Reusable rocket engine turbopump health monitoring system, part 3

Degradation mechanisms and sensor identification/selection resulted in a list of degradation modes and a list of sensors that are utilized in the diagnosis of these degradation modes. The sensor list is divided into primary and secondary indicators of the corresponding degradation modes. The signal conditioning requirements are discussed, describing the methods of producing the Space Shuttle Main Engine (SSME) post-hot-fire test data to be utilized by the Health Monitoring System. Development of the diagnostic logic and algorithms is also presented. The knowledge engineering approach, as utilized, includes the knowledge acquisition effort, characterization of the expert's problem solving strategy, conceptually defining the form of the applicable knowledge base, and rule base, and identifying an appropriate inferencing mechanism for the problem domain. The resulting logic flow graphs detail the diagnosis/prognosis procedure as followed by the experts. The nature and content of required support data and databases is also presented. The distinction between deep and shallow types of knowledge is identified. Computer coding of the Health Monitoring System is shown to follow the logical inferencing of the logic flow graphs/algorithms

Neural Collaborative Ranking

Recommender systems are aimed at generating a personalized ranked list of items that an end user might be interested in. With the unprecedented success of deep learning in computer vision and speech recognition, recently it has been a hot topic to bridge the gap between recommender systems and deep neural network. And deep learning methods have been shown to achieve state-of-the-art on many recommendation tasks. For example, a recent model, NeuMF, first projects users and items into some shared low-dimensional latent feature space, and then employs neural nets to model the interaction between the user and item latent features to obtain state-of-the-art performance on the recommendation tasks. NeuMF assumes that the non-interacted items are inherent negative and uses negative sampling to relax this assumption. In this paper, we examine an alternative approach which does not assume that the non-interacted items are necessarily negative, just that they are less preferred than interacted items. Specifically, we develop a new classification strategy based on the widely used pairwise ranking assumption. We combine our classification strategy with the recently proposed neural collaborative filtering framework, and propose a general collaborative ranking framework called Neural Network based Collaborative Ranking (NCR). We resort to a neural network architecture to model a user's pairwise preference between items, with the belief that neural network will effectively capture the latent structure of latent factors. The experimental results on two real-world datasets show the superior performance of our models in comparison with several state-of-the-art approaches.Comment: Proceedings of the 2018 ACM on Conference on Information and Knowledge Managemen

A search for new hot subdwarf stars by means of Virtual Observatory tools

Hot subdwarf stars are faint, blue objects, and are the main contributors to the far-UV excess observed in elliptical galaxies. They offer an excellent laboratory to study close and wide binary systems, and to scrutinize their interiors through asteroseismology, as some of them undergo stellar oscillations. However, their origins are still uncertain, and increasing the number of detections is crucial to undertake statistical studies. In this work, we aim at defining a strategy to find new, uncatalogued hot subdwarfs. Making use of Virtual Observatory tools we thoroughly search stellar catalogues to retrieve multi-colour photometry and astrometric information of a known sample of blue objects, including hot subdwarfs, white dwarfs, cataclysmic variables and main sequence OB stars. We define a procedure to discriminate among these spectral classes, particularly designed to obtain a hot subdwarf sample with a low contamination factor. In order to check the validity of the method, this procedure is then applied to two test sky regions: the Kepler FoV and to a test region of around (RA:225, DEC:5) deg. As a result, we obtained 38 hot subdwarf candidates, 23 of which had already a spectral classification. We have acquired spectroscopy for three other targets, and four additional ones have an available SDSS spectrum, which we used to determine their spectral type. A temperature estimate is provided for the candidates based on their spectral energy distribution, considering two-atmospheres fit for objects with clear infrared excess. Eventually, out of 30 candidates with spectral classification, 26 objects were confirmed to be hot subdwarfs, yielding a contamination factor of only 13%. The high rate of success demonstrates the validity of the proposed strategy to find new uncatalogued hot subdwarfs. An application of this method to the entire sky will be presented in a forthcoming work.Comment: 13 pages, 7 figure

Assessing Destination Competitiveness: An Application to the Hot Springs Tourism Sector

This paper proposes a model to identify the factors determining the competitiveness of the hot springs tourism sector, with particular application to Taiwan. The proposed conceptual framework brings together two approaches, namely the theories of industry organization (10) and the resource-based view (RBV). The proposition underlying this framework is that destination competitiveness is achieved by the adoption of policies and strategies aligned with market opportunities, drawing upon the unique or distinctive tourism features offered by the destination. It is proposed that three major influences are evident in the case of hot springs tourism, namely Tourism Destination Resources and Attractors, Tourism Destination Strategies and Tourism Destination Environments. An evaluation is provided of the administration of a three-round Delphi survey, which was intended to validate the determinants of destination competitiveness that were derived from the literature. Drawing upon the results of the pilot study it is concluded that the development of a sector-specific model of destination competitiveness is capable of capturing the nature and characteristics of the hot springs tourism sector

Noncooperatively Optimized Tolerance: Decentralized Strategic Optimization in Complex Systems

We introduce noncooperatively optimized tolerance (NOT), a generalization of highly optimized tolerance (HOT) that involves strategic (game theoretic) interactions between parties in a complex system. We illustrate our model in the forest fire (percolation) framework. As the number of players increases, our model retains features of HOT, such as robustness, high yield combined with high density, and self-dissimilar landscapes, but also develops features of self-organized criticality (SOC) when the number of players is large enough. For example, the forest landscape becomes increasingly homogeneous and protection from adverse events (lightning strikes) becomes less closely correlated with the spatial distribution of these events. While HOT is a special case of our model, the resemblance to SOC is only partial; for example, the distribution of cascades, while becoming increasingly heavy-tailed as the number of players increases, also deviates more significantly from a power law in this regime. Surprisingly, the system retains considerable robustness even as it becomes fractured, due in part to emergent cooperation between neighboring players. At the same time, increasing homogeneity promotes resilience against changes in the lightning distribution, giving rise to intermediate regimes where the system is robust to a particular distribution of adverse events, yet not very fragile to changes

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio