A novel method for prediction of paraffin deposit in sucker rod pumping system based on CNN indicator diagram feature deep learning

2021 ◽  
pp. 108986
Author(s):  
Hui Tian ◽  
Song Deng ◽  
Caibao Wang ◽  
Xingya Ni ◽  
Hao Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Indicator Diagram ◽  
Pumping System ◽  
Sucker Rod ◽  
Novel Method

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

2021 ◽  
pp. 136943322098663
Author(s):  
Diana Andrushia A ◽  
Anand N ◽  
Eva Lubloy ◽  
Prince Arulraj G
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Concrete Structures ◽  
Vital Role ◽  
Subjective Knowledge ◽  
Thermal Cracks ◽  
Reinforced Cement ◽  
Novel Method ◽  
Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

A Fault Diagnosis of Suck Rod Pumping System Based on SVM

2013 ◽  
Vol 307 ◽  
pp. 285-289 ◽  
Author(s):  
Wei Wu ◽  
Yu Zhou ◽  
Hang Xin Wei
Keyword(s):  
Fault Diagnosis ◽  
Support Vector ◽  
Oil Well ◽  
Pump System ◽  
Indicator Diagram ◽  
Invariant Moments ◽  
Pumping System ◽  
System A ◽  
Fault Type ◽  
Shape Characteristics

Aiming at the defects of fault diagnosis in the traditional method for sucker rod pump system, a new method based on support vector machine (SVM) pump fault diagnosis is proposed. Through studying the theory of invariant moment and the shape characteristics of pump indicator diagram, seven invariant moments is extracted from the indicator diagram as a pumping unit well condition of the characteristic parameters. Then these parameters are pretreatment, and it makes up seven eigenvector which are regarded as the input eigenvector of the SVM. The experiment indicates that the method can not only detect the fault of the pumping oil well but also can recognize the fault type of it, which is very effective for safety protection and fault diagnosis of the pumping oil.

scholarly journals A Novel Method of Billing System Using Deep Learning

2021 ◽  
Vol 1084 (1) ◽  
pp. 012035
Author(s):  
J Haritha ◽  
K Prakash ◽  
B Navina ◽  
S Saveetha
Keyword(s):  
Deep Learning ◽  
Billing System ◽  
Novel Method

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder

2021 ◽  
pp. 104728
Author(s):  
Ahmad Jahanbakhshi ◽  
Yousef Abbaspour-Gilandeh ◽  
Kobra Heidarbeigi ◽  
Mohammad Momeny
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Novel Method

scholarly journals Technical Note: Quantifying music-dance synchrony with the application of a deep learning-based 2D pose estimator

2020 ◽  
Author(s):  
Filip Potempski ◽  
Andrea Sabo ◽  
Kara K Patterson
Keyword(s):  
Deep Learning ◽  
Motor Performance ◽  
Data Extraction ◽  
Effective Means ◽  
Technical Note ◽  
Novel Method ◽  
Synchronous Condition ◽  
The Mean ◽  
Repeated Training

AbstractDance interventions are more effective at improving gait and balance outcomes than other rehabilitation interventions. Repeated training may culminate in superior motor performance compared to other interventions without synchronization. This technical note will describe a novel method using a deep learning-based 2D pose estimator: OpenPose, alongside beat analysis of music to quantify movement-music synchrony during salsa dancing. This method has four components: i) camera setup and recording, ii) tempo/downbeat analysis and waveform cleanup, iii) OpenPose estimation and data extraction, and iv) synchronization analysis. Two trials were recorded: one in which the dancer danced synchronously to the music and one where they did not. The salsa dancer performed a solo basic salsa step continuously for 90 seconds to a salsa track while their movements and the music were recorded with a webcam. This data was then extracted from OpenPose and analyzed. The mean synchronization value for both feet was significantly lower in the synchronous condition than the asynchronous condition, indicating that this is an effective means to track and quantify a dancer’s movement and synchrony while performing a basic salsa step.

scholarly journals A Deep Learning Model for Molecular Label Transfer that Enables Cancer Cell Identification from Histopathology Images

2021 ◽  
Author(s):  
Andrew Su ◽  
HoJoon Lee ◽  
Xiao Tan ◽  
Carlos J Suarez ◽  
Noemi Andor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Initial Step ◽  
Classification Systems ◽  
Interactive Tools ◽  
Histopathologic Evaluation ◽  
Histopathological Images ◽  
Novel Method ◽  
Histopathology Images ◽  
Transfer Method ◽  
Learning Software

Deep learning cancer classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches depends on prior annotation through a pathologist. This initial step relying on a manual, low-resolution, time-consuming process is highly variable and subject to observer variance. To address this issue, we developed a novel method, H&E Molecular neural network (HEMnet). This two-step process utilises immunohistochemistry as an initial molecular label for cancer cells on a H&E image and then we train a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, we show that HEMnet accurately distinguishes colorectal cancer from normal tissue at high resolution without the need for an initial manual histopathologic evaluation. Our validation study using histopathology images from TCGA samples accurately estimates tumour purity. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at: https://github.com/BiomedicalMachineLearning/HEMnet

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