Deep Learning for Crack-Like Object Detection

Cracks are common defects on surfaces of man-made structures such as pavements, bridges, walls of nuclear power plants, ceilings of tunnels, etc. Timely discovering and repairing of the cracks are of great significance and importance for keeping healthy infrastructures and preventing further damages. Traditionally, the cracking inspection was conducted manually which was labor-intensive, time-consuming and costly. For example, statistics from the Central Intelligence Agency show that the world’s road network length has reached 64,285,009 km, of which the United States has 6,586,610 km. It is a huge cost to maintain and upgrade such an immense road network. Thus, fully automatic crack detection has received increasing attention. With the development of artificial intelligence (AI), the deep learning technique has achieved great success and has been viewed as the most promising way for crack detection. Based on deep learning, this research has solved four important issues existing in crack-like object detection. First, the noise problem caused by the textured background is solved by using a deep classification network to remove the non-crack region before conducting crack detection. Second, the computational efficiency is highly improved. Third, the crack localization accuracy is improved. Fourth, the proposed model is very stable and can be used to deal with a wide range of crack detection tasks. In addition, this research performs a preliminary study about the future AI system, which provides a concept that has potential to realize fully automatic crack detection without human’s intervention

Phase Reversal Diffraction in incoherent light

Phase reversal occurs in the propagation of an electromagnetic wave in a negatively refracting medium or a phase-conjugate interface. Here we report the experimental observation of phase reversal diffraction without the above devices. Our experimental results and theoretical analysis demonstrate that phase reversal diffraction can be formed through the first-order field correlation of chaotic light. The experimental realization is similar to phase reversal behavior in negatively refracting media.Comment: 8 pages, 5 figure

Brake Strategy Analysis for Industrial Normal-closed Brake Based on Rotational Inertia Test and Simulation

Industrial brakes pose the dilemma of weighing brake capability against brake impact since the brake torque cannot be adjusted. On the one hand, the brake torque may be insufficient to stop the movement within a limited distance or parking position. On the other hand, the brake torque may be so high it can damage the transmission chain. In this study, the traditional brake strategy and the field oriented control (FOC) brake strategy were compared through simulation and a rotational inertia test. The influence of the rated brake torque and the open-closed ratio were obtained. Based on the test and simulation results, a semi-empirical formula that defines the relationship between relative brake capability and open-closed ratio was developed. Additional simulations were performed to analyze the performance of the brake in a flexible transmission chain. As an industrial application example, the benefits and the cost of a 'smart brake' based on the FOC brake strategy were analyzed. The results indicate that the equivalent brake torque with the FOC brake strategy is a function of the real-time controllable input and open-closed ratio, which can be conducted during the braking procedure. This can be an efficient way to solve the above problems