A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks

2019 ◽  
Vol 32 (11) ◽  
pp. 7563-7579 ◽  
Author(s):  
Ebrahim Eslami ◽  
Ahmed Khan Salman ◽  
Yunsoo Choi ◽  
Alqamah Sayeed ◽  
Yannic Lops
Keyword(s):  
Neural Networks ◽  
Air Quality ◽  
Real Time ◽  
Deep Neural Networks ◽  
Extremely Randomized Trees ◽  
Ensemble Approach ◽  
Air Quality Forecasting

scholarly journals Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

2021 ◽  
Vol 35 (2) ◽  
pp. 251-264
Author(s):  
Dimitrios Boursinos ◽  
Xenofon Koutsoukos
Keyword(s):  
Neural Networks ◽  
Distance Learning ◽  
Real Time ◽  
Speaker Recognition ◽  
Deep Neural Networks ◽  
Cyber Physical Systems ◽  
Error Rates ◽  
Traffic Sign ◽  
Conformal Prediction ◽  
Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

Multi-Constrained Real-Time Entry Guidance Using Deep Neural Networks

2021 ◽  
pp. 1-1 ◽  
Author(s):  
Lin Cheng ◽  
Fanghua Jiang ◽  
Zhenbo Wang ◽  
Junfeng Li
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Deep Neural Networks ◽  
Entry Guidance

scholarly journals Diagnostic data integration using deep neural networks for real-time plasma analysis

2021 ◽  
pp. 1-1
Author(s):  
A. Rigoni Garola ◽  
R. Cavazzana ◽  
M. Gobbin ◽  
R.S. Delogu ◽  
G. Manduchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Data Integration ◽  
Real Time ◽  
Deep Neural Networks ◽  
Plasma Analysis ◽  
Diagnostic Data

Real-time strawberry detection using deep neural networks on embedded system (rtsd-net): An edge AI application

2022 ◽  
Vol 192 ◽  
pp. 106586
Author(s):  
Yanchao Zhang ◽  
Jiya Yu ◽  
Yang Chen ◽  
Wen Yang ◽  
Wenbo Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Embedded System ◽  
Real Time ◽  
Deep Neural Networks

Real-Time Downhole Geosteering Data Processing Using Deep Neural Networks On FPGA

2021 ◽  
Author(s):  
Qiyu Wan ◽  
Yuchen Jin ◽  
Xuqing Wu ◽  
Jiefu Chen ◽  
Xin Fu
Keyword(s):  
Neural Networks ◽  
Data Processing ◽  
Real Time ◽  
Deep Neural Networks

Toward Coordination Control of Multiple Fish-Like Robots: Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks

2021 ◽  
Vol 8 (12) ◽  
pp. 1964-1976
Author(s):  
Tianhao Zhang ◽  
Jiuhong Xiao ◽  
Liang Li ◽  
Chen Wang ◽  
Guangming Xie
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Pose Estimation ◽  
Deep Neural Networks ◽  
Coordination Control
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