Variants of Windows XP for embedded systems have different support policies: Windows XP Embedded SP3 and Windows Embedded for Point of Service SP3 were supported until January and April 2016, respectively. Windows Embedded Standard 2009, which was succeeded by Windows Embedded Standard 7, and Windows Embedded POSReady 2009, which was succeeded by Windows Embedded POSReady 7, were supported until January and April 2019, respectively.[116] These updates, while intended for the embedded editions, could also be downloaded on standard Windows XP with a registry hack, which enabled unofficial patches until April 2019. However, Microsoft advised Windows XP users against installing these fixes, citing incompatibility issues.[9][117]
This is one of the most advanced features that Microsoft uses to protect its revenue. Since a Windows license costs money, any PC without a license results in lost earnings for Microsoft. The same applies to any Windows PC user that tries to bypass these licensing restrictions using activation cracks or online license keys.
Windows Embedded Standard 7 Activation Crack
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.
The nuclear reactions analysis technique is mainly based on the relative method or the use of activation cross sections. In order to validate nuclear data for the calculated cross section evaluated from systematic studies, we used the neutron activation analysis technique (NAA) to determine the various constituent concentrations of certified samples for animal blood, milk and hay. In this analysis, the absolute method is used. The neutron activation technique involves irradiating the sample and subsequently performing a measurement of the activity of the sample. The fundamental equation of the activation connects several physical parameters including the cross section that is essential for the quantitative determination of the different elements composing the sample without resorting to the use of standard sample. Called the absolute method, it allows a measurement as accurate as the relative method. The results obtained by the absolute method showed that the values are as precise as the relative method requiring the use of standard sample for each element to be quantified.
The DT program at the Tokamak Fusion Test Reactor (TFTR) created requirements on 14 MeV neutron measurements to measure from 10sup 6 n/cmsup 2 (for triton burnup and Ohmic tritium plasmas) to gt10sup 12 n/cmsup 2 (characteristic of gt10 MW DT plasmas) with an accuracy of 7% (one-sigma).1 To maintain an absolute calibration over this dynamic range with active neutron detectors required one to go from some absolute standard at one fluence level to a measurement at a much higher fluence. Maintaining accuracy requires an extremely linear set of measurements not systematically affected over this dynamic range. Neutron activation canmore provide such linearity when care is taken with a number of effects such as gamma-ray detection efficiency and sample contamination.2 Absolutely calibrated neutron yield measurements using dosimetric (well-known cross section) reactions with thin (low-mass) elemental foils is be described. This technique makes the detector comparison to an absolute standard of gamma-ray activity correspond to all neutron fluences by reducing the sample mass while keeping the activation detectors operating in a linear counting mode; i.e., low count rates which minimize pileup effects. The International Thermonuclear Experimental Reactor is projected to have 1000 s burn durations at fluxes of few 10sup 13 n/cmsup 2s, or more neutron fluence ital per second than entire TFTR discharges. Extrapolating neutron activation to these higher fluences will require yet more care. Some of the issues at such high fluences will be discussed.3 The National Ignition Facility (NIF) is projected to yield 10 MJ of fusion energy, or up to 10sup 12 n/cmsup 2 at the vacuum vessel wall, similar to TFTR DT conditions. It is expected that much interesting physics will be performed at yields far less than those from ignition, covering an even greater dynamic range than needed on TFTR. Thin foil techniques do not have the sensitivity required at low fluences. less
Three standard reference materials: flyash, soil, and ASI 4340 steel, are analyzed by a method of absolute instrumental neutron activation analysis. Two different light water pool-type reactors were used to produce equivalent analytical results even though the epithermal to thermal flux ratio in one reactor was higher than that in the other by a factor of two. 2ff7e9595c
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