On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing

机译：人工神经网络补偿波长感测非线性的能力

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An intelligent sensor for light wavelength readout, suitable for visible range optical applications, has been developed. Using buried triple photo-junction as basic pixel sensing element in combination with artificial neural network (ANN), the wavelength readout with a full-scale error of less than 1.5% over the range of 400 to 780 nm can be achieved. Through this work, the applicability of the ANN approach in optical sensing is investigated and compared with conventional methods, and a good compromise between accuracy and the possibility for on-chip implementation was thus found. Indeed, this technique can serve different purposes and may replace conventional methods.

机译：已经开发出一种适用于可见光范围光学应用的光波长读出智能传感器。结合人工神经网络（ANN），使用掩埋三重光电结作为基本像素感测元件，可以实现在400至780 nm范围内具有小于1.5％的满量程误差的波长读数。通过这项工作，对ANN方法在光学传感中的适用性进行了研究，并将其与常规方法进行了比较，从而发现了精度与片上实现可能性之间的良好折衷。实际上，该技术可以用于不同的目的，并且可以代替常规方法。

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期刊名称 Sensors (Basel Switzerland)
作者
Mohamed Lamine Hafiane; Zohir Dibi; Otto Manck;
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作者单位

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年(卷),期 2009(9),4
年度 2009
页码 2884–2894
总页数 11
原文格式 PDF
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关键词
Buried photo PN junctions Artificial Neural Network wavelength measurement;

机译：埋入式光电PN结;人工神经网络;波长测量;

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专利

1. On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing [J] . Mohamed Lamine Hafiane, Otto Manck, Zohir Dibi Sensors . 2009,第4期

机译：人工神经网络补偿波长传感非线性的能力
2. PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Convolutional Neural Networks [J] . Journal of hydrometeorology . 2019,第12期

机译：Persiann-CNN：使用人工神经网络 - 卷积神经网络从远程感测信息的降水估计
3. Comparing the Prediction Capabilities of Artificial Neural Network (ANN) and Nonlinear Regression Models in Pet-Poy Yarn Characteristics and Optimization of Yarn Production Conditions [J] . Kenan Y?ld?r?m, Hamdi Ogut, Yusuf Ulcay Journal of Engineered Fibers and Fabrics . 2017,第3期

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4. Natural neural networks for quantitative sensing of neurochemicals: an artificial neural network anlsysi [C] . Christiane Xiegler, Annette Harsch, Wolfgang Gopel International meeting on chemical sensors . 1998

机译：神经化学定量传感的自然神经网络：人工神经网络分析
5. Thyristor controlled series compensated transmission lines protection using artificial neural networks based symmetrical components analyzer. [D] . Elmoudi, Ramadan. 2013

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6. Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model nonlinear autoregressive exogenous artificial neural networks and Wood’s model [O] . Wilhelm Grzesiak, Daniel Zaborski, Iwona Szatkowska, 2021

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7. On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing [O] . Hafiane, Mohamed Lamine, Dibi, Zohir, Manck, Otto 2009

机译：人工神经网络补偿波长感测非线性的能力

On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing

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