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This data was created with open-source tooling for the purpose of running a public competition. This dataset is a sample of what can be generated for the commissioned competition runner to be able to set up their testing infrastructure. It contains images of publicly available spacecraft models in a Blender scene composed of a light source, a background image, and the spacecraft model with bounding box labels.
This paper describes how damage propagation can be modeled within the modules of aircraft gas turbine engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine as a function of variations of flow and efficiency of the modules of interest. An exponential rate of change for flow and efficiency loss was imposed for each data set, starting at a randomly chosen initial deterioration set point. The rate of change of the flow and efficiency denotes an otherwise unspecified fault with increasingly worsening effect. The rates of change of the faults were constrained to an upper threshold but were otherwise chosen randomly. Damage propagation was allowed to continue until a failure criterion was reached. A health index was defined as the minimum of several superimposed operational margins at any given time instant and the failure criterion is reached when health index reaches zero. Output of the model was the time series (cycles) of sensed measurements typically available from aircraft gas turbine engines. The data generated were used as challenge data for the Prognostics and Health Management (PHM) data competition at PHM 08.
Competitive pressures in the oil and gas industry are requiring a much tighter integration of technical data into E and P business processes. The development of new systems to accommodate this business need must comprehend the significant numbers of large, complex data objects which the industry generates. The life cycle of the data objects is a four phase progression from data acquisition, to data processing, through data interpretation, and ending finally with data archival. In order to implement a cost effect system which provides an efficient conversion from data to information and allows effective use of this information, an organization must consider the technical data management requirements in all four phases. A set of technical issues which may differ in each phase must be addressed to insure an overall successful development strategy. The technical issues include standardized data formats and media for data acquisition, data management during processing, plus networks, applications software, and GUI's for interpretation of the processed data. Mass storage hardware and software is required to provide cost effective storage and retrieval during the latter three stages as well as long term archival. Mobil Oil Corporation's Exploration and Producing Technical Center (MEPTEC) has addressed the technical and cost issues of designing, building, and implementing an Advanced Computing Environment (ACE) to support the petroleum E and P function, which is critical to the corporation's continued success. Mobile views ACE as a cost effective solution which can give Mobile a competitive edge as well as a viable technical solution.
Residual vector quantization (RVQ), or multistage VQ, as it is also called, has recently been shown to be a competitive technique for data compression. The competitive performance of RVQ reported in results from the joint optimization of variable rate encoding and RVQ direct-sum code books. In this paper, necessary conditions for the optimality of variable rate RVQ's are derived, and an iterative descent algorithm based on a Lagrangian formulation is introduced for designing RVQ's having minimum average distortion subject to an entropy constraint. Simulation results for these entropy-constrained RVQ's (EC-RVQ's) are presented for memory less Gaussian, Laplacian, and uniform sources. A Gauss-Markov source is also considered. The performance is superior to that of entropy-constrained scalar quantizers (EC-SQ's) and practical entropy-constrained vector quantizers (EC-VQ's), and is competitive with that of some of the best source coding techniques that have appeared in the literature.
Momentarily-best compression algorithm selected. Proposed competitive-parallel-processing system compresses data for transmission in channel of limited band-width. Likely application for compression lies in high-resolution, stereoscopic color-television broadcasting. Data from information-rich source like color-television camera compressed by several processors, each operating with different algorithm. Referee processor selects momentarily-best compressed output.
