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You searched for +publisher:"Delft University of Technology" +contributor:("Pan, Wei"). Showing records 1 – 18 of 18 total matches.

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Delft University of Technology

1. Koning, Tim (author). Low level quadcopter control using Reinforcement Learning: Developing a self-learning drone.

Degree: 2020, Delft University of Technology

 Reinforcement Learning (RL) is a learning paradigm where an agent learns a task by trial and error. The agent needs to explore its environment and… (more)

Subjects/Keywords: Reinforcement Learning; AI; DNN; quadcopter; TD3; DDPG

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APA (6th Edition):

Koning, T. (. (2020). Low level quadcopter control using Reinforcement Learning: Developing a self-learning drone. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0b9e0796-13b5-42ba-b231-fbb6aadd5233

Chicago Manual of Style (16th Edition):

Koning, Tim (author). “Low level quadcopter control using Reinforcement Learning: Developing a self-learning drone.” 2020. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:0b9e0796-13b5-42ba-b231-fbb6aadd5233.

MLA Handbook (7th Edition):

Koning, Tim (author). “Low level quadcopter control using Reinforcement Learning: Developing a self-learning drone.” 2020. Web. 18 Jan 2021.

Vancouver:

Koning T(. Low level quadcopter control using Reinforcement Learning: Developing a self-learning drone. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:0b9e0796-13b5-42ba-b231-fbb6aadd5233.

Council of Science Editors:

Koning T(. Low level quadcopter control using Reinforcement Learning: Developing a self-learning drone. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:0b9e0796-13b5-42ba-b231-fbb6aadd5233


Delft University of Technology

2. Jonker, Arnoud (author). Accelerating neural networks embedded on a microcomputer trained on KITTI.

Degree: 2020, Delft University of Technology

Due to a high contribution of human error in fatal traffic accidents, efforts in research and industry for automating vehicles steadily increased the last 3… (more)

Subjects/Keywords: vehicle; neural; network; safety; automated; artificial; intelligence; convolutional; pruning; accelerating; microcomputer; raspberry; pi; object; detection; road; user; pedestrian; cyclist; car; filter; kernel; layer

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APA (6th Edition):

Jonker, A. (. (2020). Accelerating neural networks embedded on a microcomputer trained on KITTI. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:4d60c66f-5292-4616-a72f-ba055f3b0cc8

Chicago Manual of Style (16th Edition):

Jonker, Arnoud (author). “Accelerating neural networks embedded on a microcomputer trained on KITTI.” 2020. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:4d60c66f-5292-4616-a72f-ba055f3b0cc8.

MLA Handbook (7th Edition):

Jonker, Arnoud (author). “Accelerating neural networks embedded on a microcomputer trained on KITTI.” 2020. Web. 18 Jan 2021.

Vancouver:

Jonker A(. Accelerating neural networks embedded on a microcomputer trained on KITTI. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:4d60c66f-5292-4616-a72f-ba055f3b0cc8.

Council of Science Editors:

Jonker A(. Accelerating neural networks embedded on a microcomputer trained on KITTI. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:4d60c66f-5292-4616-a72f-ba055f3b0cc8


Delft University of Technology

3. Tian, Yuan (author). Model Free Reinforcement Learning with Stability Guarantee.

Degree: 2019, Delft University of Technology

Model-free reinforcement learning has proved to be successful in many tasks such as robotic manipulator, video games, and even stock trading. However, as the dynamics… (more)

Subjects/Keywords: Reinforcement Learning

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APA (6th Edition):

Tian, Y. (. (2019). Model Free Reinforcement Learning with Stability Guarantee. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c

Chicago Manual of Style (16th Edition):

Tian, Yuan (author). “Model Free Reinforcement Learning with Stability Guarantee.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c.

MLA Handbook (7th Edition):

Tian, Yuan (author). “Model Free Reinforcement Learning with Stability Guarantee.” 2019. Web. 18 Jan 2021.

Vancouver:

Tian Y(. Model Free Reinforcement Learning with Stability Guarantee. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c.

