Searched for: subject%3A%22Convolutional%255C+Neural%255C+Network%22
(81 - 100 of 206)
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Kopbayev, Alibek (author), Khan, Faisal (author), Yang, M. (author), Halim, Syeda Zohra (author)Natural gas leakage can impose significant danger on a facility and its surrounding communities. Methods for early detection and diagnosis of such leakages have been developed and widely used for gas pipelines and storage tanks. Most techniques include inspection of sensor-aided mathematical models. Application of machine learning techniques to...journal article 2022
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Chang, Z. (author), Wan, Z. (author), Xu, Y. (author), Schlangen, E. (author), Šavija, B. (author)Extrusion-based 3D concrete printing (3DCP) results in deposited materials with complex microstructures that have high porosity and distinct anisotropy. Due to the material heterogeneity and rapid growth of cracks, fracture analysis in these air-void structures is often complex, resulting in a high computational cost. This study proposes a...journal article 2022
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de Jong, D.B. (author), Paredes-Vallés, Federico (author), de Croon, G.C.H.E. (author)End-to-end trained convolutional neural networks have led to a breakthrough in optical flow estimation. The most recent advances focus on improving the optical flow estimation by improving the architecture and setting a new benchmark on the publicly available MPI-Sintel dataset. Instead, in this article, we investigate how deep neural...journal article 2022
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Cristiani, D.L.M. (author), Falcetelli, F. (author), Yue, N. (author), Sbarufatti, Claudio (author), Di Sante, Raffaella (author), Zarouchas, D. (author), Giglio, Marco (author)Machine learning (ML) methods for the structural health monitoring (SHM) of composite structures rely on sufficient domain knowledge as they typically demand to extract damage-sensitive features from raw data before training the ML model. In practice, prior knowledge is not available in most cases. Deep learning (DL) methods, on the other...journal article 2022
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Zhang, Xinyu (author), Abbasi, Qammer H. (author), Fioranelli, F. (author), Romain, Olivier (author), Le Kernec, Julien (author)Population ageing has become a severe problem worldwide. Human activity recognition (HAR) can play an important role to provide the elders with in-time healthcare. With the advantages of environmental insensitivity, contactless sensing and privacy protection, radar has been widely used for human activity detection. The micro-Doppler...conference paper 2022
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Hafner, Frank M. (author), Zeller, Matthias (author), Schutera, Mark (author), Abhau, Jochen (author), Kooij, J.F.P. (author)Customization of a convolutional neural network (CNN) to a specific compute platform involves finding an optimal pareto state between computational complexity of the CNN and resulting throughput in operations per second on the compute platform. However, existing inference performance benchmarks compare complete backbones that entail many...conference paper 2022
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Chilakala, Koteswararao (author)Diabetic Retinopathy (DR) is one of the leading causes of permanent vision loss. Its current prevalence is around 45 millions across the globe and is projected to 70 million by 2045. Most of the people with this disease condition belong to remote and low income settings. We can reduce this incidence, if quality medical care is accessible in...master thesis 2021
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van Gruijthuijsen, Coen (author)Semantic Segmentation of medical images are used to improve diagnosis and treatment. In recent years, the application of machine learning methods are increasingly used. However, the design of these models is difficult and time-consuming. In this thesis, we investigated the automation of this process using an Automated Machine Learning (AutoML)...master thesis 2021
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Wiersma, Mark (author)Automated bin-picking is a difficult task that requires solving multiple robotic vision problems including object detection and grasp proposal generation. Current methods use deep learning to approach each of the vision problems of bin-picking separately with the main focus on generating the grasp proposals. For grasp proposal generation, neural...master thesis 2021
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Verkerk, Gertjan (author)Despite the increasing amount of automation in manual control tasks, such as driving a car or piloting an aircraft, the human ability to adapt to unexpected events still makes us an essential part of the control loop. Before we can remove the human completely, a better understanding of this unique characteristic is necessary so that we can apply...master thesis 2021
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Zhu, B. (author)In recent years, the accuracy of Deep Neural Networks (DNNs) has improved significantly because of three main factors: the availability of massive amounts training data, the introduction of powerful low-cost computational resources, and the development of complex deep learning models. The cloud can provide powerful computational resources to...doctoral thesis 2021
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Yang, Wei-Tse (author)We present the first deep learning approach to estimate the human skeletal system of the musculoskeletal model from monocular video. The current practice of musculoskeletal modeling relies on a motion capture system and OpenSim. The data is recorded in a restricted environment, and OpenSim workflow for musculoskeletal modeling is costly. Our...master thesis 2021
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Rajasekhar, Rajesh (author)The quality of a product is an important factor in the manufacturing industry. Maintaining a standard product quality ensures customer satisfaction and loyalty to the brand and reduces the risk and cost involved in the production. A system that reviews the product quality in production is the quality control system. Quality control ensures that...master thesis 2021
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Saldanha, Nikhil (author)A structured CNN filter basis allows incorporating priors about natural image statistics and thus require less training examples to learn, saving valuable annotation time. Here, we build on the Gaussian derivative CNN filter basis that learn both the orientation and scale of the filters. However, this Gaussian filter basis definition depends on...master thesis 2021
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Chandra, Anant (author)A Low-Pressure Micro-resistojet (LPM) is a type of in-space electrothermal propulsion system for satellites that works by heating low-pressure (50 to 300 Pa) fluid flowing through microchannels/slots (typically <1 mm diameter) using resistive heating elements like thin-film Molybdenum. This thesis delineates a response surface based method to...master thesis 2021
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Chen, Y. (author)Single-photon emission computed tomography (SPECT) is a well-established nuclear imaging modality for studying functional and pathological properties of the brain. Conventional general purpose SPECT systems typically offer a spatial resolution of about 10 mm with a sensitivity of 0.01-0.02%. A few dedicated brain SPECT scanners have been...doctoral thesis 2021
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Vallendar, André (author)Plastic pollution is one of the most challenging global environmental problems. Currently, more than 1000 rivers transport approximately 80% of the plastic influx into the oceans. Naturally, more and more companies are interested in tackling this problem. One of them is Noria Sustainable Innovators, a company based in Delft (Netherlands). It is...master thesis 2021
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Hoogenberg, Ruben (author)In this thesis we have looked into the complexity of neural networks. Especially convolutional neural networks (CNNs), which are useful for image recognition, are looked into. In order to better understand the process in the neural networks, in the first half of this report a mathematical foundation for neural networks and CNNs is constructed....bachelor thesis 2021
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Biesheuvel, Julian (author)Yes, convolutional neural networks are domain-invariant, albeit to some limited extent. We explored the performance impact of domain shift for convolutional neural networks. We did this by designing new synthetic tasks, for which the network’s task was to map images to their mean, median, standard deviation, and variance pixel intensities. We...bachelor thesis 2021
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den Heijer, Remco (author)Does a convolutional neural network (CNN) always have to be deep to learn a task? This is an important question as deeper networks are generally harder to train. We trained shallow and deep CNNs and evaluated their performance on simple regression tasks, such as computing the mean pixel value of an image. For these simple tasks we show that...bachelor thesis 2021
Searched for: subject%3A%22Convolutional%255C+Neural%255C+Network%22
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