In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only de...In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations.This tends to cause a large amount of calculation and low detection precision.To solve these problems,in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network(CDCGAN)was designed.In the traditional DCGAN,the image generated by the generator has a certain degree of randomness.Here,a small number of labeled belt images are taken as conditions and added them to the generator and discriminator,so the generator can generate images with the characteristics of belt damage under the aforementioned conditions.Moreover,because the discriminator cannot identify multiple types of damage,the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities,and it can accurately classify cracks,scratches,and tears.To avoid the features learned incompletely,skiplayer connection is adopted in the generator and discriminator.This not only can minimize the loss of features,but also improves the convergence speed.Compared with other algorithms,experimental results show that the loss value of the generator and discriminator is the least.Moreover,its convergence speed is faster,and the mean average precision of the proposed algorithm is up to 96.2%,which is at least 6%higher than that of other algorithms.展开更多
Reinsurance is an effective way for an insurance company to control its risk.How to design an optimal reinsurance contract is not only a key topic in actuarial science,but also an interesting research question in math...Reinsurance is an effective way for an insurance company to control its risk.How to design an optimal reinsurance contract is not only a key topic in actuarial science,but also an interesting research question in mathematics and statistics.Optimal reinsurance design problems can be proposed from different perspectives.Risk measures as tools of quantitative risk management have been extensively used in insurance and finance.Optimal reinsurance designs based on risk measures have been widely studied in the literature of insurance and become an active research topic.Different research approaches have been developed and many interesting results have been obtained in this area.These approaches and results have potential applications in future research.In this article,we review the recent advances in optimal reinsurance designs based on risk measures in static models and discuss some interesting problems on this topic for future research.展开更多
Through temporal mode direct numerical simulation, flow field database of a fully developed turbulent boundary layer on a flat plate with Mach number 4.5 and Reynolds number Reθ =1094 has been obtained. Commonly used...Through temporal mode direct numerical simulation, flow field database of a fully developed turbulent boundary layer on a flat plate with Mach number 4.5 and Reynolds number Reθ =1094 has been obtained. Commonly used detection meth- ods in experiments are applied to detecting coherent structures in the flow field, and it is found that coherent structures do exist in the wall region of a supersonic turbulent boundary layer. The detected results show that a low-speed streak is de- tected by using the Mu-level method, the rising parts of this streak are detected by using the second quadrant method, and the crossing regions from a low-speed streak to the high-speed one are detected by using the VITA method respectively. Notwithstanding that different regions are detected by different methods, they are all accompanied by quasi-stream-wise vortex structures.展开更多
在邻域粗糙集中,基于信息度量的属性约简具有重要应用意义.然而,条件邻域熵具有粒化非单调性,故其属性约简具有应用局限性.对此,采用粒计算技术及相关的3层粒结构,构建具有粒化单调性的条件邻域熵,进而研究其相关属性约简.首先,揭示条...在邻域粗糙集中,基于信息度量的属性约简具有重要应用意义.然而,条件邻域熵具有粒化非单调性,故其属性约简具有应用局限性.对此,采用粒计算技术及相关的3层粒结构,构建具有粒化单调性的条件邻域熵,进而研究其相关属性约简.首先,揭示条件邻域熵的粒化非单调性及其根源;其次,采用3层粒结构,自底向上构建一种新型条件邻域熵,获得其粒化单调性;进而,基于粒化单调的条件邻域熵,建立属性约简及启发式约简算法;最后,采用UCI(University of CaliforniaIrvine)数据实验,验证改进条件邻域熵的单调性与启发式约简算法的有效性.所得结果表明:新建条件邻域熵具有粒化单调性,改进了条件邻域熵,其诱导的属性约简具有应用前景.展开更多
