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The signal-to-noise ratio (SNR) of avalanche photodiode (APD) in optical detection system is greatly influenced by background radiation and operating temperature, so an APD optimal bias voltage compensation method based on machine learning is designed to accurately judge the current laser emission state and APD working state so that dichotomy compensation can be carried out to make APD work in optimal state. By means of cross-verification, the accuracy of judging laser emission state and APD working state is as high as 100% and 99.3% separately, then the number of input variables in the model is reduced appropriately by experiment and the prediction speed of the algorithm is further improved. Finally, road detection application is taken as the experimental background and comparison between the proposed method and the most widely used signal amplitude feedback compensation method is carried out. The results of this study suggest that the proposed APD optimal bias voltage compensation method based on machine learning offers a new and promising approach.
This article was published in the following journal.
Name: ISA transactions
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A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.
Experience-based techniques for problem-solving, learning, and discovery that find a solution which is not guaranteed to be optimal, but sufficient for a given set of goals.
A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.