Structural health monitoring(SHM)in-service is very important for wind turbine system.Because the central wavelength of a fiber Bragg grating(FBG)sensor changes linearly with strain or temperature,FBG-based sensors ar...Structural health monitoring(SHM)in-service is very important for wind turbine system.Because the central wavelength of a fiber Bragg grating(FBG)sensor changes linearly with strain or temperature,FBG-based sensors are easily applied to structural tests.Therefore,the monitoring of wind turbine blades by FBG sensors is proposed.The method is experimentally proved to be feasible.Five FBG sensors were set along the blade length in order to measure distributed strain.However,environmental or measurement noise may cover the structural signals.Dual-tree complex wavelet transform(DT-CWT)is suggested to wipe off the noise.The experimental studies indicate that the tested strain fluctuate distinctly as one of the blades is broken.The rotation period is about 1 s at the given working condition.However,the period is about 0.3 s if all the wind blades are in good conditions.Therefore,strain monitoring by FBG sensors could predict damage of a wind turbine blade system.Moreover,the studies indicate that monitoring of one blade is adequate to diagnose the status of a wind generator.展开更多
A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet pac...A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and multi-resolution.An improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features.The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem.The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning.Experimental results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.展开更多
The risk of gas leakage due to geological flaws in offshore carbon capture, utilization, and storage, as well as leakage from underwater oil or gas pipelines, highlights the need for underwater gas leakage monitoring ...The risk of gas leakage due to geological flaws in offshore carbon capture, utilization, and storage, as well as leakage from underwater oil or gas pipelines, highlights the need for underwater gas leakage monitoring technology. Remotely operated vehicles(ROVs) and autonomous underwater vehicles(AUVs) are equipped with high-resolution imaging sonar systems that have broad application potential in underwater gas and target detection tasks. However, some bubble clusters are relatively weak scatterers, so detecting and distinguishing them against the seabed reverberation in forward-looking sonar images are challenging. This study uses the dual-tree complex wavelet transform to extract the image features of multibeam forward-looking sonar. Underwater gas leakages with different flows are classified by combining deep learning theory. A pool experiment is designed to simulate gas leakage, where sonar images are obtained for further processing. Results demonstrate that this method can detect and classify underwater gas leakage streams with high classification accuracy. This performance indicates that the method can detect gas leakage from multibeam forward-looking sonar images and has the potential to predict gas leakage flow.展开更多
针对静态表情特征缺乏时间信息,不能充分体现表情的细微变化,该文提出一种针对非特定人的动态表情识别方法:基于动态时间规整(Dynamic Time Warping,DTW)和主动外观模型(Active Appearance Model,AAM)的动态表情识别。首先采用基于局部...针对静态表情特征缺乏时间信息,不能充分体现表情的细微变化,该文提出一种针对非特定人的动态表情识别方法:基于动态时间规整(Dynamic Time Warping,DTW)和主动外观模型(Active Appearance Model,AAM)的动态表情识别。首先采用基于局部梯度DT-CWT(Dual-Tree Complex Wavelet Transform)主方向模式(Dominant Direction Pattern,DDP)特征的DTW对表情序列进行规整。然后采用AAM定位出表情图像的66个特征点并进行跟踪,利用中性脸的特征点构建人脸几何模型,通过人脸几何模型的匹配克服不同人呈现表情的差异,并通过计算表情序列中相邻两帧图像对应特征点的位移获得表情的变化特征。最后采用最近邻分类器进行分类识别。在CK+库和实验室自建库HFUT-FE(He Fei University of Technology-Face Emotion)上的实验结果表明,所提算法具有较高的准确性。展开更多
基金supported by the National Natural Science Foundation of China(No.11402112)the National Key Technology Support Program (No.2012BAA01B02)。
文摘Structural health monitoring(SHM)in-service is very important for wind turbine system.Because the central wavelength of a fiber Bragg grating(FBG)sensor changes linearly with strain or temperature,FBG-based sensors are easily applied to structural tests.Therefore,the monitoring of wind turbine blades by FBG sensors is proposed.The method is experimentally proved to be feasible.Five FBG sensors were set along the blade length in order to measure distributed strain.However,environmental or measurement noise may cover the structural signals.Dual-tree complex wavelet transform(DT-CWT)is suggested to wipe off the noise.The experimental studies indicate that the tested strain fluctuate distinctly as one of the blades is broken.The rotation period is about 1 s at the given working condition.However,the period is about 0.3 s if all the wind blades are in good conditions.Therefore,strain monitoring by FBG sensors could predict damage of a wind turbine blade system.Moreover,the studies indicate that monitoring of one blade is adequate to diagnose the status of a wind generator.
基金Supported by the National Natural Science Foundation of China(61672032,61401001)the Natural Science Foundation of Anhui Province(1408085MF121)the Opening Foundation of Anhui Key Laboratory of Polarization Imaging Detection Technology(2016-KFKT-003)
文摘A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and multi-resolution.An improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features.The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem.The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning.Experimental results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.
文摘The risk of gas leakage due to geological flaws in offshore carbon capture, utilization, and storage, as well as leakage from underwater oil or gas pipelines, highlights the need for underwater gas leakage monitoring technology. Remotely operated vehicles(ROVs) and autonomous underwater vehicles(AUVs) are equipped with high-resolution imaging sonar systems that have broad application potential in underwater gas and target detection tasks. However, some bubble clusters are relatively weak scatterers, so detecting and distinguishing them against the seabed reverberation in forward-looking sonar images are challenging. This study uses the dual-tree complex wavelet transform to extract the image features of multibeam forward-looking sonar. Underwater gas leakages with different flows are classified by combining deep learning theory. A pool experiment is designed to simulate gas leakage, where sonar images are obtained for further processing. Results demonstrate that this method can detect and classify underwater gas leakage streams with high classification accuracy. This performance indicates that the method can detect gas leakage from multibeam forward-looking sonar images and has the potential to predict gas leakage flow.
文摘针对静态表情特征缺乏时间信息,不能充分体现表情的细微变化,该文提出一种针对非特定人的动态表情识别方法:基于动态时间规整(Dynamic Time Warping,DTW)和主动外观模型(Active Appearance Model,AAM)的动态表情识别。首先采用基于局部梯度DT-CWT(Dual-Tree Complex Wavelet Transform)主方向模式(Dominant Direction Pattern,DDP)特征的DTW对表情序列进行规整。然后采用AAM定位出表情图像的66个特征点并进行跟踪,利用中性脸的特征点构建人脸几何模型,通过人脸几何模型的匹配克服不同人呈现表情的差异,并通过计算表情序列中相邻两帧图像对应特征点的位移获得表情的变化特征。最后采用最近邻分类器进行分类识别。在CK+库和实验室自建库HFUT-FE(He Fei University of Technology-Face Emotion)上的实验结果表明,所提算法具有较高的准确性。
