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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
URL: http://resolver.tudelft.nl/uuid:0b9e0796-13b5-42ba-b231-fbb6aadd5233 
► 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)
▼ 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 by simultaneously receiving rewards it learns what is appropriate behaviour. Even though it has roots in machine learning, RL is essentially different from other machine learning methods. In contrast to others, RL agent has to generate its own data to learn from. In this thesis, we aim to train an RL agent to fly a quadcopter to track any target position (way-point) in three dimensional space. Where conventional control strategies for quadcopters involve a separate attitude and position controller and most RL solutions focus on one of the two controllers, our goal is to design a low level RL controller capable of computing motor commands directly from sensor input, therefore replacing both attitude and position controller with one RL policy. The policies we develop utilize the algorithm ’Twin Delayed Deep Deterministic Policy Gradient’ (TD3) for learning. TD3 is a variant of the Deep Deterministic Policy Gradient (DDPG) algorithm. The policy for attitude control trained for 3500 episodes 3, around 6.1e5 time steps. The learned policy is able to stabilize the attitude of the quadcopter (in simulation) with a success rate of 94 %. For position control, two policies are generated with two different types of dense reward. The resulting type 1 policy has high fluctuations in motor commands and therefore oscillating attitude and position values. In none of the evaluation trajectories a steady state value is reached. The type 2 produces a working policy after a shorter training time of 1200 episodes, 1.1푒6 time steps. For all tested trajectories, this policy achieves steady state for almost each way-point. This thesis proves that TD3 can be used for low-level quadcopter control, replacing both inner and outer loops of the quadcopter control. Using the dense reward function and applying negative reward on position control only results in a stable policy that can track way-points all throughout the 3D space. Future work requires the twostep parameter estimation to be tested on a real life quadcopter, as well as enrolling the policy onto a real life quadcopter.
Advisors/Committee Members: Pan, Wei (mentor), Delft University of Technology (degree granting institution).
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
URL: http://resolver.tudelft.nl/uuid:4d60c66f-5292-4616-a72f-ba055f3b0cc8
► 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)
▼ 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 decades. To reduce accidents with other road users, automated recognition and classification of road users is crucial. The most accurate models are large convolutional neural networks, however they require expensive and powerful hardware to be implemented. The hardware requirement hinders wide embedded use in education and industry, slowing down the potential increase of safety on the road. In pursue of reduced inference time on GPUs, research has focused to reduce the computational demand of neural networks. However the accelerations on microcomputers are not reported, potentially leading to different results. To contribute to increased insight of the applicability of previous research on microcomputers, this thesis compares accelerations on a microcomputer and GPU. This is pursued by using the pretrained neural network "Squeezedet", trained on the "KITTI" road user dataset. The neural network is pruned in 3 different ways: by reducing the number of filters, by reducing the size of filter kernels along a layer and by reducing the amount of layers. The accelerations are measured on a Raspberry Pi 3b+ and a Tesla K80 GPU. The process of embedding the neural network on the Raspberry Pi is described in great detail, to promote further research and educational use. Acceleration of the network is up to 12 % better on a Raspberry Pi than a GPU, when pruning filters from the network. When pruning full layers, or decreasing filter size the accelerations are up to 4% worse than on a GPU. This means when working with a microcomputer, the choice of pruning method can not be based on reported accelerations on GPUs. Therefore the recommendation is to do more research on accelerating neural networks for microcomputers. This will lead to wider use in industry and education and eventually safer roads.
Mechanical Engineering | Vehicle Engineering
Advisors/Committee Members: Pan, Wei (mentor), Delft University of Technology (degree granting institution).
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
URL: http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c
► 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)
▼ 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 of the environment is unmodelled, it is fundamentally difficult to ensure the learned policy to be absolutely reliable and its performance is guaranteed. In this thesis, we borrow the concept of stability and Lyapunov analysis in control theory to design a policy with stability guarantee and assure the guaranteed behaviors of the agent. A novel sample-based approach is proposed for analyzing the stability of a learning control system, and on the basis of the theoretical result, we establish a practical model-free learning framework with provable stability, safety and performance guarantees. % Specifically, a novel locally constrained method is proposed to solve the safety constrained problems with lower conservatism. In our solution, a Lyapunov function is searched automatically to guarantee the closed-loop system stability, which also guides the simultaneous learning (covering both the policy and value-based learning methods). Our approach is evaluated on a series of discrete and continuous control benchmarks and largely outperforms the state-of-the-art results concerning unconstrained and constrained problems. It is also shown that the algorithm has the ability of recovery to equilibrium under perturbation using the policy with stability guarantee. (Anonymous code is available to reproduce the experimental esults\footnote{\url{https://github.com/RLControlTheoreticGuarantee/GuaranteeLearningControl}}.) Since sometimes the constraint is hard to define, we introduce a novel method to learn a constraint by representing the bad cases or situations as a distribution, and the constraint is the Wasserstein distance between the distribution.
