基于二级排序元学习算法的轴承智能故障诊断

面对不同轴承故障数据集,由于小样本及不同数据集分布存在差异,导致模型在跨数据集诊断中精度不高。受人类学习过程启发,提出基于二级排序元学习算法对跨数据集的轴承故障诊断。首先对两个不同数据集进行第一级排序,将其对应为简单数据集和复杂数据集;其次把简单数据集分为元训练集和元测试集,仅对元训练集的所有任务进行第二级排序,再用少量元测试集的数据对二级排序后的模型进行微调,完成对简单数据集的故障诊断;最后用少量复杂数据集的数据对二级排序后的模型进行微调,完成对复杂数据集的故障诊断,实现跨数据集故障诊断。试验证明,与其他模型相比,该模型在轴承的小样本数据集及跨数据集上的分类结果表现更优异。

基于数据场的数据势能竞争与K-means融合的聚类算法

K-means算法采用欧氏距离进行数据点的划分,不能够准确地刻画数据集特征,而随机选取聚类中心点的机制,也不能获得好的聚类结果。为此,提出一种基于数据场的数据势能竞争与K-means算法融合的聚类算法。算法中定义了数据场的概念,利用局部最小距离进行数据聚合势能的竞争,然后利用势能熵提取基于数据集分布的最优截断距离,根据截断距离与斜率确定出簇中心点,实现K-means聚类。在UCI数据集上的测试结果表明,融合后的算法具有更好的聚类结果。

基于改进Apriori算法的毕业生就业智能推荐系统

针对高校毕业生就业盲目性、签约速度回笼时间长的问题,研究毕业生行为与就业岗位需求的关系。以广东省若干高校的历届毕业生为研究对象,依托Spark计算框架提出对原Apriori算法的改进算法Apriori_S,提高算法的执行效率和分析复杂数据集的适用性,并应用于挖掘历届毕业生数据和就业信息之间的关联规则。实验分析表明,改进算法的执行效率远高于原算法,且适用于分析多维度的复杂数据集。最后利用挖掘结果构建就业岗位“黏性”模型,构建应届毕业生就业智能推荐管理系统,实现就业岗位智能推荐。

A multi-attention RNN-based relation linking approach for question answering over knowledge base

Aiming at the relation linking task for question answering over knowledge base,especially the multi relation linking task for complex questions,a relation linking approach based on the multi-attention recurrent neural network(RNN)model is proposed,which works for both simple and complex questions.First,the vector representations of questions are learned by the bidirectional long short-term memory(Bi-LSTM)model at the word and character levels,and named entities in questions are labeled by the conditional random field(CRF)model.Candidate entities are generated based on a dictionary,the disambiguation of candidate entities is realized based on predefined rules,and named entities mentioned in questions are linked to entities in knowledge base.Next,questions are classified into simple or complex questions by the machine learning method.Starting from the identified entities,for simple questions,one-hop relations are collected in the knowledge base as candidate relations;for complex questions,two-hop relations are collected as candidates.Finally,the multi-attention Bi-LSTM model is used to encode questions and candidate relations,compare their similarity,and return the candidate relation with the highest similarity as the result of relation linking.It is worth noting that the Bi-LSTM model with one attentions is adopted for simple questions,and the Bi-LSTM model with two attentions is adopted for complex questions.The experimental results show that,based on the effective entity linking method,the Bi-LSTM model with the attention mechanism improves the relation linking effectiveness of both simple and complex questions,which outperforms the existing relation linking methods based on graph algorithm or linguistics understanding.

Violence against Women with Breast Neoplasms

The aim of this study is to assess the occurrence and type of violence suffered by women with breast cancer in the High Complexity Care Unit of a municipality in the South of Minas and patients in a support group of the University of the South of Minas Gerais. For that aim, a descriptive-exploratory methodology was applied through the quantitative method. Data were collected through a semi-structured form applied in individual interviews over a period of three months. We interviewed 57 patients and among those, 20 women （35.08%） reported having experienced some form of violence at some stage of their life, and the most frequently mentioned was the psychological violence followed by physical aggression. Although it was possible to identify that violence against affected these women, complaints against the aggressor were not affected.

基于拓扑反变的路面裂纹识别新方法

根据路面裂纹的多个本征属性,将拓扑反变理论应用到裂纹的本征属性识别中,构建复杂数据集的度量空间,组成拓扑空间,推导拓扑反变关系,利用路面裂纹原始空间到目标空间的拓扑反变关系,实现裂纹数据集的特征提取,为路面裂纹的识别奠定基础,提高了路面裂纹检测的准确度和快速性。通过仿真,验证了提出的理论方法的可行性。