To accurately obtain the tensile strength of rock and fully understand the evolution process of rock failure is one of the key issues to the research of rock mechanics theories and rock mass engineering applications. Using direct tensile, Brazilian splitting, and three‐point bending test methods, we performed indoor and numerical simulation experiments on marble, granite, and diabase and investigated the tensile strength and damage evolution process of several typical rocks in the three different tests. Our experiments demonstrate that (1) the strength is about 10% greater in the Brazilian splitting than in the direct tensile, while the tensile modulus is lower; it is the highest in the three‐point bending, which is actually subjected to the bending moment and suggested as one of the indexes to evaluate the tensile strength of rock; (2) the strength in splitting tests is strikingly different, while the strain law is basically similar; the direct tensile test with precut slits is more attainable than that with no‐cut slits, with an uninfluenced strength; (3) the failure modes of rocks using different methods are featured by different lithology, while their final modes are basically the same under the same method; (4) PFC and RFPA numerical simulation tests are effective to analyze the internal crack multiplication and acoustic emission changes in the rock as well as the damage evolution process of rock in different tests.

Most existing ICP (Iterative Closet Point)-based 3D ear recognition approaches resort to the coarse-to-fine ICP algorithms to match 3D ear models. With such an approach, the gallery-probe pairs are coarsely aligned based on a few local feature points and then finely matched using the original ear point cloud. However, such an approach ignores the fact that not all the points in the coarsely segmented ear data make positive contributions to recognition. As such, the coarsely segmented ear data which contains a lot of redundant and noisy data could lead to a mismatch in the recognition scenario. Additionally, the fine ICP matching can easily trap in local minima without the constraint of local features. In this paper, an efficient and fully automatic 3D ear recognition system is proposed to address these issues. The system describes the 3D ear surface with a local feature—the Local Surface Variation (LSV), which is responsive to the concave and convex areas of the surface. Instead of being used to extract discrete key points, the LSV descriptor is utilized to eliminate redundancy flat non-ear data and get normalized and refined ear data. At the stage of recognition, only one-step modified iterative closest points using local surface variation (ICP-LSV) algorithm is proposed, which provides additional local feature information to the procedure of ear recognition to enhance both the matching accuracy and computational efficiency. On an Inter^®Xeon^®W3550, 3.07 GHz work station (DELL T3500, Beijing, China), the authors were able to extract features from a probe ear in 2.32 s match the ear with a gallery ear in 0.10 s using the method outlined in this paper. The proposed algorithm achieves rank-one recognition rate of 100% on the Chinese Academy of Sciences’ Institute of Automation 3D Face database (CASIA-3D FaceV1, CASIA, Beijing, China, 2004) and 98.55% with 2.3% equal error rate (EER) on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2, University of Notre Dame, South Bend, IN, USA, between 2003 and 2005).

Electrical resistance strain gauges are widely used in asymmetric structures for measurement and monitoring, but their thermal output in changing temperature environments has a significant impact on the measurement results. Since thermal output is related to the coefficient of thermal expansion of the strain gauge’s sensitive grating material and the measured object, the temperature self-compensation technique of strain gauges fails to eliminate the additional strain caused by temperature because it cannot match the coefficient of thermal expansion of various measured objects. To address this problem, in this study, the principle of the thermal output of electrical resistance strain gauges was analyzed, a calibration experiment for thermal output in the case of a mismatch between the coefficient of linear expansion of the measured object and the strain gauge grating material was conducted, and the mechanism for temperature influence on thermal output was revealed. A method was proposed to obtain the thermal output curves for different materials by using thermostats with dual temperatures to conduct temperature calibration experiments. A linear regression method was used to obtain a linear formula for the thermal output corresponding to each temperature. The thermal output conversion relationship was derived for materials with different coefficients of linear expansion. An in situ temperature compensation technique for electrical resistance strain gauges that separates the measured strain into thermal and mechanical strains was proposed. The results showed that the thermal output curve for the measured object can be calibrated in advance and then deducted from the measured strain, thus reducing the influence of temperature-induced additional strain on the mechanical strain. In addition, a new method was provided for the calculation of the thermal output among materials with similar coefficients of linear expansion, providing a reference for the health monitoring of asymmetric structures.

We propose an ear recognition system based on 2D ear images which includes three stages: ear enrollment, feature extraction, and ear recognition. Ear enrollment includes ear detection and ear normalization. The ear detection approach based on improved Adaboost algorithm detects the ear part under complex background using two steps: offline cascaded classifier training and online ear detection. Then Active Shape Model is applied to segment the ear part and normalize all the ear images to the same size. For its eminent characteristics in spatial local feature extraction and orientation selection, Gabor filter based ear feature extraction is presented in this paper. Kernel Fisher Discriminant Analysis (KFDA) is then applied for dimension reduction of the high-dimensional Gabor features. Finally distance based classifier is applied for ear recognition. Experimental results of ear recognition on two datasets (USTB and UND datasets) and the performance of the ear authentication system show the feasibility and effectiveness of the proposed approach.