Laser spot is not the only IRT technique for proper crack detection. UET has also been evaluated for this task . This evaluation concludes that UET can effectively detect closed cracks considered undetectable by traditional NDT methods, including optically-stimulated IRT. Thanks to its large area imaging capability, high test productivity and safety, ultrasound IRT is a powerful NDT tool for the inspection of cracks in large aluminum structures. Its disadvantage is the requirement of a coupling element.
The only conclusion I can come up with was that it overheated and cracked, while charging. My iPhone 8plus was also charging when it cracked inside a protected case with absolutely nothing that could get to it in order to crack it
I would have not believed a screen could break without dropping it either but it happened to me too. I put the phone on the charger overnight on the floor beside the plug in between the bed and the wall. The phone was the only thing on the floor where it could not even be reached except by your hand as nothing else could fit there. Yet, somehow when I retrieved it this morning, the screen was cracked inside an otter box case.
Finished solar cells are occasionally found to be defective or faulty. The defects fall into two groups: (i) intrinsic and (ii) extrinsic. Grain boundaries are an example of intrinsic defect, while micro-cracks belong to the second category. The former type of defects diminish the short-circuit current of the cell, and this leads to loss in the efficiency. The latter defects form a class of cracks that are entirely invisible to the naked eye. With dimensions smaller than 30 μm , this type of defect can only be visualized electronically like using the electroluminescence (EL) technique and high-resolution cameras.
In practice, there are various shapes and sizes of micro-cracks in a solar cell depending on how they are formed. For example a line-shaped micro-crack is caused by scratches, and it generally occurs during cell fabrication . This type of defect can also be due to wafer sawing or laser cutting, which propagates and causes the detachment or internal breakage of the grainy materials within the solar cells . In contrast, star-shaped micro-crack is formed due to a sharp point impact which induces several line cracks with a tendency to cross each other . There are other types of micro-crack defects, but these two are the most commonly found in solar cell production. Köntges et al.  reported that there may be a risk of failure for PV modules containing cells that have micro-cracks or other types of defects. Hence, it is important to have high-quality, defect-free cells in the production of PV modules.
For the thoroughness of analysis, the proposed segmentation technique is compared with standard methods such as Otsu's thresholding, the Canny hysteresis, the Sobel edge detector, and the Laplacian of Gaussian (LoG) filter. In addition, a recent method based on Fourier image reconstruction (FIR)  is also implemented. Figure 15 shows the close-up view of the results of these different segmentation techniques using images in Figure 14a (i-iv) as input images. In this case, the ground truth images are plotted manually by an expert human inspector. It can be seen from Figure 15b that the segmentation using Otsu's global thresholding technique is able to detect micro-crack as well as other pixels. Meanwhile, both the Sobel detector and Canny hysteresis thresholding resulted in incomplete or disjointed micro-crack pixels. On the other hand, the LoG is only effective in detecting a limited number of micro-crack pixels, particularly the large ones as evident from Figure 15e. In contrast, the FIR method is accurate when detecting well-defined micro-crack pixels especially the ones appearing like straight lines. This method failed to completely detect star-shaped micro-crack pixels as evident from Figure 15f. In contrast, the results from the proposed segmentation technique are shown in Figure 15g. Clearly, the proposed method is able to detect all shapes and sizes of micro-crack pixels in the image. Close examination of this figures revealed that some unwanted pixels also appeared in the segmented images. They are mostly due to the presence of dark regions in the solar cell. Since their appearance are distinctly different from micro-crack pixels, the use of the ART shape descriptor helped reduce the error resulting from misdetection.
