otherwise, which either led to life or which occurred on rocky planets that remained lifeless. Even a Mars without a biosphere would reveal much about life. How better to understand the origin and impact of a biosphere than to compare Earth with another similar but lifeless planet? Still, several relatively recent discoveries offer encouragement that a Martian biosphere indeed might have existed. The ancient Martian surface was extensively sculptured by volcanism and the activity of liquid water. Such observations invoke impressions of an ancient martian atmosphere and environment that resembled ancient Earth more than present-day Mars. Since Viking, we have learned that our own biosphere began prior to 3.5 billion years ago, during an early period when our solar system apparently was sustaining clement conditions on at least two of its planets. Also, we have found that microorganisms can survive, even flourish, in environments more extreme in temperature and water availability than had been previously recognized. The common ancestor of life on Earth probably was adapted to elevated temperatures, raising the possibility that hydrothermal systems played a central role in sustaining our early biosphere. If a biosphere ever arose on Mars, at least some of its constituents probably dwelled in the subsurface. Even today, conditions on Mars and Earth become more similar with increasing depth beneath their surfaces. For example, under the martian permafrost, the geothermal gradient very likely maintains liquid water in environments which resemble aquifers on Earth. Indigenous bacteria have recently been recovered from deep aquifers on Earth. Liquid groundwater very likely persisted throughout Mars' history. Thus, martian biota, if they ever existed, indeed might have survived in subsurface environments.
The Window Observational Research Facility (WORF) is an ISPR-based rack facility designed to take advantage of the high optical quality US Lab Nadir research window. The WORF is based on the ISS Expedite the Processing of Experiments to Space Station (EXPRESS) rack mechanical structure and electronic systems. The WORF has a unique payload volume located at the center of the rack that provides access to the window. The interior dimensions of the payload volume are 34-in. (86.36 cm) wide by 33-in. (83.82 cm) high by 23-in. (58.42 cm) deep. This facility supports the deployment of payloads such as 9 in. aerial photography cameras and 12 in. diameter optical equipment. The WORF coupled with the optical quality of the United States Lab window support the deployment of various payload disciplines. The WORF provides payloads with power, data command and control, air cooling, water cooling, and video processing. The WORF's payload mounting surfaces and interfaces include the interior payload mounting shelf and the interior and exterior aircraft-like seat tracks. The payload mounting shelf is limited to a maximum mass of 136 kg (299 pounds). The WORF can accommodate large payloads such as the commonly used Leica-Heerbrug RC-30 aerial photography camera (whose dimensions are 53.3 cm (21-in.) wide by 50.8 cm (20-in.) deep by 76.2 cm (30-in.) long). The performance characteristics of the WORF allow it to support an array of payload disciplines. The WORF provides a maximum of 3 Kw at 28 Vdc and has a maximum data rate of 10 Mbps. The WORF's unique payload volume is designed to be light-tight, down to 2.8 x 10(exp -11) Watts/cm2/steradian, and have low-reflective surfaces. This specially designed WORF interior supports payload investigations that observe low-light-level phenomenon such as aurora. Although the WORF rack does not employ any active rack isolation (i.e., vibration dampening) technology, the rack provides a very stable environment for payload operations (on the order of X microradians). The facility's software is capable of being updated during its period of deployment. The WORF project also includes a Suitcase Simulator to allow for a payload developer to verify data interfaces at his development site, a trainer rack for astronauts to learn how to operate the WORF prior to flight, and the use of the EXPRESS Functional Checkout Units to allow for payload checkout at the KSC prior to launch.
Dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN) is a method of sequential principal-component analysis (PCA) that is well suited for such applications as data compression and extraction of features from sets of data. In comparison with a prior method of gradient-descent-based sequential PCA, this method offers a greater rate of learning convergence. Like the prior method, DOGEDYN can be implemented in software. However, the main advantage of DOGEDYN over the prior method lies in the facts that it requires less computation and can be implemented in simpler hardware. It should be possible to implement DOGEDYN in compact, low-power, very-large-scale integrated (VLSI) circuitry that could process data in real time.
A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.
