Automatic generation of assembly dimension chain based on tolerance cell

In the process of generating assembly dimension chain automatically,there are many problems such as complex assembly relationship,variety of constraints of components and complex dimension and tolerance relations of components.These problems lead to many difficulties in search process and need lots of man-machine interaction.Because of these difficulties,this paper presents a method of automatic generation of assembly dimension chain based on tolerance cell.This method is realized through obtaining assembly tolerance cell and part tolerance cell first.These two types of tolerance cell are extracted and expressed in the form of linked list structure in computer further.Then assembly dimension chain is searched automatically based on obtained tolerance cell,and the correct assembly dimension chain is extracted finally.A system of generating assembly dimension chain automatically has been developed based on DELMIA and successfully deployed in aircraft assembly simulation in the engineering practice.     

一种基于分形的木材细胞图像特征提取方法*

木材纹理图像的分形维数可以代表木材很多的纹理特征,它是木材树种的一项重要数字特征参数。为了提取木材纹理特征,提出采用改进差分盒子法计算木材纹理细胞图像的分形维数值。理论分析和实验结果表明：该方法能够提取木材纹理特征,并且能在一定程度上克服图像比例大小对于特征提取所造成的影响,是木材纹理特征参数提取的一种重要方法。     

Derivation of the functional relations between fractal dimension of and shape indices of urban form

The relationships between the size, scale, shape, and dimension of urban settlements are basic problems remained to be further resolved, and this paper provides an available perspective for understanding these problems. Based on the standard circle, the relations between the fractal dimension of urban boundary and the compactness ratios of urban shape were derived from a geometric measure relation in a simple way. The compactness ratios proved to be the exponential functions of the reciprocal of the boundary dimension. The results can be generalized and applied to the common indices of shape including circularity ratio, ellipticity index, and form ratio, which are defined by urban area, perimeter, or Feret’s diameter. The mathematical models are empirically verified by the remote sensing data of China’s 31 mega-cities in 1990 and 2000 and lend support to the assumption that urban boundaries are pre-fractals rather than real fractals. A conclusion can be drawn that there exist certain functional relations between the shape indices and the boundary dimension, and within certain range of scales, the fractal parameters can be indirectly estimated by the ratios of size measurements to reflect the features of urban shapes.     

汽车操纵稳定性稳态响应参数的单位与量纲

针对表征汽车操纵稳定性状态的稳定性因数等参数的单位规定比较混乱的现状,根据角度物理量有单位但无量纲的特点,在轮胎侧偏刚度取两种不同单位的情况下,对汽车转向稳态响应的稳定性因数及稳态横摆角速度增益等重要参数的单位及量纲进行了全面分析.     

Determining number of clusters and prototype locations via multi-scale clustering

In clustering algorithms, it is usually assumed that the number of clusters is known or given. In the absence of such a priori information, a procedure is needed to find an appropriate number of clusters. This paper presents a clustering algorithm that incorporates a mechanism for finding the appropriate number of clusters as well as the locations of cluster prototypes. This algorithm, called multi-scale clustering, is based on scale-space theory by considering that any prominent data structure ought to survive over many scales. The number of clusters as well as the locations of cluster prototypes are found in an objective manner by defining and using lifetime and drift speed clustering criteria. The outcome of this algorithm does not depend on the initial prototype locations that affect the outcome of many clustering algorithms. As an application of this algorithm, it is used to enhance the Hough transform technique.     

图像矩阵降维压缩的一种新方法

由于矩阵的SVD分解方法反映了图像的内在属性和本质特征,在图像处理中得到了广泛应用．但在矩阵降维压缩过程中对奇异值的最小数日还没有定量的确定方法,这给SVD在图像特征提取中的应用带来很大困难．为此,在研究确定奇异值可压缩到最小数日的基础上。提出一个奇异值压缩的降秩准则。并给出了具体的证明过程和计算方法．计算机仿真研究验证了所提出计算方法的正确性．     

The hybrid method of FSIR and FSAVE for functional effective dimension reduction

Functional Sliced Inverse Regression (FSIR) and Functional Sliced Average Variance Estimation (FSAVE) are two popular functional effective dimension reduction methods. However, both of them have restrictions: FSIR is vulnerable to symmetric dependencies and FSAVE has low efficiency for monotone dependencies and is sensitive to the number of slices. To avoid aforementioned disadvantages, a hybrid method of FSIR and FSAVE is developed. Theoretical properties for the hybrid method and the consistency result of the proposed hybrid estimator are derived. Simulation studies show that the hybrid method has better performance than those of FSIR and FSAVE. The proposed method is also applied on the Tecator data set.     

