This paper advances a new approach based on wavelet and wavelet packet transforms in tandem with a fuzzy cluster neural network,abbreviated WPFCNN.Wavelets and wavelet packets decompose a vibration signal into differe...This paper advances a new approach based on wavelet and wavelet packet transforms in tandem with a fuzzy cluster neural network,abbreviated WPFCNN.Wavelets and wavelet packets decompose a vibration signal into different bands at different levels and provides multiresolution or multiscale views of a signal which is stationary or nonstationary. Fuzzy mathematics processes uncertain problems in engineering and converts the attributes extracted by wavelet packets to fuzzy membership degree.To achieve self-organizing classification,the MAXNET neural network is employed.WPFCNN integrates the advantages of wavelet packets and fuzzy cluster with MAXNET.The approach is adopted to process and classify vibration signal of a NH_3 compressor in a petrochemical plant.The results indicate that it is a useful and effective intelligence classification in the field of condition monitoring and fault diagnosis.展开更多
In this paper,we introduce an adaptive clustering algorithm for intrusion detection based on wavecluster which was introduced by Gholamhosein in 1999 and used with success in image processing.Because of the non-statio...In this paper,we introduce an adaptive clustering algorithm for intrusion detection based on wavecluster which was introduced by Gholamhosein in 1999 and used with success in image processing.Because of the non-stationary characteristic of network traffic,we extend and develop an adaptive wavecluster algorithm for intrusion detection.Using the multiresolution property of wavelet transforms,we can effectively identify arbitrarily shaped clusters at different scales and degrees of detail,moreover,applying wavelet transform removes the noise from the original feature space and make more accurate cluster found.Experimental results on KDD-99 intrusion detection dataset show the efficiency and accuracy of this algorithm.A detection rate above 96% and a false alarm rate below 3% are achieved.展开更多
It is well known that Fourier analysis or wavelet analysis is a very powerful and useful tool for a function since they convert time-domain problems into frequency-domain problems. Are there similar tools for a matrix...It is well known that Fourier analysis or wavelet analysis is a very powerful and useful tool for a function since they convert time-domain problems into frequency-domain problems. Are there similar tools for a matrix？ By pairing a matrix to a piecewise function,a Haar-like wavelet is used to set up a similar tool for matrix analyzing, resulting in new methods for matrix approximation and orthogonal decomposition. By using our method, one can approximate a matrix by matrices with different orders. Our method also results in a new matrix orthogonal decomposition, reproducing Haar transformation for matrices with orders of powers of two. The computational complexity of the new orthogonal decomposition is linear. That is, for an m × n matrix, the computational complexity is O（mn）. In addition,when the method is applied to k-means clustering, one can obtain that k-means clustering can be equivalently converted to the problem of finding a best approximation solution of a function. In fact, the results in this paper could be applied to any matrix related problems.In addition, one can also employ other wavelet transformations and Fourier transformation to obtain similar results.展开更多
Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, d...Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.展开更多
