The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- ...The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- sents a novel signal processing scheme, adaptive morpho- logical update lifting wavelet （AMULW）, for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration sig- nals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective roiling element bearings.展开更多
Supervised classification of hyperspectral images is a challenging task because of the higher dimensionality of a pixel signature. The conventional classifiers require large training data set;however, practically limi...Supervised classification of hyperspectral images is a challenging task because of the higher dimensionality of a pixel signature. The conventional classifiers require large training data set;however, practically limited numbers of labeled pixels are available due to complexity and cost involved in sample collection. It is essential to have a method that can reduce such higher dimensional data into lower dimensional feature space without the loss of useful information. For classification purpose, it will be useful if such a method takes into account the nature of the underlying signal when extracting lower dimensional feature vector. The lifting framework provides the required flexibility. This article proposes the adaptive lifting wavelet transform to extract the lower dimensional feature vectors for the classification of hyperspectral signatures. The proposed adaptive update step allows the decomposition filter to adapt to the input signal so as to retain the desired characteristics of the signal. A three-layer feed forward neural network is used as a supervised classifier to classify the extracted features. The effectiveness of the proposed method is tested on two hyperspectral data sets (HYDICE & ROSIS sensors). The performance of the proposed method is compared with first generation discrete wavelet transform (DWT) based feature extraction method and previous studies that use the same data. The indices used for measuring performance are overall classification accuracy and Kappa value. The experimental results show that the proposed adaptive lifting scheme (ALS) has excellent results with a small size training set.展开更多
In this paper,the adaptive lifting scheme (ALS) and local gradient maps (LGM) are proposed to isolate the transient feature components from the gearbox vibration signals. Based on entropy minimization rule,the ALS is ...In this paper,the adaptive lifting scheme (ALS) and local gradient maps (LGM) are proposed to isolate the transient feature components from the gearbox vibration signals. Based on entropy minimization rule,the ALS is employed to change properties of an initial wavelet and design adaptive wavelet. Then LGM is applied to characterize the transient feature components in detail signal of decomposition results using ALS. In the present studies, the orthogonal Daubechies 4 (Db 4) wavelet is used as the initial wavelet. The proposed method is applied to both simulated signals and vibration signals acquired from a gearbox for periodic impulses detection. The two conventional methods (cepstrum analysis and Hilbert envelope analysis) and the orthogonal Db4 wavelet are also used to analyze the same signals for comparison. The results demonstrate that the proposed method is more effective in extracting transient components from noisy signals.展开更多
The paper presents a class of nonlinear adaptive wavelet transforms for lossless image compression. In update step of the lifting the different operators are chosen by the local gradient of original image. A nonlinear...The paper presents a class of nonlinear adaptive wavelet transforms for lossless image compression. In update step of the lifting the different operators are chosen by the local gradient of original image. A nonlinear morphological predictor follows the update adaptive lifting to result in fewer large wavelet coefficients near edges for reducing coding. The nonlinear adaptive wavelet transforms can also allow perfect reconstruction without any overhead cost. Experiment results are given to show lower entropy of the adaptive transformed images than those of the non-adaptive case and great applicable potentiality in lossless image compresslon.展开更多
For lessening the weight and volume of flow control system,enlarging the circulation control applying area of angle of attack(AOA),and achieving nice controlling characteristics,a novel lift enhancement method based o...For lessening the weight and volume of flow control system,enlarging the circulation control applying area of angle of attack(AOA),and achieving nice controlling characteristics,a novel lift enhancement method based on dual synthetic jet actuators(DSJAs)and synthetic jet actuator(SJA),and an adaptive proportional integral and differential(PID)algorithm based on radial basis function neural network are introduced.DSJAs are uniformly located along the chord to suppress the separation and trailing-edge SJA is applied to achieve the high circulation.Velocities of actuators are modulated to realize the real speed profile and on–off controlling laws of DSJAs are designed.Numerical simulations show that DSJAs and SJA could suppress the separation completely and move leading-edge stagnation point and trailing-edge separation point downstream even at AOA of 19°,hence achieve the highest lift and nose-down moment augmentation(ΔCl_(max)=0.92,ΔCm_(max)=0.02534),andΔL/D can reach 11.39 at AOA of 18°.Stalling is delayed to more than 19°.Linear lift area and pitch-break angle are both increased to 16°.ΔCl/C_(μ) can reach 76.7,indicating the greatest control efficiency.The results of adaptive PID control,whose controlling effects are proved better than PID,indicate that lift could track the objective with the rise time of 0.0325 s and finally keep steady,suggesting the nice stability and rapidness.展开更多
基金Supported by National Natural Science Foundation of China(51705431,51375078)Natural Sciences and Engineering Research Council of Canada(RGPIN-2015-04897)
文摘The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- sents a novel signal processing scheme, adaptive morpho- logical update lifting wavelet （AMULW）, for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration sig- nals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective roiling element bearings.
