Image Dedusting with Deep Learning based Closed-Loop Network

2021 ◽  
Author(s):  
Xibin Song ◽  
Dingfu Zhou ◽  
Jin Fang ◽  
Liangjun Zhang
Keyword(s):  
Deep Learning ◽  
Closed Loop ◽  
Loop Network

Seismic Inversion via Closed-Loop Fully Convolutional Residual Network and Transfer Learning

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Lingling Wang ◽  
Delin Meng ◽  
Bangyu Wu
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Seismic Data ◽  
Closed Loop ◽  
Seismic Inversion ◽  
Open Loop ◽  
Residual Network ◽  
Velocity Inversion ◽  
Good Convergence ◽  
Loop Network

Because deep learning networks can 'learn' the complex mapping function between the labeled inputs and outputs, they have shown great potential in seismic inversion. Conventional deep learning algorithms require a large amount of labeled data for sufficient training. However, in practice, the number of well logs is limited. To address this problem, we propose a closed-loop fully convolutional residual network (FCRN) combined with transfer learning strategy for seismic inversion. This closed-loop FCRN consists of an inverse network and a forward network. The inverse network predicts the inversion target from seismic data, whereas the forward network calculates seismic data from the inversion target. The inverse network is initialized by pre-training on the Marmousi2 model and fine-tuned with the limited labeled data around the wells through transfer learning, to suit the target seismic data. The forward network is initialized by training with the limited labeled data around the wells. In this way, the closed-loop network is well initialized to ensure relatively good convergence. Then, the misfit of the limited labeled data and the error between the true and the forward seismic data are used to regularize the training of the initialized closed-loop network. The inverse network of the optimized closed-loop network is used to obtain the final inversion results. The proposed work flow can be used for velocity, density, and impedance inversion from post-stack seismic data. This paper takes velocity inversion as an example to illustrate the effectiveness of the method. The experimental results show that the closed-loop FCRN with transfer learning is superior than the open-loop FCRN with better lateral continuity and velocity details. The closed-loop FCRN can effectively predict the velocity with high accuracy on the synthetic data, has good anti-noise performance, and also can be effectively used for the field data with spatial heterogeneity.

Closed-loop feedback control of microfluidic cell manipulation via deep-learning integrated sensor networks

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Ningquan Wang ◽  
Ruxiu Liu ◽  
Norh Asmare ◽  
Chia-Heng Chu ◽  
Ozgun Civelekoglu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sensor Networks ◽  
Feedback Control ◽  
Closed Loop ◽  
Microfluidic System ◽  
Cell Manipulation ◽  
Integrated Sensor ◽  
Loop Feedback ◽  
Closed Loop Feedback ◽  
On Chip

An adaptive microfluidic system changing its operational state in real-time based on cell measurements through an on-chip electrical sensor network.

scholarly journals Deep learning enhanced digital twin for Closed-Loop In-Process quality improvement

CIRP Annals ◽  
2020 ◽  
Vol 69 (1) ◽  
pp. 369-372
Author(s):  
Pasquale Franciosa ◽  
Mikhail Sokolov ◽  
Sumit Sinha ◽  
Tianzhu Sun ◽  
Dariusz Ceglarek
Keyword(s):  
Quality Improvement ◽  
Deep Learning ◽  
Closed Loop ◽  
Process Quality ◽  
Digital Twin

scholarly journals An Intelligent Microwave Oven with Thermal Imaging and Temperature Recommendation Using Deep Learning

2020 ◽  
Vol 3 (1) ◽  
pp. 13 ◽  
Author(s):  
Tareq Khan
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Thermal Imaging ◽  
Closed Loop ◽  
Thermal Image ◽  
Microwave Oven ◽  
Closed Loop Control ◽  
Target Temperature ◽  
Thermal Camera ◽  
Loop Control

Whenever food in a microwave oven is heated, the user estimates the time to heat. This estimation can be incorrect, leading the food to be too hot or still cold. In this research, an intelligent microwave oven is designed. After the food is put into the microwave oven and the door is closed, it captures the image of the food, classifies the image and then suggests the food’s target temperature by learning from previous experiences, so the user does not have to recall the target food temperature each time the same food is warmed. The temperature of the food is measured using a thermal camera. The proposed microwave incorporates a display to show a real-time colored thermal image of the food. The microwave automatically stops the heating when the temperature of the food hits the target temperature using closed-loop control. The deep learning-based image classifier gradually learns the type of foods that are consumed in that household and becomes smarter in temperature recommendation. The system can classify and recommend target temperature with 93% accuracy. A prototype is developed using a microcontroller-based system and successfully tested.

Closed-loop Network Automation with Generic Programmable Data Plane (G-PDP) : (Invited Paper)

2021 ◽  
Author(s):  
Shaofei Tang ◽  
Hui Liang ◽  
Min Wang ◽  
Tingyu Li ◽  
Zuqing Zhu
Keyword(s):  
Closed Loop ◽  
Data Plane ◽  
Loop Network

scholarly journals Semiactive Nonsmooth Control for Building Structure with Deep Learning

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Qing Wang ◽  
Jianhui Wang ◽  
Xiaofang Huang ◽  
Li Zhang
Keyword(s):  
Deep Learning ◽  
Control Algorithm ◽  
Closed Loop ◽  
Passive Control ◽  
Harmful Effect ◽  
Structural Vibration ◽  
Stable Theory ◽  
Seismic Action ◽  
Building Structure ◽  
Closed Loop System

Aiming at suppressing harmful effect for building structure by surface motion, semiactive nonsmooth control algorithm with Deep Learning is proposed. By finite-time stable theory, the building structure closed-loop system’s stability is discussed under the proposed control algorithm. It is found that the building structure closed-loop system is stable. Then the proposed control algorithm is applied on controlling the building structural vibration. The seismic action is chosen as El Centro seismic wave. Dynamic characteristics have comparative analysis between semiactive nonsmooth control and passive control in two simulation examples. They demonstrate that the designed control algorithm has great robustness and anti-interference. The proposed control algorithm is more effective than passive control in suppressing structural vibration.

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