现有分阶段解码的实体关系抽取模型仍存在着阶段间特征融合不充分的问题,会增大曝光偏差对抽取性能的影响。为此,提出一种双关系预测和特征融合的实体关系抽取模型(entity relation extraction model with dual relation prediction and...现有分阶段解码的实体关系抽取模型仍存在着阶段间特征融合不充分的问题,会增大曝光偏差对抽取性能的影响。为此,提出一种双关系预测和特征融合的实体关系抽取模型(entity relation extraction model with dual relation prediction and feature fusion,DRPFF),该模型使用预训练的基于Transformer的双向编码表示模型(bidirectional encoder representation from transformers,BERT)对文本进行编码,并设计两阶段的双关系预测结构来减少抽取过程中错误三元组的生成。在阶段间通过门控线性单元(gated linear unit,GLU)和条件层规范化(conditional layer normalization,CLN)组合的结构来更好地融合实体之间的特征。在NYT和WebNLG这2个公开数据集上的试验结果表明,该模型相较于基线方法取得了更好的效果。展开更多
Numerous models have been proposed to reduce the classification error of Naive Bayes by weakening its attribute independence assumption and some have demonstrated remarkable error performance. Considering that ensembl...Numerous models have been proposed to reduce the classification error of Naive Bayes by weakening its attribute independence assumption and some have demonstrated remarkable error performance. Considering that ensemble learning is an effective method of reducing the classifmation error of the classifier, this paper proposes a double-layer Bayesian classifier ensembles （DLBCE） algorithm based on frequent itemsets. DLBCE constructs a double-layer Bayesian classifier （DLBC） for each frequent itemset the new instance contained and finally ensembles all the classifiers by assigning different weight to different classifier according to the conditional mutual information. The experimental results show that the proposed algorithm outperforms other outstanding algorithms.展开更多
关系抽取是自然语言处理中一项基础的上游任务.句子的结构信息在某种意义上蕴含了实体及其关系信息,有助于提高关系抽取的准确率,然而使用现有自然语言处理(Natural Language Processing,NLP)语言工具进行句法分析时会引入一定的错误传...关系抽取是自然语言处理中一项基础的上游任务.句子的结构信息在某种意义上蕴含了实体及其关系信息,有助于提高关系抽取的准确率,然而使用现有自然语言处理(Natural Language Processing,NLP)语言工具进行句法分析时会引入一定的错误传播问题,且现有的基于图结构的关系抽取模型在一定程度上忽略了句子的时序信息.通过结合双向长短时记忆网络(Bi-directional Long Short-Term Memory,Bi LSTM)捕获句子序列的上下文关系,同时使用传统条件随机场(Conditional Random Field,CRF)的关系标注结果矫正NLP工具的错误传播问题,提出了一种用于关系抽取的双层时空图卷积神经网络(Bilayer Spatiotemporal Graph Convolution Neural Network,Bi SpGCN)模型.该模型在中文糖尿病数据集和中文人物关系数据集上的实验结果表明,相较于传统的多头注意力引导的图卷积神经网络(Attention Guided Graph Convolutional Networks for Relation Extraction,AGGCN)模型,BiSpGCN模型能够充分利用句子的有效信息,具有更好的关系抽取性能.展开更多
基金This work was supported by the Shanxi Province Applied Basic Research Project,China(Grant No.201901D111100).Xiaoli Hao received the grant,and the URL of the sponsors’website is http://kjt.shanxi.gov.cn/.
文摘In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations.This tends to cause a large amount of calculation and low detection precision.To solve these problems,in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network(CDCGAN)was designed.In the traditional DCGAN,the image generated by the generator has a certain degree of randomness.Here,a small number of labeled belt images are taken as conditions and added them to the generator and discriminator,so the generator can generate images with the characteristics of belt damage under the aforementioned conditions.Moreover,because the discriminator cannot identify multiple types of damage,the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities,and it can accurately classify cracks,scratches,and tears.To avoid the features learned incompletely,skiplayer connection is adopted in the generator and discriminator.This not only can minimize the loss of features,but also improves the convergence speed.Compared with other algorithms,experimental results show that the loss value of the generator and discriminator is the least.Moreover,its convergence speed is faster,and the mean average precision of the proposed algorithm is up to 96.2%,which is at least 6%higher than that of other algorithms.
基金the support from the Natural Sciences and Engineering Research Council of Canada(NSERC)(grant No.RGPIN-2016-03975)supported by grants from the National Natural Science Foundation of China(Grant No.11971505)111 Project of China(No.B17050).
