Marine instruments deployed in seawater inevitably experience biofouling, which severely reduces their service life and hinders ocean monitoring. Marine biofouling greatly affects the service life of marine optical instruments and thus has a detrimental impact on ocean monitoring. The fouling community exhibits an attachment succession phenomenon. Macroscopic fouling organisms have adherent and stubborn attachments, whereas microorganisms during early fouling stages are easy to remove, but excessive cleaning also greatly increases energy consumption. Therefore, monitoring biofouling and selecting appropriate removal timing is critical. Due to the complex and dynamic nature of the marine environment, in-situ detection of microbial fouling on optical window of marine optical instrument is challenging because of many factors such as target characteristics, seawater turbidity, light refraction and scattering. Currently, there are no mature technologies available for in-situ fouling detection so as to remove timely micro fouling. To solve this problem, this study deployed thin poly methyl methacrylate (PMMA) coupons within the coastal seawaters of Qingdao, followed by in-situ mapping of photoacoustic signals using a self-built excitation and detection platform, along with along with of transmittance spectrum analysis on fouled PMMA thin films using PerkinElmer LAMBDA750. By combining results from both techniques with microscopic morphology analysis, we explored the relationship between microbial fouling and photoacoustic signal. The research results will provide a novel approach and technical basis for in-situ detection and timely clearance of microbial fouling on optical windows of marine optical instruments.
This paper researches a grid-optimized DBSCAN algorithm called Grid-DBSCAN. Different from the traditional DBSCAN algorithm, we introduce a square grid array with side length ε. The whole study area is divided twice into several grids to study the computation. In this algorithm, any data point only needs to make a distance operation with the data points in the range of four different 3*3 grids that it may be in. This significantly reduces the computational complexity and required runtime of the algorithm. In addition, the two different center positions of the grid partition ensure the transitivity of the clustering algorithm and reduce the possibility of data points being misclassified. The simulation results indicate that the Grid-DBSCAN algorithm can effectively handle various clustering conditions. Both the computational time and clustering performance are superior to those of traditional DBSCAN and K-means algorithms.
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