993 resultados para 120499 Engineering Design not elsewhere classified


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Formal specifications can precisely and unambiguously define the required behavior of a software system or component. However, formal specifications are complex artifacts that need to be verified to ensure that they are consistent, complete, and validated against the requirements. Specification testing or animation tools exist to assist with this by allowing the specifier to interpret or execute the specification. However, currently little is known about how to do this effectively. This article presents a framework and tool support for the systematic testing of formal, model-based specifications. Several important generic properties that should be satisfied by model-based specifications are first identified. Following the idea of mutation analysis, we then use variants or mutants of the specification to check that these properties are satisfied. The framework also allows the specifier to test application-specific properties. All properties are tested for a range of states that are defined by the tester in the form of a testgraph, which is a directed graph that partially models the states and transitions of the specification being tested. Tool support is provided for the generation of the mutants, for automatically traversing the testgraph and executing the test cases, and for reporting any errors. The framework is demonstrated on a small specification and its application to three larger specifications is discussed. Experience indicates that the framework can be used effectively to test small to medium-sized specifications and that it can reveal a significant number of problems in these specifications.

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Spatial characterization of non-Gaussian attributes in earth sciences and engineering commonly requires the estimation of their conditional distribution. The indicator and probability kriging approaches of current nonparametric geostatistics provide approximations for estimating conditional distributions. They do not, however, provide results similar to those in the cumbersome implementation of simultaneous cokriging of indicators. This paper presents a new formulation termed successive cokriging of indicators that avoids the classic simultaneous solution and related computational problems, while obtaining equivalent results to the impractical simultaneous solution of cokriging of indicators. A successive minimization of the estimation variance of probability estimates is performed, as additional data are successively included into the estimation process. In addition, the approach leads to an efficient nonparametric simulation algorithm for non-Gaussian random functions based on residual probabilities.

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Powder metallurgy alloys are typically inhomogeneous with a significant amount of porosity. This complicates conventional transmission electron microscopy sample preparation. However, the use of focused ion beam milling allows site specific transmission electron microscopy samples to be prepared in a short amount of time. This paper presents a method that can be used to produce transmission electron microscopy samples from an Al-Cu-Mg PM alloy. (C) 2003 IoM Communications Ltd. Published by Maney for the Institute of Materials, Minerals and Mining.

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Analytical transmission electron microscopy indicates that liquid film migration occurs during sintering of an Al-Cu-Mg alloy, that intragranular liquid pools develop from migrating films and that iron segregates to these pools. It is suggested that a high localised iron concentration retards the liquid film migration rate by reducing the coherency strain in the retreating grain, causing a region of the film to detach from the boundary, thus forming an intragranular pool in the advancing grain. Alloys with low iron levels develop few intragranular pools and have high sintered densities. (C) 2003 Acta Materialia Inc. Published by Elsevier Science Ltd. All rights reserved.

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Minimum/maximum autocorrelation factor (MAF) is a suitable algorithm for orthogonalization of a vector random field. Orthogonalization avoids the use of multivariate geostatistics during joint stochastic modeling of geological attributes. This manuscript demonstrates in a practical way that computation of MAF is the same as discriminant analysis of the nested structures. Mathematica software is used to illustrate MAF calculations from a linear model of coregionalization (LMC) model. The limitation of two nested structures in the LMC for MAF is also discussed and linked to the effects of anisotropy and support. The analysis elucidates the matrix properties behind the approach and clarifies relationships that may be useful for model-based approaches. (C) 2003 Elsevier Science Ltd. All rights reserved.

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The role of the resin type on the sintering of maraging steel with boron additions has been investigated. Two different resins were added to the steel mixture and their subsequent debinding was evaluated and sintering responses compared with that of a resin-free alloy. The two resins used, nylon and a mixture of phenolic resin and synthetic wax, possessed different debinding behaviour, with the latter causing significant carbon contamination of the parts. This caused the formation of a Ti-Mo carbide, depleting the matrix of these elements. Consequently, the microstructure consisted of the equilibrium Fe-Fe2B eutectic, as well as a Mo-rich boride. The liquid phase also appeared to contain significant amounts of carbon, which lowered the temperature at which the liquid formed, resulting in high density occurring at a much lower temperature. When nylon was used as the binder, a similar sintering response to the resin-free alloy was observed. (C) 2002 Published by Elsevier Science B.V.