Council of Science Editors:

Tian Y(. Model Free Reinforcement Learning with Stability Guarantee. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c


Delft University of Technology

4. Hafner, Frank (author). Cross-Modal Re-identification of Persons between RGB and Depth.

Degree: 2018, Delft University of Technology

 Cross-modal person re-identification is the task to re-identify a person which was sensedin a first modality, like in visible light (RGB), in a second modality,… (more)

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APA (6th Edition):

Hafner, F. (. (2018). Cross-Modal Re-identification of Persons between RGB and Depth. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6797b0e2-5a20-444f-8d32-a73581e00ff5

Chicago Manual of Style (16th Edition):

Hafner, Frank (author). “Cross-Modal Re-identification of Persons between RGB and Depth.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:6797b0e2-5a20-444f-8d32-a73581e00ff5.

MLA Handbook (7th Edition):

Hafner, Frank (author). “Cross-Modal Re-identification of Persons between RGB and Depth.” 2018. Web. 18 Jan 2021.

Vancouver:

Hafner F(. Cross-Modal Re-identification of Persons between RGB and Depth. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:6797b0e2-5a20-444f-8d32-a73581e00ff5.

Council of Science Editors:

Hafner F(. Cross-Modal Re-identification of Persons between RGB and Depth. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:6797b0e2-5a20-444f-8d32-a73581e00ff5


Delft University of Technology

5. Li, Mingxi (author). Efficient Neural Architecture Search for Language Modeling.

Degree: 2019, Delft University of Technology

Neural networks have achieved great success in many difficult learning tasks like image classification, speech recognition and natural language processing. However, neural architectures are hard… (more)

Subjects/Keywords: NAS; Deep learning; Artificial intelligence

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APA (6th Edition):

Li, M. (. (2019). Efficient Neural Architecture Search for Language Modeling. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:aa5c948d-43c4-480d-9818-43949c67a3b5

Chicago Manual of Style (16th Edition):

Li, Mingxi (author). “Efficient Neural Architecture Search for Language Modeling.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:aa5c948d-43c4-480d-9818-43949c67a3b5.

MLA Handbook (7th Edition):

Li, Mingxi (author). “Efficient Neural Architecture Search for Language Modeling.” 2019. Web. 18 Jan 2021.

Vancouver:

Li M(. Efficient Neural Architecture Search for Language Modeling. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:aa5c948d-43c4-480d-9818-43949c67a3b5.

Council of Science Editors:

Li M(. Efficient Neural Architecture Search for Language Modeling. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:aa5c948d-43c4-480d-9818-43949c67a3b5


Delft University of Technology

6. Bhoraskar, Akshay (author). Prediction of Fuel Consumption of Long Haul Heavy Duty Vehicles using Machine Learning and Comparison of the Performance of Various Learning Techniques.

Degree: 2019, Delft University of Technology

 This study aims at a possible solution to predict the fuel consumption of heavy duty diesel trucks, particularly, the tractor semitrailer for their long haul… (more)

Subjects/Keywords: Fuel Prediction; Machine Learning; Heavy duty vehicles

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APA (6th Edition):

Bhoraskar, A. (. (2019). Prediction of Fuel Consumption of Long Haul Heavy Duty Vehicles using Machine Learning and Comparison of the Performance of Various Learning Techniques. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:acf934e3-ceeb-49ba-b98e-11f384324aea

Chicago Manual of Style (16th Edition):

Bhoraskar, Akshay (author). “Prediction of Fuel Consumption of Long Haul Heavy Duty Vehicles using Machine Learning and Comparison of the Performance of Various Learning Techniques.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:acf934e3-ceeb-49ba-b98e-11f384324aea.

MLA Handbook (7th Edition):

Bhoraskar, Akshay (author). “Prediction of Fuel Consumption of Long Haul Heavy Duty Vehicles using Machine Learning and Comparison of the Performance of Various Learning Techniques.” 2019. Web. 18 Jan 2021.

Vancouver:

Bhoraskar A(. Prediction of Fuel Consumption of Long Haul Heavy Duty Vehicles using Machine Learning and Comparison of the Performance of Various Learning Techniques. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:acf934e3-ceeb-49ba-b98e-11f384324aea.