文摘Reinsurance is an effective way for an insurance company to control its risk.How to design an optimal reinsurance contract is not only a key topic in actuarial science,but also an interesting research question in mathematics and statistics.Optimal reinsurance design problems can be proposed from different perspectives.Risk measures as tools of quantitative risk management have been extensively used in insurance and finance.Optimal reinsurance designs based on risk measures have been widely studied in the literature of insurance and become an active research topic.Different research approaches have been developed and many interesting results have been obtained in this area.These approaches and results have potential applications in future research.In this article,we review the recent advances in optimal reinsurance designs based on risk measures in static models and discuss some interesting problems on this topic for future research.
基金Supported by the National Natural Science Foundation of China (Grant No. 90205021)the Foundation for the Author of National Excellent Doctoral Dissertation of China (Grant No. 200328)Liu-Hui Center of Applied Mathematics, Nankai University and Tianjin University
文摘Through temporal mode direct numerical simulation, flow field database of a fully developed turbulent boundary layer on a flat plate with Mach number 4.5 and Reynolds number Reθ =1094 has been obtained. Commonly used detection meth- ods in experiments are applied to detecting coherent structures in the flow field, and it is found that coherent structures do exist in the wall region of a supersonic turbulent boundary layer. The detected results show that a low-speed streak is de- tected by using the Mu-level method, the rising parts of this streak are detected by using the second quadrant method, and the crossing regions from a low-speed streak to the high-speed one are detected by using the VITA method respectively. Notwithstanding that different regions are detected by different methods, they are all accompanied by quasi-stream-wise vortex structures.
文摘在邻域粗糙集中,基于信息度量的属性约简具有重要应用意义.然而,条件邻域熵具有粒化非单调性,故其属性约简具有应用局限性.对此,采用粒计算技术及相关的3层粒结构,构建具有粒化单调性的条件邻域熵,进而研究其相关属性约简.首先,揭示条件邻域熵的粒化非单调性及其根源;其次,采用3层粒结构,自底向上构建一种新型条件邻域熵,获得其粒化单调性;进而,基于粒化单调的条件邻域熵,建立属性约简及启发式约简算法;最后,采用UCI(University of CaliforniaIrvine)数据实验,验证改进条件邻域熵的单调性与启发式约简算法的有效性.所得结果表明:新建条件邻域熵具有粒化单调性,改进了条件邻域熵,其诱导的属性约简具有应用前景.
文摘现有分阶段解码的实体关系抽取模型仍存在着阶段间特征融合不充分的问题,会增大曝光偏差对抽取性能的影响。为此,提出一种双关系预测和特征融合的实体关系抽取模型(entity relation extraction model with dual relation prediction and feature fusion,DRPFF),该模型使用预训练的基于Transformer的双向编码表示模型(bidirectional encoder representation from transformers,BERT)对文本进行编码,并设计两阶段的双关系预测结构来减少抽取过程中错误三元组的生成。在阶段间通过门控线性单元(gated linear unit,GLU)和条件层规范化(conditional layer normalization,CLN)组合的结构来更好地融合实体之间的特征。在NYT和WebNLG这2个公开数据集上的试验结果表明,该模型相较于基线方法取得了更好的效果。
基金supported by National Natural Science Foundation of China (Nos. 61073133, 60973067, and 61175053)Fundamental Research Funds for the Central Universities of China(No. 2011ZD010)
文摘Numerous models have been proposed to reduce the classification error of Naive Bayes by weakening its attribute independence assumption and some have demonstrated remarkable error performance. Considering that ensemble learning is an effective method of reducing the classifmation error of the classifier, this paper proposes a double-layer Bayesian classifier ensembles （DLBCE） algorithm based on frequent itemsets. DLBCE constructs a double-layer Bayesian classifier （DLBC） for each frequent itemset the new instance contained and finally ensembles all the classifiers by assigning different weight to different classifier according to the conditional mutual information. The experimental results show that the proposed algorithm outperforms other outstanding algorithms.
