Ghost imaging with probability estimation using convolutional neural network: improving estimation accuracy using parallel convolutional neural network

2021 ◽  
Author(s):  
Shoma Kataoka ◽  
Yasuhiro Mizutani ◽  
Tsutomu Uenohara ◽  
Yasuhiro Takaya
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Probability Estimation ◽  
Estimation Accuracy ◽  
Ghost Imaging

Computational Ghost Imaging Method Based on Convolutional Neural Network

2020 ◽  
Vol 49 (6) ◽  
pp. 610002-610002
Author(s):  
冯维 Wei FENG ◽  
赵晓冬 Xiao-dong ZHAO ◽  
吴贵铭 Gui-ming WU ◽  
叶忠辉 Zhong-hui YE ◽  
赵大兴 Da-xing ZHAO
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Imaging Method ◽  
Ghost Imaging

scholarly journals Online Capacity Estimation for Lithium-Ion Batteries Based on Semi-Supervised Convolutional Neural Network

2021 ◽  
Vol 12 (4) ◽  
pp. 256
Author(s):  
Yi Wu ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Capacity Estimation ◽  
Practical Applications

Accurate capacity estimation can ensure the safe and reliable operation of lithium-ion batteries in practical applications. Recently, deep learning-based capacity estimation methods have demonstrated impressive advances. However, such methods suffer from limited labeled data for training, i.e., the capacity ground-truth of lithium-ion batteries. A capacity estimation method is proposed based on a semi-supervised convolutional neural network (SS-CNN). This method can automatically extract features from battery partial-charge information for capacity estimation. Furthermore, a semi-supervised training strategy is developed to take advantage of the extra unlabeled sample, which can improve the generalization of the model and the accuracy of capacity estimation even in the presence of limited labeled data. Compared with artificial neural networks and convolutional neural networks, the proposed method is demonstrated to improve capacity estimation accuracy.

scholarly journals Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1795 ◽  
Author(s):  
Xiao Lin ◽  
Dalila Sánchez-Escobedo ◽  
Josep R. Casas ◽  
Montse Pardàs
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Depth Estimation ◽  
Input Image ◽  
Estimation Accuracy ◽  
Single Task ◽  
Qualitative And Quantitative ◽  
Highly Correlated

Semantic segmentation and depth estimation are two important tasks in computer vision, and many methods have been developed to tackle them. Commonly these two tasks are addressed independently, but recently the idea of merging these two problems into a sole framework has been studied under the assumption that integrating two highly correlated tasks may benefit each other to improve the estimation accuracy. In this paper, depth estimation and semantic segmentation are jointly addressed using a single RGB input image under a unified convolutional neural network. We analyze two different architectures to evaluate which features are more relevant when shared by the two tasks and which features should be kept separated to achieve a mutual improvement. Likewise, our approaches are evaluated under two different scenarios designed to review our results versus single-task and multi-task methods. Qualitative and quantitative experiments demonstrate that the performance of our methodology outperforms the state of the art on single-task approaches, while obtaining competitive results compared with other multi-task methods.

scholarly journals Detection of Motion on a Trampoline with a Smartwatch

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8413
Author(s):  
Satoshi Kobayashi ◽  
Tatsuhito Hasegawa
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Estimation Accuracy ◽  
Battery Consumption

In this study, we develop a method for detecting the motions performed on a trampoline using an accelerometer mounted on a smartwatch. This method will lead to a system that can be used to promote trampoline exercise using a home trampoline by detecting motions on the trampoline using a smartwatch. We proposed a method based on the convolutional neural network to detect the motions on a trampoline. As a result of the performance evaluation by leave-one-subject-out cross-validation on eight subjects, our method achieves 78.8% estimation accuracy, which is the best estimation accuracy compared to the baseline methods. We also evaluate the inference time and the battery consumption when the model is actually running on a smartwatch. Our method is effective for on-device prediction.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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