On covariate factor detection and removal for robust gait recognition

We propose a novel bolt-on module capable of boosting the robustness of various single compact 2D gait representations. Gait recognition is negatively influenced by covariate factors including clothing and time which alter the natural gait appearance and motion. Contrary to traditional gait recognition, our bolt-on module remedies this by a dedicated covariate factor detection and removal procedure which we quantitatively and qualitatively evaluate. The fundamental concept of the bolt-on module is founded on exploiting the pixel-wise composition of covariate factors. Results demonstrate how our bolt-on module is a powerful component leading to significant improvements across gait representations and datasets yielding state-of-the-art results.

FE simulation and experimental tests of high-strength structural bolts under tension

Experimental tests have been completed for high-strength 8.8 bolts for studying their mechanical performance subjected to tensile loading. As observed from these tests, failure of structural bolts has been identified as in one of two ways: threads stripping and necking of the threaded portion of the bolt shank, which is possibly due to the degree of fit between internal and external threads. Following the experimental work, a numerical approach has been developed for demonstration of the tensile performance with proper consideration of tolerance class between bolts and nuts. The degree of fit between internal and external threads has been identified as a critical factor affecting failure mechanisms of high-strength structural bolts in tension, which is caused by the machining process. In addition, different constitutive material laws have been taken into account in the numerical simulation, demonstrating the entire failure mechanism for structural bolts with different tolerance classes in their threads. It is also observed that the bolt capacities are closely associated with their failure mechanisms.