Number Of Center Points Research Articles

Acoustic measurement of velocity filed using improved radial basic function neural network

In coal-fired boilers, the aerodynamic field directly affects the combustion conditions of the boiler and safety of the furnace. In this work, a new method based on acoustic tomography (AT) is proposed to measure the velocity field by using radial basis function neural network (RBFNN). First, we establish the proposed RBFNN model to reconstruct the velocity field and study the influence of various parameters including the number and arrangement of sensors, the shape parameters and the number of center points in RBF, and the hyper parameters in the neural network to improve the reconstruction performance. Second, a novel improved RBFNN acoustic algorithm is used to reconstruct different two-dimensional velocity fields based on numerical simulations. The reconstruction results obtained using the proposed algorithms have higher accuracy, better real-time performance, and noise immunity as compared with other classical acoustic algorithms. Third, the physical experiments are conducted in lab to further assess the proposed algorithm. The results exhibit that the proposed RBFNN acoustic method provided satisfactory consistency as compared to the traditional measurement approaches. This proves the effectiveness of the proposed acoustic method in practical velocity field measurement.

MapReduce distributed parallel computing framework for diagnosis and treatment of knee joint Kashin-Beck disease

To improve the accuracy and computational efficiency of the MapReduce distributed parallel computing framework, thereby mining the diagnosis and treatment data of Kashin-Beck Disease (KBD) of the knee joint. Based on the shortcomings of the traditional K-means Clustering Algorithm (KCA), a simplified method for distance calculation was proposed. The Manhattan distance was used instead of Euclidean distance. Further improvement strategies were proposed to implement and compare KCA of MapReduce (MR-KCA) and Improved MR-KCA (IMR-KCA). With the same data, the sum of squared errors of MR-KCA and IMR-KCA decreased with the increase in the number of center points. Compared with MR-KCA, the quality of IMR-KCA was higher, and their difference was especially evident at 8 GB data capacity. The total execution time of both MR-KCA and IMR-KCA increased with the increase in the number of center points. Compared to MR-KCA, the total execution time of IMR-KCA was significantly reduced, especially when the data capacity was 8 GB. When the number of center points was 5000, IMR-KCA could reduce the total execution time by 50%. Through experiments, IMR-KCA was proved to better present the diagnosis and treatment data of patients with knee joint KBD. The scalability rates of MR-KCA and IMR-KCA decreased as the number of nodes increased, but the scalability rates of both algorithms could be maintained above 0.80, which had better scalability. Compared with MR-KCA, IMR-KCA had significantly higher scalability. The IMR-KCA proposed in this study had high accuracy and computing efficiency, which could be used in the visualization of KBD diagnosis and treatment.

Response surface methodology for advanced manufacturing technology optimization: theoretical fundamentals, practical guidelines, and survey literature review

Process optimization normally involves the combination of mathematical and statistical techniques which can be approached by distinct ways. Despite the fact that different methods can be found in the literature, the response surface methodology raised as one of the most effective ways for performing process optimization, by combining design and analysis of experiments, modeling techniques, and optimization methods. However, practical guidelines for response surface methodology and critical analysis of its applications are quite scarce. Thus, this paper aims to present the theoretical principles and practical guidelines for carrying response surface methodology as well as to provide empirical evidence on its critical aspects for manufacturing optimization. In order to accomplish with this objective, 49 papers published in the International Journal of Advanced Manufacturing Technology (IJAMT) from 2014 to 2017 were investigated and reproduced, allowing the analysis of 123 response surfaces. Surprisingly, more than 75.29% of the models have presented a saddle shape. The practical meaning of this finding is that the stationary point is not a suitable solution for the optimization of those surfaces. Besides, multiple response surfaces are more commonly found in the literature than individual ones. From this amount of papers, 71.88% of the works investigated have presented significant correlation with their peers and 87.61% have convexity incompatible with the optimization direction. Most of the optimization solutions have found outside of experimental region which reveals a preponderant neglect of the nonlinear constraints involving the definition of the experimental region. It was also verified that the proportion of functions in saddle format corresponds to 96.86% of the models estimated in flat regions. Moreover, it was found that the number of center points is commonly changed and all the manufacturing processes investigated are driven by at most five control parameters. Finally, considering the theoretical principles, the practical guidelines, and the obtained results, a follow-along example involving the optimization of AISI H13-hardened steel turning with PCBN wiper, previously published by the authors in IJAMT, was revisited by using response surface methodology. The results corroborated the proposed framework suitability.

Equiradial designs under changing axial distances, design sizes and varying center runs with their relationships to the central composite designs

In assessing the preferences of equiradial designs based on design size, axial distance and number of center points in relation to the central composite designs, D-absolute deviation (D-AD) and G-absolute deviation (G-AD) are proposed as new design measures of closeness of experimental designs. Each absolute deviation is positive or zero. The G-absolute deviation is zero or approximately zero at equals 1 center point. For greater than 1, G-absolute deviation decreases for increasing values of . On the other hand, the D-absolute deviation decreases as the design size increases. Designs having G-AD values of zero or approximately zero are identical or near identical in G-optimality properties. Also, designs having D-AD values of zero or approximately zero are identical or near identical in D-optimality properties. It is conjecturally hoped that at some increased design size, the equiradial designs and the central composite designs, having same axial or radial distance will coincide (be identical) in their properties, with D-AD value of zero or approximately zero.

Modeling dermatome selectivity of single-and multiple-current source spinal cord stimulation systems.

A recently published computational modeling study of spinal cord stimulation (SCS) predicted that a multiple current source (MCS) system could generate a greater number of central points of stimulation in the dorsal column (DC) than a single current source (1 CS) system. However, the clinical relevance of this finding has not been established. The objective of this work was to compare the dermatomal zone selectivity of MCS and 1 CS systems. A finite element method (FEM) model was built with a representation of the spinal cord anatomy and a 2 × 8 paddle electrode array. Using a contact configuration with two aligned tripoles, the FEM model was used to solve for DC field potentials across incremental changes in current between the two cathodes, modeling the MCS and 1 CS systems. The activation regions within the DC were determined by coupling the FEM output to a biophysical nerve fiber model, and coverage was mapped to dermatomal zones. Results showed marginal differences in activated dermatomal zones between 1 CS and MCS systems. This indicates that a MCS system may not provide incremental therapeutic benefit as suggested in prior analysis.