12,000 research outputs found
SiSeRHMap v1.0: A simulator for mapped seismic response using a hybrid model
SiSeRHMap is a computerized methodology capable of drawing up prediction maps of
seismic response. It was realized on the basis of a hybrid model which combines different
approaches and models in a new and non-conventional way. These approaches
5 and models are organized in a code-architecture composed of five interdependent
modules. A GIS (Geographic Information System) Cubic Model (GCM), which is a layered
computational structure based on the concept of lithodynamic units and zones,
aims at reproducing a parameterized layered subsoil model. A metamodeling process
confers a hybrid nature to the methodology. In this process, the one-dimensional linear
10 equivalent analysis produces acceleration response spectra of shear wave velocitythickness
profiles, defined as trainers, which are randomly selected in each zone. Subsequently,
a numerical adaptive simulation model (Spectra) is optimized on the above
trainer acceleration response spectra by means of a dedicated Evolutionary Algorithm
(EA) and the Levenberg–Marquardt Algorithm (LMA) as the final optimizer. In the fi15
nal step, the GCM Maps Executor module produces a serial map-set of a stratigraphic
seismic response at different periods, grid-solving the calibrated Spectra model. In addition,
the spectra topographic amplification is also computed by means of a numerical
prediction model. This latter is built to match the results of the numerical simulations
related to isolate reliefs using GIS topographic attributes. In this way, different sets
20 of seismic response maps are developed, on which, also maps of seismic design response
spectra are defined by means of an enveloping technique
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
Complex computational models are often designed to simulate real-world
physical phenomena in many scientific disciplines. However, these simulation
models tend to be computationally very expensive and involve a large number of
simulation input parameters which need to be analyzed and properly calibrated
before the models can be applied for real scientific studies. We propose a
visual analysis system to facilitate interactive exploratory analysis of
high-dimensional input parameter space for a complex yeast cell polarization
simulation. The proposed system can assist the computational biologists, who
designed the simulation model, to visually calibrate the input parameters by
modifying the parameter values and immediately visualizing the predicted
simulation outcome without having the need to run the original expensive
simulation for every instance. Our proposed visual analysis system is driven by
a trained neural network-based surrogate model as the backend analysis
framework. Surrogate models are widely used in the field of simulation sciences
to efficiently analyze computationally expensive simulation models. In this
work, we demonstrate the advantage of using neural networks as surrogate models
for visual analysis by incorporating some of the recent advances in the field
of uncertainty quantification, interpretability and explainability of neural
network-based models. We utilize the trained network to perform interactive
parameter sensitivity analysis of the original simulation at multiple
levels-of-detail as well as recommend optimal parameter configurations using
the activation maximization framework of neural networks. We also facilitate
detail analysis of the trained network to extract useful insights about the
simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic
Multi-touch Detection and Semantic Response on Non-parametric Rear-projection Surfaces
The ability of human beings to physically touch our surroundings has had a profound impact on our daily lives. Young children learn to explore their world by touch; likewise, many simulation and training applications benefit from natural touch interactivity. As a result, modern interfaces supporting touch input are ubiquitous. Typically, such interfaces are implemented on integrated touch-display surfaces with simple geometry that can be mathematically parameterized, such as planar surfaces and spheres; for more complicated non-parametric surfaces, such parameterizations are not available. In this dissertation, we introduce a method for generalizable optical multi-touch detection and semantic response on uninstrumented non-parametric rear-projection surfaces using an infrared-light-based multi-camera multi-projector platform. In this paradigm, touch input allows users to manipulate complex virtual 3D content that is registered to and displayed on a physical 3D object. Detected touches trigger responses with specific semantic meaning in the context of the virtual content, such as animations or audio responses. The broad problem of touch detection and response can be decomposed into three major components: determining if a touch has occurred, determining where a detected touch has occurred, and determining how to respond to a detected touch. Our fundamental contribution is the design and implementation of a relational lookup table architecture that addresses these challenges through the encoding of coordinate relationships among the cameras, the projectors, the physical surface, and the virtual content. Detecting the presence of touch input primarily involves distinguishing between touches (actual contact events) and hovers (near-contact proximity events). We present and evaluate two algorithms for touch detection and localization utilizing the lookup table architecture. One of the algorithms, a bounded plane sweep, is additionally able to estimate hover-surface distances, which we explore for interactions above surfaces. The proposed method is designed to operate with low latency and to be generalizable. We demonstrate touch-based interactions on several physical parametric and non-parametric surfaces, and we evaluate both system accuracy and the accuracy of typical users in touching desired targets on these surfaces. In a formative human-subject study, we examine how touch interactions are used in the context of healthcare and present an exploratory application of this method in patient simulation. A second study highlights the advantages of touch input on content-matched physical surfaces achieved by the proposed approach, such as decreases in induced cognitive load, increases in system usability, and increases in user touch performance. In this experiment, novice users were nearly as accurate when touching targets on a 3D head-shaped surface as when touching targets on a flat surface, and their self-perception of their accuracy was higher