Laser Interferometer Space Antenna (LISA) is a planned NASA-ESA mission to be launched around 2012. The Gravitational Wave detection is fundamentally the determination of frequency, source parameters, and waveform amplitude derived in a specific order from the interferometric time-series of the rotating LISA spacecrafts. The LISA Science Team has developed a Mock LISA Data Challenge intended to promote the testing of complicated nested search algorithms to detect the 100-1 millihertz frequency signals at amplitudes of 10E-21. However, it has become clear that, sequential search of the parameters is very time consuming and ultra-sensitive; hence, a new strategy has been developed. Parallelization of existing sequential search algorithms of Gravitational Wave signal identification consists of decomposing sequential search loops, beginning with outermost loops and working inward. In this process, the main challenge is to detect interdependencies among loops and partitioning the loops so as to preserve concurrency. Existing parallel programs are based upon either shared memory or distributed memory paradigms. In PVM, master and node programs are used to execute parallelization and process spawning. The PVM can handle process management and process addressing schemes using a virtual machine configuration. The task scheduling and the messaging and signaling can be implemented efficiently for the LISA Gravitational Wave search process using a master and 6 nodes. This approach is accomplished using a server that is available at NASA Ames Research Center, and has been dedicated to the LISA Data Challenge Competition. Historically, gravitational wave and source identification parameters have taken around 7 days in this dedicated single thread Linux based server. Using PVM approach, the parameter extraction problem can be reduced to within a day. The low frequency computation and a proxy signal-to-noise ratio are calculated in separate nodes that are controlled by the master using message and vector of data passing. The message passing among nodes follows a pattern of synchronous and asynchronous send-and-receive protocols. The communication model and the message buffers are allocated dynamically to address rapid search of gravitational wave source information in the Mock LISA data sets.
Collects space systems cost and related data (flight rate, payload, etc.) over time. Gathers only public data. Non-recurring and recurring. Minimal data processing. Graph, visualize, add context. Focus on US space systems competitiveness. Keep fresh update as data arises, launches occur, etc. Keep fresh focus on recent data, indicative of the future.
Big data and artificial intelligence/machine learning are transforming the global business environment. Data is now the most valuable asset for enterprises in every industry. Companies are using data-driven insights for competitive advantage. With that, the adoption of machine learning-based data analytics is rapidly taking hold across various industries, producing autonomous systems that support human decision-making. This work explored the application of machine learning to aircraft engine conceptual design. Supervised machine-learning algorithms for regression and classification were employed to study patterns in an existing, open-source database of production and research turbofan engines, and resulting in predictive analytics for use in predicting performance of new turbofan designs. Specifically, the author developed machine learning-based analytics to predict cruise thrust specific fuel consumption (TSFC) and core sizes of high-efficiency turbofan engines, using engine design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks application program interface (API) written in Python, with Google’s TensorFlow (an open source library for numerical computation) serving as the backend engine. The promising results of the predictive analytics show that machine-learning techniques merit further exploration for application in aircraft engine conceptual design.
Through the Minority University Research and Education Project (MUREP), NASA engages underrepresented populations via various initiatives, including by means of three year competitive awards for Data Science Equity, Access, and Priority in Research and Education (DEAP) Institutes led by Historically Black Colleges and Universities (HBCUs). MUREP DEAP Institutes, multi-institutional consortia, conduct data science research and capacity building initiatives that aim to enhance the research, academic, and technological capabilities of the participating HBCUs while providing NASA-specific knowledge, skills and opportunities for students and faculty who have historically been underrepresented and underserved in the STEM workforce. Three MUREP DEAP Institutes, led by Bethune-Cookman University, North Carolina A&T University, and North Carolina Central University, are conducting data science and natural hazard related research utilizing remote sensing and earth observations, with mentorship for the Institutes provided by scientists from NASA’s Marshall Space Flight Center. Each Institute has unique capabilities, needs, and goals, but overlap in their common interest to conduct cutting edge data science and natural hazard related research and to build capacity, broaden participation, and increase retention of a diverse group of participating students and faculty. Here, successes and challenges from the first year of activities at each MUREP DEAP institute are discussed, where activities focused primarily on the recruitment of a diverse group of students and the upskilling of students and faculty in data science and remote sensing concepts. The planned activities and goals of each DEAP Institute for the upcoming two years of activities will also be discussed, both from research and community-building perspectives.