Council of Science Editors:

Bhoraskar A(. Prediction of Fuel Consumption of Long Haul Heavy Duty Vehicles using Machine Learning and Comparison of the Performance of Various Learning Techniques. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:acf934e3-ceeb-49ba-b98e-11f384324aea


Delft University of Technology

7. Wan, Shiyu (author). Path Following Control Design for Passenger Comfort Under Disturbances.

Degree: 2018, Delft University of Technology

In recent years, enormous progress has been made in the field of automated driving. As a consequence, automated driving technologies are becoming increasingly popular. Research… (more)

Subjects/Keywords: Path Following; Passenger Comfort; Model Predictive Control; Disturbance Rejection

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APA (6th Edition):

Wan, S. (. (2018). Path Following Control Design for Passenger Comfort Under Disturbances. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:16bf2d14-5f31-4e26-8e2e-43cb9f2cfba4

Chicago Manual of Style (16th Edition):

Wan, Shiyu (author). “Path Following Control Design for Passenger Comfort Under Disturbances.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:16bf2d14-5f31-4e26-8e2e-43cb9f2cfba4.

MLA Handbook (7th Edition):

Wan, Shiyu (author). “Path Following Control Design for Passenger Comfort Under Disturbances.” 2018. Web. 18 Jan 2021.

Vancouver:

Wan S(. Path Following Control Design for Passenger Comfort Under Disturbances. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:16bf2d14-5f31-4e26-8e2e-43cb9f2cfba4.

Council of Science Editors:

Wan S(. Path Following Control Design for Passenger Comfort Under Disturbances. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:16bf2d14-5f31-4e26-8e2e-43cb9f2cfba4


Delft University of Technology

8. YANG, MINGHAO (author). Efficient Neural Network Architecture Search.

Degree: 2019, Delft University of Technology

One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training. It can be treated as a Network… (more)

Subjects/Keywords: NAS; Deep Learning; ICML; Artificial Intelligence

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APA (6th Edition):

YANG, M. (. (2019). Efficient Neural Network Architecture Search. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9985c543-cb4e-4259-b6f8-b44ba433f1e3

Chicago Manual of Style (16th Edition):

YANG, MINGHAO (author). “Efficient Neural Network Architecture Search.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:9985c543-cb4e-4259-b6f8-b44ba433f1e3.

MLA Handbook (7th Edition):

YANG, MINGHAO (author). “Efficient Neural Network Architecture Search.” 2019. Web. 18 Jan 2021.

Vancouver:

YANG M(. Efficient Neural Network Architecture Search. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:9985c543-cb4e-4259-b6f8-b44ba433f1e3.

Council of Science Editors:

YANG M(. Efficient Neural Network Architecture Search. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:9985c543-cb4e-4259-b6f8-b44ba433f1e3


Delft University of Technology

9. Fris, Rein (author). The Landing of a Quadcopter on Inclined Surfaces using Reinforcement Learning.

Degree: 2020, Delft University of Technology

 Deep Reinforcement Learning (DRL) enables us to design controllers for complex tasks with a deep learning approach. It allows us to design controllers that are… (more)

Subjects/Keywords: Reinforcement Learning (RL); Autonomous Control; Quadcopter; Deep Learning

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APA (6th Edition):

Fris, R. (. (2020). The Landing of a Quadcopter on Inclined Surfaces using Reinforcement Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5b6fd0d1-5d18-4de7-878d-e22e4df45d3c

Chicago Manual of Style (16th Edition):

Fris, Rein (author). “The Landing of a Quadcopter on Inclined Surfaces using Reinforcement Learning.” 2020. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:5b6fd0d1-5d18-4de7-878d-e22e4df45d3c.

MLA Handbook (7th Edition):

Fris, Rein (author). “The Landing of a Quadcopter on Inclined Surfaces using Reinforcement Learning.” 2020. Web. 18 Jan 2021.

Vancouver:

Fris R(. The Landing of a Quadcopter on Inclined Surfaces using Reinforcement Learning. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:5b6fd0d1-5d18-4de7-878d-e22e4df45d3c.

Council of Science Editors:

Fris R(. The Landing of a Quadcopter on Inclined Surfaces using Reinforcement Learning. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:5b6fd0d1-5d18-4de7-878d-e22e4df45d3c


Delft University of Technology

10. GAO, Xinyu (author). Sensor Data Fusion of Lidar and Camera for Road User Detection.

Degree: 2018, Delft University of Technology

Object detection is one of the most important research topics in autonomous vehicles. The detection systems of autonomous vehicles nowadays are mostly image-based ones which… (more)

Subjects/Keywords: 3D object detection; Lidar; Camera; sensor fusion

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APA (6th Edition):

GAO, X. (. (2018). Sensor Data Fusion of Lidar and Camera for Road User Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a

Chicago Manual of Style (16th Edition):

GAO, Xinyu (author). “Sensor Data Fusion of Lidar and Camera for Road User Detection.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a.