Mechanical Engineering | Vehicle Engineering
Advisors/Committee Members: Pan, Wei (mentor), Zhou, Hongpeng (graduation committee), Delft University of Technology (degree granting institution).
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
URL: http://resolver.tudelft.nl/uuid:6797b0e2-5a20-444f-8d32-a73581e00ff5
► 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)
▼ 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, like depth. Therefore, the challenge is to sense between inputs from separate modalities, without information from both modalities at the same time step. Lately, the scientific challenge of cross-modal person re-identification between depth and RGB is getting more and more attention due to the needs of intelligent vehicles, but also interested parties in the surveillance domain, where sensing in poor illumination is desirable. Techniques for cross-modal person re-identification have to solve several concurrent tasks. First, techniques have to be robust against variations in the single modalities. Occurring challenges are viewpoint changes, pose variations or variations in camera resolution. Second, the challenge of re-identifying a person has to be solved across the modalities within a heterogeneous network of RGB and depth cameras. At the present day, work in cross-modal re-identification between infrared images and RGB images exist. At the same time almost no work was done in re-identification between depth images and visible light images. The objective of this work is to fill this gap by comparing the performance of different techniques for cross-modal re-identification of persons. The main contributions of this work are two-fold. First, different deep neural network architectures for cross-modal re-identification of persons between depth and visible light are investigated and compared. Second, a new technique for cross-modal person re-identification is presented. Thet echnique is based on two-step cross-distillation and allows to extract similar features from the depth and visible light modality. Therefore, the task of matching persons sensed between depth and visible light is facilitated and can be solved with higher accuracy. Within the evaluation, it was possible to report state-of-the-art results for two relevant datasets for cross-modal person re-identification between depth and RGB. For the BIWI RGBD-ID dataset the pre-existing state-of-the-art was improved by more than 15% in mean average precision. Additionally, it was possible to validate the performance of the method with the RobotPKU dataset. Although the method was successfully applied in cross-modal person re-identification between depth and RGB, it was shown that in another modality combinations, like RGB and infrared, the technique in its current definition cannot be considered state-of-the-art. Finally, it is possible to give a lookout on the implications of the results for the intelligent vehicles domain. For a successful deployment in this area more thorough datasets have to be developed and the performance on sparse depth maps, as provided by lidars or radars, have to be investigated.
Advisors/Committee Members: Gavrila, Dariu (mentor), Kooij, Julian (mentor), Tax, David (mentor), Pan, Wei (mentor), Delft University of Technology (degree granting institution).
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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
URL: http://resolver.tudelft.nl/uuid:aa5c948d-43c4-480d-9818-43949c67a3b5
► 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)
▼ 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 to design, which requires lots of knowledge and time of human experts. Therefore, there has been a growing interest in automating the process of designing neural architectures. Though these searched architectures have achieved competitive performance on various tasks, the efficiency of NAS still needs to be improved. Moreover, current neural architecture search approach disregards the dependency between a node and its predecessors and successors. This thesis builds upon BayesNAS which employs the classic Bayesian learning method to search for CNN architectures, and extends it to the problem of neural architecture search for recurrent architectures. Hierarchical sparse priors are used to model the architecture parameters to alleviate the dependency issue. Since the update of posterior variance is based on Laplace approximation, an efficient method to compute the Hessian of recurrent layer is proposed. We can find candidated architectures after training the over-parameterized network for only one epoch. Our experiments on Penn Treebank and WikiText-2 show that competitive architectures can be found in 0.3 GPU days using a single GPU for language modeling task. We find that our algorithm is more efficient than state-of-the-art.