Recent work with NASA's Jet Propulsion Laboratory has allowed for external access to five of JPL's real-world requirements models, anonymized to conceal proprietary information, but retaining their computational nature. Experimentation with these models, reported herein, demonstrates a dramatic speedup in the computations performed on them. These models have a well defined goal: select mitigations that retire risks which, in turn, increases the number of attainable requirements. Such a non-linear optimization is a well-studied problem. However identification of not only (a) the optimal solution(s) but also (b) the key factors leading to them is less well studied. Our technique, called KEYS, shows a rapid way of simultaneously identifying the solutions and their key factors. KEYS improves on prior work by several orders of magnitude. Prior experiments with simulated annealing or treatment learning took tens of minutes to hours to terminate. KEYS runs much faster than that; e.g for one model, KEYS ran 13,000 times faster than treatment learning (40 minutes versus 0.18 seconds). Processing these JPL models is a non-linear optimization problem: the fewest mitigations must be selected while achieving the most requirements. Non-linear optimization is a well studied problem. With this paper, we challenge other members of the PROMISE community to improve on our results with other techniques.
NASA supports the community of mission principal investigators by helping them ideate, mature, and propose concepts for new missions. As NASA's Federally Funded Research and Development Center (FFRDC), JPL is a primary resource for providing this service. The environmental context for the formulation lifecycle evolves continuously. Contemporary trends include: more competitors; more-complex mission ideas; scarcer formulation resources; and higher standards for technical evaluation. Derived requirements for formulation support include: stable, clear, reliable methods tailored for each stage of the formulation lifecycle; on-demand access to standout technical and programmatic subject-matter experts; optimized, outfitted facilities; smart access to learning embodied in a vast oeuvre of prior formulation work; hands-on method coaching. JPL has retooled its provision of integrated formulation lifecycle support to PIs, teams, and program offices in response to this need. This mission formulation enterprise is the JPL Innovation Foundry.
Background: Computerized dynamic posturography is widely used to measure balance control performance. Clinically, performance is assessed by comparing individual data against standards obtained from a normative population. When performing repeated assessments to track performance changes, one must be concerned with the influence of learning effects. Subjects do not have the opportunity to practice before the first session, and often a second session is not performed prior to an experiment. Thus, the objective of this activity was to examine learning effects on balance control performance. We hypothesize that subjects will perform better on the second session when compared to the first, and that the difference will be greater for more difficult conditions. Methods: Data were collected from 204 subjects using the NeuroCom Equitest system during quiet stance with arms crossed at the chest on up to two sessions. All subjects performed standard sensory organization tests (SOTs) including 1) normal vision, fixed support; 2) absent vision, fixed support; 3) sway-referenced vision, fixed support; 4) normal vision, swayreferenced support; 5) absent vision, sway-referenced support; and 6) sway-referenced vision, sway-referenced support. 120 of these subjects performed modified sensory organization tests (mSOTs 2 and 5) which included static (20 back) and dynamic (20, 0.33Hz) head tilts. Median equilibrium scores (mEQ) were calculated from peak-to-peak anterior-posterior sway across trials. Data collected on the first session were then compared with the second to examine learning effect. Results: There were no differences in mEQ scores between the first and second sessions for SOTs 1, 2, and 4, while mEQ scores were higher for the second session when compared to the first for SOTs 3, 5, and 6 and for all mSOTs. Discussion: An additional familiarization session or practice trials prior to the first session may be necessary for more challenging SOT and mSOT conditions to minimize learning effect.
Prior to the successful flight test validation of a new avionics prototype, participants from Boeing, Honeywell, and United Airlines underwent group training at NASA Langley Research Center. New prototype software for an algorithm which enables greater efficiency in high-density airspace, called Interval Management, was to be incorporated into Electronic Flight Bags and placed in the cockpit for pilot usage. The goals of the training were to teach the flight test pilots how to operate the new software, establish techniques to simultaneously position three aircraft prior to each test scenario, and ensure a common communication protocol among team members when coordinating the position of aircraft for the next scenario. The multi-tiered interactive training regimen consisted of a process that continually built upon previous foundational material. The primary learning elements were 1) a portable computer-based trainer that was provided to the pilots prior to classroom training sessions, 2) classroom learning, 3) full mock-up simulator training, and 4) refresher training just prior to the flight test. Each part of the regimen was designed to repeat and build upon the previous element. The purpose of this Technical Memorandum is to inform the aviation industry how flight training for Interval Management was conducted at Langley Research Center in order to reduce overall development costs of future Interval Management training programs. Secondly, the paper provides insight regarding the decision-making process when attempting to conduct a flight test.