MLA Handbook (7th Edition):

GAO, Xinyu (author). “Sensor Data Fusion of Lidar and Camera for Road User Detection.” 2018. Web. 18 Jan 2021.

Vancouver:

GAO X(. Sensor Data Fusion of Lidar and Camera for Road User Detection. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a.

Council of Science Editors:

GAO X(. Sensor Data Fusion of Lidar and Camera for Road User Detection. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a


Delft University of Technology

11. Rueda Arjona, Antonio (author). Implementing Symbolic Controllers into FPGAs.

Degree: 2019, Delft University of Technology

Embedded control systems are processor-based systems that need to run an application for an extended amount of time, such as months or years. Typically, they… (more)

Subjects/Keywords: FPGA; Symbolic Control; BDD; LabVIEW

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APA (6th Edition):

Rueda Arjona, A. (. (2019). Implementing Symbolic Controllers into FPGAs. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:259c9c49-8dd1-44e5-baa5-c0ef537828ba

Chicago Manual of Style (16th Edition):

Rueda Arjona, Antonio (author). “Implementing Symbolic Controllers into FPGAs.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:259c9c49-8dd1-44e5-baa5-c0ef537828ba.

MLA Handbook (7th Edition):

Rueda Arjona, Antonio (author). “Implementing Symbolic Controllers into FPGAs.” 2019. Web. 18 Jan 2021.

Vancouver:

Rueda Arjona A(. Implementing Symbolic Controllers into FPGAs. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:259c9c49-8dd1-44e5-baa5-c0ef537828ba.

Council of Science Editors:

Rueda Arjona A(. Implementing Symbolic Controllers into FPGAs. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:259c9c49-8dd1-44e5-baa5-c0ef537828ba


Delft University of Technology

12. Zhou, Moyu (author). Analysis of current Dutch traffic management effectiveness with automated vehicles: a ramp-metering case study: Simulation Study.

Degree: 2019, Delft University of Technology

 Automated vehicles are conventional vehicles equipped with advanced sensors, controller and actuators. They achieve intelligent information exchange with the environment through the onboard sensing and… (more)

Subjects/Keywords: Dynamic Traffic Management; Ramp Metering

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APA (6th Edition):

Zhou, M. (. (2019). Analysis of current Dutch traffic management effectiveness with automated vehicles: a ramp-metering case study: Simulation Study. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:ecb3796f-ff68-400f-bf2d-a1ad3b340154

Chicago Manual of Style (16th Edition):

Zhou, Moyu (author). “Analysis of current Dutch traffic management effectiveness with automated vehicles: a ramp-metering case study: Simulation Study.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:ecb3796f-ff68-400f-bf2d-a1ad3b340154.

MLA Handbook (7th Edition):

Zhou, Moyu (author). “Analysis of current Dutch traffic management effectiveness with automated vehicles: a ramp-metering case study: Simulation Study.” 2019. Web. 18 Jan 2021.

Vancouver:

Zhou M(. Analysis of current Dutch traffic management effectiveness with automated vehicles: a ramp-metering case study: Simulation Study. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:ecb3796f-ff68-400f-bf2d-a1ad3b340154.

Council of Science Editors:

Zhou M(. Analysis of current Dutch traffic management effectiveness with automated vehicles: a ramp-metering case study: Simulation Study. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:ecb3796f-ff68-400f-bf2d-a1ad3b340154


Delft University of Technology

13. Kokkalis, Konstantinos (author). LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning.

Degree: 2018, Delft University of Technology

 Learning capabilities are a key requisite for an autonomous agent operating in dynamically changing and complex environments, where pre-programming is not anymore possible. Furthermore, it… (more)

Subjects/Keywords: Stability Analysis; Learning Control; LMI; ReLU; Deep neural networks; Linear Matrix Inequality; Locally Weighted Learning

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APA (6th Edition):

Kokkalis, K. (. (2018). LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0d004272-2a0e-4c50-8b51-66a007e89a47

Chicago Manual of Style (16th Edition):

Kokkalis, Konstantinos (author). “LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:0d004272-2a0e-4c50-8b51-66a007e89a47.

