Owing to the significantly installed capacity of solar energy during the past decades, the operation and maintenance of the large photovoltaic power station is a big challenge, as the manual inspection is labour‐intensive and expensive. This Letter presents a solution for the intelligent inspection of the hotspot with the unmanned aerial vehicle in the large‐scale photovoltaic plant. First, a traditional image processing method is presented to eliminate the noise and crop the infrared image, which can make the defected feature more obvious. Then, the module is extracted by the line segments detection. Finally, considering the great advance in the realm of the computer vision, a deep‐learning‐based method for automatic hotspot detection is proposed for locating the hotspot with the data augmentation. The deep‐learning‐based model can extract the hotspot feature by the training process in the data set. In the end, the performance of the method proposed is extensively evaluated and the numerical results prove the accuracy and precision of the model.
Large spectral fluctuations bring large errors in laser-induced breakdown spectroscopy (LIBS) analysis, which impedes the improvement of precision and accuracy, limiting the large-scale application and commercialization. In this work, we...
Lead detection is critical for maintaining drinking water safety. Laser-induced breakdown spectroscopy (LIBS) is widely used for elemental analysis, but its sensitivity is reduced in the presence of water. To...
The single sample calibration laser-induced breakdown spectroscopy (SSC-LIBS) is quite suitable for the fields where the standard sample is hard to obtain, including space exploration, geology, archaeology, and jewelry identification. But in practice, the self-absorption effect of plasma destroys the linear relationship of spectral intensity and element concentration based on the Lomakin-Scherbe formula which is the guarantee of the high accuracy of the SSC-LIBS. Thus, the self-absorption effect limits the quantitative accuracy of SSC-LIBS greatly. In this work, an improved SSC-LIBS with self-absorption correction (SSC-LIBS with SAC) is proposed for the promotion of quantitative accuracy of SSC-LIBS. The SSC-LIBS with SAC can correct the intensity ratio of spectral lines in the calculation of SSC-LIBS through relative self-absorption coefficient K without complicated preparatory information. The alloy samples and pressed ore samples were used to verify the effect of the SSC-LIBS with SAC. Compared with SSC-LIBS, for alloy samples, the average RMSEP and average ARE of SSC-LIBS with SAC decreased from 0.83 wt.% and 13.75% to 0.40 wt.% and 4.06%, respectively. For the pressed ore samples, the average RMSEP and average ARE of SSC-LIBS with SAC decreased from 4.77 wt.% and 90.48% to 2.34 wt.% and 14.60%. The experimental result indicates that SSC-LIBS with SAC has a great improvement of quantitative accuracy and better universality compared with traditional SSC-LIBS, which is a mighty promotion of the wide application of SSC-LIBS.
Electrolyte disturbance is very common and harmful, increasing the mortality of critical patients. Hence, rapid and accurate detection of electrolyte levels is vital in clinical practice. Laser-induced breakdown spectroscopy (LIBS) has the advantage of rapid and simultaneous detection of multiple elements, which meets the needs of clinical electrolyte detection. However, the cracking caused by serum drying and the effect of the coffee-ring led to the unstable spectral signal of LIBS and inaccurate detection results. Herein, we propose the ordered microarray silicon substrates (OMSS) obtained by laser microprocessing, to solve the disturbance caused by cracking and the coffee-ring effect in LIBS detection. Moreover, the area of OMSS is optimized to obtain the optimal LIBS detection effect; only a 10 uL serum sample is required. Compared with the silicon wafer substrates, the relative standard deviation (RSD) of the serum LIBS spectral reduces from above 80.00% to below 15.00% by the optimized OMSS, improving the spectral stability. Furthermore, the OMSS is combined with LIBS to form a sensing platform for electrolyte disturbance detection. A set of electrolyte disturbance simulation samples (80% of the ingredients are human serum) was prepared for this platform evaluation. Finally, the platform can achieve an accurate quantitative detection of Na and K elements (Na: RSD < 6.00%, R2 = 0.991; K: RSD < 4.00%, R2 = 0.981), and the detection time is within 5 min. The LIBS sensing platform has a good prospect in clinical electrolyte detection and other blood-related clinical diagnoses.
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