The Earth science community has long advocated placing numerous instruments in space to study the Earth and its environment. Space agencies from many countries have responded to this call with a wide range of orbiting satellites. Scientists also envisioned placing some satellites in constellations, to enable diverse remote sensing instruments to observe the same part of the Earth (or its atmosphere) at about the same time, thereby increasing the opportunities for coincident science observations. The Earth Science Afternoon Constellation is answering this call, but there have been unique challenges on the way to its deployment. Currently, the Afternoon Constellation is to comprise six satellites. Three are currently on orbit: NASA's Earth Observing System (EOS)-Aqua (2002) and EOS-Aura (2004), and CNES's Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) (2004). Two more satellites, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Cloudsat, are to be jointly launched in late 2005, followed by the Orbiting Carbon Observatory (OCO) in 2008. The Afternoon Constellation is unlike most satellite constellations in that: 1) It is not a homogenous mix of identical satellites; rather it comprises several satellites with complementary observational capabilities; 2) The satellites are not spaced around the Earth to provide instantaneous, global coverage (as for a communications satellite constellation); rather they orbit in close proximity so observations occur at about the same time over approximately the same region; and 3) Lastly, the satellites are not managed and controlled by one organization; rather the list of organizations is diverse: CNES in France, NASA Centers at Goddard, Langley, and the Jet Propulsion Laboratory, and the US Air Force facility in New Mexico. The PARASOL launch and early orbit (L&EO) phase proved to be a learning experience for constellation members (including Constellation management). Prior to launch, all members signed an operations coordination document that spelled out basic requirements for keeping the constellation safe and resolving non-nominal events. Once PARASOL reached orbit and the mission teams gained experience using the newly-developed constellation monitoring tools, it became clear that some of the guidelines in the signed agreements had to be adjusted. This paper presents the L&EO lessons learned and how they were used to prepare for the next phase - the period following the CloudSat/CALIPSO launch.
There have been five Materials International Space Station Experiment (MISSE) passive experiment carriers (PECs) (MISSE 1-5) to date that have been launched, exposed in space on the exterior of International Space Station (ISS) and then returned to Earth for analysis. An additional four MISSE PECs (MISSE 6A, 6B, 7A, and 7B) are in various stages of completion. The PECs are two-sided suitcase to size sample carriers that are intended to provide information on the effects of the low Earth orbital environment on a wide variety of materials and components. As a result of post retrieval analyses of the retrieved MISSE 2 experiments and numerous prior space experiments, there have been valuable lessons learned and needs identified that are worthy of being documented so that planning, design, and analysis of future space environment experiments can benefit from the experience in order to maximize the knowledge gained. Some of the lessons learned involve the techniques, concepts, and issues associated with measuring atomic oxygen erosion yields. These are presented along with several issues to be considered when designing experiments, such as the uncertainty in mission duration, scattering and contamination effects on results, and the accuracy of measuring atomic oxygen erosion.
Energetic particles are accelerated in rich profusion at sites throughout the heliosphere. They come from solar flares in the low corona, from shock waves driven outward by coronal mass ejections (CMEs), from planetary magnetospheres and bow shocks. They come from corotating interaction regions (CIRs) produced by high-speed streams in the solar wind, and from the heliospheric termination shock at the outer edge of the heliospheric cavity. We sample all these populations near Earth, but can distinguish them readily by their element and isotope abundances, ionization states, energy spectra, angular distributions and time behavior. Remote spacecraft have probed the spatial distributions of the particles and examined new sources in situ. Most acceleration sources can be "seen" only by direct observation of the particles; few photons are produced at these sites. Wave-particle interactions are an essential feature in acceleration sources and, for shock acceleration, new evidence of energetic-proton-generated waves has come from abundance variations and from local cross-field scattering. Element abundances often tell us the physics the source plasma itself, prior to acceleration. By comparing different populations, we learn more about the sources, and about the physics of acceleration and transport, than we can possibly learn from one source alone.
The Orion Pad Abort 1 Flight Test occurred on May 6th, 2010 at White Sands Missile Range in New Mexico. The test was the first for the Orion vehicle and was primarily a test of the Orion Launch Abort System’s ability to execute an abort from the launch pad. The test was successful, and, in most cases, results were well within predicted outcomes. For this test, the thermal control subsystem was an inherent part of the flight operations team. The thermal team provided pre-launch predictions of component temperatures, set component temperature limits, and monitored those limits during the flight operations process as part of the primary flight operations team. The thermal team was also responsible for day-of-flight predictions of the main abort motor’s propellant mean bulk temperature, a key launch constraint for the test. After the test, thermal data was collected, reviewed, and compared against pre-launch predictions showing very good agreement in most areas. Based on the pre-test and flight operations experience, a detailed list of lessons learned has been developed and will be reviewed prior to potential future Orion or other such flight tests.