MLA Handbook (7th Edition):

Kokkalis, Konstantinos (author). “LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning.” 2018. Web. 18 Jan 2021.

Vancouver:

Kokkalis K(. LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:0d004272-2a0e-4c50-8b51-66a007e89a47.

Council of Science Editors:

Kokkalis K(. LMI-based Stability Analysis for Learning Control: Deep Neural Networks and Locally Weighted Learning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:0d004272-2a0e-4c50-8b51-66a007e89a47


Delft University of Technology

14. Kroezen, Dave (author). Online Reinforcement Learning for Flight Control: An Adaptive Critic Design without prior model knowledge.

Degree: 2019, Delft University of Technology

Online Reinforcement Learning is a possible solution for adaptive nonlinear flight control. In this research an Adaptive Critic Design (ACD) based on Dual Heuristic Dynamic… (more)

Subjects/Keywords: Reinforcement Learning (RL); Adaptive Control; Online Learning; Adaptive Critic Designs; Flight Control Systems; Adaptive Flight Control; Machine Learning

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APA (6th Edition):

Kroezen, D. (. (2019). Online Reinforcement Learning for Flight Control: An Adaptive Critic Design without prior model knowledge. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:38547b1d-0535-4b30-a348-67ac40c7ddcc

Chicago Manual of Style (16th Edition):

Kroezen, Dave (author). “Online Reinforcement Learning for Flight Control: An Adaptive Critic Design without prior model knowledge.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:38547b1d-0535-4b30-a348-67ac40c7ddcc.

MLA Handbook (7th Edition):

Kroezen, Dave (author). “Online Reinforcement Learning for Flight Control: An Adaptive Critic Design without prior model knowledge.” 2019. Web. 18 Jan 2021.

Vancouver:

Kroezen D(. Online Reinforcement Learning for Flight Control: An Adaptive Critic Design without prior model knowledge. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:38547b1d-0535-4b30-a348-67ac40c7ddcc.

Council of Science Editors:

Kroezen D(. Online Reinforcement Learning for Flight Control: An Adaptive Critic Design without prior model knowledge. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:38547b1d-0535-4b30-a348-67ac40c7ddcc


Delft University of Technology

15. Sankararaman, Shyam Prasadh (author). Tissue Characterization by Deep Learning in Medical Hyperspectral Images.

Degree: 2019, Delft University of Technology

Hyperspectral imaging (HSI) is a promising imaging modality in medical applications, especially for non-invasive and non-contact disease diagnosis and image-guided surgery. Encoding both spatial and… (more)

Subjects/Keywords: hyperspectral imaging; Deep Learning; Medical imaging; Philips; convolutional neural networks

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APA (6th Edition):

Sankararaman, S. P. (. (2019). Tissue Characterization by Deep Learning in Medical Hyperspectral Images. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:cd37823a-9434-49d0-a3ec-65ea101b89cd

Chicago Manual of Style (16th Edition):

Sankararaman, Shyam Prasadh (author). “Tissue Characterization by Deep Learning in Medical Hyperspectral Images.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:cd37823a-9434-49d0-a3ec-65ea101b89cd.

MLA Handbook (7th Edition):

Sankararaman, Shyam Prasadh (author). “Tissue Characterization by Deep Learning in Medical Hyperspectral Images.” 2019. Web. 18 Jan 2021.

Vancouver:

Sankararaman SP(. Tissue Characterization by Deep Learning in Medical Hyperspectral Images. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:cd37823a-9434-49d0-a3ec-65ea101b89cd.

Council of Science Editors:

Sankararaman SP(. Tissue Characterization by Deep Learning in Medical Hyperspectral Images. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:cd37823a-9434-49d0-a3ec-65ea101b89cd


Delft University of Technology

16. van Beelen, Ruben (author). Adaptive Observer for Automated Emergency Maneuvers: Fusing cost-efficient onboard sensors with computer vision into a robust estimate of sideslip angle using online covariance calculation.

