Results 21 to 30 of about 60,104 (158)
Producing Synthetic Dataset for Human Fall Detection in AR/VR Environments
Applied Sciences, 2021 Human poses and the behaviour estimation for different activities in (virtual reality/augmented reality) VR/AR could have numerous beneficial applications.
Denis Zherdev +5 more
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Nature Communications, 2023 Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling
Shu-Wen Li +4 more
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Student driven learning in Synthetic Environments
Procedia CIRP, 2019 113 master students of multiple engineering study backgrounds were challenged to develop solutions for the Fraunhofer Project Center at the University of Twente (FPC@UT). In 20 groups the students had to develop a synthetic environment to monitor, manage and control a pilot plant or virtual factory.
Damgrave, R.G.J., Lutters, E.
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Synthetic environment for machine learning experiments
, 2022 This thesis addresses the problem of data scarcity in human deep-learning applications. Automated estimation of human shape and pose from an image is challenging. It is even more difficult to map the identified human pixels onto a 3D model. Existing deep-learning models learn to map manually labelled human pixels in 2D images onto human surface, which ...
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Sensors, 2022 Structural health monitoring systems that employ vision data are under constant development. Generating synthetic vision data is an actual issue. It allows, for example, for obtention of additional data for machine learning techniques or predicting the ...
Paweł Zdziebko +2 more
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Synthetic Spatial Foraging With Active Inference in a Geocaching Task
Frontiers in Neuroscience, 2022 Humans are highly proficient in learning about the environments in which they operate. They form flexible spatial representations of their surroundings that can be leveraged with ease during spatial foraging and navigation. To capture these abilities, we
Victorita Neacsu +3 more
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Synthetic Monitoring Environments for Reinforcement Learning
CoRRReinforcement Learning (RL) lacks benchmarks that enable precise, white-box diagnostics of agent behavior. Current environments often entangle complexity factors and lack ground-truth optimality metrics, making it difficult to isolate why algorithms fail.
Leonard S. Pleiss +2 more
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The role of engagement in synthetic learning environments
, 2015 Synthetic learning environments (SLEs) are often lauded for their ability to "motivate" trainees. However, little empirical evidence exists to support the popular claim that SLEs work because they are motivating. The reason for the lack of evidence supporting these claims lies in the often inadequate definition and measurement of the motivation ...
Nelson, Tristan Quinn, author +4 more
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Semantic Segmentation with Transfer Learning for Off-Road Autonomous Driving
Sensors, 2019 Since the state-of-the-art deep learning algorithms demand a large training dataset, which is often unavailable in some domains, the transfer of knowledge from one domain to another has been a trending technique in the computer vision field.
Suvash Sharma +5 more
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Design and Verification of Spacecraft Pose Estimation Algorithm using Deep Learning
Journal of Astronomy and Space SciencesThis study developed a real-time spacecraft pose estimation algorithm that combined a deep learning model and the leastsquares method. Pose estimation in space is crucial for automatic rendezvous docking and inter-spacecraft communication. Owing to the
Shinhye Moon +3 more
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