The cpt and crt indices calculated from defected cell images in Figure 15 are tabulated in Table 1. These indices are also calculated for the remaining 110 defected cells which are not shown in this paper. The average values are listed in the last column of Table 1. Referring to this table, the completeness of Otsu's method is the highest compared to other algorithms. But this is not the case for correctness as the crt index for this algorithm is the second lowest. Consequently, Otsu's method reconstructs many micro-crack pixels as well as noise as evident visually in the examples in Figure 15. As expected, the Sobel edge detection and Canny hysteresis methods produce only average results for both completeness and correctness. The same trend is observed for the FIR method. In contrast, the LoG filter produces the lowest cpt and crt scores, suggesting that this method does not correctly or completely detect micro-crack pixels. Meanwhile, the proposed segmentation technique yields the highest crt and the second highest cpt scores. This result suggests that this method has the ability to completely and correctly characterize micro-crack with small amount of noise.
Shape analysis is performed in order to primarily distinguish between micro-crack and other arbitrary pixels. This is due to the fact that the micro-crack pixels form shapes which are visually distinct like line or star patterns. On the other hand, shapes formed by the spurious intensity variation or gray level discontinuities produce arbitrarily patterns which are also detected by the proposed image processing algorithm. In doing so, the ART shape descriptor discussed earlier in Section 2.4 is implemented. The algorithm is evaluated using 114 defected and 126 intact cells. Altogether, 5,598 shapes have been detected of which 218 belong to the micro-crack category and the remaining are arbitrary patterns. The ART is applied to these shapes, and the results are visualized in principal component plots in Figure 20a. In this case, only the first two dominant components, i.e., first and second components, are used in the visualization.
In the present work, an image processing model that automatically detects and analyzes cracks on the surfaces of building elements in the digital image is established. The proposed model does not only automatically recognize crack pixels out of image background but also perform various measurements of crack characteristics including the area, perimeter, width, length, and orientation. At the center of the proposed model, an image enhancement algorithm called Min-Max Gray Level Discrimination (M2GLD) is put forward as a preprocessing step to improve the Otsu binarization approach, followed by shape analyses for meliorating the crack detection performance. The crack detected by the proposed approach was compared with that acquired by the conventional technique. The experimental results show that the crack on various structure surfaces can be accurately recognized and analyzed using the proposed image processing model. The paper is organized as follows: the next section reviews previous works pertinent to the current study; the third section describes the improved Otsu method based on the M2GLD, followed by the proposed image processing model for the detection of surface crack; the model experimental results are reported in the fifth section; and the final section provides some conclusions of the study.
April 4, 2022: Yet some more bug fixes by Andreas Wank. March 2, 2022: Some bug fixes by Andreas Wank. August 17, 2019: Support up to Matlab R2019a(beta). Simulink model warnings fixed by Behnam Tamimi. June 7, 2016: Support for Matlab R2016a(beta). Several GUIs' bugs have been fixed by Hantao Cui. May 26, 2016: PSAT version 2.1.10. Compatible with Matlab R2014b, R2015a and R2015b. Some bugs have been fixed. September 6, 2014: PSAT version 2.1.9. PSAT Documentation available for purchase. Compatible with Matlab R2014a and syntax consolidation. Fixed a few bugs. January 6, 2013: PSAT version 2.1.8. Added 2 compact solar photo-voltaic generator models (thanks to B. Tamimi and C. Cañizares). Fixed a few bugs. July 30, 2012: PSAT version 2.1.7. Compatible with Matlab 7.14 (R2012a). Added 4 hydro turbine models (thanks to W. Li and L. Vanfretti). Fixed the exponential recovery load model and several other bugs. May 13, 2010: PSAT version 2.1.6. Yet another minor release. Compatible with Matlab 7.10 (R2010a). November 1, 2009: PSAT version 2.1.5. Yet another minor release. June 17, 2009: PSAT version 2.1.4. Minor release that fixes a few bugs. April 