Electrical Engineer | Embedded Systems
Advisors/Committee Members: Oliehoek, Frans (mentor), Pan, Wei (graduation committee), van Gemert, Jan (graduation committee), Zhou, Hongpeng (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: NAS; Deep learning; Artificial intelligence
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APA ·
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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
URL: http://resolver.tudelft.nl/uuid:acf934e3-ceeb-49ba-b98e-11f384324aea
► 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)
▼ 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 operations using various machine learning techniques. It intends to provide a possible alternative to simulation or physics based models, which often are very complicated. The stringent laws on emission control set by the Paris Agreement and the fact that heavy duty trucks contribute to almost 27% of CO2 emissions from road transport and their dependence on diesel for operations (in long haul) makes it the need of the hour to first, have an estimate on the emissions being produced and second, to develop technologies to reduce those emissions. This study focuses specifically on the first part i.e., estimating the amount of fuel consumed by heavy duty trucks in the European Union and thereby determine the emissions being produced. The main objective is to examine whether an approach of machine learning could be a viable option to predict fuel consumption. This thesis is part of the AEROFLEX project and was done in collaboration with TNO, which provided all the data-sets required for the study. The idea was to explore the regime of machine learning for one time step ahead prediction of fuel consumption. Furthermore, this study also focused on the development of another model by not using any variables affected by the driver as input into the training model. This exclusion was necessary to make sure the model remained adaptive to new routes and new trucks, especially because large scale on-road testing of the newly developed trucks is impossible and also because this way would help predict the fuel consumed by a truck without the necessity of it driving on a road. The study concludes with a comparison with an existing simulation model at TNO and provide an alternative machine learning solution. It also provides a comparison between different machine learning techniques and suggest the most accurate one. It was found that machine learning could potentially be used to predict the amount of fuel consumed by a long haul heavy duty truck driving on a motorway. It was also found that engine torque was the variable that affected the fuel consumption of the truck the most. Furthermore, Neural Network was the most potent algorithm among all the other learning techniques for both the models developed in this study with it performing better than the simulation tool by a factor of approximately 3.8 in the model where the driver/drive influenced inputs were not considered in the training data-set. The results obtained from this work at a sampling frequency of 10 Hz. (i.e., 0.1 seconds) are comparable to the ones reported by other sources at a sampling rate of 0.016 Hz. (i.e., 1 minute) or 0.0016 Hz. (i.e., 10 minutes). This goes on to say that the machine learning algorithms are also potent at much higher sampling frequencies.
Advisors/Committee Members: Pan, Wei (mentor), Wisse, Martijn (graduation committee), Knoop, Victor (graduation committee), van Eijk, Emiel (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Fuel Prediction; Machine Learning; Heavy duty vehicles
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APA ·
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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
URL: http://resolver.tudelft.nl/uuid:16bf2d14-5f31-4e26-8e2e-43cb9f2cfba4
► 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)
▼ 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 on comfort for autonomous vehicles, however, is still limited and unexplored. Some researchers address the comfort issue in path planning by velocity profiles, which regulates the instantaneous values of vehicle acceleration and jerk. Meanwhile, the actuator response to external disturbances and the inaccurate following can result in the violation to the pre-designed path, and therefore causes an uncomfortable driving experience. In order to tackle the passenger comfort issue from the perspective of path following control, this study proposes a frequency shaped model predictive control scheme that is (1) robust under external disturbances and (2) able to optimize passenger comfort by regulating the vehicle lateral acceleration with respect to its corresponding frequency. The frequency is selected based on the comfort evaluation criteria proposed in ISO 2631. Further, the proposed controller is tested in three simulation scenarios, compared to three baseline controllers with respect to tracking accuracy and driving comfort. Finally, our analysis shows that the FSMPC controller can improve driving comfort, especially at the velocity higher than 60 km/h.
Mechanical Engineering
Advisors/Committee Members: Happee, Riender (mentor), Ferranti, Laura (mentor), Pan, Wei (graduation committee), Delft University of Technology (degree granting institution).
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
URL: http://resolver.tudelft.nl/uuid:9985c543-cb4e-4259-b6f8-b44ba433f1e3
► 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)
▼ 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 Compression problem on the architecture parameters from an overparameterized network. However, there are two issues associated with most one-shot NAS methods. First, dependencies between a node and its predecessors and successors are often disregarded which result in improper treatment over zero operations. Second, architecture parameters pruning based on their magnitude is questionable. In this thesis, classic Bayesian learning approach is applied to alleviate these two issues. Unlike other NAS methods, we train the over-parameterized network for only one epoch before update network architecture. Impressively, this enabled us to find the optimal architecture in both proxy and proxyless tasks on CIFAR-10 within only 0.2 GPU days using a single GPU. As a byproduct, our approach can be transferred directly to convolutional neural networks compression by enforcing structural sparsity that is able to achieve extremely sparse networks without accuracy deterioration.
