The S-kernel: A measure of symmetry of objects

In this paper we introduce a new symmetry feature named “symmetry kernel” (SK) to support a measure of symmetry. Given any symmetry transform S, SK of a pattern P is the maximal included symmetric sub-set of P for all directions and shifts. We provide a first algorithm to exhibit this kernel where the centre of symmetry is assumed to be the centre of mass. Then we prove that, in any direction, the optimal axis corresponds to the maximal correlation of a pattern with its symmetric version. That leads to a second algorithm. The associated symmetry measure is a modified difference between the respective surfaces of a pattern and its kernel. A series of experiments supports the actual algorithm validation.     

浅谈沥青混凝土桥面铺装

通过对钢筋混凝土桥面柔性铺装的早期病害及其产生原因的分析,从桥面设计、材料和施工三方面提出了预防措施,对混凝土桥面沥青铺装的设计与施工有重要的参考价值。     

Maintenance of the undersea section of the Seikan Tunnel

This paper introduces the results of measurements made over a period of 14 years after the Seikan Tunnel was opened for service. Because this tunnel is a very long undersea tunnel with the length of 54 km, the necessity of clarifying the long-term behavior of the tunnel structure was recognized from the start. Various measurements are made to survey the condition of the ground and lining concrete and data thus obtained are used to assess the soundness of the tunnel structure. Judging from the data thus far obtained, the tunnel structure appears to remain in a good condition.     

利用测量头测量圆孔中心操作的简化

利用机床提供的顾客软体 (变量 ) ,通过三点确定一个圆心的公理 ,精确测定圆心位置座标     

圆锥孔径测量方法与误差分析

圆锥孔径的测量往往比较困难、且精度较低。提出长度测量仪器与环规配合使用,应 用比较测量的方法检测圆锥孔的直径。通过计算和分析可以得出,当被测圆锥孔径不大于180 mm 时,其测量不确定度可小于1.84 μm。给出了测量方法和不确定度计算公式供操作时参考。     

浅谈油库计量工作的风险管理

结合石油库的生产情况，分析了计量工作中的风险来源，提出了跟踪和杜绝风险的方法，为石油库计量管理和减少损失提供了依据。     

钢筋混凝土防撞护栏早期裂缝成因分析及预防

从设计方面、施工方面及混凝土原材料的质量及配合比设计三方面分析了钢筋混凝土防撞护栏表面早期裂缝出现的成因,并提出了相应的预防措施,以有效防止护栏表面裂缝的产生。     

埋弧焊方法的使用

通过对埋弧焊工作原理、特点和焊接工艺的介绍,提出了鸿源卷管的焊接参数,埋弧焊焊接缺陷及其防止措施。     

浅谈提高沥青路面平整度的施工措施

本文对施工过程中影响沥青路面平整度的原因进行了分析,提出了相应的施工措施。     

A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering

Fuzzy clustering has played an important role in solving many problems. In this paper, we design an unsupervised neural network model based on a fuzzy objective function, called OFUNN. The learning rule for the OFUNN model is a result of the formal derivation by the gradient descent method of a fuzzy objective function. The performance of the cluster analysis algorithm is often evaluated by counting the number of crisp clustering errors. However, the number of clustering errors alone is not a reliable and consistent measure for the performance of clustering, especially in the case of input data with fuzzy boundaries. We introduce two measures to evaluate the performance of the fuzzy clustering algorithm. The clustering results on three data sets, Iris data and two artificial data sets, are analyzed using the proposed measures. They show that OFUNN is very competitive in terms of speed and accuracy compared to the fuzzy c-means algorithm.