Semi-automated detection of surface degradation on bridges based on a level set method

Due to the effect of climate factors, natural phenomena and human usage, buildings and infrastructures are subject of progressive degradation. The deterioration of these structures has to be monitored in order to avoid hazards for human beings and for the natural environment in their neighborhood. Hence, on the one hand, monitoring such infrastructures is of primarily importance. On the other hand, unfortunately, nowadays this monitoring effort is mostly done by expert and skilled personnel, which follow the overall data acquisition, analysis and result reporting process, making the whole monitoring procedure quite expensive for the public (and private, as well) agencies. This paper proposes the use of a partially user-assisted procedure in order to reduce the monitoring cost and to make the obtained result less subjective as well. The developed method relies on the use of images acquired with standard cameras by even inexperienced personnel. The deterioration on the infrastructure surface is detected by image segmentation based on a level sets method. The results of the semi-automated analysis procedure are remapped on a 3D model of the infrastructure obtained by means of a terrestrial laser scanning acquisition. The proposed method has been successfully tested on a portion of a road bridge in Perarolo di Cadore (BL), Italy

Nondestructive evaluation of ceramic and metal matrix composites for NASA's HITEMP and enabling propulsion materials programs

In a preliminary study, ultrasonic, x-ray opaque, and fluorescent dye penetrants techniques were used to evaluate and characterize ceramic and metal matrix composites. Techniques are highlighted for identifying porosity, fiber alignment, fiber uniformity, matrix cracks, fiber fractures, unbonds or disbonds between laminae, and fiber-to-matrix bond variations. The nondestructive evaluations (NDE) were performed during processing and after thermomechanical testing. Specific examples are given for Si3N4/SiC (SCS-6 fiber), FeCrAlY/Al2O3 fibers, Ti-15-3/SiC (SCS-6 fiber) materials, and Si3N4/SiC (SCS-6 fiber) actively cooled panel components. Results of this study indicate that the choice of the NDE tools to be used can be optimized to yield a faithful and accurate evaluation of advanced composites

Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks

Quantized Neural Networks (QNNs), which use low bitwidth numbers for representing parameters and performing computations, have been proposed to reduce the computation complexity, storage size and memory usage. In QNNs, parameters and activations are uniformly quantized, such that the multiplications and additions can be accelerated by bitwise operations. However, distributions of parameters in Neural Networks are often imbalanced, such that the uniform quantization determined from extremal values may under utilize available bitwidth. In this paper, we propose a novel quantization method that can ensure the balance of distributions of quantized values. Our method first recursively partitions the parameters by percentiles into balanced bins, and then applies uniform quantization. We also introduce computationally cheaper approximations of percentiles to reduce the computation overhead introduced. Overall, our method improves the prediction accuracies of QNNs without introducing extra computation during inference, has negligible impact on training speed, and is applicable to both Convolutional Neural Networks and Recurrent Neural Networks. Experiments on standard datasets including ImageNet and Penn Treebank confirm the effectiveness of our method. On ImageNet, the top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7\%, which is superior to the state-of-the-arts of QNNs

Characterisation of spatial network-like patterns from junctions' geometry

We propose a new method for quantitative characterization of spatial network-like patterns with loops, such as surface fracture patterns, leaf vein networks and patterns of urban streets. Such patterns are not well characterized by purely topological estimators: also patterns that both look different and result from different morphogenetic processes can have similar topology. A local geometric cue -the angles formed by the different branches at junctions- can complement topological information and allow to quantify the large scale spatial coherence of the pattern. For patterns that grow over time, such as fracture lines on the surface of ceramics, the rank assigned by our method to each individual segment of the pattern approximates the order of appearance of that segment. We apply the method to various network-like patterns and we find a continuous but sharp dichotomy between two classes of spatial networks: hierarchical and homogeneous. The first class results from a sequential growth process and presents large scale organization, the latter presents local, but not global organization.Comment: version 2, 14 page