Mechanical Engineering | Vehicle Engineering
Advisors/Committee Members: Pan, Wei (mentor), Zhou, Hongpeng (mentor), Gavrila, Dariu (graduation committee), van de Plas, Raf (graduation committee), Delft University of Technology (degree granting institution).
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
URL: http://resolver.tudelft.nl/uuid:5b6fd0d1-5d18-4de7-878d-e22e4df45d3c
► 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)
▼ 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 otherwise cumbersome to design with conventional control methodologies. Often, an objective for RL is binary in nature. However, exploring in environments with sparse rewards is a problem in RL, and finding positive reward becomes exponentially more difficult with increased environment complexity. For this project, our objective is to design an RL based controller for the landing of a quadcopter on inclined surfaces. Landing is defined as reaching these inclined surfaces with reasonable speed, such that no damage is done to either the quadcopter or the surface to land on upon impact. We aim to use a binary reward for this task. We use methods to aid exploration in sparse reward environments, namely Hindsight Experience Replay (HER), and non-optimized demonstrations. HER can resample goals from the demonstrator data and the policy rollouts. The resampling of goals is done by considering a portion of the visited states during policy rollouts as the intended goals. The demonstrations are non-optimized in the sense that the demonstrations do not follow the same objective as ours. We consider demonstrations valid if these demonstrations are obtained from arbitrary stable policies. Our results show that the RL system does generalize to other goals when using HER and demonstrations. The demonstrations are not imitated as were to happen in pure imitation learning. HER, on the other hand, enabled us to receive reward in our complex environment, while also allowing us to experience multiple goals in one policy rollout. We found that lack of HER and demonstrations were not able to overcome the problems of exploration in sparse reward environments. We found that landing a quadcopter on inclined surfaces using an RL controller is feasible. Our trajectories clearly showed a swinging motion which in theory should be a valid control strategy for this problem. This swinging motion results in dead spots with the quadcopter being in a state with a minimal translational and rotational velocities under a relatively large angle. Further research is needed to increase the accuracy and robustness of our RL based controller.
Advisors/Committee Members: Babuska, Robert (mentor), Pan, Wei (graduation committee), Ferreira de Brito, Bruno (graduation committee), Delft University of Technology (degree granting institution).
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
URL: http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a
► 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)
▼ 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 detect target objects in the images. Although image-based detectors can provide a rather accurate 2D position of the object in the image, it is necessary to get the accurate 3D position of the object for an autonomous vehicle since it operates in the real 3D world. The relative position of the objects will heavily influence the vehicle control strategy. This thesis work aims to find out a solution for the 3D object detection by combining the Lidar point cloud and camera images, considering that these are two of the most commonly used perception sensors of autonomous vehicles. Lidar performs much better than the camera in 3D object detection since it rebuilds the surface of the surroundings by the point cloud. What’s more, combing Lidar with the camera provides the system redundancy in case of a single sensor failure. Due to the development of Neural Network (NN), past researches achieved great success in detecting objects in the images. Similarly, by applying the deep learning algorithms to parsing the point cloud, the proposed 3D object detection system obtains a competitive result in the KITTI 3D object detection benchmark.
Vehicle Engineering
Advisors/Committee Members: Gavrila, Dariu (mentor), Domhof, Joris (mentor), Kooij, Julian (graduation committee), Pan, Wei (graduation committee), Delft University of Technology (degree granting institution).
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
URL: http://resolver.tudelft.nl/uuid:259c9c49-8dd1-44e5-baa5-c0ef537828ba
► 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)
▼ 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 implement a realtime function to control a system. Embedded systems are implemented using hardware and software to perform an specific task. This is why they can be optimized to reduce its size and cost and increase its reliability and performance. In embedded control systems, a discrete time embedded system is controlling a continuous time plant. In order to deal with this complex interactions, there are some tools that synthesize symbolic controllers. However, the size of these controllers is still too large to be widely implemented in embedded systems for real-time applications. Although it is possible to implement them in CPUs with large memory, their time-step is limited by a few GHz. On the other hand, FPGAs can run at a higher frequency (MHz) but they have limited memory. In this project, we propose a tool that automate the process of compressing, determinizing and generating the necessary files to flash a symbolic controller into an FPGA. We propose three different ways of transforming the original controllers and we compare them with another similar tool from the Technische Universität München. We also simulate in real-time the controlled closed-loop of some of those symbolic controllers using a simulated plant to validate the entire process.
