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A robust stability assessment approach is presented to efficiently estimate eigenvalues in microgrids in the presence of bounded uncertainties. Through this method, all possible locations of eigenvalues can be directly obtained, which makes repeatedly eigenvalue calculation unnecessary when dealing with uncertainties. More importantly, a quasi-diagonalization technique is established to reduce the...
In this paper multilinear mean component analysis (MMCA) is introduced as a new algorithm for gait recognition. Compared with traditional PCA and MPCA, the new method MMCA is based on dimensionality reduction by preserving the squared length, and implicitly also the direction of the mean vector of the each mode's original input. The solution is not necessarily corresponding to the top eigenvalues...
We show theoretically and experimentally that photonic lattices constructed from random components residing on a ring in momentum space are amorphous, yet they exhibit a bandgap, and support linear and nonlinear defect-state guidance.
The paper puts forward a new method to identify similar C codes based on weighted attributes eigenvector. According to the characteristics of physical and structure attributes of C codes, the weighing theory of attributes eigenvector is adopted to strengthen the infection of attribute elements with covert codes, which are based on the theory and method about existing kinds of attribute eigenvector...
Log image was acquired by X-ray real-time digital imaging system without log destruction. This paper presents the average density value of the specific spot of log is measured quickly and exactly according to log perimeter and log image information using the method of artificial neural network. According to the basic knowledge of X-ray testing technique, the method of getting high quality digital...
Effective and robust visual tracking is one of the most important tasks for the intelligent visual surveillance. In this paper, we proposed a novel method for detecting and tracking moving people using the spatiotemporal latent semantic cues and the incremental eigenspace tracking techniques. During tracking process, the target appearance model is incrementally learned in low dimensional tensor eigenspace...
Contrasting to the original method of identifying the types of wood defects which requires the experienced technical staff with good discrimination to consider the characteristics of wood defects in the image, this paper presents a new method which can identify the types of internal wood defects rapidly and accurately by BP neural network which can analyse the visual characteristics parameters of...
We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and compare it against several competing techniques: Principal Component Analysis (PCA), Kernel PCA (KPCA), and linear local pooling in classification problems. We evaluate the classification performance of the nearest-neighbor rule...
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