Error correction algorithm for grating Moiré fringes based on QM-ANN

Chang, Li; Lu, Qiuyue; Guo, Yumei; Zhou, Bo; Xiu, Guoyi

doi:10.1016/j.measurement.2024.114155

Measurement

2024

DOI: 10.1016/j.measurement.2024.114155

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Error correction algorithm for grating Moiré fringes based on QM-ANN

Li Chang,

Qiuyue Lu,

Yumei Guo

et al.

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2024

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Cited by 2 publications

References 14 publications

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Research on grating nano-measurement algorithm based on TCN

Chang,

Li,

Zhou

et al. 2024

Meas. Sci. Technol.

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In order to compensate for the errors caused by disturbing factors such as temperature, humidity, vibration and pressure in the environment during the measurement of grating sensor, a measurement algorithm based on an improved temporal convolutional network (TCN) is proposed. The environmental signals are first collected to construct the data set, and then the improved TCN model with S-shaped rectified linear activation unit activation function is used to subdivide the grating sensor signals and compensate the environmental errors. Experimental results show that in the training of the data set with the least error reduction, the error of the original TCN after compensation is about 4.52 nm, the error of the improved TCN after compensation is about 3.46 nm. Therefore, the improved TCN reduces the error by at least about 23.5%. Compared with the other same type of algorithm, the improved TCN can reduce the error in the verification set by at least 44.3%, which proves the feasibility and effectiveness of the proposed error compensation algorithm, and lays a certain foundation for the realization of ultra-precision measurement of gratings.

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Research on grating nano-measurement algorithm based on TCN

Chang,

Li,

Zhou

et al. 2024

Meas. Sci. Technol.

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Use of artificial neural network to assess rural anthropization impacts

Santana,

Santos,

Carvalho

et al. 2024

Rev. Bras. Geog. Fis.

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This study evaluated the environmental conditions in different land occupation types in an urbanized rural area, compared their microclimates, and described their characteristics using a computational algorithm that assigned an environmental quality class for each area. The experiment was carried out in the city of Dourados-MS, Brazil, at the Federal University of Grande Dourados, between the summer of 2020 and winter of 2021. Temperature and relative air humidity data were collected to estimate temperature and humidity index (THI) during 40 days of winter (cold) and 40 days of summer (heat). The data were collected by wireless datalogger systems installed in the nine microenvironments evaluated plus INMET information. Secondly, a logical-mathematical model was developed involving an Artificial Neural Network to classify the scenarios (the environments) according to THI and human well-being index (HWBI). The proposed neural network was composed of an input layer with twelve neurons, a hidden layer with eighteen neurons, and an output layer with five neurons. The system proved to be efficient, with about 90% accuracy in its training and 80% in testing phase. As the first complex architecture built for multi-class classification of environmental comfort, the algorithm well reflected the studied environments, encompassing the interactions between natural resources and built spaces.

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Error correction algorithm for grating Moiré fringes based on QM-ANN

Cited by 2 publications

References 14 publications

Research on grating nano-measurement algorithm based on TCN

Research on grating nano-measurement algorithm based on TCN

Use of artificial neural network to assess rural anthropization impacts

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