Electrical Engineer | Embedded Systems
Advisors/Committee Members: Mazo Espinosa, Manuel (mentor), Mazo Espinosa, Manuel (graduation committee), Pan, Wei (graduation committee), Kok, Manon (graduation committee), Delft University of Technology (degree granting institution).
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
URL: http://resolver.tudelft.nl/uuid:ecb3796f-ff68-400f-bf2d-a1ad3b340154
► 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)
▼ 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 cooperative system. vehicles are possible to have situation awareness and automatically analyze the safety and dangerous state of journeys. Finally vehicles can reach destinations following drivers' willing. The ongoing research on intelligent vehicles is mainly about improving the safety, comfort, efficiency and provide an excellent human-car interface. As a self-organizing system, the traffic system is quite complicated. There are many disturbance factors to lead to various traffic problems. One of the daily occurring problems is congestion on the motorway. In order to reduce congestion, Rijkswaterstaat applies various dynamic traffic management (DTM) measures to guide the traffic. It works well nowadays in conventional traffic. However, automated vehicles entered the market recently and will start to play an essential role in future traffic. The automated vehicles' reaction to DTM measures may be different from conventional vehicles while the traffic problems still exist. Therefore, it is necessary to research the effectiveness of current Dutch traffic management in automated vehicles. This thesis aims to investigate the effectiveness of current Dutch DTM measures with driver assistant and partially automated vehicles. Due to the time limitation, only the ramp metering measure will be researched through a simulation study. Therefore the main research question is 'How partial automated driving influences the performance of current Dutch dynamic traffic management system and how can this be evaluated via simulation?'. Three methods are applied, including literature review, simulation and statistical analysis. The literature part reviews levels of automation, various longitudinal and lateral vehicle motion models, which are chosen and modified in the simulation. Many ramp metering algorithms are also introduced in the literature review. The ramp metering controller in the simulation follows RWS algorithm. Besides, the motorway demand and the penetration rate of level 1 and 2 vehicles are two input of the simulation. From the simulation results, it is concluded that the level 2 automation consisting of Adaptive Cruise Control (ACC) and Lane Change Assistance (LCA) system brings a negative impact on the motorway capacity. The ramp metering measure remains efficient if the penetration rate of level 2 vehicles is low. However, when the capacity reduces to the critical flow set up in the ramp metering controller, Ramp metering loses its efficiency. The parameters in the ramp metering controller therefore, require an update. For further research, it is recommended to simulate the same scenarios with different ramp metering algorithms. Since the functions of the algorithms are different, there might be other robust control algorithms for automated vehicles. Besides, another limitation of this thesis is that the automation…
Advisors/Committee Members: van Lint, Hans (graduation committee), Calvert, Simeon (mentor), Taale, Henk (mentor), Schakel, Wouter (mentor), Pan, Wei (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Dynamic Traffic Management; Ramp Metering
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APA ·
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Export
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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
URL: http://resolver.tudelft.nl/uuid:0d004272-2a0e-4c50-8b51-66a007e89a47
► 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)
▼ 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 is essential to guarantee that the learning agent will act safely by considering its stability properties. In this thesis, novel conditions are proposed, aiming to examine stability of the learned dynamics for two important model classes; namely Rectified Linear Unit (ReLU) Deep Neural Networks (DNNs) and Locally Weighted Learning (LWL). For the former method, a theoretical and computational framework is developed by establishing an equivalence between ReLU DNN models and Piecewise Affine (PWA) systems. This allows to leverage well-known tools of PWA system analysis, and consequently compute, characterize equilibria and determine their region of attraction for ReLU DNNs. Due to their increased complexity, a structured search for appropriate stability conditions was performed for LWL methods until the optimal trade-off between conservativeness and computational efficiency was obtained. These stability conditions are given as Linear Matrix Inequality (LMI) problems and they consist the first stability results in literature for these two model classes. Their efficacy is assessed in numerical and real-world dynamical systems and it is shown that the proposed LMIs are not unreasonably conservative, as they can evaluate accurately the stability properties of these two representations. Finally, this work demonstrates how to formulate appropriate stability conditions for learning methods in a principled manner.
