scholarly journals Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series

Forests ◽  
2017 ◽  
Vol 8 (8) ◽  
pp. 275 ◽  
Author(s):  
Valerie Pasquarella ◽  
Bethany Bradley ◽  
Curtis Woodcock
Keyword(s):  
Time Series ◽  
Real Time ◽  
Real Time Monitoring ◽  
Insect Defoliation

scholarly journals Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1233 ◽  
Author(s):  
Chen ◽  
Xie ◽  
Yuan ◽  
Huang ◽  
Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Tool Wear ◽  
Real Time ◽  
Original Data ◽  
Attention Mechanism ◽  
Cnc Machining ◽  
Numerical Control ◽  
Real Time Monitoring ◽  
Monitoring Method

To monitor the tool wear state of computerized numerical control (CNC) machining equipment in real time in a manufacturing workshop, this paper proposes a real-time monitoring method based on a fusion of a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network with an attention mechanism (CABLSTM). In this method, the CNN is used to extract deep features from the time-series signal as an input, and then the BiLSTM network with a symmetric structure is constructed to learn the time-series information between the feature vectors. The attention mechanism is introduced to self-adaptively perceive the network weights associated with the classification results of the wear state and distribute the weights reasonably. Finally, the signal features of different weights are sent to a Softmax classifier to classify the tool wear state. In addition, a data acquisition experiment platform is developed with a high-precision CNC milling machine and an acceleration sensor to collect the vibration signals generated during tool processing in real time. The original data are directly fed into the depth neural network of the model for analysis, which avoids the complexity and limitations caused by a manual feature extraction. The experimental results show that, compared with other deep learning neural networks and traditional machine learning network models, the model can predict the tool wear state accurately in real time from original data collected by sensors, and the recognition accuracy and generalization have been improved to a certain extent.

Landslide mapping, monitoring and early warning by using optical remote sensing, InSAR and ground-based sensors: case study of the Qingpo landslide (Wenchuan, China)

2020 ◽  
Author(s):  
Weihua Zhao ◽  
Mingli Xie ◽  
Nengpan Ju
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Real Time ◽  
Early Warning ◽  
Deformation Monitoring ◽  
Monitoring Methods ◽  
Real Time Monitoring ◽  
Deformation Velocity ◽  
Monitoring And Early Warning

<p> Studies of landslide evolution that improve the knowledge of ground movements are essential to understand the mechanism of deformation for landslide-prone territories to mitigate the associated risk. The large Qingpo landslide, with a volume of about 200,0000 m<sup>3</sup>, is located in a mega ancient landslide (with a width of 1300 m and a height difference about 400 meters), and a pylon is just located on the boundary of Qingpo landslide. How to accurately judge the historical evolution process, current evolution stage and the future evolution trend of the large landslides is very important for landslide and pylon monitoring and early warning. In this study, on the basis of a detailed on-site investigation, a total of 114 Sentinel-1A Images over five years with Level-1 Single Look Complex (SLC) mode and Interferometric Wide (IW) acquisition mode were downloaded from Copernicus Open Access Hub and were preprocessed by time series InSAR model, which allow us to produce deformation time series and mean deformation velocity maps. An automatic monitoring and warning scheme was designed, 10 sets of ground-based sensors, containing self-adapting crack meter, rain gauge, strain gauge and dip meter were installed, followed by real-time monitoring within one month. Ultimately, the temporal and spatial evolution characteristics of the landslide were comprehensively analyzed through on-site deformation investigation, long-term deformation monitoring by InSAR and ground-based real-time monitoring. The applicability of long-term remote sensing monitoring and real-time monitoring methods and how to use them together have also been verified. This study may can also provide a typical case for the comprehensive use of multi-source data.</p>

1618: Real-Time Monitoring of Nitric Oxide and Blood Flow During Ischemia-Reperfusion in the Rat Testis

2006 ◽  
Vol 175 (4S) ◽  
pp. 521-521
Author(s):  
Motoaki Saito ◽  
Tomoharu Kono ◽  
Yukako Kinoshita ◽  
Itaru Satoh ◽  
Keisuke Satoh
Keyword(s):  
Nitric Oxide ◽  
Blood Flow ◽  
Real Time ◽  
Ischemia Reperfusion ◽  
Real Time Monitoring ◽  
Rat Testis

GaN heteroepitaxy by remote plasma MOCVD : Real time monitoring by laser reflectance interferometry

2001 ◽  
Vol 11 (PR3) ◽  
pp. Pr3-1175-Pr3-1182 ◽  
Author(s):  
M. Losurdo ◽  
A. Grimaldi ◽  
M. Giangregorio ◽  
P. Capezzuto ◽  
G. Bruno
Keyword(s):  
Real Time ◽  
Real Time Monitoring ◽  
Remote Plasma ◽  
Laser Reflectance

INVESTIGATION OF HAND KINEMATICS TOWARDS FRONT CRAWL SWIMMING USING NON-INVASIVE REAL-TIME MONITORING SYSTEM

2014 ◽  
Author(s):  
Rozaimi Ghazali ◽  
◽  
Asiah Mohd Pilus ◽  
Wan Mohd Bukhari Wan Daud ◽  
Mohd Juzaila Abd Latif ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Real Time Monitoring ◽  
Front Crawl ◽  
Non Invasive ◽  
Hand Kinematics

Model of multicomponent narrow-band periodical non-stationary random signal

2020 ◽  
Vol 2020 (48) ◽  
pp. 17-24
Author(s):  
I.M. Javorskyj ◽  
◽  
R.M. Yuzefovych ◽  
P.R. Kurapov ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Hilbert Transform ◽  
Spectral Properties ◽  
Narrow Band ◽  
Analytic Signal ◽  
Random Signal ◽  
Hilbert Transformation ◽  
The Hilbert Transform

The correlation and spectral properties of a multicomponent narrowband periodical non-stationary random signal (PNRS) and its Hilbert transformation are considered. It is shown that multicomponent narrowband PNRS differ from the monocomponent signal. This difference is caused by correlation of the quadratures for the different carrier harmonics. Such features of the analytic signal must be taken into account when we use the Hilbert transform for the analysis of real time series.

101-LB: Real-Time Monitoring of Cholesterol Levels Using a Microfluidic Device

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 101-LB
Author(s):  
ABHINAV BHUSHAN ◽  
SONALI J. KARNIK
Keyword(s):  
Real Time ◽  
Microfluidic Device ◽  
Real Time Monitoring ◽  
Cholesterol Levels
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