文摘Supervised classification of hyperspectral images is a challenging task because of the higher dimensionality of a pixel signature. The conventional classifiers require large training data set;however, practically limited numbers of labeled pixels are available due to complexity and cost involved in sample collection. It is essential to have a method that can reduce such higher dimensional data into lower dimensional feature space without the loss of useful information. For classification purpose, it will be useful if such a method takes into account the nature of the underlying signal when extracting lower dimensional feature vector. The lifting framework provides the required flexibility. This article proposes the adaptive lifting wavelet transform to extract the lower dimensional feature vectors for the classification of hyperspectral signatures. The proposed adaptive update step allows the decomposition filter to adapt to the input signal so as to retain the desired characteristics of the signal. A three-layer feed forward neural network is used as a supervised classifier to classify the extracted features. The effectiveness of the proposed method is tested on two hyperspectral data sets (HYDICE & ROSIS sensors). The performance of the proposed method is compared with first generation discrete wavelet transform (DWT) based feature extraction method and previous studies that use the same data. The indices used for measuring performance are overall classification accuracy and Kappa value. The experimental results show that the proposed adaptive lifting scheme (ALS) has excellent results with a small size training set.
基金Higher School Specialized Research Fund for the Doctoral Program Funding Issue(No.2011021120032)Fundamental Research Funds for the Central Universities(No.2012jdhz23)
文摘In this paper,the adaptive lifting scheme (ALS) and local gradient maps (LGM) are proposed to isolate the transient feature components from the gearbox vibration signals. Based on entropy minimization rule,the ALS is employed to change properties of an initial wavelet and design adaptive wavelet. Then LGM is applied to characterize the transient feature components in detail signal of decomposition results using ALS. In the present studies, the orthogonal Daubechies 4 (Db 4) wavelet is used as the initial wavelet. The proposed method is applied to both simulated signals and vibration signals acquired from a gearbox for periodic impulses detection. The two conventional methods (cepstrum analysis and Hilbert envelope analysis) and the orthogonal Db4 wavelet are also used to analyze the same signals for comparison. The results demonstrate that the proposed method is more effective in extracting transient components from noisy signals.
基金Supported by the National Natural Science Foundation of China (69983005)
文摘The paper presents a class of nonlinear adaptive wavelet transforms for lossless image compression. In update step of the lifting the different operators are chosen by the local gradient of original image. A nonlinear morphological predictor follows the update adaptive lifting to result in fewer large wavelet coefficients near edges for reducing coding. The nonlinear adaptive wavelet transforms can also allow perfect reconstruction without any overhead cost. Experiment results are given to show lower entropy of the adaptive transformed images than those of the non-adaptive case and great applicable potentiality in lossless image compresslon.
基金This work was supported by the National Natural Science Foundation of China(Grants 11972369,11872374,and 52075538).
文摘For lessening the weight and volume of flow control system,enlarging the circulation control applying area of angle of attack(AOA),and achieving nice controlling characteristics,a novel lift enhancement method based on dual synthetic jet actuators(DSJAs)and synthetic jet actuator(SJA),and an adaptive proportional integral and differential(PID)algorithm based on radial basis function neural network are introduced.DSJAs are uniformly located along the chord to suppress the separation and trailing-edge SJA is applied to achieve the high circulation.Velocities of actuators are modulated to realize the real speed profile and on–off controlling laws of DSJAs are designed.Numerical simulations show that DSJAs and SJA could suppress the separation completely and move leading-edge stagnation point and trailing-edge separation point downstream even at AOA of 19°,hence achieve the highest lift and nose-down moment augmentation(ΔCl_(max)=0.92,ΔCm_(max)=0.02534),andΔL/D can reach 11.39 at AOA of 18°.Stalling is delayed to more than 19°.Linear lift area and pitch-break angle are both increased to 16°.ΔCl/C_(μ) can reach 76.7,indicating the greatest control efficiency.The results of adaptive PID control,whose controlling effects are proved better than PID,indicate that lift could track the objective with the rise time of 0.0325 s and finally keep steady,suggesting the nice stability and rapidness.
