{"title":"Analog-Digital Converter with Neural Network and DC-DC Converter for Underwater Solar Charging","authors":"W. Lai","doi":"10.1109/IS3C50286.2020.00042","DOIUrl":null,"url":null,"abstract":"In this article, author describe a time-interleaved analog-to-digital converter (TI-ADC) approach to DC-DC converter and rectifier with resonator for underwater wireless power transfer (UWPT) and underwater solar array charging. The proposed paper investigates the wireless power propagation model for TI-ADC with neural network (NN) based UWPT. The proposed neural network algorithms also can support fault detection for solar array charging. In the proposed calibration TI-ADC, a pilot signal is sent to train the neural network and then the sampled signals could be directly calibrated with the trained network.","PeriodicalId":143430,"journal":{"name":"2020 International Symposium on Computer, Consumer and Control (IS3C)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Symposium on Computer, Consumer and Control (IS3C)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IS3C50286.2020.00042","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this article, author describe a time-interleaved analog-to-digital converter (TI-ADC) approach to DC-DC converter and rectifier with resonator for underwater wireless power transfer (UWPT) and underwater solar array charging. The proposed paper investigates the wireless power propagation model for TI-ADC with neural network (NN) based UWPT. The proposed neural network algorithms also can support fault detection for solar array charging. In the proposed calibration TI-ADC, a pilot signal is sent to train the neural network and then the sampled signals could be directly calibrated with the trained network.