A Machine Learning Approach to Reduce the Number of Simulations for Long-Term Well Control Optimization

2020 ◽  
Author(s):  
Daniel Rodrigues dos Santos ◽  
André Ricardo Fioravanti ◽  
Antonio Alberto de Souza dos Santos ◽  
Denis José Schiozer
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Well Control ◽  
Control Optimization ◽  
Machine Learning Approach

scholarly journals Machine Learning Approach to Quantity Management for Long-Term Sustainable Development of Dockless Public Bike: Case of Shenzhen in China

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qingfeng Zhou ◽  
Chun Janice Wong ◽  
Xian Su
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Clustering Algorithms ◽  
Utilization Efficiency ◽  
Impact Factors ◽  
Classification Algorithms ◽  
Learning Approach ◽  
High Efficient ◽  
Machine Learning Approach

Since the number of bicycles is critical to the sustainable development of dockless PBS, this research practiced the introduction of a machine learning approach to quantity management using OFO bike operation data in Shenzhen. First, two clustering algorithms were used to identify the bicycle gathering area, and the available bike number and coefficient of available bike number variation were analyzed in each bicycle gathering area’s type. Second, five classification algorithms were compared in the accuracy of distinguishing the type of bicycle gathering areas using 25 impact factors. Finally, the application of the knowledge gained from the existing dockless bicycle operation data to guide the number planning and management of public bicycles was explored. We found the following. (1) There were 492 OFO bicycle gathering areas that can be divided into four types: high inefficient, normal inefficient, high efficient, and normal efficient. The high inefficient and normal inefficient areas gathered about 110,000 bicycles with low usage. (2) More types of bicycle gathering area will affect the accuracy of the classification algorithm. The random forest classification had the best performance in identifying bicycle gathering area types in five classification algorithms with an accuracy of more than 75%. (3) There were obvious differences in the characteristics of 25 impact factors in four types of bicycle gathering areas. It is feasible to use these factors to predict area type to optimize the number of available bicycles, reduce operating costs, and improve utilization efficiency. This work helps operators and government understand the characteristics of dockless PBS and contributes to promoting long-term sustainable development of the system through a machine learning approach.

A Machine‐Learning Approach to Derive Long‐Term Trends of Thermospheric Density

2020 ◽  
Vol 47 (6) ◽  
Author(s):  
Libin Weng ◽  
Jiuhou Lei ◽  
Jiahao Zhong ◽  
Xiankang Dou ◽  
Hanxian Fang
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Long Term Trends

scholarly journals O9.7. INDIVIDUALIZED LONG-TERM OUTCOME PREDICTION OF PSYCHOSIS IN AN OBSERVATIONAL STUDY: A MACHINE LEARNING APPROACH

2018 ◽  
Vol 44 (suppl_1) ◽  
pp. S101-S102 ◽  
Author(s):  
Jessica De Nijs ◽  
Daniel P J van Opstal ◽  
Ronald J Janssen ◽  
Wiepke Cahn ◽  
Hugo Schnack ◽  
...  
Keyword(s):  
Machine Learning ◽  
Observational Study ◽  
Outcome Prediction ◽  
Learning Approach ◽  
Term Outcome ◽  
Long Term Outcome ◽  
Machine Learning Approach

scholarly journals CloudBridge Waste Segregator Automation using Machine Learning

2019 ◽  
Vol 8 (9) ◽  
pp. 2794-2798
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Strategies ◽  
Agricultural Fields ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Waste Segregation ◽  
Recycling Of Wastes ◽  
Growing Population

There is a huge problem in creating space today because of growing population and research is going on profusely in finding space to dump waste. The waste has been dumped to rivers, underground and mixed with soil and by other methods. But all these methods are harmful to environment in long term. Our research is done on finding efficient way to segregate waste followed by recycling of wastes. The difficulties in isolation of various products are dealt using machine learning approach. The framework used to robotize the procedure of waste isolation to deal with the junk effectively and productively is one of the Machine Learning strategies called Convolutional Neural Network (CNN). The experiments showed that the performance of CNN is better because it recognizes the components in an image and recombines these components to recognize other structures while other methods learn to recognize as they go through it. The work will be segregated into 6 bins consisting of biodegradable, non- biodegradable. Here we have used the TensorFlow algorithm which uses Python. The applications of TensorFlow are Python application itself. The application of our research includes waste segregation in society, in industries, in agricultural fields. The recycled wastes can be used as organic material in many places

scholarly journals Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data

2020 ◽  
Vol 117 (31) ◽  
pp. 18240-18250 ◽  
Author(s):  
Hector A. Orengo ◽  
Francesc C. Conesa ◽  
Arnau Garcia-Molsosa ◽  
Agustín Lobo ◽  
Adam S. Green ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Google Earth ◽  
Learning Approach ◽  
Indus Civilization ◽  
Machine Learning Classification ◽  
Cholistan Desert ◽  
Machine Learning Approach ◽  
Accurate Probability

This paper presents an innovative multisensor, multitemporal machine-learning approach using remote sensing big data for the detection of archaeological mounds in Cholistan (Pakistan). The Cholistan Desert presents one of the largest concentrations of Indus Civilization sites (fromca. 3300 to 1500 BC). Cholistan has figured prominently in theories about changes in water availability, the rise and decline of the Indus Civilization, and the transformation of fertile monsoonal alluvial plains into an extremely arid margin. This paper implements a multisensor, multitemporal machine-learning approach for the remote detection of archaeological mounds. A classifier algorithm that employs a large-scale collection of synthetic-aperture radar and multispectral images has been implemented in Google Earth Engine, resulting in an accurate probability map for mound-like signatures across an area that coversca. 36,000 km2. The results show that the area presents many more archaeological mounds than previously recorded, extending south and east into the desert, which has major implications for understanding the archaeological significance of the region. The detection of small (<5 ha) to large mounds (>30 ha) suggests that there were continuous shifts in settlement location. These shifts are likely to reflect responses to a dynamic and changing hydrological network and the influence of the progressive northward advance of the desert in a long-term process that culminated in the abandonment of much of the settled area during the Late Harappan period.

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