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- W4310346534 abstract "Convolutional neural networks (CNNs) have proven to be highly efficient in performing arduous tasks such as image reconstruction, image classification, etc. on multimode fiber (MMF) with intensity-based detection. In this work, a CNN model known as AlexNet has been employed for weight-location recognition analysis by performing an image classification task. A simple experiment is performed in which random weights in the range 0.5 kg-3 kg are applied on the plastic optical fiber (POF) at six unequally-spaced, pre-determined locations along the length of the POF for data acquisition comprising of specklegram images. The experiment is repeated for 1 m, 2 m and 3 m fiber length. These images are split into training, validation and test dataset in the ratio of 80 %, 10 % and 10 % respectively. The images in the training dataset are employed for training the model, whereas the validation dataset is used for validating the model. The model makes output predictions of weight-location on the test dataset with optimal recognition (classification) accuracy. The recognition accuracy of 100 % for 1 m and 2 m, whereas 99.8 % accuracy is achieved for 3 m. The results suggest that there is a low loss in recognition accuracy with the increase in fiber lengths. Hence, there is a negligible impact of fiber length on recognition accuracy." @default.
- W4310346534 created "2022-12-09" @default.
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- W4310346534 date "2023-01-01" @default.
- W4310346534 modified "2023-09-30" @default.
- W4310346534 title "Weight-location recognition in a plastic optical fiber using a convolutional neural network" @default.
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- W4310346534 doi "https://doi.org/10.1016/j.yofte.2022.103166" @default.
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