This paper describes application of Artificial Intelligence using machine learning and deep learning at our laser diode module manufacturing facility. Implementing A.I. into data analysis and classification problems, ...
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ISBN:
(纸本)9781728133379
This paper describes application of Artificial Intelligence using machine learning and deep learning at our laser diode module manufacturing facility. Implementing A.I. into data analysis and classification problems, various benefits such as quality control, human work reduction and efficient usage of big data have been obtained.
Turbofan engines are known as the heart of the aircraft,as important equipment of the aircraft,the health state of the engine determines the aircraft’s operational ***,the equipment monitoring and maintenance of the ...
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Turbofan engines are known as the heart of the aircraft,as important equipment of the aircraft,the health state of the engine determines the aircraft’s operational ***,the equipment monitoring and maintenance of the engine is an important part of ensuring the healthy and stable operation of the aircraft,and the remaining useful life（RUL） prediction of the engine is an important part of *** monitoring data of turbofan engines have a high dimension and a long time span,which brings difficulties to predicting the remaining useful life of the *** paper proposes a residual life prediction model based on autoencoder and temporal convolutional network（TCN）.Among them,autoencoder is used to reduce the dimension of the data and extract features from the engine monitoring *** obtained low-dimensional data is trained in the TCN network to predict the remaining useful *** model mentioned in this article is verified on the NASA public dataset（C-MAPSS）and compared with common machine learning methods and other deep neural *** experimental results show that the model proposed in this paper performs best in the evaluation methods,and this conclusion has important implications for engine health.
Face identification from low quality and low resolution Near-Infrared (NIR) face images is a challenging problem. Since surveillance cameras typically acquire images at a large standoff distance, the effective resolut...
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ISBN:
(纸本)9781467399623
Face identification from low quality and low resolution Near-Infrared (NIR) face images is a challenging problem. Since surveillance cameras typically acquire images at a large standoff distance, the effective resolution of the face is not large enough to identify the individuals. Moreover for a 24-hour surveillance footage, images in low light and at nighttime are acquired in NIR mode which makes the identification problem even more challenging. We propose an effective method using both hand-crafted and learned features for face identification of low resolution NIR images. We show that learned features contribute considerably to the performance of identification algorithm, and that using both feature level and score level fusion in a hierarchal approach gives good performance. The results demonstrate the effectiveness of the proposed approach on images which are of low quality, low resolution and acquired under challenging illumination conditions in near-infrared mode by surveillance cameras.
As more and more single-cell RNA-seq (scRNA-seq) datasets become available, carrying out compare between them is key. However, this task is challengeable due to differences caused by different experiment. We proposed ...
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ISBN:
(纸本)9781728118680
As more and more single-cell RNA-seq (scRNA-seq) datasets become available, carrying out compare between them is key. However, this task is challengeable due to differences caused by different experiment. We proposed a single cell alignment method using deep autoencoder followed by k-nearst-neighbor cells (scadKNN), which learns the feature representation of the data while eliminating batch effects and dropouts through deep autoencoder and uses the low-dimensional feature to align cell types, thereby reducing calculation effort and improving alignment accuracy. Experiments using different real datasets are employed to showcase the effectiveness of the proposed approach.
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