Stimulus duration thresholds for reading numerical time information: Effects of visual size and number of time units

We examined the effects of the visual size and the number of digits on reading numerical time information in young adults. Using an adaptive staircase procedure, minimal stimulus presentation duration (MSPD) for 80%-correct responses was determined for visual sizes ranging from 0.1° to 15°, when reading 1 (“mm”), 2 (“hh:mm”) or 3 (“hh:mm:ss”) 2-digit units of time information. All three time types revealed U-shaped relations between MSPD and visual size, with the characteristics of the relation depending on the number of time units. Time type had two different effects. First, longer time types gave rise to longer MSPDs, as more elements needed to be encoded into working memory. Second, longer time types gave rise to smaller ranges of optimal visual character size, decreasing from 0.2–2° for the 1-unit time type to 0.3–0.5° for the 3-unit time type. The lower boundary of the optimal range of visual size may be understood as resulting from acuity limitations. The shift in the upper boundary of the optimal range of visual size is suggested to reflect the change in size of the visual span associated with larger visual character sizes.     

Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm

The main difficulty with EM algorithm for mixture model concerns the number of components, say g. This is the question of model selection, and the EM algorithm itself could not estimate g. On the contrary, the algorithm requires g to be specified before the remaining parameters can be estimated. To solve this problem, a new algorithm, which is called stepwise split-and-merge EM (SSMEM) algorithm, is proposed. The SSMEM algorithm alternately splits and merges components, estimating g and other parameters of components simultaneously. Also, two novel criteria are introduced to efficiently select the components for split or merge. Experimental results on simulated and real data demonstrate the effectivity of the proposed algorithm.     

Global and local choice of the number of nearest neighbors in locally linear embedding

The crux in the locally linear embedding algorithm is the selection of the number of nearest neighbors k. Some previous techniques have been developed for finding this parameter based on embedding quality measures. Nevertheless, they do not achieve suitable results when they are tested on several kind of manifolds. In this work is presented a new method for automatically computing the number of neighbors by means of analyzing global and local properties of the embedding results. Besides, it is also proposed a second strategy for choosing the parameter k, on manifolds where the density and the intrinsic dimensionality of the neighborhoods are changeful. The first proposed technique, called preservation neighborhood error, calculates a unique value of k for the whole manifold. Moreover, the second method, named local neighborhood selection, computes a suitable number of neighbors for each sample point in the manifold. The methodologies were tested on artificial and real-world datasets which allow us to visually confirm the quality of the embedding. According to the results our methods aim to find suitable values of k and appropriated embeddings.     

A dynamic programming approach for minimizing the number of drawing stages and heat treatments in cylindrical shell multistage deep drawing

Deep drawing is an important sheet metal forming process that appears in many industrial fields. It involves pressing a blank sheet against a hollow cavity that takes the form of the desired product. Due to limitations related to the properties of the blank sheet material, several drawing stages may be needed before the required shape and dimensions of the final product can be obtained. Heat treatment may also be needed during the process in order to restore the formability of the material so that failure is avoided. In this paper, the problem of minimizing the number of drawing stages and heat treatments needed for the multistage deep drawing of cylindrical shells is addressed. This problem is directly related to minimizing manufacturing costs and lead time. It is required to determine the post-drawing shell diameters along with whether heat treatment is to be conducted after each drawing stage such that the aforementioned objectives are achieved and failure is avoided. Conventional computer-aided process planning (CAPP) rules are used to define the search space for a dynamic programming (DP) approach in which both the post-drawing shell diameter and material condition are used to define the states in the problem. By discretizing the range of feasible shell diameters starting from the initial blank diameter down to the final shell diameter, the feasible transitions from state to another is represented by a directed graph, based upon which the DP functional equation is easily defined. The DP generates a set of feasible optimized process plans that are then verified by carrying out finite element analysis in which the deformation severity and the resulting strains and thickness variations are investigated. Two case studies are presented to demonstrate the effectiveness of the developed approach. The results suggest that the proposed approach is a valuable, reliable and quick computer aided process planning approach to this complicated problem.     

Estimation of the number of clusters and influence zones

Whereas estimating the number of clusters is directly involved in the first steps of unsupervised classification procedures, the problem still remains topical. In our attempt to propose a solution, we focalize on procedures that do not make any assumptions on the cluster shapes. Indeed the classification approach we use is based on the estimation of the probability density function (PDF) using the Parzen–Rosenblatt method. The modes of the PDF lead to the construction of influence zones which are intrinsically related to the number of clusters. In this paper, using different sizes of kernel and different samplings of the data set, we study the effects they imply on the relation between influence zones and the number of clusters. This ends up in a proposal of a method for counting the clusters. It is illustrated in simulated conditions and then applied on experimental results chosen from the field of multi-component image segmentation.     