Degree: 2019, Delft University of Technology

One of the most promising ideas in autonomous vehicle control systems is letting the vehicle drive autonomously outside the normal, linear, operating region and letting… (more)

Subjects/Keywords: sideslip estimation; computer vision; vehicle dynamics control; covariance calculation

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

APA (6th Edition):

van Beelen, R. (. (2019). Adaptive Observer for Automated Emergency Maneuvers: Fusing cost-efficient onboard sensors with computer vision into a robust estimate of sideslip angle using online covariance calculation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c6fe2ac1-8b96-4557-9d5f-8a1c4b885bb3

Chicago Manual of Style (16th Edition):

van Beelen, Ruben (author). “Adaptive Observer for Automated Emergency Maneuvers: Fusing cost-efficient onboard sensors with computer vision into a robust estimate of sideslip angle using online covariance calculation.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:c6fe2ac1-8b96-4557-9d5f-8a1c4b885bb3.

MLA Handbook (7th Edition):

van Beelen, Ruben (author). “Adaptive Observer for Automated Emergency Maneuvers: Fusing cost-efficient onboard sensors with computer vision into a robust estimate of sideslip angle using online covariance calculation.” 2019. Web. 18 Jan 2021.

Vancouver:

van Beelen R(. Adaptive Observer for Automated Emergency Maneuvers: Fusing cost-efficient onboard sensors with computer vision into a robust estimate of sideslip angle using online covariance calculation. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:c6fe2ac1-8b96-4557-9d5f-8a1c4b885bb3.

Council of Science Editors:

van Beelen R(. Adaptive Observer for Automated Emergency Maneuvers: Fusing cost-efficient onboard sensors with computer vision into a robust estimate of sideslip angle using online covariance calculation. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:c6fe2ac1-8b96-4557-9d5f-8a1c4b885bb3


Delft University of Technology

17. Wang, Yizhou (author). Binary Neural Networks for Object Detection.

Degree: 2019, Delft University of Technology

In the past few years, convolutional neural networks (CNNs) have been widely utilized and shown state-of-the-art performances on computer vision tasks. However, CNN based approaches… (more)

Subjects/Keywords: Neural Network Quantization; Deep Learning; Object Detection; Computer Vision; Artificial Intelligence

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

APA (6th Edition):

Wang, Y. (. (2019). Binary Neural Networks for Object Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9f0da106-82ea-4f2e-9cd5-8bc834885d6f

Chicago Manual of Style (16th Edition):

Wang, Yizhou (author). “Binary Neural Networks for Object Detection.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:9f0da106-82ea-4f2e-9cd5-8bc834885d6f.

MLA Handbook (7th Edition):

Wang, Yizhou (author). “Binary Neural Networks for Object Detection.” 2019. Web. 18 Jan 2021.

Vancouver:

Wang Y(. Binary Neural Networks for Object Detection. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:9f0da106-82ea-4f2e-9cd5-8bc834885d6f.

Council of Science Editors:

Wang Y(. Binary Neural Networks for Object Detection. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:9f0da106-82ea-4f2e-9cd5-8bc834885d6f


Delft University of Technology

18. Anil Meera, Ajith (author). Informative Path Planning for Search and Rescue using a UAV.

Degree: 2018, Delft University of Technology

Target search in an obstacle filled environment is a practically relevant challenge in robotics that has a huge impact in the society. The wide range… (more)

Subjects/Keywords: Path Planning; Robotics; Unmanned Aerial Vehicle; Computer Vision; Optimization

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

APA (6th Edition):

Anil Meera, A. (. (2018). Informative Path Planning for Search and Rescue using a UAV. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:34ce9384-9352-41bc-99ce-2a54bd1f3361

Chicago Manual of Style (16th Edition):

Anil Meera, Ajith (author). “Informative Path Planning for Search and Rescue using a UAV.” 2018. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:34ce9384-9352-41bc-99ce-2a54bd1f3361.

MLA Handbook (7th Edition):

Anil Meera, Ajith (author). “Informative Path Planning for Search and Rescue using a UAV.” 2018. Web. 18 Jan 2021.

Vancouver:

Anil Meera A(. Informative Path Planning for Search and Rescue using a UAV. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 18]. Available from: http://resolver.tudelft.nl/uuid:34ce9384-9352-41bc-99ce-2a54bd1f3361.

Council of Science Editors:

Anil Meera A(. Informative Path Planning for Search and Rescue using a UAV. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:34ce9384-9352-41bc-99ce-2a54bd1f3361

.