scholarly journals Ming-style chair parts and its group technology application research

2021 ◽  
Vol 2125 (1) ◽  
pp. 012049
Author(s):  
Yufan Wang ◽  
Yi Ren ◽  
Xinyan Yang ◽  
Zhongfeng Zhang

Abstract In order to apply the group technology to the production of Ming style furniture, enhance the ability of enterprises to cope with the production of small-batch, multi-variety furniture, this article will take Ming-style chair furniture as an example to make a reasonable division of its parts. The types of Ming-style chair parts are combed, the characteristic information of each component is extracted and the joint mode of mortise and tenon between two parts is summarized. In this way, the process of each mortise and tenon structure is summarized, and the correspondence between mortise and tenon structure and the machinery is established. According to this method, the Ming-style chair parts are summarized and classified into 211 kinds. Based on the similarity of parts processing machinery, all parts of the Ming-style chairs are classified into groups by fuzzy clustering algorithm and MATLAB software calculation. The purpose of this article is to provide a divisional idea for the pre-part classification preparation of Ming-style chair parts in groups for the part family.

1989 ◽  
Vol 54 (10) ◽  
pp. 2692-2710 ◽  
Author(s):  
František Babinec ◽  
Mirko Dohnal

The problem of transformation of data on the reliability of chemical equipment obtained in particular conditions to other equipment in other conditions is treated. A fuzzy clustering algorithm is defined for this problem. The method is illustrated on a case study.


2021 ◽  
pp. 1-14
Author(s):  
Yujia Qu ◽  
Yuanjun Wang

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.


1995 ◽  
Vol 05 (02) ◽  
pp. 239-259
Author(s):  
SU HWAN KIM ◽  
SEON WOOK KIM ◽  
TAE WON RHEE

For data analyses, it is very important to combine data with similar attribute values into a categorically homogeneous subset, called a cluster, and this technique is called clustering. Generally crisp clustering algorithms are weak in noise, because each datum should be assigned to exactly one cluster. In order to solve the problem, a fuzzy c-means, a fuzzy maximum likelihood estimation, and an optimal fuzzy clustering algorithms in the fuzzy set theory have been proposed. They, however, require a lot of processing time because of exhaustive iteration with an amount of data and their memberships. Especially large memory space results in the degradation of performance in real-time processing applications, because it takes too much time to swap between the main memory and the secondary memory. To overcome these limitations, an extended fuzzy clustering algorithm based on an unsupervised optimal fuzzy clustering algorithm is proposed in this paper. This algorithm assigns a weight factor to each distinct datum considering its occurrence rate. Also, the proposed extended fuzzy clustering algorithm considers the degree of importances of each attribute, which determines the characteristics of the data. The worst case is that the whole data has an uniformly normal distribution, which means the importance of all attributes are the same. The proposed extended fuzzy clustering algorithm has better performance than the unsupervised optimal fuzzy clustering algorithm in terms of memory space and execution time in most cases. For simulation the proposed algorithm is applied to color image segmentation. Also automatic target detection and multipeak detection are considered as applications. These schemes can be applied to any other fuzzy clustering algorithms.


2007 ◽  
Vol 27 (3) ◽  
pp. 237-248 ◽  
Author(s):  
Xiao-Ying Wang ◽  
Jonathan M. Garibaldi ◽  
Benjamin Bird ◽  
Michael W. George

Sign in / Sign up

Export Citation Format

Share Document