An area of increasing interest for the next generation of aircraft is autonomy and the integration of increasingly autonomous systems into the national airspace. Such an integration requires humans to work closely with autonomous systems, forming teams. Our hypothesis is that a team composed of both humans and autonomous systems will operate better than either entity alone. We have existing procedures for certifying pilots to operate in the national airspace and are currently working on methods for validating the function of autonomous systems, however we have no method in place for assessing the interaction of these two disparate systems. Communication is one avenue. This paper will examine the use of language as a metric for ascertaining human-machine teaming effectiveness. A proof-of-concept of the application of two communication-based analysis techniques, Linguistic Inquiry and Word Count (LIWC) and Latent Semantic Analysis (LSA), for the prediction of success in human/chatbot teaming was conducted. By running these analyses over data from the 2014 and 2015 Loebner Prize competitions of human/chatbot teaming, numerical scores were obtained that can be associated with scores provided by human judges during the competition. Correlating their LIWC and LSA data with the scores provided by the judges, and using linear regression over this correlation, formulae were obtained that predict the score of human/chatbot interaction. These formulae were tested over the 2013 Loebner Prize transcripts, determining that, though there was strong correlation between predicted and actual scores, the predictive success of this method was not strong. However, with specialized topic spaces and lexica, as well as larger data sets, the predictive power of these metrics will improve. Given the importance of providing metrics for human-machine system team success and given the promise shown by the communication-basedLIWCand LSAmethods, continuing research in this area is necessary. After examining the potential for using communication and spoken language as a metric for the success of human/autonomous system teaming, this paper then examines aspects inherent to communication systems that may contribute to unreliability and reduced trust. Modern natural language processing tools rely on deep learning algorithms to create language rules that produce accurate results, but these rules are uninterpretable. The resulting blackbox system lacks transparency necessary for full validation and complete trust. Additionally, speech-based interfaces pose other difficulties to developing coordinated teamwork between humans and autonomous systems. Human communication is infrequently limited to speech only, instead usually relying on a combination of verbal, gestural, and general body language communication. Reducing an analysis of team effectiveness to a study of spoken language alone is problematic as it leaves these other equally important forms of communication out. This paper will examine these problems and the general deficiencies in speech-based metrics for human-machine teaming.
The heterogeneous reactions of ClONO2 + H2O yields HNO3 + HOCl (1), ClONO2 + HCl yields C12 + HNO3 (2), and HOCl + HCl yields Cl2 + H2O (3) on liquid sulfuric acid surfaces have been studied using a fast flow reactor coupled to a quadrupole mass spectrometer. The main objectives of the study are to investigate: (a) the temperature dependence of these reactions at a fixed H2O partial pressure typical of the lower stratosphere (that is, by changing temperature at a constant water partial pressure, the H2SO4 content of the surfaces is also changed), (b) the relative importance or competition between reactions 1 and 2, and (c) the effect of HNO3 on the reaction probabilities due to the formation of a H2SO4/HNO3/H2O ternary system. The measurements show that all the reactions depend markedly on temperature at a fixed H2O partial pressure: they proceed efficiently at temperatures near 200 K and much slower at temperatures near 220 K. The reaction probability (gamma(sub 1)) for ClONO2 hydrolysis approaches 0.01 at temperatures below 200 K, whereas the values for gamma(sub 2) and gamma(sub 3) are on the order of a few tenths at 200 K. Although detailed mechanisms for these reactions are still unknown, the present data indicate that the competition between ClONO2 hydrolysis and ClONO2 reaction with HCl may depend on temperature (or H2SO4 Wt %): in the presence of gaseous HCl at stratospheric concentrations, reaction 2 is dominant at lower temperatures (less than 200 K), but reaction 1 becomes important at temperatures above 210 K. Furthermore, reaction probability measurements performed on the H2SO4/HNO3/ H2O ternary solutions do not exhibit noticeable deviation from those performed on the H2SO4/H2O binary system, suggesting little effect of HNO3 in sulfate aerosols on the ClONO2 and HOCl reactions with HCl. The results reveal that significant reductions in the chlorine-containing reservoir species (such as ClONO2 and HCl) can take place on stratospheric sulfate aerosols at high latitudes in winter and early spring, even at temperatures too warm for Polar Stratospheric Clouds (PSCs) to form or in regions where nucleation of PSCs is sparse. This is particularly true under elevated sulfuric acid loading, such as that after the eruption of Mt. Pinatubo. Comparisons between our results and those presently available have also been made.
