Searched for: subject%3A%22Transfer%255C+learning%22
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van Putten, Noël (author)
In this paper, the Proximal Policy Optimization (PPO) algorithm is used to perform a constrained wing shape optimization. The PPO algorithm is a Machine Learning (ML) algorithm that improves itself by repeatedly performing the same optimization and learning from its results. The complete adaptation of the PPO framework to the design problem is...
master thesis 2024
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Kapoor, T. (author), Wang, H. (author), Nunez, Alfredo (author), Dollevoet, R.P.B.J. (author)
This paper proposes a novel framework for simulating the dynamics of beams on elastic foundations. Specifically, partial differential equations modeling Euler–Bernoulli and Timoshenko beams on the Winkler foundation are simulated using a causal physics-informed neural network (PINN) coupled with transfer learning. Conventional PINNs encounter...
journal article 2024
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Paldino, Gian Marco (author), De Caro, Fabrizio (author), De Stefani, J. (author), Vaccaro, Alfredo (author), Bontempi, Gianluca (author)
Dynamic Thermal Rating (DTR) enhances grid flexibility by adapting line capabilities to weather conditions. For this purpose, DTR-based technologies require reliable and continuous measurement of the conductor temperature along the line route, which could hinder their wide-scale deployment due to the prohibitively high number of required...
journal article 2024
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van Leeuwen, Sander (author)
Language is an intuitive and effective way for humans to communicate. Large Language Models (LLMs) can interpret and respond well to language. However, their use in deep reinforcement learning is limited as they are sample inefficient. State-of-the-art deep reinforcement learning algorithms are more sample efficient but cannot understand...
master thesis 2023
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WANG, NIAN RU (author)
This thesis presents LightLetter, a system that is designed for recognizing fingertip air-writing of both numbers and letters. This provides a cost-effective and privacy-conscious method for interacting with public devices, such as touchscreens. The current iteration of LightLetter allows for the input of letters and numbers to public devices...
master thesis 2023
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Haarman, Luuk (author)
Convolutional Neural Networks (CNNs) benefit from fine-grained details in high-resolution images, but these images are not always easily available as data collection can be expensive or time-consuming. Transfer learning pre-trains models on data from a related domain before fine-tuning on the main domain, and is a common strategy to deal with...
master thesis 2023
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Mullaj, Dajt (author)
Deep convolutional neural networks (CNNs) have achieved current state-of-the-art in image denoising, but require large datasets for training. Their performance remains limited on smaller real-noise datasets. In this paper, we investigate robust deep learning denoising using transfer learning. We explore the impact of dataset sizes, CNN parameter...
master thesis 2023
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HADJIGEORGIOU, MARIOS (author)
Federated Learning (FL) is widely favoured in the training of machine learning models due to its privacy-preserving and data diversity benefits. In this research paper, we investigate an extension of FL referred to as Personalized Federated Learning (PFL) for the purpose of training diffusion models. We explore the personalization technique of...
bachelor thesis 2023
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Witting, Emiel (author)
Domain adaptation allows machine learning models to perform well in a domain that is different from the available train data. This non-trivial task is approached in many ways and often relies on assumptions about the source (train) and target (test) domains. Unsupervised domain adaptation uses unlabeled target data to mitigate a shift or bias...
bachelor thesis 2023
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Verschuren, Wim (author)
Non-Intrusive Load Monitoring (NILM) is a technique used to disaggregate household power consumption data into individual appliance components without the need for dedicated meters for each appliance. This paper focuses on improving the generalizability of NILM algorithms to unseen households using Convolutional Neural Networks (CNNs) and one...
bachelor thesis 2023
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Wang, Heqi (author)
Accurate and trustworthy short-term traffic prediction is crucial in the modern world for the comfort of drivers and decision-makers as it is used to improve the performance of traffic management systems, lessen congestion, increase safety, and shorten journey times. It is possible to discover useful information for network transportation...
master thesis 2023
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Mourragui, S.M.C. (author)
Extensive efforts in cancer research over the past decades have markedly improved diagnosis and treatments, leading to better outcomes for cancer patients. Paradoxically, however, these discoveries have begun to shed light on a level of complexity that rules out the emergence of a universal cancer treatment. As any tumor is now known to be...
doctoral thesis 2023
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Schürmann, Femke (author)
The integration of large-scale battery storage systems can aid the transition to renewable energy and stabilize energy systems for optimization. However, batteries can be cost-prohibitive and unprofitable, highlighting the need for a more comprehensive understanding and modelling of battery degradation. Battery degradation prediction models play...
master thesis 2023
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Kerimov, B. (author), Bentivoglio, Roberto (author), Garzón Díaz, J.A. (author), Isufi, E. (author), Tscheikner-Gratl, Franz (author), Steffelbauer, David Bernhard (author), Taormina, R. (author)
Metamodels accurately reproduce the output of physics-based hydraulic models with a significant reduction in simulation times. They are widely employed in water distribution system (WDS) analysis since they enable computationally expensive applications in the design, control, and optimisation of water networks. Recent machine-learning-based...
journal article 2023
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Rudra, Koustav (author), Fernando, Zeon Trevor (author), Anand, A. (author)
Pre-trained contextual language models such as BERT, GPT, and XLnet work quite well for document retrieval tasks. Such models are fine-tuned based on the query-document/query-passage level relevance labels to capture the ranking signals. However, the documents are longer than the passages and such document ranking models suffer from the token...
journal article 2023
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Krcek, M. (author), Perin, G. (author)
Hyperparameter tuning represents one of the main challenges in deep learning-based profiling side-channel analysis. For each different side-channel dataset, the typical procedure to find a profiling model is applying hyperparameter tuning from scratch. The main reason is that side-channel measurements from various targets contain different...
journal article 2023
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Dekhovich, A. (author), Turan, O.T. (author), Jiaxiang, Y. (author), Bessa, M.A. (author)
Data-driven modeling in mechanics is evolving rapidly based on recent machine learning advances, especially on artificial neural networks. As the field matures, new data and models created by different groups become available, opening possibilities for cooperative modeling. However, artificial neural networks suffer from catastrophic...
journal article 2023
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Gao, Q. (author), Yang, Haoyu (author), Shanbhag, S.M. (author), Schweidtmann, A.M. (author)
Process design is a creative task that is currently performed manually by engineers. Artificial intelligence provides new potential to facilitate process design. Specifically, reinforcement learning (RL) has shown some success in automating process design by integrating data-driven models that learn to build process flowsheets with process...
book chapter 2023
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Glynis, K.G. (author), Kapelan, Z. (author), Bakker, Martijn (author), Taormina, R. (author)
Researchers and engineers employ machine learning (ML) tools to detect pipe bursts and prevent significant non-revenue water losses in water distribution systems (WDS). Nonetheless, many approaches developed so far consider a fixed number of sensors, which requires the ML model redevelopment and collection of sufficient data with the new...
journal article 2023
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Singh, Guru Deep (author)
The automotive industry currently has been working on developing various levels of autonomy to assist in different Advanced Driver Assistance Systems (ADAS) with the ultimate aim of moving closer to the realization of an autonomous vehicle. For such ADAS, the industry has been using multiple sensors like Cameras, Radar, LiDAR, etc. LiDAR has...
master thesis 2022
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