Minimizing the number of late jobs for the permutation flowshop problem with secondary resources

This paper considers a permutation flowshop problem with secondary resources with the objective of minimizing the number of tardy jobs. The number of secondary resources assigned to the machines (workcenters), as well as the allocation of resources among the various machines, will play a significant role in the time required to process each job by its specified due date. This problem finds application in a large number of environments including manufacturing, maintenance, warehousing operations, as well as in healthcare. The research presents a lower bound for the permutation flowshop problem and evaluates its performance against the optimal solution for small, medium, and large instances. Several heuristics, including neighborhood search and simulated annealing, are presented to generate the secondary resource assignment and the allocation of jobs to the schedule. The computational complexity of the lower bound and computational examples for the heuristics are discussed.     

Minimizing the number of tardy jobs in the flowshop problem with operation and resource flexibility

In this paper, we propose a reformulation for the flowshop problem based on the concept of operation to workstation flexibility. While we assume a fixed sequence of operations in the reformulation, the “location” of each operation within the flow is not fixed. The proposed flowshop problem is important because the effective use of this flexibility can lead to significant changes in production performance. Solving the proposed problem involves two sub-problems: assignment of operations to workstations and sequencing of the jobs. We present a lower bound procedure and efficient solution approaches to solve each sub-problem. Furthermore, an improvement method based on neighborhood search and simulated annealing is implemented. A set of experiments is analyzed under different combinations of the proposed heuristics achieving high quality results. These experiments demonstrate that the heuristic factor is of significance for the problem.     

A hierarchical Gamma Mixture Model-based method for estimating the number of clusters in complex data

This paper proposes a new method for estimating the true number of clusters and initial cluster centers in a dataset with many clusters. The observation points are assigned to the data space to observe the clusters through the distributions of the distances between the observation points and the objects in the dataset. A Gamma Mixture Model (GMM) is built from a distance distribution to partition the dataset into subsets, and a GMM tree is obtained by recursively partitioning the dataset. From the leaves of the GMM tree, a set of initial cluster centers are identified and the true number of clusters is estimated. This method is implemented in the new GMM-Tree algorithm. Two GMM forest algorithms are further proposed to ensemble multiple GMM trees to handle high dimensional data with many clusters. The GMM-P-Forest algorithm builds GMM trees in parallel, whereas the GMM-S-Forest algorithm uses a sequential process to build a GMM forest. Experiments were conducted on 32 synthetic datasets and 15 real datasets to evaluate the performance of the new algorithms. The results have shown that the proposed algorithms outperformed the existing popular methods: Silhouette, Elbow and Gap Statistic, and the recent method I-nice in estimating the true number of clusters from high dimensional complex data.     

Why concept lattices are large: extremal theory for generators,concepts, and VC-dimension

Abstract

A unique type of subcontexts is always present in formal contexts with many concepts: the contranominal scales. We make this precise by giving an upper bound for the number of minimal generators (and thereby for the number of concepts) of contexts without contranominal scales larger than a given size. We give an interpretation of this bound in terms of the Vapnik–Chervonenkis dimension of the concept lattice. Extremal contexts are constructed which meet this bound exactly. They are completely classified.     

A model-based method for computation of correlation dimension,Lyapunov exponents and synchronization from depth-EEG signals

In order to predict epileptic seizures many precursory features, extracted from the EEG signals, have been introduced. Before checking out the performance of features in detection of pre-seizure state, it is required to see whether these features are accurately extracted. Evaluation of feature estimation methods has been less considered, mainly due to the lack of a ground truth for the real EEG signals’ features. In this paper, some simulated long-term depth-EEG signals, with known state spaces, are generated via a realistic neural mass model with physiological parameters. Thanks to the known ground truth of these synthetic signals, they are suitable for evaluating different algorithms used to extract the features. It is shown that conventional methods of estimating correlation dimension, the largest Lyapunov exponent, and phase coherence have non-negligible errors. Then, a parameter identification-based method is introduced for estimating the features, which leads to better estimation results for synthetic signals. It is shown that the neural mass model is able to reproduce real depth-EEG signals accurately; thus, assuming this model underlying real depth-EEG signals, can improve the accuracy of features’ estimation.     

柴油机喷油泵试验台集油控制器的设计与实现

喷油泵试验台在工作过程中需要对燃油喷射量进行测量,以检查被试泵在各种转速工况下的工作性能。可通过集油量筒采集多次喷射油量,再除以喷油次数得到平均油量。采用C8051F310芯片作为主控制部件,可设计并实现喷油泵试验台集油控制器。