Advanced learning algorithms for cross-language patent retrieval and classification

We study several machine learning algorithms for cross-language patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, which basically used learning techniques for monolingual sub-tasks, our learning algorithms exploit the bilingual training documents and learn a semantic representation from them. We study Japanese–English cross-language patent retrieval using Kernel Canonical Correlation Analysis (KCCA), a method of correlating linear relationships between two variables in kernel defined feature spaces. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. We also investigate learning algorithms for cross-language document classification. The learning algorithm are based on KCCA and Support Vector Machines (SVM). In particular, we study two ways of combining the KCCA and SVM and found that one particular combination called SVM_2k achieved better results than other learning algorithms for either bilingual or monolingual test documents.     

支持向量分类器及其在原核生物基因计算识别中的应用

以支持向量机为分类器,序列的k-letter词为特征,建立了原核生物的基因识别模型。分别选取已知功能的基因为正样本,和与等长正样本的随机突变序列为负样本组成训练集。5倍交叉实验的结果表示,对于具有不同核函数的支持向量机以及不同长度的词特征,其预测准确率不同,最高的可达94％以上,最差的低于60％;长度为3的词的特征的分类结果最好,其次是长度为4。这说明3联核苷酸为基因序列比较好的统计特征。     

一种基于机器视觉的交通标识符自动识别方法

在交通标志实时识别时,由于参考图像与实测图像非同时获取,摄像机与被摄交通标志之间的位置难以保证完全相同,致使所获取的交通标志参考图像与实测图像之间可能产生几何失真。几何失真对交通标志图像的识别结果有较大影响,因此寻找一种具有旋转和比例不变性的图像识别方法势在必行。基于不变矩的交通标志识别算法,首先对交通标志图像进行归一化处理,然后求其Hu矩,最后利用相关性测度算法进行比较。结果表明,算法针对旋转或缩放后的交通标志图像具有100%的识别率。     

Performance of a novel carrier frequency offset estimation algorithm for OFDM-based WLANs

INTRODUCTION Orthogonal frequency division multiplexing (OFDM) has been treated as the key transmission method in Wireless Local Area Networks (WLANs) based on IEEE802.11a protocol (IEEE Standard 802.11a- 1999). However, the sensitivity of the scheme to the frequency offset limits its development. Moose (1994) proposed maximum likelihood esti-mation for the carrier frequency offset by using two different received symbols, but the limitation of this method is that the acquisition r…     

数控加工实训中常见的操作错误及防范措施

提高学生的数控加工操作能力,避免或减少操作错误,是数控机床实训中应特别关注的问题。本文通过分析学生在数控车床、数控铣床加工实训中产生的常见操作错误,提出具体的防范措施。     

在普通铣床上镗阶梯孔的铣夹头设计

在机加工过程中经常遇到在普通机床上不好加工的一些表面，需要设计一些简单实用的工艺装备。本文介绍一种在普通铣床上加工阶梯孔专用夹头的设计方法。     

XIO及PLCBUS在智能家居网络中的应用与分析

从电力线载波类、无线射频类、总线类三种按传输介质分类的协议，分析智能家居网络技术，重点阐述分析了电力线载波类的x10协议、PLCBUS协议以及两协议在智能家居网络中的应用，认为基于电力线载波技术组成智能家居控制网络具有广阔的发展前景。     

Carrier phase shifted SPWM based on current sourced multi-modular converter for active power filter

A novel current-source active power filter(APF)based on multi-modular converter with carrier phase-shifted SPWM(CPS-SPWM)technique is proposed.With this technique,the effect of equivalent high switching frequency converter is obtained with low switching frequency converter.It is very promising in current-source APF that adopt superconducting magnetic energy storage component.     

数控机床机械故障分类新方法及其振动模型的研究

数控机床机械部件故障诊断的关键是首先要清晰地界定故障的类型,从而准确判断故障源。本文从故障机理和故障模式的角度重新对数控机床机械部分故障进行分类。由此得到齿轮和轴承的故障是数控机床机械部分最主要的故障源的结论。同时又给出了它们的振动模型。     

Predicting scientific research trends based on link prediction in keyword networks

The rapid development of scientific fields in this modern era has raised the concern for prospective scholars to find a proper research field to conduct their future studies. Thus, having a vision of future could be helpful to pick the right path for doing research and ensuring that it is worth investing in. In this study, we use article keywords of computer science journals and conferences, assigned by INSPEC controlled indexing, to construct a temporal scientific knowledge network. By observing keyword networks snapshots over time, we can utilize the link prediction methods to foresee the future structures of these networks. We use two different approaches for this link prediction problem. First, we have utilized three topology-based link prediction algorithms, two of which are commonly used in literature. We have also proposed a third algorithm based on nodes (keywords) clustering coefficient, their centrality measures like eigenvector centrality, and nodes community information. Then, we used nodes topological features and the outputs of aforementioned topology-based link prediction algorithms as features to feed five machine learning link prediction algorithms (SVM, Random Forest Classifier, K-Nearest Neighbors, Gaussian Naïve Bayes, and Multinomial Naïve Bayes). All tested predictors have shown considerable performance and their results are discussed in this paper.