21, 2009: PSAT version 2.1.3. Minor release that consolidates device classes and fixes several bugs. June 26, 2008: PSAT version 2.1.2. Minor release that fixes some bugs in the CPF analysis. June 18, 2008: PSAT version 2.1.1. Minor release that fixes a bug in the AVR class. June 16, 2008: PSAT version 2.1.0. PSAT can run on any Matlab version back to 5.3 and on GNU Octave. The GUI for 3D visualization for static and dynamic power system analyses has been completed. The library for Numerical Linear Analysis has been completely rewritten and split into a Linear Analysis library and a Numerical Differentiation library. A filter for the ODM (open data model) has been included. The GUIs have been optimized for running on Quartz (Mac OS X) and X11 (Linux) graphical systems. February 2, 2008: PSAT version 2.0.0. The whole PSAT code has been rethinked and rewritten using classes and object oriented programming techniques. Each device is defined by a class with attributes and methods. The algorithms of PF, CPF, OPF, SSSA and TD have been rewritten, improved and made more robust. The Simulink library has been renewed using \"physical\" components. This avoids the directionality of control blocks and allows producing high quality network schemes. Added the status field to most components. A component can be put on-line or off-line by toggling its status. New more reliable versions of TCSC, SSSC and UPFC devices and Power Oscillations damper model has been provided by H. Ayres, M. S. Castro and A. Del Rosso. The HVDC model has also been rewritten. Several new filters for data format conversion have been added. Most filters has been provided by J. C. Morataya. PSAT has been tested with very large static and dynamic networks (up to 15000 buses). The logo of PSAT has been changed. November 20, 2007: PSAT version 2.0.0 beta 4. Fully class-based version. 3D visualization of power systems. Several components and models have been completely revised and rewritten. This is an almost stable version and is only compatible with Matlab 7.0 or newer (no GNU/Octave compatibility). March 8, 2007: PSAT version 2.0.0 beta 3 . This is still a development version and is only compatible with Matlab 7.0 or newer (at the moment this version is NOT compatible with Octave). Further class development. Several improvements. December 14, 2006: PSAT version 2.0.0 beta 2. This is a development version and is only compatible with Matlab 7.0 or newer (at the moment this version is NOT compatible with Octave). Further class development. Introduced the connection status for several comments. Several improvements. March 24, 2006: PSAT version 2.0.0 beta 1. This is a development version and is only compatible with Matlab 7.0 or newer (at the moment this version is NOT compatible with Octave). First version of PSAT which uses classes. New more reliable FACTS models and new Power Oscillation Damper model for FACTS devices (by H. Ayres, M. S. Castro and A. Del Rosso). New Simulink library with physical components. New filters for data format conversion (by J. C. Morataya). Improved PF, CPF, OPF, SSSA and TD algorithms. Tested on a 15000 bus network. July 14, 2005: PSAT version 1.3.4. Added multiperiod market clearing model for the PSAT-GAMS interface and many other improvements. January 26, 2005: PSAT version 1.3.3. Minor release with a few bug-fixes and a revised documentation. October 8, 2004: PSAT version 1.3.2. First version tested on Matlab 7 (R14). New Physical Model Component (PMC) Simulink Library. Several bug fixes and improvements. August 2, 2004: PSAT version 1.3.1. Numeric Linear Analysis library by Alberto del Rosso. New model for direct drive synchronous generator wind turbine. PSS/E 29 filter. Several bug fixes and improvements. May 2, 2004: PSAT version 1.3.0. Added a command line version and basic compatibility with GNU/Octave. New wind turbine models and bus frequency measurement. Several bug fixes and improvements. November 25, 2003: PSAT version 1.2.2. Several bug fixes and improvements. Added utilities to convert data files into BPA format and to export PF results to MS Excel sheets and to LaTeX tables. September 11, 2003: PSAT version 1.2.1. Includes previous patch and several other bug fixes. August 31, 2003: PSAT version 1.2.0. Matlab version independent. Several bugs and typos were removed thanks to Liulin. August 16, 2003: Created the PSAT Forum (available at ). August 1, 2003: PSAT version 1.1.0. Many addings (GAMS and UWPFLOW interfaces, phase shifting transformer, etc.), improvements and bugs fixing. November 11, 2002: PSAT version 1.0.0. 153554b96e