Advisors/Committee Members: Trimpe, Sebastian (mentor), Kober, Jens (mentor), Hellendoorn, Hans (graduation committee), Nunez Vicencio, Alfredo (graduation committee), Pan, Wei (graduation committee), Delft University of Technology (degree granting institution).
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
URL: http://resolver.tudelft.nl/uuid:38547b1d-0535-4b30-a348-67ac40c7ddcc
► 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)
▼ 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 Programming (DHP) is developed and implemented on a simulated Cessna Citation 550 aircraft. Using an online identified system model approximation, the method is independent of prior model knowledge. The agent consists of two Artificial Neural Networks (ANNs) which form the Adaptive Critic Design and is supplemented with a Recursive Least Squares (RLS) online model estimation. The implemented agent is demonstrated to learn a near optimal control policy for different operating points, which is capable of tracking pitch and roll rate while actively minimizing the sideslip angle in a faster than real-time simulation. Providing limited model knowledge is shown to increase the learning, performance and robustness of the controller.
Aerospace Engineering | Control and Simulation
Advisors/Committee Members: van Kampen, Erik-jan (mentor), de Croon, Guido (graduation committee), Mitici, Mihaela (graduation committee), Pan, Wei (graduation committee), Delft University of Technology (degree granting institution).
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 ·
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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
URL: http://resolver.tudelft.nl/uuid:cd37823a-9434-49d0-a3ec-65ea101b89cd
► 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)
▼ 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 spectral information, it can detect subtle changes in the biochemical and morphological properties of a tissue, revealing the early progression of a pathological condition like cancer. Previous medical hyperspectral image analysis approaches depended on handcrafted features or feature extraction principle, requiring considerable domain expertise. To overcome this, automatic feature learning approaches like convolutional neural networks (CNN), previously used in tasks like classification, detection and segmentation in medical images were applied to hyperspectral data, although in a limited number of research studies. This thesis was proposed to review the state-of-the-art in medical hyperspectral image analysis, identify the limitations in current methods, and present a proof-of-concept for using limited hyperspectral image data in CNN-driven tissue characterization. The goal of this thesis is to characterize, using CNNs, ex vivo head and neck (tongue) tissue of patients affected by tumors. While previous work in this field implemented patch-based classification of tissue, in this thesis, a pixel-wise classification approach was proposed to obtain a smooth and continuous segmentation of hyperspectral images. To this end, two types of CNN models were trained from scratch using limited labelled training data, one to automatically learn the spectral features present in the hyperspectral data and the other to learn the combined spectral-spatial features from the hyperspectral data. Performance of four different trained models was evaluated by using a leave-one-out testing scheme, with the spectral-spatial learning approach with larger input spatial dimensions outperforming the other considered approaches.
Mechanical Engineering | Systems and Control
Advisors/Committee Members: van de Plas, Raf (mentor), Shan, Caifeng (graduation committee), Vdovin, Gleb (graduation committee), Pan, Wei (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: hyperspectral imaging; Deep Learning; Medical imaging; Philips; convolutional neural networks
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APA ·
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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
URL: http://resolver.tudelft.nl/uuid:c6fe2ac1-8b96-4557-9d5f-8a1c4b885bb3
► 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)
▼ 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 it "drift". By doing so, the maneuverability of the vehicle could be enhanced. To enable systems that can control this behaviour, estimation of certain vehicle states is needed with high accuracy and high frequency.In this project, a new solution to this problem is proposed by combining a mixed dynamic-kinematic observer with a single camera that produces velocity measurements based on tracking the ground plane. To improve filtering of the camera velocity measurements, the measurement error covariance matrix is updated online based on a model of the camera measurement error. Evaluation of the new methodology was done on data recorded from a 1:10 scale test vehicle and performance was assessed based on ground truth data obtained using a Motion Capture System.In normal driving conditions with correctly identified vehicle parameters, an observer without camera performs better by 25% in terms of RMSE on lateral velocity and sideslip angle estimation. However, the online adaptation of the covariance matrix results in an estimate that is at least 45% more accurate in terms of RMSE than the same observer without online covariance adaptation. Next to that, experiments show that the proposed observer with camera has better robustness to uncertainty in model parameters by almost a factor five in terms of RMSE than the observer without camera.When the grip of the tires is physically lowered and the vehicle is drifting, the proposed observer can track large sideslip angles (>30°), where the state-of-the-art observer without camera is not able. The state-of-the-art observer has an increase in RMSE of 75% on all estimated quantities in comparison to the proposed methodology. These results show that adding a camera to an existing sideslip angle observer greatly enhances robustness of the observer to uncertainty in model parameters and violation of model assumptions. This comes dat the cost of losing some accuracy in normal driving conditions.
