scholarly journals Flow pattern identification of liquid-liquid (oil and water) in vertical pipelines using machine learning techniques

2022 ◽  
Vol 2163 (1) ◽  
pp. 012001
Author(s):  
C M Ruiz-Diaz ◽  
J A Gómez-Camperos ◽  
M M Hernández-Cely
Keyword(s):  
Petrochemical Industry ◽  
Room Temperature ◽  
Volume Fraction ◽  
Intelligent System ◽  
Flow Patterns ◽  
Machine Learning Techniques ◽  
Transition Flow ◽  
Learning Techniques ◽  
Two Phases ◽  
Flow Pattern Identification

Abstract Given the importance of process control in the petrochemical industry, there is a need to determine the behavior of the fluids inside the pipes. In this work a methodology is developed for the identification of flow patterns in vertical pipes with diameters between 0.01 m and 0.10 m, from the implementation of artificial intelligence techniques, for a liquid combination of two phases composed of oil with viscosity in the range of 792 Kg/m3 to 1823 Kg/m3 and water at room temperature. The predictive models generated in the structuring of the methodology were trained with 70% of data based on viscosity parameters, pipe diameter, volume fraction and surface velocities of the working fluids stored in a database. The remaining information, equivalent to 30% of the total, was used to develop the automatic model validation. The flow patterns identified by the intelligent system for oil and water flow, without considering the predominant substance, are churning, dispersed, very fine dispersion, transition flow, intermittent, and annular

scholarly journals Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 369 ◽  
Author(s):  
Semin Ryu ◽  
Seung-Chan Kim
Keyword(s):  
Intelligent System ◽  
Surface Characteristics ◽  
Machine Learning Techniques ◽  
Test Accuracy ◽  
Learning Approaches ◽  
Well Test ◽  
Mechanical Responses ◽  
Learning Techniques ◽  
The Impact ◽  
Mechanical Impulse

Inspired by spiders that can generate and sense vibrations to obtain information regarding a substrate, we propose an intelligent system that can recognize the type of surface being touched by knocking the surface and listening to the vibrations. Hence, we developed a system that is equipped with an electromagnetic hammer for hitting the ground and an accelerometer for measuring the mechanical responses induced by the impact. We investigate the feasibility of sensing 10 different daily surfaces through various machine-learning techniques including recent deep-learning approaches. Although some test surfaces are similar, experimental results show that our system can recognize 10 different surfaces remarkably well (test accuracy of 98.66%). In addition, our results without directly hitting the surface (internal impact) exhibited considerably high test accuracy (97.51%). Finally, we conclude this paper with the limitations and future directions of the study.

Relationship between Microstructure and Tensile Strength in the Directionally Solidified (23-27) at. % Al-Ni Alloys

2006 ◽  
Vol 510-511 ◽  
pp. 458-461 ◽  
Author(s):  
Y. Lu ◽  
H.C. Kim ◽  
Je Hyun Lee ◽  
Myung Hoon Oh ◽  
Dang Moon Wee ◽  
...  
Keyword(s):  
Room Temperature ◽  
Eutectic Composition ◽  
Volume Fraction ◽  
Solidification Rate ◽  
Transgranular Fracture ◽  
Hypoeutectic Alloy ◽  
Directionally Solidified ◽  
Dendritic Microstructure ◽  
Ni Alloys ◽  
Two Phases

Directional or single crystal technique was applied to enhance the ductility, and two phases of γ (Ni) phase or β (NiAl) phase in γ‘(Ni3Al) matrix were also considered to increase the strength and ductility. In this study, directionally solidified rods were prepared at the solidification rate of 50µm/s in 23-27 at.% Al-Ni alloys, and tensile strengths of these rods were analyzed at room temperature. Directionally solidified samples showed the γ dendrite fibers formed in the Ni3Al matrix in the hypo eutectic composition of 23 at.% Al, the γ‘ single phase in the eutectic composition of 24.5 at. % Al, and the β dendrite fibers in the γ‘ matrix in the hyper eutectic compositions of 25, 26, 27 at.% Al. The hypoeutectic alloy including γ dendrites with γ‘ matrix exhibited a large elongation of over 70% with ductile transgranular fracture at room temperature. With increasing Al contents, the γ dendritic microstructure changed to the β dendrite in the γ‘ matrix, which resulted in decreasing the elongation by increasing the volume fraction of the brittle β dendrites in the ductile γ’ matrix.

scholarly journals Classical and Deep Learning Paradigms for Detection and Validation of Key Genes of Risky Outcomes of HCV

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 73
Author(s):  
Nagwan M. Abdel Samee
Keyword(s):  
Hepatic Cirrhosis ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Second Phase ◽  
Selection Methods ◽  
Neural Network Approach ◽  
Learning Techniques ◽  
Key Genes ◽  
Two Phases

