The rapid advancement of deep neural network (DNN) models has enabled their widespread application across various domains, including face recognition and natural language processing. However, data-driven DNN models are prone to erroneous behavior when inadequately trained, necessitating extensive predictive labeling of test data to identify and mitigate defects. Manual labeling, however, remains both labor-intensive and inefficient.
The UOWC system employing 450 nm blue laser with 1.25 Gbps in the 6-meter distance was measured under the various environmental parameters. The transmission channel was improved using the slight reflected angle. This condition can compensate for the attenuation caused by the scattering and absorption in the transmission channel. The UOWC transmission system experiment was carried out under several environmental parameters, including depth of surface turbulence, temperature, and turbidity.
In this brief,
