10,360 research outputs found
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Stacking-based visualization of trajectory attribute data
Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well
Categorizing Flight Paths using Data Visualization and Clustering Methodologies
This work leverages the U.S. Federal Aviation Administration's Traffic Flow
Management System dataset and DV8, a recently developed tool for highly
interactive visualization of air traffic data, to develop clustering algorithms
for categorizing air traffic by their varying flight paths. Two clustering
methodologies, a spatial-based geographic distance model, and a vector-based
cosine similarity model, are demonstrated and compared for their clustering
effectiveness. Examples of their applications reveal successful, realistic
clustering based on automated clustering result determination and
human-in-the-loop processes, with geographic distance algorithms performing
better for enroute portions of flight paths and cosine similarity algorithms
performing better for near-terminal operations, such as arrival paths. A point
extraction technique is applied to improve computation efficiency.Comment: Published in the 9th International Conference on Research in Air
Transportation (ICRAT'20):
https://www.icrat.org/previous-conferences/9th-international-conference/papers
A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys
Defining cellular and molecular identities within the kidney is necessary to understand its organization and function in health and disease. Here we demonstrate a reproducible method with minimal artifacts for single-nucleus Droplet-based RNA sequencing (snDrop-Seq) that we use to resolve thirty distinct cell populations in human adult kidney. We define molecular transition states along more than ten nephron segments spanning two major kidney regions. We further delineate cell type-specific expression of genes associated with chronic kidney disease, diabetes and hypertension, providing insight into possible targeted therapies. This includes expression of a hypertension-associated mechano-sensory ion channel in mesangial cells, and identification of proximal tubule cell populations defined by pathogenic expression signatures. Our fully optimized, quality-controlled transcriptomic profiling pipeline constitutes a tool for the generation of healthy and diseased molecular atlases applicable to clinical samples
Inferring transportation modes from GPS trajectories using a convolutional neural network
Identifying the distribution of users' transportation modes is an essential
part of travel demand analysis and transportation planning. With the advent of
ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach
for inferring commuters' mobility mode(s) is to leverage their GPS
trajectories. A majority of studies have proposed mode inference models based
on hand-crafted features and traditional machine learning algorithms. However,
manual features engender some major drawbacks including vulnerability to
traffic and environmental conditions as well as possessing human's bias in
creating efficient features. One way to overcome these issues is by utilizing
Convolutional Neural Network (CNN) schemes that are capable of automatically
driving high-level features from the raw input. Accordingly, in this paper, we
take advantage of CNN architectures so as to predict travel modes based on only
raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving,
and train. Our key contribution is designing the layout of the CNN's input
layer in such a way that not only is adaptable with the CNN schemes but
represents fundamental motion characteristics of a moving object including
speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the
quality of GPS logs through several data preprocessing steps. Using the clean
input layer, a variety of CNN configurations are evaluated to achieve the best
CNN architecture. The highest accuracy of 84.8% has been achieved through the
ensemble of the best CNN configuration. In this research, we contrast our
methodology with traditional machine learning algorithms as well as the seminal
and most related studies to demonstrate the superiority of our framework.Comment: 12 pages, 3 figures, 7 tables, Transportation Research Part C:
Emerging Technologie
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Large-scale automated investigation of free-falling paper shapes via iterative physical experimentation
Free-falling paper shapes exhibit rich, complex and varied behaviours which are extremely challenging to model analytically. Physical experimentation aids in system understanding but is time-consuming, sensitive to initial conditions and reliant on subjective visual behavioural classification. In this study robotics, computer vision and machine learning are used to autonomously fabricate, drop, analyse and classify the behaviours of hundreds of shapes. The system is validated by reproducing results for falling disks, which exhibit four falling styles: tumbling, chaotic, steady and periodic. A previously-determined mapping from a non-dimensional parameter space to behaviour groups is shown to be consistent with these new experiments for tumbling and chaotic behaviours. Steady or periodic behaviours, however, are observed in previously unseen areas of the parameter space. More complex hexagon, square and cross shapes are investigated, showing that the non-dimensional parameter space generalises to these shapes. The system highlights the potential of robotics for the investigation of complex physical systems, of which falling paper is one example, and provides a template for future investigation of such systems
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