Based on the theory of dynamic adaptive chemistry and dynamic cell clustering, a 3D model for fuel combustion in the cylinder is constructed to study the fast calculation algorithm to accelerate the engine combustion ...
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Based on the theory of dynamic adaptive chemistry and dynamic cell clustering, a 3D model for fuel combustion in the cylinder is constructed to study the fast calculation algorithm to accelerate the engine combustion simulation and analyze the impacts of the basic structure and the main parameters of dynamic adaptive chemistry and dynamic cell clustering on diesel engine simulation. It is found that the error threshold value of the dynamic adaptive chemistry algorithm should be set appropriately to ensure the accuracy of the simulation, and the dynamic adaptive chemistry algorithm should employ different initial search species and threshold values to meet different research objectives. Regarding the dynamic cell clustering algorithm, the data accuracy in the overall spatial range can be guaranteed, but the local location information will be missing, which will have a great impact on the prediction of pollutants. Therefore, suitable cell clustering conditions should be selected to meet the requirements of different targets.
作者：
Zhao, JSChew, WCUniv Illinois
Dept Elect & Comp Engn Electromagnet Lab Ctr Computat Electromagnet Urbana IL 61801 USA
In this paper, the wire-surface basis is reviewed. The original hard wire-surface basis is divided into independent small wire-surface bases. This new wire-surface basis consists of only one wire segment and one trian...
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In this paper, the wire-surface basis is reviewed. The original hard wire-surface basis is divided into independent small wire-surface bases. This new wire-surface basis consists of only one wire segment and one triangular patch. It simulates more accurately the current distribution near the junction, and is easier to form the wire-surface global loop basis for low-frequency problems. For complex structures with a global loop basis, LF-MLFMA directly based on a loop-tree basis is inefficient and cannot be applied. To solve this problem, LF-MLFMA based on a RWG basis, wire basis, and wire-surface basis for efficiently solving low-frequency problems is developed. But the intrinsic expansion bases are still loop-tree bases. (C) 2001 John Wiley & Sons, Inc.
The fault diagnosis of bevel gearbox is of great significance. At present, the commonly used methods are based on pattern recognition, such as support vector machine, convex hull classifier and hyperdisk classifier. H...
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The fault diagnosis of bevel gearbox is of great significance. At present, the commonly used methods are based on pattern recognition, such as support vector machine, convex hull classifier and hyperdisk classifier. However, the number of elements in the kernel matrix of these kernel function-based classification methods increases squarely with the data size, resulting in intolerable training time. Based on this, a sparse random projection-based hyperdisk classifier model is proposed. The proposed method has the following novelties: First, based on sparse random projection and the geometrical characteristics of the hyperdisk model, a method is designed to efficiently screen out the core samples, and these samples are given different weights in this process. Second, the proposed method introduces slack variables and the dynamic penalty parameter to obtain a hyperdisk model with more reasonable boundary. Last, a strategy is developed to minimize the adverse effects of imbalanced training data. The effectiveness and applicability of the proposed method are verified on bevel gearbox fault data. The experimental results show that compared with other classifiers, the proposed method can greatly reduce the training time while guaranteeing a high classification accuracy. What's more, it has better performance and efficiency in fault diagnosis with imbalanced training data.
This paper examines the problem of exponentially-weighted H∞ adaptive filtering and shows that its suboptimal solution reduces to a recursive algorithm which is slightly different from the RLS algorithm. Bas...
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This paper examines the problem of exponentially-weighted H∞ adaptive filtering and shows that its suboptimal solution reduces to a recursive algorithm which is slightly different from the RLS algorithm. Based on this similarity, its fast array form is immediately obtained by following the derivation of the fast RLS array algorithm. Also a theoretical expression for its steady-state mean-square error is provided. Several numerical examples indicate that the exponentially-weighted H∞ filter can achieve a proper balance between H∞ and H2 (least squares) filtering criteria.
Sonography is an established noninvasive diagnostic tool in the clinical context of an emergency department. Its use in the prehospital setting is still rare despite its importance to use someone’s resources purposef...
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Sonography is an established noninvasive diagnostic tool in the clinical context of an emergency department. Its use in the prehospital setting is still rare despite its importance to use someone’s resources purposeful and its importance in emergency medicine guidelines. In this article we show the advantages and disadvantages of prehospital point-of-care ultrasound (pPOCUS). We reflect organizational hurdles implementing pPOCUS as well as describing the technical preconditions for an easy and meaningful use. Furthermore, we explain teaching issues for pPOCUS and with a standard operating procedure (SOP) we show how pPOCUS could be implemented in the prehospital setting using some cardinal symptoms as examples.
