23,012 research outputs found
Stepping from Graph Transformation Units to Model Transformation Units
Graph transformation units are rule-based entities that allow to transform source graphs into target graphs via sets of graph transformation rules according to a control condition. The graphs and rules are taken from an underlying graph transformation approach. Graph transformation units specify model transformations whenever the transformed graphs represent models. This paper is based on the observation that in general models are not always suitably represented as single
graphs, but they may be specified as the composition of a variety of different formal structures such as sets, tuples, graphs, etc., which should be transformed by compositions of different types of rules and operations instead of single graph
transformation rules. Consequently, in this paper, graph transformation units are generalized to model transformation units that allow to transform such kind of composed
models in a rule-based and controlled way. Moreover, two compositions of model transformation units are presented
Online identification and nonlinear control of the electrically stimulated quadriceps muscle
A new approach for estimating nonlinear models of the electrically stimulated quadriceps muscle group under nonisometric conditions is investigated. The model can be used for designing controlled neuro-prostheses. In order to identify the muscle dynamics (stimulation pulsewidth-active knee moment relation) from discrete-time angle measurements only, a hybrid model structure is postulated for the shank-quadriceps dynamics. The model consists of a relatively well known time-invariant passive component and an uncertain time-variant active component. Rigid body dynamics, described by the Equation of Motion (EoM), and passive joint properties form the time-invariant part. The actuator, i.e. the electrically stimulated muscle group, represents the uncertain time-varying section. A recursive algorithm is outlined for identifying online the stimulated quadriceps muscle group. The algorithm requires EoM and passive joint characteristics to be known a priori. The muscle dynamics represent the product of a continuous-time nonlinear activation dynamics and a nonlinear static contraction function described by a Normalised Radial Basis Function (NRBF) network which has knee-joint angle and angular velocity as input arguments. An Extended Kalman Filter (EKF) approach is chosen to estimate muscle dynamics parameters and to obtain full state estimates of the shank-quadriceps dynamics simultaneously. The latter is important for implementing state feedback controllers. A nonlinear state feedback controller using the backstepping method is explicitly designed whereas the model was identified a priori using the developed identification procedure
Evolution of the Potential Energy Surface with Size for Lennard-Jones Clusters
Disconnectivity graphs are used to characterize the potential energy surfaces
of Lennard-Jones clusters containing 13, 19, 31, 38, 55 and 75 atoms. This set
includes members which exhibit either one or two `funnels' whose low-energy
regions may be dominated by a single deep minimum or contain a number of
competing structures. The graphs evolve in size due to these specific size
effects and an exponential increase in the number of local minima with the
number of atoms. To combat the vast number of minima we investigate the use of
monotonic sequence basins as the fundamental topographical unit. Finally, we
examine disconnectivity graphs for a transformed energy landscape to explain
why the transformation provides a useful approach to the global optimization
problem.Comment: 13 pages, 8 figures, revte
A Domain-Specific Language and Editor for Parallel Particle Methods
Domain-specific languages (DSLs) are of increasing importance in scientific
high-performance computing to reduce development costs, raise the level of
abstraction and, thus, ease scientific programming. However, designing and
implementing DSLs is not an easy task, as it requires knowledge of the
application domain and experience in language engineering and compilers.
Consequently, many DSLs follow a weak approach using macros or text generators,
which lack many of the features that make a DSL a comfortable for programmers.
Some of these features---e.g., syntax highlighting, type inference, error
reporting, and code completion---are easily provided by language workbenches,
which combine language engineering techniques and tools in a common ecosystem.
In this paper, we present the Parallel Particle-Mesh Environment (PPME), a DSL
and development environment for numerical simulations based on particle methods
and hybrid particle-mesh methods. PPME uses the meta programming system (MPS),
a projectional language workbench. PPME is the successor of the Parallel
Particle-Mesh Language (PPML), a Fortran-based DSL that used conventional
implementation strategies. We analyze and compare both languages and
demonstrate how the programmer's experience can be improved using static
analyses and projectional editing. Furthermore, we present an explicit domain
model for particle abstractions and the first formal type system for particle
methods.Comment: Submitted to ACM Transactions on Mathematical Software on Dec. 25,
201
Calipso: Physics-based Image and Video Editing through CAD Model Proxies
We present Calipso, an interactive method for editing images and videos in a
physically-coherent manner. Our main idea is to realize physics-based
manipulations by running a full physics simulation on proxy geometries given by
non-rigidly aligned CAD models. Running these simulations allows us to apply
new, unseen forces to move or deform selected objects, change physical
parameters such as mass or elasticity, or even add entire new objects that
interact with the rest of the underlying scene. In Calipso, the user makes
edits directly in 3D; these edits are processed by the simulation and then
transfered to the target 2D content using shape-to-image correspondences in a
photo-realistic rendering process. To align the CAD models, we introduce an
efficient CAD-to-image alignment procedure that jointly minimizes for rigid and
non-rigid alignment while preserving the high-level structure of the input
shape. Moreover, the user can choose to exploit image flow to estimate scene
motion, producing coherent physical behavior with ambient dynamics. We
demonstrate Calipso's physics-based editing on a wide range of examples
producing myriad physical behavior while preserving geometric and visual
consistency.Comment: 11 page
Accelerating the Fourier split operator method via graphics processing units
Current generations of graphics processing units have turned into highly
parallel devices with general computing capabilities. Thus, graphics processing
units may be utilized, for example, to solve time dependent partial
differential equations by the Fourier split operator method. In this
contribution, we demonstrate that graphics processing units are capable to
calculate fast Fourier transforms much more efficiently than traditional
central processing units. Thus, graphics processing units render efficient
implementations of the Fourier split operator method possible. Performance
gains of more than an order of magnitude as compared to implementations for
traditional central processing units are reached in the solution of the time
dependent Schr\"odinger equation and the time dependent Dirac equation
Analysis of the 24-Hour Activity Cycle: An illustration examining the association with cognitive function in the Adult Changes in Thought (ACT) Study
The 24-hour activity cycle (24HAC) is a new paradigm for studying activity
behaviors in relation to health outcomes. This approach captures the
interrelatedness of the daily time spent in physical activity (PA), sedentary
behavior (SB), and sleep. We illustrate and compare the use of three popular
approaches, namely isotemporal substitution model (ISM), compositional data
analysis (CoDA), and latent profile analysis (LPA) for modeling outcome
associations with the 24HAC. We apply these approaches to assess an association
with a cognitive outcome, measured by CASI item response theory (IRT) score, in
a cohort of 1034 older adults (mean [range] age = 77 [65-100]; 55.8% female;
90% White) who were part of the Adult Changes in Thought (ACT) Activity
Monitoring (ACT-AM) sub-study. PA and SB were assessed with thigh-worn activPAL
accelerometers for 7 days. We highlight differences in assumptions between the
three approaches, discuss statistical challenges, and provide guidance on
interpretation and selecting an appropriate approach. ISM is easiest to apply
and interpret; however, the typical ISM model assumes a linear association.
CoDA specifies a non-linear association through isometric logratio
transformations that are more challenging to apply and interpret. LPA can
classify individuals into groups with similar time-use patterns. Inference on
associations of latent profiles with health outcomes need to account for the
uncertainty of the LPA classifications which is often ignored. The selection of
the most appropriate method should be guided by the scientific questions of
interest and the applicability of each model's assumptions. The analytic
results did not suggest that less time spent on SB and more in PA was
associated with better cognitive function. Further research is needed into the
health implications of the distinct 24HAC patterns identified in this cohort.Comment: 51 pages, 11 tables, 8 figure
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