871 research outputs found
Estimating true changes when categorical panel data are affected by uncorrelated and correlated errors; an application to unemployment data
No full textConclusions about changes in categorical characteristics based on observed panel data can be incorrect when (even a small amount of) measurement error is present. Random measurement errors, referred to as independent classification errors, usually lead to over-estimation of the total amount of gross change, whereas systematic, correlated errors usually cause underestimation of the transitions. Furthermore, the patterns of true change may be seriously distorted by independent or systematic classification errors. Latent class models and directed log-linear analysis are excellent tools to correct for both independent and correlated measurement errors. An extensive example on labor market states taken from the Survey of Income and Program Participation panel is presented
Light Axion Emission and the Formation of Merging Black Holes Binaries
No full text# Reproduction Package for the Paper "Light axion emission and the formation of merging binary black holes"
## Authors:
Djuna Croon ([email protected])
Jeremy Sakstein ([email protected])
## Software
MESA version 15140 (http://mesa.sourceforge.net/)
MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk)
GFORTRAN GCC version 9.2.0
## Example Directories
**work:** Contains inlists, run\_star\_extras, and run\_binary_\extras needed to reproduce our results. These were adapted from the reproduction package of A&A 650, A107 (2021). A small number of models did not converge using the default controls. These can be made to complete by either setting the control make\_gradr\_sticky\_in\_solver\_iters = .true. (see inlist1, this works for models with small initial periods) or by relaxing delta\_HR\_limit and delta\_HR\_hard\_limit (see run\_binary\_extras line 900).
## Citation Policy
If you use any part of this reproduction package for independent work we recommend you cite the following papers:
- https://arxiv.org/abs/2208.01110
- Phys.Dark Univ. 32 (2021) 100801
- Phys.Rev.D 102 (2020) 11, 115024
- Phys.Rev.Lett. 125 (2020) 26, 261105
- Astrophys.J.Lett. 916 (2021) 2, L16
- Phys.Rev.D 105 (2022) 095038
- A&A 650, A107 (2021)
- Astrophys. J. Suppl. 192, 3 (2011)
- Astrophys. J. Suppl. 208, 4 (2013)
- Astrophys. J. Suppl. 234, 34 (2018)
- ApJS 243, 10 (2019
Learning Depth from Single Monocular Images Using Stereo Supervisory Input
No full textStereo vision systems are often employed in robotics as a means for obstacle avoidance and navigation. These systems have inherent depth-sensing limitations, with significant problems in occluded and untextured regions, leading to sparse depth maps. We propose using a monocular depth estimation algorithm to tackle these problems, in a Self-Supervised Learning (SSL) framework. The algorithm learns online from the sparse depth map generated by a stereo vision system, producing a dense depth map. The algorithm is designed to be computationally efficient, for implementation onboard resource-constrained mobile robots and unmanned aerial vehicles. Within that context, it can be used to provide both reliability against a stereo camera failure, as well as more accurate depth perception, by filling in missing depth information, in occluded and low texture regions. This in turn allows the use of more efficient sparse stereo vision algorithms. We test the algorithm offline on a new, high resolution, stereo dataset, of scenes shot in indoor environments, and processed using both sparse and dense stereo matching algorithms. It is shown that the algorithm’s performance doesn’t deteriorate, and in fact sometimes improves, when learning only from sparse, high confidence regions rather than from the computationally expensive, dense, occlusion-filled and highly post-processed dense depth maps. This makes the approach very promising for self- supervised learning on autonomous robots.Aerospace EngineeringControl and OperationsControl & Simulatio
What is normal? Revisting normative data for Scottish children's phonological processes
No full textPrsentation which examines normative data for Scottish children's phonological processes
Three-dimensional relative localization and synchronized movement with wireless ranging
No full textRelative localization is a key capability for autonomous robot swarms, and it is a substan-
tial challenge, especially for small flying robots, as they are extremely restricted in terms of
sensors and processing while other robots may be located anywhere around them in three-
dimensional space. In this article, we generalize wireless ranging-based relative localiza-
tion to three dimensions. In particular, we show that robots can localize others in three
dimensions by ranging to each other and only exchanging body velocities and yaw rates.
