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You searched for +publisher:"Delft University of Technology" +contributor:("Lin, Hai Xiang"). Showing records 1 – 8 of 8 total matches.

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Delft University of Technology

1. Ament, Tjalling (author). GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets.

Degree: 2020, Delft University of Technology

To optimize the exploitation of oil and gas reservoirs both on- and offshore, Biodentfiy has developed a method to predict prospectivity of hydrocarbons before drilling.… (more)

Subjects/Keywords: Machine Learning; Elastic Net; GPU; Grid Search; Feature selection

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APA (6th Edition):

Ament, T. (. (2020). GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f18f345f-9049-46cf-9d1e-0d3fd668474a

Chicago Manual of Style (16th Edition):

Ament, Tjalling (author). “GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets.” 2020. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:f18f345f-9049-46cf-9d1e-0d3fd668474a.

MLA Handbook (7th Edition):

Ament, Tjalling (author). “GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets.” 2020. Web. 26 Jan 2021.

Vancouver:

Ament T(. GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:f18f345f-9049-46cf-9d1e-0d3fd668474a.

Council of Science Editors:

Ament T(. GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:f18f345f-9049-46cf-9d1e-0d3fd668474a


Delft University of Technology

2. Guan, Siyu (author). PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM.

Degree: 2018, Delft University of Technology

This thesis project developed an alternative PM2.5 concentration prediction model and early warning system of extreme air pollution based on the long short-term memory (LSTM)… (more)

Subjects/Keywords: LSTM; extreme air pollutation; PM2.5 prediction

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APA (6th Edition):

Guan, S. (. (2018). PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9484abe6-43ba-4f68-97fe-62a3c818fa33

Chicago Manual of Style (16th Edition):

Guan, Siyu (author). “PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM.” 2018. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:9484abe6-43ba-4f68-97fe-62a3c818fa33.

MLA Handbook (7th Edition):

Guan, Siyu (author). “PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM.” 2018. Web. 26 Jan 2021.

Vancouver:

Guan S(. PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:9484abe6-43ba-4f68-97fe-62a3c818fa33.

Council of Science Editors:

Guan S(. PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:9484abe6-43ba-4f68-97fe-62a3c818fa33


Delft University of Technology

3. Xie, Yu (author). Deep Learning Architectures for PM2.5 and Visibility Predictions.

Degree: 2018, Delft University of Technology

 Facing the severe air pollution phenomenon in urban areas and the subsequent low visibility event in airports, it is urgent to conduct air quality and… (more)

Subjects/Keywords: PM2.5 predictions; PM10 predictions; Visibility predictions; Deep learning; LSTM; Transfer learning

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APA (6th Edition):

Xie, Y. (. (2018). Deep Learning Architectures for PM2.5 and Visibility Predictions. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c7ea9e3e-d6c2-426c-b4d5-8c7143052257

Chicago Manual of Style (16th Edition):

Xie, Yu (author). “Deep Learning Architectures for PM2.5 and Visibility Predictions.” 2018. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:c7ea9e3e-d6c2-426c-b4d5-8c7143052257.

MLA Handbook (7th Edition):

Xie, Yu (author). “Deep Learning Architectures for PM2.5 and Visibility Predictions.” 2018. Web. 26 Jan 2021.

Vancouver:

Xie Y(. Deep Learning Architectures for PM2.5 and Visibility Predictions. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:c7ea9e3e-d6c2-426c-b4d5-8c7143052257.

Council of Science Editors:

Xie Y(. Deep Learning Architectures for PM2.5 and Visibility Predictions. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:c7ea9e3e-d6c2-426c-b4d5-8c7143052257


Delft University of Technology

4. Huang, Jie (author). Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem.

Degree: 2019, Delft University of Technology

This thesis focuses on the construction and optimization of a prediction model for the errors resulting from a model order reduction (MOR) procedure in oil… (more)

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APA (6th Edition):

Huang, J. (. (2019). Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:2bc14761-3800-4315-aa7b-0455b7e393cf

Chicago Manual of Style (16th Edition):

Huang, Jie (author). “Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem.” 2019. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:2bc14761-3800-4315-aa7b-0455b7e393cf.

MLA Handbook (7th Edition):

Huang, Jie (author). “Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem.” 2019. Web. 26 Jan 2021.

Vancouver:

Huang J(. Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:2bc14761-3800-4315-aa7b-0455b7e393cf.

Council of Science Editors:

Huang J(. Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:2bc14761-3800-4315-aa7b-0455b7e393cf


Delft University of Technology

5. Hegeman, Rick (author). Predicting the air quality by combining model simulations with machine learning.

Degree: 2020, Delft University of Technology

 Combating air pollution has proven to be a difficult task for countries with rapidly developing economies. Poor air quality can be hazardous to people doing… (more)

Subjects/Keywords: PM2.5 predictions; LSTM; Deep Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hegeman, R. (. (2020). Predicting the air quality by combining model simulations with machine learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0eefc9a3-5462-4eee-8907-955ae4b95813

Chicago Manual of Style (16th Edition):

Hegeman, Rick (author). “Predicting the air quality by combining model simulations with machine learning.” 2020. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:0eefc9a3-5462-4eee-8907-955ae4b95813.