The potential market for LANDSAT products through the 1990's was determined. Results are presented in a matrix format. Improved resolution is a major factor in the marketability of LANDSAT data, the 10 meter resolution (projected for 1995) having a significant impact on the federal, private, and international users, and on the agricultural, minerals, and national defense applications. Data delivery time and competition from the French remote sensing system are considered.
The spatial, geometric, and radiometric qualities of LANDSAT 4 thematic mapper (TM) and multispectral scanner (MSS) data were evaluated by interpreting, through visual and computer means, film and digital products for selected agricultural and forest cover types in California. Multispectral analyses employing Bayesian maximum likelihood, discrete relaxation, and unsupervised clustering algorithms were used to compare the usefulness of TM and MSS data for discriminating individual cover types. Some of the significant results are as follows: (1) for maximizing the interpretability of agricultural and forest resources, TM color composites should contain spectral bands in the visible, near-reflectance infrared, and middle-reflectance infrared regions, namely TM 4 and TM % and must contain TM 4 in all cases even at the expense of excluding TM 5; (2) using enlarged TM film products, planimetric accuracy of mapped poins was within 91 meters (RMSE east) and 117 meters (RMSE north); (3) using TM digital products, planimetric accuracy of mapped points was within 12.0 meters (RMSE east) and 13.7 meters (RMSE north); and (4) applying a contextual classification algorithm to TM data provided classification accuracies competitive with Bayesian maximum likelihood.
The Grace DAKASEP separator was originally developed as a wicking layer for nickel-zinc alkaline batteries. The DAKASEP is a filled non-woven separator which is flexible and heat sealable. Through modification of formulation and processing variables, products with a variety of properties can be produced. Variations of DAKASEP were tested in Ni-H2, Ni-Zn, Ni-Cd, and primary alkaline batteries with good results. The properties of DAKASEP which are optimized for Hg-Zn primary batteries are shown in tabular form. This separator has high tensile strength, 12 micron average pore size, relatively low porosity at 46-48 percent, and consequently moderately high resistivity. Versions were produced with greater than 70 percent porosity and resistivities in 33 wt percent KOH as low as 3 ohm cm. Performance data for Hg-Zn E-1 size cells containing DAKASEP with the properties shown in tabular form, are more reproducible than data obtained with a competitive polypropylene non-woven separator. In addition, utilization of active material is in general considerably improved.
Collects space systems cost and related data (flight rate, payload, etc.) over time. Gathers only public data. Non-recurring and recurring. Minimal data processing. Graph, visualize, add context. Focus on US space systems competitiveness. Keep fresh update as data arises, launches occur, etc. Keep fresh focus on recent data, indicative of the future.
Collects space systems cost and related data (flight rate, payload, etc.) over time. Gathers only public data. Non-recurring and recurring. Minimal data processing. Graph, visualize, add context. Focus on US space systems competitiveness. Keep fresh update as data arises, launches occur, etc. Keep fresh focus on recent data, indicative of the future.
This slide presentation reviews the development of smart payloads instruments systems with high data rates. On-board computation has become a bottleneck for advanced science instrument and engineering capabilities. In order to improve the computation capability on board, smart payloads have been proposed. A smart payload is a Localized instrument, that can offload the flight processor of extensive computing cycles, simplify the interfaces, and minimize the dependency of the instrument on the flight system. This has been proposed for the Mars mission, Mars Atmospheric Trace Molecule Spectroscopy (MATMOS). The design of this system is discussed; the features of the Virtex-4, are discussed, and the technical approach is reviewed. The proposed Hybrid Field Programmable Gate Array (FPGA) technology has been shown to deliver breakthrough performance by tightly coupling hardware and software. Smart Payload designs for instruments such as MATMOS can meet science data return requirements with more competitive use of available on-board resources and can provide algorithm acceleration in hardware leading to implementation of better (more advanced) algorithms in on-board systems for improved science data return