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The integration of geo-information from multiple sources and of diverse nature in developing mineral favourability indexes (MFIs) is a well-known problem in mineral exploration and mineral resource assessment. Fuzzy set theory provides a convenient framework to combine and analyse qualitative and quantitative data independently of their source or characteristics. A novel, data-driven formulation for calculating MFIs based on fuzzy analysis is developed in this paper. Different geo-variables are considered fuzzy sets and their appropriate membership functions are defined and modelled. A new weighted average-type aggregation operator is then introduced to generate a new fuzzy set representing mineral favourability. The membership grades of the new fuzzy set are considered as the MFI. The weights for the aggregation operation combine the individual membership functions of the geo-variables, and are derived using information from training areas and L, regression. The technique is demonstrated in a case study of skarn tin deposits and is used to integrate geological, geochemical and magnetic data. The study area covers a total of 22.5 km(2) and is divided into 349 cells, which include nine control cells. Nine geo-variables are considered in this study. Depending on the nature of the various geo-variables, four different types of membership functions are used to model the fuzzy membership of the geo-variables involved. (C) 2002 Elsevier Science Ltd. All rights reserved.

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A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

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Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was classed by the neural net into the middle group—these rocks should be subject to full freeze-thaw tests. Thus, use of the probabilistic neural network would meanthat the extended test would only need be applied to 43% of the samples, and 11% of the rocks classed as acceptable would fail early.

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Papers in this issue of Natural Resources Research are from the “Symposium on the Application of Neural Networks to the Earth Sciences,” held 20–21 August 2002 at NASA Moffet Field, Mountain View, California. The Symposium represents the Seventh International Symposium on Mineral Exploration (ISME-02). It was sponsored by the Mining and Materials Processing Institute of Japan (MMIJ), the US Geological Survey, the Circum-Pacific Council, and NASA. The ISME symposia have been held every two years in order to bring together scientists actively working on diverse quantitative methods applied to the earth sciences. Although the title, International Symposium on Mineral Exploration, suggests exclusive focus on mineral exploration, interests and presentations always have been wide-ranging—talks presented at this symposium are no exception.

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The effect of the addition of elemental Mg, Sri and Pb on the sintering of impressed prealloyed 2124 and 6061 powder has been investigated. Despite being sintered at a temperature that resulted in similar to20 vol.% liquid phase, high density was possible only upon the addition of similar to1% elemental Mg as well as trace amounts of Sri or Pb. Additions of Mg facilitated oxide disruption, while Sri and Pb segregated to the liquid phase, where they reduced the liquid's surface tension, thus improving sintering. (C) 2002 Elsevier Science B.V. All rights reserved.

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A lithographic method was used to produce polycrystalline diamond films having highly defined surface geometry, showing an array of diamond tips for possible application as a field emitter device. The films grown in this study used microwave plasma assisted chemical vapour deposition (MACVD) on a silicon substrate; the substrate was then dissolved away to reveal the surface features on the diamond film. It is possible to align the crystallite direction and affect the electron emission properties using a voltage bias to enhance the nucleation process and influence the nuclei to a preferred orientation. This study focuses on the identification of the distribution of crystal directions in the film, using electron backscattering diffraction (EBSD) to identify the crystallographic character of the film surface. EBSD allows direct examination of the individual diamond grains, grains boundaries and the crystal orientation of each individual crystallite. The EBSD maps of the bottom (nucleation side) of the films, following which a layer of film is ion-milled away and the mapping process repeated. The method demonstrates experimentally that oriented nucleation occurs and the thin sections allow the crystal texture to be reconstructed in 3-D. (C) 2003 Elsevier B.V. All rights reserved.

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An epidemiologic survey among four administrative villages around Poyang Lake, in Jiangxi Province, China (two experimental and two controls) is being conducted to determine if bovine infections are responsible for the persistence of human schistosomiasis transmission on Yangtze River marshlands. A previously published paper presented the experimental design and baseline data for humans and bovines. This paper presents basic data for the four villages using remote sensing, and baseline data for snails that includes geographic information systems and remote sensing technology to classify the areas of bovine grazing ranges and habitats suitable for snails. A new method for sampling Oncomelania snails in China is used to determine the distribution, density, and infection rates of snails throughout the grazing ranges from season to season over a four-year period. Hypothetically, treating bovines should reduce infection rates in snails to below the critical number necessary to maintain infections in man and bovines.

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Different amorphous structures have been induced in monocrystalline silicon by high pressure in indentation and polishing. Through the use of high-resolution transmission electron microscopy and nanodiffraction, it was found that the structures of amorphous silicon formed at slow and fast loading/unloading rates are dissimilar and inherit the nearest-neighbor distance of the crystal in which they are formed. The results are in good agreement with recent theoretical predictions. (C) 2004 American Institute of Physics.

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The core structure of a dislocation complex in SiGe/Si system composed of a perfect 60degrees dislocation and an extended 60 dislocation has been revealed at atomic level. This is attained by applying the image deconvolution technique in combination with dynamical diffraction effect correction to an image taken with a 200 kV field-emission high-resolution electron microscope. The possible configuration of the dislocation complex is analyzed and their Burgers vectors are determined. (C) 2003 Elsevier B.V. All rights reserved.