Enhanced Kick Detection with Low-Cost Rig Sensors Through Automated Pattern Recognition and Real-Time Sensor Calibration

2015 ◽  
Author(s):  
Parham Pournazari ◽  
Pradeepkumar Ashok ◽  
Eric van Oort ◽  
Sean Unrau ◽  
Stephen Lai
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Low Cost ◽  
Sensor Calibration ◽  
Automated Pattern Recognition

Automated Pattern Recognition in Real-Time Drilling Data for Early Karst Detection

2021 ◽  
Author(s):  
Danil Maksimov ◽  
Marius Alexander Løken ◽  
Alexey Pavlov ◽  
Sigbjørn Sangesland
Keyword(s):  
Pattern Recognition ◽  
Early Detection ◽  
Real Time ◽  
Geophysical Methods ◽  
Service Companies ◽  
Drilling Data ◽  
Automated Pattern Recognition ◽  
Carbonate Formations ◽  
Fluid Losses ◽  
Real Challenge

Abstract Drilling in carbonate formations often poses a real challenge to operators, contractors and service companies. Severe fluid losses, gas kicks and other unwanted situations increase drilling risks. These risks are closely related to drilling through karsts — vugs, cavities and fractures. Therefore it is important to detect karsts early enough to avoid drilling into them or, once drilling in a karstification region is detected, to prepare risk mitigating actions. Some geophysical methods can be used for karsts detection, however, they have limitations and cannot guarantee early detection of karsts. One of the recent studies has shown that certain patterns in real-time drilling data can serve as indicators of zones with a higher likelihood of encountering karsts. In this paper, we demonstrate how these patterns can be detected in an automated manner with an adaptive differential filter algorithm. The method has been validated on real drilling data.

The Design and Implementation of a Low Cost and High Precision Angle Measuring Device

2014 ◽  
Vol 1044-1045 ◽  
pp. 889-892
Author(s):  
Xin Lei Bi ◽  
Guo Wei Gao ◽  
Hong Sheng Pan ◽  
Ming Ming Chen
Keyword(s):  
Real Time ◽  
High Precision ◽  
Low Cost ◽  
Signal Transmission ◽  
Mean Shift ◽  
Angle Measurement ◽  
Sensor Calibration ◽  
Piecewise Linearization ◽  
Angle Sensor ◽  
Measuring Device

Based on SCA830 accelerometer designed a low cost high precision Angle measurement device. The device adopts the real-time mean shift filtering algorithms to reduce the interference of the external environment and noise, uses piecewise linearization method for Angle sensor calibration. The experimental results show that the designed angle detection device precision can reach 0.02 °, the system is stability, digital signal transmission is complete, and it can collect real-time carrier tilt Angle.

LOW COST REAL TIME ROCKET TELEMETRY SYSTEM DESIGN

2019 ◽  
Author(s):  
Gabriel de Almeida Souza ◽  
Larissa Barbosa ◽  
Glênio Ramalho ◽  
Alexandre Zuquete Guarato
Keyword(s):  
System Design ◽  
Real Time ◽  
Low Cost ◽  
Telemetry System

Low-Cost High-Speed Techniques for Real-Time Simulation of Power Electronic Systems

2007 ◽  
Author(s):  
R. E. Crosbie ◽  
J. J. Zenor ◽  
R. Bednar ◽  
D. Word ◽  
N. G. Hingorani
Keyword(s):  
Real Time ◽  
High Speed ◽  
Low Cost ◽  
Electronic Systems ◽  
Power Electronic ◽  
Real Time Simulation ◽  
Time Simulation

scholarly journals Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu
Keyword(s):  
Deep Learning ◽  
Integrated Pest Management ◽  
Pest Management ◽  
Real Time ◽  
Oilseed Rape ◽  
Data Augmentation ◽  
Low Cost ◽  
Response Speed ◽  
Original Model ◽  
Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

scholarly journals Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Action Recognition ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Data ◽  
Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

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