We perform a nonlinear observability analysis, investigating the observability of relative
locations for different cases. Furthermore, we show both in simulation and with real-world
experiments that the proposed method can be used for successfully achieving various
swarm behaviours. In order to demonstrate the method’s generality, we demonstrate it both
on tiny quadrotors and lightweight flapping wing robots
Modelling 2DEG charges in AlGaN/GaN heterostructures
No full textIn this paper we compare different
approaches to calculating the charge density in the
2DEG layer of AlGaN/GaN HEMTs. The methods
used are (i) analytical theory implemented in
MATLAB, (ii) finite-element analysis using
semiconductor TCAD software that implements only
the Poisson and continuity equations, and (iii) 1D
software that solves the Poisson and Schrödinger
equations self-consistently. By using the 1D PoissonSchrödinger solver, we highlight the consequences of
neglecting the Schrödinger equation. We conclude
that the TCAD simulator predicts with a reasonable
level of accuracy the electron density in the 2DEG
layer for both a conventional HEMT structure and
one featuring an extra GaN cap layer. In addition,
while the sheet charge density is not significantly
affected by including Schrödinger, its confinement in
the channel is found to be modified
Pak energiearmoede gericht aan
No full textDe verduurzaming van onze woningvoorraad is naast een technische operatie ook een verdelingsvraagstuk. Huishoudens met lage inkomens in slechte woningen moeten prioriteit krijgen bij het renovatiebeleid. En totdat iedereen in een duurzaam huis woont, moeten huishoudens die in de knel komen door hoge energieprijzen, worden gecompenseerd.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Urban Development Managemen
Automatic Design of Verifiable Robot Swarms
No full textThe paradigm of swarm robotics aims to enable several independent robots to collaborate together toward collective goals. The distributed nature of a swarm, whereby each robot acts independently in accordance with its perceived environment, is expected to provide the system with a high degree of flexibility, robustness, and scalability. However, this comes at the cost of increased system complexity. This thesis explores how to automatically design a collective behavior in a way that is transparent and verifiable. The thesis begins by taking a step back and analyzing the design choices that need to be made when designing a swarm of robots. Through an in-depth literature study, focusing on swarms of small drones as a case study, we found how sensor and actuator choices can create constraints for the swarm behavior that can be achieved, and how desired swarm behaviors can create requirements for the hardware design and local-level controllers. Coincidentally, we found a prominent example of this in our own research on relative localization sensors for swarms of tiny drones (performed in addition to the research in this thesis), whereby we developed a communication-based relative localization approach that enabled teams of tiny drones to fly together in tight areas, the advantages being: omni-directional sensing, independence from lighting conditions and/or visual clutter, low mass, and low computational costs. However, this solution also comes with the restriction of ensuring that robots never move parallel to each other, as this will present an unobservable situation. Based on such lessons, the remainder of the thesis aims for a framework that is agnostic with respect to the robot and the swarm's collective task. The framework proposed in this thesis is centered around the following notion: a collective goal can be broken down into a set of locally observable objectives which the robots can sense, referred to as ``desired'' objectives. The robots then take actions in order to reach these desired objectives. When all robots achieve the desired objectives, then the global goal and/or collective behavior emerges. This framework was first developed for the specific case study of pattern formation by cognitively limited robots, which could only sense the relative location of close-by neighbors. It was later generalized, and its use was demonstrated on other collective tasks, namely: aggregation, consensus, and foraging. Through a local model of agent transitions, it was possible to: 1) identify potential obstructions to achieving the collective goal, and 2) optimize the behavior of the robots so as to maximize the likelihood of achieving the desired objectives. The optimization is performed by an evolutionary algorithm that leverages the local model, whereby the fitness function maximizes the probability of being in a desired local state. Using this approach, the policy evaluation only scales with the size of the local state space, and demands much less computation than swarm simulations would. In the final stage of this research, a complete framework was further developed to alleviate the need to manually define the desired objectives as well as the local models required for potential verification and/or optimization. The framework uses a data-driven approach to automatically extract two models: 1) a deep neural network that estimates the global performance of the swarm from the distribution of local sensor data, and 2) a probabilistic state transition model that explicitly models the local state transitions (i.e., transitions in observations from the perspective of a single robot in a swarm) given a policy. The framework can efficiently lead to effective controllers, as demonstrated via multiple case studies. It can also be used in combination with an evolutionary optimization process, leading to higher efficiency, or for heterogeneous online learning. Overall, the methods and insights developed in this thesis propose a new way to approach the development of verifiable and understandable behaviors for swarms of robots, using models in order to perform analysis, verification, and optimization.Control & SimulationSpace Systems Egineerin
Self-supervised Monocular Multi-robot Relative Localization with Efficient Deep Neural Networks
No full textRelative localization is an important ability for multiple robots to perform cooperative tasks in GPS-denied environments. This paper presents a novel autonomous positioning framework for monocular relative localization of multiple tiny flying robots. This approach does not require any groundtruth data from external systems or manual labeling. Instead, the proposed framework is able to label real-world images with 3D relative positions between robots based on another onboard relative estimation technology, using ultra-wideband (UWB). After training in this self-supervised manner, the proposed deep neural network (DNN) can predict relative positions of peer robots by purely using a monocular camera. This deep learning-based visual relative localization is scalable, distributed, and autonomous. We also built an open-source and lightweight simulation pipeline by using Blender for 3D rendering, which allows synthetic image generation of other robots, and generalized training of the neural network. The proposed localization framework is tested on two real-world Crazyflie2 quadrotors by running the DNN on the onboard AIdeck (a tiny AI chip and monocular camera). All results demonstrate the effectiveness of the self-supervised multi-robot localization method. Video: https://youtu.be/7arkaIblPpsGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio
Visual Homing for Micro Aerial Vehicles using Scene Familiarity
No full textAutonomous navigation is a major challenge in the development of MAVs. When an algorithm has to be efficient, insect intelligence can be a source of inspiration. An elementary navigation task is homing, which means autonomously returning to the initial location. A promising approach makes use of visual familiarity of a route to determine reference headings during homing. In this thesis an existing biological proof of concept based on desert ants is transferred to MAVs. Vision-in-the-loop experiments in different environments are performed, to investigate the viability of scene familiarity for visual navigation. Trained images are used to determine which control actions to take during homing. To determine familiarity, either a database of stored images is kept or an artificial neural network is used. Different image representations are compared in multiple simulated environments. The use of textons for determining familiarity gives the best performance, but HSV color histograms also perform well and are very efficient. It is concluded that to make this method competitive with other visual navigation approaches, route familiarity should be combined with other methods to improve robustness.Aerospace EngineeringControl and Simulatio
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