基于自编码器的深度聚类是无监督学习的代表算法,近年来在计算机视觉领域获得了诸多关注。相较于传统算法,自动编码器隐藏层紧凑的表示空间为聚类任务提供了更为灵活的条件。现有的自编码器聚类大多使用单分支编码器网络,而采用多个网...基于自编码器的深度聚类是无监督学习的代表算法,近年来在计算机视觉领域获得了诸多关注。相较于传统算法,自动编码器隐藏层紧凑的表示空间为聚类任务提供了更为灵活的条件。现有的自编码器聚类大多使用单分支编码器网络,而采用多个网络结合的双编码器结构还有较大的探索空间。为此,提出了一种基于双分支小波卷积自编码器和数据增强的深度聚类方法DB-WCAE-DA(Deep Clustering Method Based on Dual-Branch Wavelet Convolutional Autoencoder and Data Augmentation)。首先,融合小波变换设计了一种双分支的卷积自编码器结构,将数据映射到低维特征空间进行聚类。其次,在一个分支上采用VMF混合模型构建聚类软分配,保留数据的几何结构和方向信息;在另一个分支上引入数据增强技术,并在嵌入空间中添加噪声,提高编码器对特征的学习能力。通过这种双分支嵌套式优化过程不断提炼数据特征,使得聚类结果更加可靠。最后,在多个基准数据集上验证了该模型的有效性。展开更多
基金This project was supported by National Natural Science Foundation of China
文摘This paper advances a new approach based on wavelet and wavelet packet transforms in tandem with a fuzzy cluster neural network,abbreviated WPFCNN.Wavelets and wavelet packets decompose a vibration signal into different bands at different levels and provides multiresolution or multiscale views of a signal which is stationary or nonstationary. Fuzzy mathematics processes uncertain problems in engineering and converts the attributes extracted by wavelet packets to fuzzy membership degree.To achieve self-organizing classification,the MAXNET neural network is employed.WPFCNN integrates the advantages of wavelet packets and fuzzy cluster with MAXNET.The approach is adopted to process and classify vibration signal of a NH_3 compressor in a petrochemical plant.The results indicate that it is a useful and effective intelligence classification in the field of condition monitoring and fault diagnosis.
文摘In this paper,we introduce an adaptive clustering algorithm for intrusion detection based on wavecluster which was introduced by Gholamhosein in 1999 and used with success in image processing.Because of the non-stationary characteristic of network traffic,we extend and develop an adaptive wavecluster algorithm for intrusion detection.Using the multiresolution property of wavelet transforms,we can effectively identify arbitrarily shaped clusters at different scales and degrees of detail,moreover,applying wavelet transform removes the noise from the original feature space and make more accurate cluster found.Experimental results on KDD-99 intrusion detection dataset show the efficiency and accuracy of this algorithm.A detection rate above 96% and a false alarm rate below 3% are achieved.
文摘It is well known that Fourier analysis or wavelet analysis is a very powerful and useful tool for a function since they convert time-domain problems into frequency-domain problems. Are there similar tools for a matrix？ By pairing a matrix to a piecewise function,a Haar-like wavelet is used to set up a similar tool for matrix analyzing, resulting in new methods for matrix approximation and orthogonal decomposition. By using our method, one can approximate a matrix by matrices with different orders. Our method also results in a new matrix orthogonal decomposition, reproducing Haar transformation for matrices with orders of powers of two. The computational complexity of the new orthogonal decomposition is linear. That is, for an m × n matrix, the computational complexity is O（mn）. In addition,when the method is applied to k-means clustering, one can obtain that k-means clustering can be equivalently converted to the problem of finding a best approximation solution of a function. In fact, the results in this paper could be applied to any matrix related problems.In addition, one can also employ other wavelet transformations and Fourier transformation to obtain similar results.
文摘Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.
文摘基于自编码器的深度聚类是无监督学习的代表算法,近年来在计算机视觉领域获得了诸多关注。相较于传统算法,自动编码器隐藏层紧凑的表示空间为聚类任务提供了更为灵活的条件。现有的自编码器聚类大多使用单分支编码器网络,而采用多个网络结合的双编码器结构还有较大的探索空间。为此,提出了一种基于双分支小波卷积自编码器和数据增强的深度聚类方法DB-WCAE-DA(Deep Clustering Method Based on Dual-Branch Wavelet Convolutional Autoencoder and Data Augmentation)。首先,融合小波变换设计了一种双分支的卷积自编码器结构,将数据映射到低维特征空间进行聚类。其次,在一个分支上采用VMF混合模型构建聚类软分配,保留数据的几何结构和方向信息;在另一个分支上引入数据增强技术,并在嵌入空间中添加噪声,提高编码器对特征的学习能力。通过这种双分支嵌套式优化过程不断提炼数据特征,使得聚类结果更加可靠。最后,在多个基准数据集上验证了该模型的有效性。