Normalized cross-correlation is an important mathematical tool in digital signal processing. This paper presents a new algorithm and its systolic structure for digital normalized cross-correlation, based on the statis...
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Normalized cross-correlation is an important mathematical tool in digital signal processing. This paper presents a new algorithm and its systolic structure for digital normalized cross-correlation, based on the statistical characteristic of inner-product. We first introduce a relationship between the inner-product in cross-correlation and a first-order moment. Then digital normalized cross-correlation is transformed into a new calculation formula that mainly includes a first-order moment. Finally, by using a fast algorithm for first-order moment, we can compute the first-order moment in this new formula rapidly, and thus develop a fast algorithm for normalized cross-correlation, which contributes to that arbitrary-length digital normalized cross-correlation being performed by a simple procedure and less multiplications. Furthermore, as the algorithm for the first-order moment can be implemented by systolic structure, we design a systolic array for normalized cross-correlation with a seldom multiplier, in order for its fast hardware implementation. The proposed algorithm and systolic array are also improved for reducing their addition complexity. The comparisons with some algorithms and structures have shown the performance of the proposed method.
The article presents a parallel hardware-oriented algorithm designed to speed up the division of two octonions. The advantage of the proposed algorithm is that the number of real multiplications is halved as compared ...
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The article presents a parallel hardware-oriented algorithm designed to speed up the division of two octonions. The advantage of the proposed algorithm is that the number of real multiplications is halved as compared to the naive method for implementing this operation. In the synthesis of the discussed algorithm, the matrix representation of this operation was used, which allows us to present the division of octonions by means of a vector-matrix product. Taking into account a specific structure of the matrix multiplicand allows for reducing the number of real multiplications necessary for the execution of the octonion division procedure.
By introducing a form of reorder for multidimensional data, we propose a unified fast algo-rithm that jointly employs one-dimensional W transform and multidimensional discrete polynomial trans-form to compute eleven t...
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By introducing a form of reorder for multidimensional data, we propose a unified fast algo-rithm that jointly employs one-dimensional W transform and multidimensional discrete polynomial trans-form to compute eleven types of multidimensional discrete orthogonal transforms, which contain three types of m-dimensional discrete cosine transforms ( m-D DCTs) ,four types of m-dimensional discrete W transforms ( m-D DWTs) ( m-dimensional Hartley transform as a special case), and four types of generalized discrete Fourier transforms ( m-D GDFTs). For real input, the number of multiplications for all eleven types of the m-D discrete orthogonal transforms needed by the proposed algorithm are only 1/m times that of the commonly used corresponding row-column methods, and for complex input, it is further reduced to 1/(2m) times. The number of additions required is also reduced considerably. Furthermore, the proposed algorithm has a simple computational structure and is also easy to be im-plemented on computer, and the numerical experiments show that the computational efficiency is con-sistent with the theoretic analysis.
This paper presents a fast fuzzy c-means (FCM) clustering algorithm with two layers, which is a mergence of hard clustering and fuzzy clustering. The result of hard clustering is used to initialize the c cluster cente...
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This paper presents a fast fuzzy c-means (FCM) clustering algorithm with two layers, which is a mergence of hard clustering and fuzzy clustering. The result of hard clustering is used to initialize the c cluster centers in fuzzy clustering, and then the number of iteration steps is reduced. The application of the proposed algorithm to image segmentation based on the two dimensional histogram is provided to show its computational efficience.
We describe an algorithm for finding Hamilton cycles in random graphs. Our model is the random graph G=G(n,m) (delta >= 3). In this model G is drawn uniformly from graphs with vertex set [n], m edges and minimum de...
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We describe an algorithm for finding Hamilton cycles in random graphs. Our model is the random graph G=G(n,m) (delta >= 3). In this model G is drawn uniformly from graphs with vertex set [n], m edges and minimum degree at least three. We focus on the case where m = cn for constant c. If c is sufficiently large then our algorithm runs in O(n1+o(1)) time and succeeds w.h.p. (c) 2014 Wiley Periodicals, Inc. Random Struct. Alg., 47, 73-98, 2015
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