MLA Handbook (7th Edition):

Hegeman, Rick (author). “Predicting the air quality by combining model simulations with machine learning.” 2020. Web. 26 Jan 2021.

Vancouver:

Hegeman R(. Predicting the air quality by combining model simulations with machine learning. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:0eefc9a3-5462-4eee-8907-955ae4b95813.

Council of Science Editors:

Hegeman R(. Predicting the air quality by combining model simulations with machine learning. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:0eefc9a3-5462-4eee-8907-955ae4b95813


Delft University of Technology

6. Dolas, Sagar (author). High Performance Data Traversal: Cache Aware Computing With Space Filling Curve.

Degree: 2017, Delft University of Technology

 "What Mathematics is to Physics, Data traversal is to High-performance computing." The world of Computational science has witnessed an exponential expansion of sophisticated numerical algorithms… (more)

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APA (6th Edition):

Dolas, S. (. (2017). High Performance Data Traversal: Cache Aware Computing With Space Filling Curve. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5503075e-fe4c-4a87-85e5-ec6c6d5ec53e

Chicago Manual of Style (16th Edition):

Dolas, Sagar (author). “High Performance Data Traversal: Cache Aware Computing With Space Filling Curve.” 2017. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:5503075e-fe4c-4a87-85e5-ec6c6d5ec53e.

MLA Handbook (7th Edition):

Dolas, Sagar (author). “High Performance Data Traversal: Cache Aware Computing With Space Filling Curve.” 2017. Web. 26 Jan 2021.

Vancouver:

Dolas S(. High Performance Data Traversal: Cache Aware Computing With Space Filling Curve. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:5503075e-fe4c-4a87-85e5-ec6c6d5ec53e.

Council of Science Editors:

Dolas S(. High Performance Data Traversal: Cache Aware Computing With Space Filling Curve. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:5503075e-fe4c-4a87-85e5-ec6c6d5ec53e


Delft University of Technology

7. Geçmen, Dilan (author). Deep Learning Techniques for Low-Field MRI.

Degree: 2020, Delft University of Technology

Delft University of Technology (TU Delft), Leiden University Medical Center (LUMC), Pennsylvania State University (PSU) and Mbarara University of Science and Technology (MUST) have an… (more)

Subjects/Keywords: Deep Learning; Magnetic Resonance Imaging

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APA (6th Edition):

Geçmen, D. (. (2020). Deep Learning Techniques for Low-Field MRI. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:ce264a44-ddd5-45c5-96d0-c82aac0e4911

Chicago Manual of Style (16th Edition):

Geçmen, Dilan (author). “Deep Learning Techniques for Low-Field MRI.” 2020. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:ce264a44-ddd5-45c5-96d0-c82aac0e4911.

MLA Handbook (7th Edition):

Geçmen, Dilan (author). “Deep Learning Techniques for Low-Field MRI.” 2020. Web. 26 Jan 2021.

Vancouver:

Geçmen D(. Deep Learning Techniques for Low-Field MRI. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:ce264a44-ddd5-45c5-96d0-c82aac0e4911.

Council of Science Editors:

Geçmen D(. Deep Learning Techniques for Low-Field MRI. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:ce264a44-ddd5-45c5-96d0-c82aac0e4911


Delft University of Technology

8. van Nieuwpoort, Ruben (author). Solving Poisson's equation with dataflow computing.

Degree: 2017, Delft University of Technology

 The finite element method (FEM) is an ubiquitous method for the analysis of boundary value problems. Specifically, it can be used to find approximations to… (more)

Subjects/Keywords: Finite element analysis; isogeometric analysis; weighted quadrature; Matrix-free solution techniques; Dataflow computing; mathematical theory; MAX5 hardware resources

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APA (6th Edition):

van Nieuwpoort, R. (. (2017). Solving Poisson's equation with dataflow computing. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c5dfd1d4-6494-47e9-90d9-486d2a7b26b3

Chicago Manual of Style (16th Edition):

van Nieuwpoort, Ruben (author). “Solving Poisson's equation with dataflow computing.” 2017. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:c5dfd1d4-6494-47e9-90d9-486d2a7b26b3.

MLA Handbook (7th Edition):

van Nieuwpoort, Ruben (author). “Solving Poisson's equation with dataflow computing.” 2017. Web. 26 Jan 2021.

Vancouver:

van Nieuwpoort R(. Solving Poisson's equation with dataflow computing. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:c5dfd1d4-6494-47e9-90d9-486d2a7b26b3.

Council of Science Editors:

van Nieuwpoort R(. Solving Poisson's equation with dataflow computing. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:c5dfd1d4-6494-47e9-90d9-486d2a7b26b3

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