An on-line Rock Type Sensor Using Machine Learning

2020 ◽  
Author(s):  
◽  
Eva Ho
Keyword(s):  
Machine Learning ◽  
Rock Type ◽  
On Line

scholarly journals Who Donates on Line? Segmentation Analysis and Marketing Strategies Based on Machine Learning for Online Charitable Donations in Taiwan

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 52728-52740
Author(s):  
Chiung-Wen Hsu ◽  
Yu-Lin Chang ◽  
Tzer-Shyong Chen ◽  
Te-Yi Chang ◽  
Yu-Da Lin
Keyword(s):  
Machine Learning ◽  
Marketing Strategies ◽  
Charitable Donations ◽  
Segmentation Analysis ◽  
On Line ◽  
Line Segmentation

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

Reinforcement and Non-Reinforcement Machine Learning Classifiers for User Movement Prediction

2013 ◽  
pp. 218-237 ◽  
Author(s):  
Theodoros Anagnostopoulos
Keyword(s):  
Machine Learning ◽  
Classification Problem ◽  
Location Prediction ◽  
Context Aware ◽  
Resonance Theory ◽  
Self Organizing Maps ◽  
Adaptive Resonance ◽  
Proposed Model ◽  
On Line ◽  
Mobile Context

Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify, and predict context in order to act efficiently, beforehand, for the benefit of the user. In this chapter, the authors propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. They rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. The authors introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Two learning methods (non-reinforcement and reinforcement learning) are presented and evaluated. They compare ART with Self-Organizing Maps (SOM), Offline kMeans, and Online kMeans algorithms. Their findings are very promising for the use of the proposed model in mobile context aware applications.

Automated Die Inking through On-line Machine Learning

2019 ◽  
Author(s):  
Constantinos Xanthopoulos ◽  
Arnold Neckermann ◽  
Paulus List ◽  
Klaus-Peter Tschernay ◽  
Peter Sarson ◽  
...  
Keyword(s):  
Machine Learning ◽  
On Line

scholarly journals Adaptive neurofuzzy control of a robotic gripper with on-line machine learning

2004 ◽  
Vol 48 (2-3) ◽  
pp. 93-110 ◽  
Author(s):  
J.A. Domínguez-López ◽  
R.I. Damper ◽  
R.M. Crowder ◽  
C.J. Harris
Keyword(s):  
Machine Learning ◽  
Neurofuzzy Control ◽  
On Line

scholarly journals An Efficient Image Classification of Malaria Parasite Using Convolutional Neural Network and ADAM Optimizer

2021 ◽  
Vol 12 (2) ◽  
pp. 3376-3384
Author(s):  
K. Kranthi Kumar, Et. al.
Keyword(s):  
Machine Learning ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Information Science ◽  
Data Intensive ◽  
Learning Techniques ◽  
Protozoan Infection ◽  
Life Threatening ◽  
On Line ◽  
Infected Cells

Machine learning can be a technique of nursing lysis that automatically develops an analytical model. It is a branch of synthetic intelligence that believes that systems are going to learn information, determine patterns of information and decide with degraded human intervention. Machine learning addresses the question of how computers can be constructed that improve mechanically through knowledge. It lies at the intersection of technology and statistics and at the center of artificial data and information science, one in all the quickest increasing technical fields of nowadays. Recent advances in machine learning were driven by the event of latest learning and theories also as by the constant explosion. The event of latest learning algorithms and also theory and the in-progress growth within the accessibility of on-line information also as low-priced computation crystal rectifier to recent progress within the field of machine learning. Additional evidence-based decision-making could be carried out in science, technology and trade, including healthcare, production, education and monetary modelling, enforcement and promotion, with adoption of mechanical learning techniques based on data-intensive methods. The results are also available. The infection can be a life-threatening disease. The bite of a nursing partner is often transmitted in dipterous Anopheles. In infected mosquitoes, plasmodium parasite is a gift. The parasite is discharged into your blood after you bite this dipterous insect once it bites you. Once your body is composed of the parasites, they mature into the liver. The mature parasites enter the blood for several days when red blood cells start to infect. In red blood cells, parasites increase over 48-72 hours, causing infected cells to divide. The parasites still infect red blood cells, which last 2 to 3 days in cycles. This paper is used for observation of protozoan infection with a deep learning idea.

scholarly journals On-Line Spot Welding Quality Prediction with Analysis of Welding Parameters

2020 ◽  
Vol 3 (1) ◽  
pp. 127-140
Author(s):  
Emin Cantez ◽  
İsmail Atalay ◽  
Oğuz Alper İsen ◽  
Serkan Aydın
Keyword(s):  
Machine Learning ◽  
Automotive Industry ◽  
Spot Welding ◽  
Welding Parameters ◽  
Quality Prediction ◽  
Welding Quality ◽  
Important Place ◽  
Machine Learning Methods ◽  
On Line

Spot welding is one of the metal joining technologies and has an important place especially in the automotive industry. A passenger car has average 5000 spots. Destructive inspection is carried out at certain periods to check these spots. However, not all parts can be checked. In this work, welding parameters were collected and analyzed. By applying different machine learning methods, the quality of the spot welding was tried to be estimated and the results were compared.

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