Hepatitis C virus (HCV) is one of the most dangerous viruses worldwide. It is the foremost cause of the hepatic cirrhosis, and hepatocellular carcinoma, HCC. Detecting new key genes that play a role in the growth of HCC in HCV patients using machine learning techniques paves the way for producing accurate antivirals. In this work, there are two phases: detecting the up/downregulated genes using classical univariate and multivariate feature selection methods, and validating the retrieved list of genes using Insilico classifiers. However, the classification algorithms in the medical domain frequently suffer from a deficiency of training cases. Therefore, a deep neural network approach is proposed here to validate the significance of the retrieved genes in classifying the HCV-infected samples from the disinfected ones. The validation model is based on the artificial generation of new examples from the retrieved genes’ expressions using sparse autoencoders. Subsequently, the generated genes’ expressions data are used to train conventional classifiers. Our results in the first phase yielded a better retrieval of significant genes using Principal Component Analysis (PCA), a multivariate approach. The retrieved list of genes using PCA had a higher number of HCC biomarkers compared to the ones retrieved from the univariate methods. In the second phase, the classification accuracy can reveal the relevance of the extracted key genes in classifying the HCV-infected and disinfected samples.

scholarly journals A Predictive Classification Method for Email Phishing Attacks using Random Forest and A-R trees

2020 ◽  
Vol 9 (10) ◽  
pp. 421-424
Keyword(s):  
Random Forest ◽  
Adaptive Learning ◽  
Intelligent System ◽  
Early Stage ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Corporate Networks ◽  
Phishing Attacks ◽  
Learning Techniques ◽  
Or Organization

Cyber-attacks are the attempts made by an individual or an organization deliberately, to breach the information system mainly computers of another individual or organization. These attacks have risen in recent years due to various reasons posing the need for systems that can use adaptive learning techniques to detect and mitigate these attacks at an early stage. Phishing is one of the significant cyber-attacks. According to global security report 2019, phishing was the major cause of attacks in corporate networks. Phishing attack uses disguised email to achieve its goal. In this attack, attacker masquerade himself as a trusted individual or a company and trick the email recipient into clicking malicious links or attachments. The proposed method provides a testbed for detecting and mitigating various types of phishing attacks. Machine learning techniques are used to build an intelligent system which can detect phishing attacks. This application uses random forest algorithm with AR-Trees (acceptance-rejection tree algorithm) to determine the attacks by considering various datasets available online and new datasets dynamically constructed for making the system ready to mitigate future phishing attacks.

A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques

2021 ◽  
pp. 267-285
Author(s):  
Ankit Kumar Jain ◽  
Sumit Kumar Yadav ◽  
Neelam Choudhary
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Second Phase ◽  
Short Message ◽  
Machine Learning Classifiers ◽  
Novel Approach ◽  
Learning Techniques ◽  
Message Service ◽  
Novel Method ◽  
Two Phases

Smishing attack is generally performed by sending a fake short message service (SMS) that contains a link of the malicious webpage or application. Smishing messages are the subclass of spam SMS and these are more harmful compared to spam messages. There are various solutions available to detect the spam messages. However, no existing solution, filters the smishing message from the spam message. Therefore, this article presents a novel method to filter smishing message from spam message. The proposed approach is divided into two phases. The first phase filters the spam messages and ham messages. The second phase filters smishing messages from spam messages. The performance of the proposed method is evaluated on various machine learning classifiers using the dataset of ham and spam messages. The simulation results indicate that the proposed approach can detect spam messages with the accuracy of 94.9% and it can filter smishing messages with the accuracy of 96% on neural network classifier.

A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques

2020 ◽  
Vol 12 (1) ◽  
pp. 21-38 ◽  
Author(s):  
Ankit Kumar Jain ◽  
Sumit Kumar Yadav ◽  
Neelam Choudhary
Keyword(s):  
Machine Learning ◽  
Short Message Service ◽  
Machine Learning Techniques ◽  
Second Phase ◽  
Short Message ◽  
Novel Approach ◽  
Learning Techniques ◽  
Message Service ◽  
Novel Method ◽  
Two Phases

Smishing attack is generally performed by sending a fake short message service (SMS) that contains a link of the malicious webpage or application. Smishing messages are the subclass of spam SMS and these are more harmful compared to spam messages. There are various solutions available to detect the spam messages. However, no existing solution, filters the smishing message from the spam message. Therefore, this article presents a novel method to filter smishing message from spam message. The proposed approach is divided into two phases. The first phase filters the spam messages and ham messages. The second phase filters smishing messages from spam messages. The performance of the proposed method is evaluated on various machine learning classifiers using the dataset of ham and spam messages. The simulation results indicate that the proposed approach can detect spam messages with the accuracy of 94.9% and it can filter smishing messages with the accuracy of 96% on neural network classifier.

Microstructures and Mechanical Behavior of Two-Phase Niobium Silicide-Niobium Alloys

1988 ◽  
Vol 133 ◽  
Author(s):  
M. G. Mendiratta ◽  
D. M. Dimiduk
Keyword(s):  
Mechanical Behavior ◽  
Phase Transformations ◽  
Room Temperature ◽  
Volume Fraction ◽  
Two Phase ◽  
Niobium Alloys ◽  
Intermetallic Matrix ◽  
Two Phases ◽  
The Individual

ABSTRACTIn the Nb-Si system, it is possible to produce in-situ composites consisting of a brittle Nb5Si3 intermetallic matrix and ductile Nb particles. The two phases are thermochemically stable up to ∼ 1500∼C and are amenable to wide microstructural variations including morphology, volume fraction, and the size of the individual microconstituents. This paper presents microstructures and phase transformations in these composites as a function of composition and heat treatments and bend properties from room-temperature to 1400°C.

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