针对当前无监督行人重识别方法因受到硬件差异、光照变化等客观因素的影响,导致同一行人图像出现较大反差,随之易带来样本错误伪标签生成的问题,使得现有无监督行人重识别方法还有待进一步提升的空间。为了解决此问题,提出了一种基于二...针对当前无监督行人重识别方法因受到硬件差异、光照变化等客观因素的影响,导致同一行人图像出现较大反差,随之易带来样本错误伪标签生成的问题,使得现有无监督行人重识别方法还有待进一步提升的空间。为了解决此问题,提出了一种基于二次重聚类的无监督行人重识别(unsupervised person re-identification based on quadratic clustering)方法。该方法主要包括全局二次聚类的无监督学习模块和基于聚类结果的有监督学习模块。具体来说,前者基于全局二次聚类分别对相机ID和行人身份ID进行无监督分析,解决了同一行人在不同摄像机视角下的统一成像风格问题;后者则采用有监督学习方式改进了内存字典的初始化与更新方式,解决了模型在训练中偏移的问题。通过此双模块的协同训练以共同抑制跨摄像头间采集的图像所产生错误伪标签的问题。所提出的算法分别在Market-1501、DukeMTMC-ReID、MSMT17、Person和VeRi-776数据集上进行实验,取得了mAP=81.2%和rank-1=91.2%、mAP=68.4%和rank-1=78.7%、mAP=31.1%和rank-1=60.4%、mAP=88.3%和rank-1=93.6%的性能,对比当前最先进的方法,分别提高了2.4、1.8、6.0、2.5和4.3个百分点的rank-1准确率。展开更多
Existing unsupervised person re-identification approaches fail to fully capture thefine-grained features of local regions,which can result in people with similar appearances and different identities being assigned the...Existing unsupervised person re-identification approaches fail to fully capture thefine-grained features of local regions,which can result in people with similar appearances and different identities being assigned the same label after clustering.The identity-independent information contained in different local regions leads to different levels of local noise.To address these challenges,joint training with local soft attention and dual cross-neighbor label smoothing(DCLS)is proposed in this study.First,the joint training is divided into global and local parts,whereby a soft attention mechanism is proposed for the local branch to accurately capture the subtle differences in local regions,which improves the ability of the re-identification model in identifying a person’s local significant features.Second,DCLS is designed to progressively mitigate label noise in different local regions.The DCLS uses global and local similarity metrics to semantically align the global and local regions of the person and further determines the proximity association between local regions through the cross information of neighboring regions,thereby achieving label smoothing of the global and local regions throughout the training process.In extensive experiments,the proposed method outperformed existing methods under unsupervised settings on several standard person re-identification datasets.展开更多
如何通过猕猴运动皮层的神经元锋电位信号估计其手指移动位置是一神经解码问题,现存方法解决该问题大多采用有监督训练,需要通过训练数据得到神经元锋电位信号与手指移动位置的关系,因此其估计性能依赖于训练数据.本文提出了一种无监督...如何通过猕猴运动皮层的神经元锋电位信号估计其手指移动位置是一神经解码问题,现存方法解决该问题大多采用有监督训练,需要通过训练数据得到神经元锋电位信号与手指移动位置的关系,因此其估计性能依赖于训练数据.本文提出了一种无监督解码方法,该方法基于状态空间模型(State space model,SSM),利用神经网络得到神经元锋电位数与手指移动位置的关系权值,再用逐次状态估计方法去估计手指移动的位置.为减少训练的复杂度和提高估计准确度,采用一种非线性的积分卡尔曼滤波(Cubature Kalman filtering,CKF)来完成神经网络的训练和手指位置的逐次状态估计.与传统方法相比,该方法的最大特点是无监督,可以由神经元锋电位簇向量直接估计手指移动位置,而无需有监督训练.实验结果显示,当采用较少的有监督数据,现存方法与本文方法相比有较大的估计误差;当采用较多的有监督数据,现存方法才具有与本文方法相近似的估计误差.展开更多
In this paper, an Artificial Neural Network (ANN) model is used for the analysis of any type of conventional building frame under an arbitrary loading in terms of the rotational end moments of its members. This is ach...In this paper, an Artificial Neural Network (ANN) model is used for the analysis of any type of conventional building frame under an arbitrary loading in terms of the rotational end moments of its members. This is achieved by training the network. The frame will deform so that all joints will rotate an angle. At the same time, a relative lateral sway will be produced at the rth floor level, assuming that the effects of axial lengths of the bars of the structure are not altered. The issue of choosing an appropriate neural network structure and providing structural parameters to that network for training purposes is addressed by using an unsupervised algorithm. The model’s parameters, as well as the rotational variables, are investigated in order to get the most accurate results. The model is then evaluated by using the iteration method of frame analysis developed by Dr. G. Kani. In general, the new approach delivers better results compared to several commonly used methods of structural analysis.展开更多
This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network(DBN).The important quality variable melt index of polypropylene is hard to measur...This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network(DBN).The important quality variable melt index of polypropylene is hard to measure in industrial processes.Lack of online measurement instruments becomes a problem in polymer quality control.One effective solution is to use soft sensors to estimate the quality variables from process data.In recent years,deep learning has achieved many successful applications in image classification and speech recognition.DBN as one novel technique has strong generalization capability to model complex dynamic processes due to its deep architecture.It can meet the demand of modelling accuracy when applied to actual processes.Compared to the conventional neural networks,the training of DBN contains a supervised training phase and an unsupervised training phase.To mine the valuable information from process data,DBN can be trained by the process data without existing labels in an unsupervised training phase to improve the performance of estimation.Selection of DBN structure is investigated in the paper.The modelling results achieved by DBN and feedforward neural networks are compared in this paper.It is shown that the DBN models give very accurate estimations of the polymer melt index.展开更多