Towards a human eye behavior model by applying Data Mining Techniques on Gaze Information from IEC
In this paper, we firstly present what is Interactive Evolutionary
Computation (IEC) and rapidly how we have combined this artificial intelligence
technique with an eye-tracker for visual optimization. Next, in order to
correctly parameterize our application, we present results from applying data
mining techniques on gaze information coming from experiments conducted on
about 80 human individuals
Flexible structure control laboratory development and technology demonstration
An experimental structure is described which was constructed to demonstrate and validate recent emerging technologies in the active control and identification of large flexible space structures. The configuration consists of a large, 20 foot diameter antenna-like flexible structure in the horizontal plane with a gimballed central hub, a flexible feed-boom assembly hanging from the hub, and 12 flexible ribs radiating outward. Fourteen electrodynamic force actuators mounted to the hub and to the individual ribs provide the means to excite the structure and exert control forces. Thirty permanently mounted sensors, including optical encoders and analog induction devices provide measurements of structural response at widely distributed points. An experimental remote optical sensor provides sixteen additional sensing channels. A computer samples the sensors, computes the control updates and sends commands to the actuators in real time, while simultaneously displaying selected outputs on a graphics terminal and saving them in memory. Several control experiments were conducted thus far and are documented. These include implementation of distributed parameter system control, model reference adaptive control, and static shape control. These experiments have demonstrated the successful implementation of state-of-the-art control approaches using actual hardware
Editing faces in videos
Editing faces in movies is of interest in the special effects industry. We aim at
producing effects such as the addition of accessories interacting correctly with
the face or replacing the face of a stuntman with the face of the main actor.
The system introduced in this thesis is based on a 3D generative face model.
Using a 3D model makes it possible to edit the face in the semantic space of pose,
expression, and identity instead of pixel space, and due to its 3D nature allows
a modelling of the light interaction. In our system we first reconstruct the 3D
face, which is deforming because of expressions and speech, the lighting, and
the camera in all frames of a monocular input video. The face is then edited by
substituting expressions or identities with those of another video sequence or by
adding virtual objects into the scene. The manipulated 3D scene is rendered back
into the original video, correctly simulating the interaction of the light with the
deformed face and virtual objects.
We describe all steps necessary to build and apply the system. This includes
registration of training faces to learn a generative face model, semi-automatic
annotation of the input video, fitting of the face model to the input video, editing
of the fit, and rendering of the resulting scene.
While describing the application we introduce a host of new methods, each
of which is of interest on its own. We start with a new method to register 3D
face scans to use as training data for the face model. For video preprocessing a
new interest point tracking and 2D Active Appearance Model fitting technique
is proposed. For robust fitting we introduce background modelling, model-based
stereo techniques, and a more accurate light model
Spike sorting for large, dense electrode arrays
Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%
Collision Detection and Merging of Deformable B-Spline Surfaces in Virtual Reality Environment
This thesis presents a computational framework for representing, manipulating and merging rigid and deformable freeform objects in virtual reality (VR) environment. The core algorithms for collision detection, merging, and physics-based modeling used within this framework assume that all 3D deformable objects are B-spline surfaces. The interactive design tool can be represented as a B-spline surface, an implicit surface or a point, to allow the user a variety of rigid or deformable tools. The collision detection system utilizes the fact that the blending matrices used to discretize the B-spline surface are independent of the position of the control points and, therefore, can be pre-calculated. Complex B-spline surfaces can be generated by merging various B-spline surface patches using the B-spline surface patches merging algorithm presented in this thesis. Finally, the physics-based modeling system uses the mass-spring representation to determine the deformation and the reaction force values provided to the user. This helps to simulate realistic material behaviour of the model and assist the user in validating the design before performing extensive product detailing or finite element analysis using commercially available CAD software. The novelty of the proposed method stems from the pre-calculated blending matrices used to generate the points for graphical rendering, collision detection, merging of B-spline patches, and nodes for the mass spring system. This approach reduces computational time by avoiding the need to solve complex equations for blending functions of B-splines and perform the inversion of large matrices. This alternative approach to the mechanical concept design will also help to do away with the need to build prototypes for conceptualization and preliminary validation of the idea thereby reducing the time and cost of concept design phase and the wastage of resources
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