铣刀健康状况直接影响实际生产加工过程,因此开展铣刀状态监测研究具有较大工程意义。以卷积神经网络为代表的深度学习模型已经逐渐用于监测加工过程中的刀具状态。但是这些模型的可解释性较差,预测结果的差异性也较大。作为一种新颖的...铣刀健康状况直接影响实际生产加工过程,因此开展铣刀状态监测研究具有较大工程意义。以卷积神经网络为代表的深度学习模型已经逐渐用于监测加工过程中的刀具状态。但是这些模型的可解释性较差,预测结果的差异性也较大。作为一种新颖的卷积神经网络变种,主成分分析模型(Principal component analysis network,PCANet)的可解释性好,但是特征自监督学习能力有待提升,且相关应用案例较少。针对以上问题,拟对PCANet模型进行优化,进而提出了一种激活主成分分析-最大池化-支持向量回归(Activated PCANet with max pooling and support vector regression,APCANet-MP-SVR)模型,用于自适应提取敏感特征并准确预测刀具磨损情况。首先引入tanh激活函数,提高模型泛化能力;再采用最大池化层替代哈希编码和直方图用于特征选择,进一步降低冗余特征规模;最后建立支持向量回归模型实时预测刀具磨损值。应用案例充分证明了所提模型能够更好地用于加工现场刀具磨损值预测。展开更多
Recent developments in computer vision applications have enabled detection of significant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Parti...Recent developments in computer vision applications have enabled detection of significant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization(SPSOM)and Incremental Deep Convolution Neural Networks(IDCNN)for detecting multiple objects.However,the study considered opticalflows resulting in assessing motion contrasts.Existing methods have issue with accuracy and error rates in motion contrast detection.Hence,the overall object detection performance is reduced significantly.Thus,consideration of object motions in videos efficiently is a critical issue to be solved.To overcome the above mentioned problems,this research work proposes a method involving ensemble approaches to and detect objects efficiently from video streams.This work uses a system modeled on swarm optimization and ensemble learning called Spatiotemporal Glowworm Swarm Optimization Model(SGSOM)for detecting multiple significant objects.A steady quality in motion contrasts is maintained in this work by using Chebyshev distance matrix.The proposed system achieves global optimization in its multiple object detection by exploiting spatial/temporal cues and local constraints.Its experimental results show that the proposed system scores 4.8%in Mean Absolute Error(MAE)while achieving 86%in accuracy,81.5%in precision,85%in recall and 81.6%in F-measure and thus proving its utility in detecting multiple objects.展开更多
Brain tumor is one of the most common tumors with high mortality.Early detection is of great significance for the treatment and rehabilitation of patients.The single channel convolution layer and pool layer of traditi...Brain tumor is one of the most common tumors with high mortality.Early detection is of great significance for the treatment and rehabilitation of patients.The single channel convolution layer and pool layer of traditional convolutional neural network(CNN)structure can only accept limited local context information.And most of the current methods only focus on the classification of benign and malignant brain tumors,multi classification of brain tumors is not common.In response to these shortcomings,considering that convolution kernels of different sizes can extract more comprehensive features,we put forward the multi-size convolutional kernel module.And considering that the combination of average-pooling with max-pooling can realize the complementary of the high-dimensional information extracted by the two structures,we proposed the dual-channel pooling layer.Combining the two structures with ResNet50,we proposed an improved ResNet50 CNN for the applications in multi-category brain tumor classification.We used data enhancement before training to avoid model over fitting and used five-fold cross-validation in experiments.Finally,the experimental results show that the network proposed in this paper can effectively classify healthy brain,meningioma,diffuse astrocytoma,anaplastic oligodendroglioma and glioblastoma.展开更多
文摘铣刀健康状况直接影响实际生产加工过程,因此开展铣刀状态监测研究具有较大工程意义。以卷积神经网络为代表的深度学习模型已经逐渐用于监测加工过程中的刀具状态。但是这些模型的可解释性较差,预测结果的差异性也较大。作为一种新颖的卷积神经网络变种,主成分分析模型(Principal component analysis network,PCANet)的可解释性好,但是特征自监督学习能力有待提升,且相关应用案例较少。针对以上问题,拟对PCANet模型进行优化,进而提出了一种激活主成分分析-最大池化-支持向量回归(Activated PCANet with max pooling and support vector regression,APCANet-MP-SVR)模型,用于自适应提取敏感特征并准确预测刀具磨损情况。首先引入tanh激活函数,提高模型泛化能力;再采用最大池化层替代哈希编码和直方图用于特征选择,进一步降低冗余特征规模;最后建立支持向量回归模型实时预测刀具磨损值。应用案例充分证明了所提模型能够更好地用于加工现场刀具磨损值预测。
文摘Recent developments in computer vision applications have enabled detection of significant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization(SPSOM)and Incremental Deep Convolution Neural Networks(IDCNN)for detecting multiple objects.However,the study considered opticalflows resulting in assessing motion contrasts.Existing methods have issue with accuracy and error rates in motion contrast detection.Hence,the overall object detection performance is reduced significantly.Thus,consideration of object motions in videos efficiently is a critical issue to be solved.To overcome the above mentioned problems,this research work proposes a method involving ensemble approaches to and detect objects efficiently from video streams.This work uses a system modeled on swarm optimization and ensemble learning called Spatiotemporal Glowworm Swarm Optimization Model(SGSOM)for detecting multiple significant objects.A steady quality in motion contrasts is maintained in this work by using Chebyshev distance matrix.The proposed system achieves global optimization in its multiple object detection by exploiting spatial/temporal cues and local constraints.Its experimental results show that the proposed system scores 4.8%in Mean Absolute Error(MAE)while achieving 86%in accuracy,81.5%in precision,85%in recall and 81.6%in F-measure and thus proving its utility in detecting multiple objects.
基金This paper is supported by the National Youth Natural Science Foundation of China(61802208)the National Natural Science Foundation of China(61873131)+5 种基金the Natural Science Foundation of Anhui(1908085MF207 and 1908085QE217)the Key Research Project of Anhui Natural Science(KJ2020A1215 and KJ2020A1216)the Excellent Youth Talent Support Foundation of Anhui(gxyqZD2019097)the Postdoctoral Foundation of Jiangsu(2018K009B)the Higher Education Quality Project of Anhui(2019sjjd81,2018mooc059,2018kfk009,2018sxzx38 and 2018FXJT02)the Fuyang Normal University Doctoral Startup Foundation(2017KYQD0008).
文摘Brain tumor is one of the most common tumors with high mortality.Early detection is of great significance for the treatment and rehabilitation of patients.The single channel convolution layer and pool layer of traditional convolutional neural network(CNN)structure can only accept limited local context information.And most of the current methods only focus on the classification of benign and malignant brain tumors,multi classification of brain tumors is not common.In response to these shortcomings,considering that convolution kernels of different sizes can extract more comprehensive features,we put forward the multi-size convolutional kernel module.And considering that the combination of average-pooling with max-pooling can realize the complementary of the high-dimensional information extracted by the two structures,we proposed the dual-channel pooling layer.Combining the two structures with ResNet50,we proposed an improved ResNet50 CNN for the applications in multi-category brain tumor classification.We used data enhancement before training to avoid model over fitting and used five-fold cross-validation in experiments.Finally,the experimental results show that the network proposed in this paper can effectively classify healthy brain,meningioma,diffuse astrocytoma,anaplastic oligodendroglioma and glioblastoma.
