电工实践教学改革与实践

电工实践教学是培养大学生电类工程能力的重要环节，本文介绍我们以培养人才为中心在电工实习基地建设；实践教学内容，方法和手段等方面进行的改革与实践。     

Post-Supervised Template Induction for Information Extraction from Lists and Tables in Dynamic Web Sources

Dynamic web sites commonly return information in the form of lists and tables. Although hand crafting an extraction program for a specific template is time-consuming but straightforward, it is desirable to automatically generate template extraction programs from examples of lists and tables in html documents. Supervised approaches have been shown to achieve high accuracy, but they require manual labelling of training examples, which is also time consuming. Fully unsupervised approaches, which extract rows and columns by detecting regularities in the data, cannot provide sufficient accuracy for practical domains. We describe a novel technique, Post-supervised Learning, which exploits unsupervised learning to avoid the need for training examples, while minimally involving the user to achieve high accuracy. We have developed unsupervised algorithms to extract the number of rows and adopted a dynamic programming algorithm for extracting columns. Our method achieves high performance with minimal user input compared to fully supervised techniques.     

Automating the identification and analysis of protein {beta}-barrels

ßBarrels are widespread and well-studied featuresof a great many protein structures. In this paper an unsuper-visedmethod for the detection of P-barrels is developed based ontechniques from graph theory. The hydrogen bonded connectivityof ß-sheets is derived using standard pattern recognitiontechniques and expressed as a graph. Barrels correspond to topologicalrings in these connectivity graphs and can thus be identifiedusing ring perception algorithms. Following from this, the characteristictopological structure of a barrel can be expressed using a novelform of reduced nomenclature that counts sequence separationsbetween successive members of the ring set These techniquesare tested by applying them to the detection of barrels in anon-redundant subset of the Brookhaven database. Results indicatethat topological rings do seem to correspond uniquely to ß-barrelsand that the technique, as implemented, finds the majority ofbarrels present in the dataset.     

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.     

唐山高技能人才队伍现状分析与思考

随着唐山市经济的快速发展,高技能人才缺乏的现象已日渐明显,对唐山市的人才现状进行了调研,并分析了造成人才短缺现象的原因,提出了加快高技能人才队伍建设的建议。     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.     

基于视频的人体行走状态分析

提出了一种基于多视角的人体行走状态分析方法.首先,利用基于自适应背景更新的背景差分法检测出运动人体;其次,利用4向链码提取人体上半身质心运动轨迹的周期;最后,通过训练不同运动状态的周期得到对应的模糊集,根据隶属度函数判别慢走、走和跑3种运动状态.实验结果表明,该方法能够准确分析人体运动速度并判断其运动状态,并且对背景噪声、视角变化有良好的鲁棒性.