文摘针对当前无监督行人重识别方法因受到硬件差异、光照变化等客观因素的影响,导致同一行人图像出现较大反差,随之易带来样本错误伪标签生成的问题,使得现有无监督行人重识别方法还有待进一步提升的空间。为了解决此问题,提出了一种基于二次重聚类的无监督行人重识别(unsupervised person re-identification based on quadratic clustering)方法。该方法主要包括全局二次聚类的无监督学习模块和基于聚类结果的有监督学习模块。具体来说,前者基于全局二次聚类分别对相机ID和行人身份ID进行无监督分析,解决了同一行人在不同摄像机视角下的统一成像风格问题;后者则采用有监督学习方式改进了内存字典的初始化与更新方式,解决了模型在训练中偏移的问题。通过此双模块的协同训练以共同抑制跨摄像头间采集的图像所产生错误伪标签的问题。所提出的算法分别在Market-1501、DukeMTMC-ReID、MSMT17、Person和VeRi-776数据集上进行实验,取得了mAP=81.2%和rank-1=91.2%、mAP=68.4%和rank-1=78.7%、mAP=31.1%和rank-1=60.4%、mAP=88.3%和rank-1=93.6%的性能,对比当前最先进的方法,分别提高了2.4、1.8、6.0、2.5和4.3个百分点的rank-1准确率。
基金supported by the National Natural Science Foundation of China under Grant Nos.62076117 and 62166026the Jiangxi Key Laboratory of Smart City under Grant No.20192BCD40002Jiangxi Provincial Natural Science Foundation under Grant No.20224BAB212011.
文摘Existing unsupervised person re-identification approaches fail to fully capture thefine-grained features of local regions,which can result in people with similar appearances and different identities being assigned the same label after clustering.The identity-independent information contained in different local regions leads to different levels of local noise.To address these challenges,joint training with local soft attention and dual cross-neighbor label smoothing(DCLS)is proposed in this study.First,the joint training is divided into global and local parts,whereby a soft attention mechanism is proposed for the local branch to accurately capture the subtle differences in local regions,which improves the ability of the re-identification model in identifying a person’s local significant features.Second,DCLS is designed to progressively mitigate label noise in different local regions.The DCLS uses global and local similarity metrics to semantically align the global and local regions of the person and further determines the proximity association between local regions through the cross information of neighboring regions,thereby achieving label smoothing of the global and local regions throughout the training process.In extensive experiments,the proposed method outperformed existing methods under unsupervised settings on several standard person re-identification datasets.
文摘如何通过猕猴运动皮层的神经元锋电位信号估计其手指移动位置是一神经解码问题,现存方法解决该问题大多采用有监督训练,需要通过训练数据得到神经元锋电位信号与手指移动位置的关系,因此其估计性能依赖于训练数据.本文提出了一种无监督解码方法,该方法基于状态空间模型(State space model,SSM),利用神经网络得到神经元锋电位数与手指移动位置的关系权值,再用逐次状态估计方法去估计手指移动的位置.为减少训练的复杂度和提高估计准确度,采用一种非线性的积分卡尔曼滤波(Cubature Kalman filtering,CKF)来完成神经网络的训练和手指位置的逐次状态估计.与传统方法相比,该方法的最大特点是无监督,可以由神经元锋电位簇向量直接估计手指移动位置,而无需有监督训练.实验结果显示,当采用较少的有监督数据,现存方法与本文方法相比有较大的估计误差;当采用较多的有监督数据,现存方法才具有与本文方法相近似的估计误差.
文摘In this paper, an Artificial Neural Network (ANN) model is used for the analysis of any type of conventional building frame under an arbitrary loading in terms of the rotational end moments of its members. This is achieved by training the network. The frame will deform so that all joints will rotate an angle. At the same time, a relative lateral sway will be produced at the rth floor level, assuming that the effects of axial lengths of the bars of the structure are not altered. The issue of choosing an appropriate neural network structure and providing structural parameters to that network for training purposes is addressed by using an unsupervised algorithm. The model’s parameters, as well as the rotational variables, are investigated in order to get the most accurate results. The model is then evaluated by using the iteration method of frame analysis developed by Dr. G. Kani. In general, the new approach delivers better results compared to several commonly used methods of structural analysis.
基金supported by National Natural Science Foundation of China (No. 61673236)the European Union (No. PIRSES-GA-2013-612230)
文摘This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network(DBN).The important quality variable melt index of polypropylene is hard to measure in industrial processes.Lack of online measurement instruments becomes a problem in polymer quality control.One effective solution is to use soft sensors to estimate the quality variables from process data.In recent years,deep learning has achieved many successful applications in image classification and speech recognition.DBN as one novel technique has strong generalization capability to model complex dynamic processes due to its deep architecture.It can meet the demand of modelling accuracy when applied to actual processes.Compared to the conventional neural networks,the training of DBN contains a supervised training phase and an unsupervised training phase.To mine the valuable information from process data,DBN can be trained by the process data without existing labels in an unsupervised training phase to improve the performance of estimation.Selection of DBN structure is investigated in the paper.The modelling results achieved by DBN and feedforward neural networks are compared in this paper.It is shown that the DBN models give very accurate estimations of the polymer melt index.
