Effect of ground rubber powder and devulcanizates on the properties of natural rubber compounds

Ground rubber powder (GRP) and devulcanized rubber (Dev.R) waste through a continuous shear‐flow reaction were used as fillers for virgin natural rubber. A Horikx plot and the gel fraction proved that both main‐chain scission and crosslink scission occurred during the devulcanization. The cure characteristics, swelling behavior, crosslink density, and mechanical properties were studied. GRP and Dev.R, generated from passenger car and light truck tires, showed applicable mechanical properties in the new rubber compounds, and the compounds containing Dev.R demonstrated better properties than those containing GRP. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 208–217, 2005     

Improvement of mechanical properties of rubber compounds using waste rubber/virgin rubber

Ground rubber powder (GRP) and devulcanized rubber waste obtained using the process of continuous shear flow reaction were used as both fillers and a part of rubber in virgin natural rubber (NR). The cure characteristics, swelling behavior, cross‐link density, mechanical properties, and morphology were studied in our laboratory. The GRP and devulcanized rubber, which were generated from passenger car and light truck tires, showed applicable mechanical properties in new rubber compounds, and the compounds that contained devulcanized rubber showed better properties than those with GRP. In particular, devulcanized rubber as a part of rubber obtained through a cross‐link reaction exhibited excellent mechanical properties, with a strong potential for future applications. POLYM. ENG. SCI., 45:1239–1246, 2005. © 2005 Society of Plastics Engineers     

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Conservation and land use planning in humid tropical lowland forests urgently need accurate remote sensing techniques to distinguish among floristically different forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain forest types can be accurately discriminated by a non-parametric k nearest neighbors (k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of forest (regrowth forest). Three floristically defined forest types were formed through clustering the old-growth forest plots (n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four forest types did not adequately distinguish two old-growth forest classes, so they were merged into a single forest class. The resulting three forest classes were most accurately classified by the k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the k-nn classifier can accurately distinguish floristically and structurally different rain forest types. The classification accuracies were higher for the k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.     

Error compensation in machine tools: a neural network approach

For a non-idealized machine tool, each point in the workspace is associated with a tool point positioning error vector. If this error map can be determined, then it is possible to substantially improve the positioning performance of the machine by introducing suitable compensation into the control loop. This paper explores the possibility of using an artifical neural network (ANN) to compute this mapping. The training set for the ANN is obtained by mounting a physical artifact whose dimensions are precisely known in the machine's workspace. The machine, equipped with a touch trigger probe, measures the positions of features on the artifact. The difference between the machine reading and the known dimension is the machine error at that point in the workspace. Using standard modeling techniques, the kinematic error model for a CNC turning center was developed. This model was parameterized by measurement of the parametric error functions using a laser interferometer, electronic levels and a precision square. The kinematic model was then used to simulate the artifact-measuring process and develop the ANN training set. The effect of changing artifact geometry was explored and a machining operation was simulated using the ANN output to provide compensation. The results show that the ANN is capable of learning the error map of a real machine, and that ANN-based compensation can significantly reduce part-dimensional errors.     

Reconstruction of General Curves,Using Factorization and Bundle Adjustment

In this paper, we extend the notion of affine shape, introduced by Sparr, from finite point sets to curves. The extension makes it possible to reconstruct 3D-curves up to projective transformations, from a number of their 2D-projections. We also extend the bundle adjustment technique from point features to curves.The first step of the curve reconstruction algorithm is based on affine shape. It is independent of choice of coordinates, is robust, does not rely on any preselected parameters and works for an arbitrary number of images. In particular this means that, except for a small set of curves (e.g. a moving line), a solution is given to the aperture problem of finding point correspondences between curves. The second step takes advantage of any knowledge of measurement errors in the images. This is possible by extending the bundle adjustment technique to curves.Finally, experiments are performed on both synthetic and real data to show the performance and applicability of the algorithm.     

Evaluation and comparison of estimation methods for failure rates and probabilities

An updated parametric robust empirical Bayes (PREB) estimation methodology is presented as an alternative to several two-stage Bayesian methods used to assimilate failure data from multiple units or plants. PREB is based on prior-moment matching and avoids multi-dimensional numerical integrations. The PREB method is presented for failure-truncated and time-truncated data. Erlangian and Poisson likelihoods with gamma prior are used for failure rate estimation, and Binomial data with beta prior are used for failure probability per demand estimation. Combined models and assessment uncertainties are accounted for. One objective is to compare several methods with numerical examples and show that PREB works as well if not better than the alternative more complex methods, especially in demanding problems of small samples, identical data and zero failures. False claims and misconceptions are straightened out, and practical applications in risk studies are presented.     

Identifying software project risks in Nigeria: an International Comparative Study

We report a study where software project risk factors were solicited in Nigeria. The study repeats a research design, which has been used in a Delphi study in the U.S., Finland, and Hong Kong to rank software development risks. The study produces a rank-order list of software risk factors and compares results with the earlier study, showing significant differences. Obtained rankings signal the importance of the infrastructure related and socio-economic software risks in developing countries like Nigeria. This demonstrates the importance of understanding the broader socio-economic context in identifying and managing software risks. The study also shows that some risks in Nigeria are located on a different scale. The choice of contingent mitigation strategies of project managers play a less important role, because the risks are so deeply engrained with the poor quality of basic infrastructure services.     

采用超临界参数的循环流化床

目前在波兰E lekrown ia Lagisza发电厂建造世界上第1座超临界蒸汽参数的循环流化床锅炉。该CFB锅炉的设计发电容量达460 MW,采用了西门子的本生垂直管(Benson Vertical)技术。该CFB锅炉预计在2006年投入运行。