Systems and Control
Advisors/Committee Members: Hellendoorn, Hans (mentor), Corno, M (graduation committee), Batselier, Kim (graduation committee), Pan, Wei (graduation committee), Flohr, Fabian (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: sideslip estimation; computer vision; vehicle dynamics control; covariance calculation
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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
URL: http://resolver.tudelft.nl/uuid:9f0da106-82ea-4f2e-9cd5-8bc834885d6f
► 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…
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▼ 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 usually require a large amount of storage, run-time memory, as well as computation power in both training and inference time, which are usually used on GPU based machines to ensure the speed for inferences. But they are usually insufficient to be deployed on low-power applications. Although many approaches were proposed to compress and accelerate the CNN models, most of them were only evaluated on relatively simple problems (e.g. image classification), which only support limited real-world applications. Especially, among those methods, binary quantization can achieve very high model compression, but only a few works have been observed to utilize it on more complex tasks. Therefore, the exploration and evaluations of applying binary quantization on more complex tasks like object detection are worthwhile, which can be used in much more applications like autonomous driving and face detection. In this project, we apply and evaluate two different binary quantization approaches, named ABC-Net and PA-Net on object detection tasks. Also, we specify the exact implementation details for the binary convolutional operations in this project. As a result, we can achieve maximally 6.1× (around 16% of the full-precision model) compression, and minimal 2.5% accuracy reduction for weight quantization. The weight quantized models were able to outperform some existing real-time detectors in terms of both accuracy and storage size. Although large accuracy reduction was observed for input quantization, the quantized model could still maintain an acceptable accuracy compared to existing real-time object detectors.
Embedded Systems
Advisors/Committee Members: Al-Ars, Zaid (mentor), Pan, Wei (graduation committee), van Genderen, Arjan (graduation committee), Zhu, Baozhou (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Neural Network Quantization; Deep Learning; Object Detection; Computer Vision; Artificial Intelligence
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APA ·
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MLA ·
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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
URL: http://resolver.tudelft.nl/uuid:34ce9384-9352-41bc-99ce-2a54bd1f3361
► 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…
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▼ 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 of applications include searching for victims in a search and rescue operation, detecting weeds in precision agriculture, patrolling borders for military and navy, automated census of endangered species in a forest etc. An efficient target search algorithm provides a data acquisition platform with least human intervention, thus improving the quality of life of humans. This thesis aims at introducing a general path planning algorithm for UAVs flying at different heights in an obstacle filled environment, searching for targets in the ground field. An adaptive informative path planning (IPP) algorithm is introduced that simultaneously trade off between area coverage, field of view, height dependent sensor performance and obstacle avoidance. It plans under uncertainties in the sensor measurements at varying heights, and is robust against wrong target detections. It generates an optimal fixed horizon plan in the form of a 3D minimum-snap trajectory that maximizes the information gain in minimum flight time by providing maximum area coverage, without any collision with the obstacles. The resulting planner is modular in terms of the mapping strategy, environment complexity, different target, changes in the sensor model and optimizer used. The planner is tested against varying environmental complexities, demonstrating its capability in handling a wide range of possible environments. The planner outperforms other planners like non-adaptive IPP planner, coverage planner and random sampling planner, by demonstrating the fastest decrease in map error while flying for a fixed time budget. A proof of concept for the algorithm is provided through real experiments by running the algorithm on a UAV flying inside a lab environment, searching for targets lying on the ground. All the targets were successfully found and mapped by the algorithm, demonstrating its applicability in a real-life target search problem.
Mechanical Engineering | Biorobotics
Advisors/Committee Members: Siegwart, Roland (mentor), Wisse, Martijn (mentor), Popović, Marija (mentor), Millane, Alexander (mentor), Pan, Wei (graduation committee), Alonso Mora, Javier (graduation committee), Mohajerin Esfahani, Peyman (graduation committee), Delft University of Technology (degree granting institution), ETH Zürich (degree granting institution).
Subjects/Keywords: Path Planning; Robotics; Unmanned Aerial Vehicle; Computer Vision; Optimization
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APA ·
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MLA ·
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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
. 
Open Access Theses and Dissertations