In order to comply with European antitrust regulations, Microsoft proposed the use of a "ballot" screen containing download links to competing web browsers, thus removing the need for a version of Windows completely without Internet Explorer, as previously planned.[212] Microsoft announced that it would discard the separate version for Europe and ship the standard upgrade and full packages worldwide, in response to criticism involving Windows 7 E and concerns from manufacturers about possible consumer confusion if a version of Windows 7 with Internet Explorer were shipped later, after one without Internet Explorer.[213]
Abstract:The data collection of Acoustic Emission (AE) method is typically based on threshold-dependent approach, where the AE system acquires data when the output of AE sensor is above the pre-defined threshold. However, this approach fails to detect flaws in noisy environment, as the signal level of noise may overcome the signal level of AE from flaws, and saturate the AE system. Time-dependent approach is based on streaming waveforms and extracting features at every pre-defined time interval. It is hypothesized that the relevant AE signals representing active flaws are embedded into the streamed signals. In this study, a decomposition method of the streamed AE signals to separate noise signal and crack signal is demonstrated. The AE signals representing fatigue crack growth in steel are obtained from the laboratory scale testing. The streamed AE signals in a noisy operational condition are obtained from the gearbox testing at the Naval Air Systems Command (NAVAIR) facility. The signal addition and decomposition is achieved to determine the minimum detectable signal to noise ratio that is embedded into the streamed AE signals. The developed decomposition approach is demonstrated on detecting burst signals embedded into the streamed signals recorded in the spline testing of the helicopter gearbox test rig located at the NAVAIR facility.Keywords: Acoustic Emission (AE); streamed signals; laboratory scale testing; gearbox spline
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Image classification: The most typical CNN approaches to perform road damage detection and classification tasks are usually trained by designing a neural network consisting of convolutional and fully connected (FC) layers. For example, An et al. [31] classified images into two types with or without potholes by replacing the backbone feature extraction network in CNN and comparing the accuracy of different backbone networks in colour and colour grayscale frames in a cross-sectional manner. Bhatia et al. [32] developed a method to predict whether an input thermal image is a pothole or a non-pothole, demonstrating that using the residual network as the backbone network can improve the model detection rate applied in night-time and foggy weather environments. Fan et al. [33] experimentally evaluated 30 CNNs for road crack image classification, where Progressive neural architecture search (PNASNet) achieved the best balance between speed and accuracy. However, the image classification only presents the object image and does not detect the details of road damage in the image.
Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.
Cracks are common distresses in both concrete and asphalt pavements. Different types of cracks can be observed due to different causes: road surface aging, climate, and traffic load. The methods currently used for road and airport pavement management system (PMS) [1,2] generally used for the classification of cracks provided by Shahin [3] and adopted by the international standard American Society for Testing and Materials (ASTM) [4]. The classification is defined on crack characteristic and causes as listed in Table 1 and Figure 1.
Recently, with the development of machine learning classified as deep learning inspired by structure of the brain called artificial neural networks (ANN) [45], many algorithms have been proposed to perform object detection and image classification tasks. ANN is employed to solve many civil engineering problems [46,47,48,49,50]. Gao and Mosalam in [51] applied the transfer learning to detect damage images with structural method, and this method can reduce the computational cost by using the pre-trained neural network model. Meanwhile, the author needs to fine the neural network to perform the crack detection. Local patch information was employed to inspect crack information by convolutional neural networks (CNN) in [52]. In CrackNet [53], the algorithm improved pixel-perfect accuracy based on CNN by discarding pooling layers. In CrackNet-R [54], a recurrent neural network (RNN) is deployed to perform automatic crack detection on asphalt road. Cha et al. [55] adopted a sliding windows based on CNN to scan and detect road crack. Fan et al. in [56] proposed a structured prediction method to detect crack pixels with CNN. The small structured pixel images (27 27 pixels) was input into the neural network, which may generate overload for the computer memory. Ensemble network is proposed to perform crack detection and measure pavement cracks generated in road pavement [57]. Maeda et al. on [58] adopted object detection network architecture to detect crack images, and the network architecture can be transferred to a smartphone to perform road crack detection. Cha et al. used the Faster-RCNN to inspect road cracks [59]. Yang et al. in [60] adopted a fully convolutional network (FCN) to inspect road pavement cracks at pixel level, which can perform crack detection by end-to-end training. Li et al. in [61] employed the you-only-look-once v3 (YOLOv3)-Lite method to inspect the aircraft structures, and the depth wise separable convolution and feature pyramid were adopted to design the network architecture and joined the low- and high-resolution for crack detection. Jenkins et al. presented an encoder-decoder architecture to perform road crack detection, and the function of the encoder and decoder layers are used to reduce the size of input image to generate lower level feature maps, and obtain the resolution of the input data with up-sampling, respectively [62]. Tisuchiya et al. proposed a data augmentation method based on YOLOv3 to perform crack detection, which can increase the accuracy effectively [63].
Then, a multi-dilation module (MDM) is designed, which is embedded into an encoder-decoder architecture to obtain cracks features of multiple context sizes. The crack features of multiple context size can be integrated into multi-dilation module by dilation convolution with different dilation rates, which can obtain much more cracks information. Next, hierarchical feature (HF) learning module is designed to obtain multi-scale feature from the high- to low- level convolutional layers. The single-scale features of each convolutional stage are used to predict pixel-wise crack detection at side output.
One sample was also taken from a sash that has a full early stratigraphy with six early generations of cream or light gray paint like the north elevation weatherboards. As with the weatherboard samples, there is evidence here of some colored pigment particles embedded in the early generations. Above generation five on the sash is a white paint that appears to contain zinc white pigment, which would indicate that it postdates 1845. This layer was not found on other window samples and is probably a later layer that flowed down a crack in the layers above. It does seem to have an appearance and color similar to generation seven.
To determine whether some elements on the exterior were indeed picked out in a different color in the eighteenth century, several color measurements were taken from the first-generation paint in uncast samples from the weatherboards, windows, and elements of the porch. The paint on all the elements was found to be fairly close in color. Before light bleaching, none of the averages of the first-generation paint measurements in any of the samples was different by ΔE value of more than 6.00. After light bleaching, the greatest difference between the average measurements of any two first-generation paints was no greater than ΔE value of 4.23. (Generally, ΔE value of around 2 to 4 is considered acceptable in the color printing industry for standard color deviation.) This slight discrepancy in color seems more likely to be due to natural variation in hand-ground paint and aging conditions than to an intentional color difference. Thus, this examination of the first-generation paint on the exterior of the Finnie House suggests that the house was originally painted with a monochromatic scheme quite unlike the current multi-colored scheme.
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