摘要：This paper presents a novel approach for human identification at a distance using gait recognition. Recognition of a person from their gait is a biometric of increasing interest. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis （PCA）, to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. Fourier transform is performed to achieve translation invariant for the gait patterns accumulated from silhouette sequences which are extracted from different circumstances. Kernel PCA is then used to extract higher order relations among the gait patterns for future recognition. A fusion strategy is finally executed to produce a final decision. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles.

摘要：Deformation modulus of a rock mass(E_m) is one of the most important design parameters in construction of rock engineering projects such as underground ***,difficulties are frequently encountered during in-situ tests which are also time-consuming and expensive for determining this *** E_m is often estimated indirectly from proposed equations by different researchers,many of these equations cannot be used in case of problematic rock conditions(thinly bedded,highly jointed rock masses,etc.) as high quality core samples are *** study aims to explore more practical and useful equation for E_m estimation using Rock Quality Designation(RQD) and point load index *** were made between available empirical equations and the proposed E_m equation in terms of the estimation *** comparison tests(ANOVA) showed that E_m can be reliably estimated using proposed equation especially at the preliminary stages of projects.