1,841 research outputs found
A Generalized Discrete Event System (G-DEVS) Flattened Simulation Structure: Application to High-Level Architecture (HLA) Compliant Simulation of Workflow
International audienceThe objective of the paper is to specify a new flattened Generalized Discrete Event System simulation engine structure and the Workflow modeling and simulation environment embedding it. We express first the new flattened simulation structure and give the corresponding transformation functions. We analyze performance tests conducted on this new simulation structure to measure its efficiency. Then, having selected the essential concepts in the elaboration of the Workflow, we present a language of description to define the Workflow processes. Finally, we define a distributed Workflow Reference Model that interfaces components of the Workflow with respect to the High-Level Architecture standard. Today enterprises can take advantage of this platform in the context of networking where interoperability, flexibility, and efficiency are challenging concepts
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
We propose a novel attention gate (AG) model for medical image analysis that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us to eliminate the necessity of using explicit external
tissue/organ localisation modules when using convolutional neural networks
(CNNs). AGs can be easily integrated into standard CNN models such as VGG or
U-Net architectures with minimal computational overhead while increasing the
model sensitivity and prediction accuracy. The proposed AG models are evaluated
on a variety of tasks, including medical image classification and segmentation.
For classification, we demonstrate the use case of AGs in scan plane detection
for fetal ultrasound screening. We show that the proposed attention mechanism
can provide efficient object localisation while improving the overall
prediction performance by reducing false positives. For segmentation, the
proposed architecture is evaluated on two large 3D CT abdominal datasets with
manual annotations for multiple organs. Experimental results show that AG
models consistently improve the prediction performance of the base
architectures across different datasets and training sizes while preserving
computational efficiency. Moreover, AGs guide the model activations to be
focused around salient regions, which provides better insights into how model
predictions are made. The source code for the proposed AG models is publicly
available.Comment: Accepted for Medical Image Analysis (Special Issue on Medical Imaging
with Deep Learning). arXiv admin note: substantial text overlap with
arXiv:1804.03999, arXiv:1804.0533
An Institutional Framework for Heterogeneous Formal Development in UML
We present a framework for formal software development with UML. In contrast
to previous approaches that equip UML with a formal semantics, we follow an
institution based heterogeneous approach. This can express suitable formal
semantics of the different UML diagram types directly, without the need to map
everything to one specific formalism (let it be first-order logic or graph
grammars). We show how different aspects of the formal development process can
be coherently formalised, ranging from requirements over design and Hoare-style
conditions on code to the implementation itself. The framework can be used to
verify consistency of different UML diagrams both horizontally (e.g.,
consistency among various requirements) as well as vertically (e.g.,
correctness of design or implementation w.r.t. the requirements)
GarmentCode: Programming Parametric Sewing Patterns
Garment modeling is an essential task of the global apparel industry and a
core part of digital human modeling. Realistic representation of garments with
valid sewing patterns is key to their accurate digital simulation and eventual
fabrication. However, little-to-no computational tools provide support for
bridging the gap between high-level construction goals and low-level editing of
pattern geometry, e.g., combining or switching garment elements, semantic
editing, or design exploration that maintains the validity of a sewing pattern.
We suggest the first DSL for garment modeling -- GarmentCode -- that applies
principles of object-oriented programming to garment construction and allows
designing sewing patterns in a hierarchical, component-oriented manner. The
programming-based paradigm naturally provides unique advantages of component
abstraction, algorithmic manipulation, and free-form design parametrization. We
additionally support the construction process by automating typical low-level
tasks like placing a dart at a desired location. In our prototype garment
configurator, users can manipulate meaningful design parameters and body
measurements, while the construction of pattern geometry is handled by garment
programs implemented with GarmentCode. Our configurator enables the free
exploration of rich design spaces and the creation of garments using
interchangeable, parameterized components. We showcase our approach by
producing a variety of garment designs and retargeting them to different body
shapes using our configurator.Comment: Supplementary video: https://youtu.be/16Yyr2G9_6E
Languages and Tools for Optimization of Large-Scale Systems
Modeling and simulation are established techniques for solving design problems in a wide range of engineering disciplines today. Dedicated computer languages, such as Modelica, and efficient software tools are available. In this thesis, an extension of Modelica, Optimica, targeted at dynamic optimization of Modelica models is proposed. In order to demonstrate the Optimica extension, supporting software has been developed. This includes a modularly extensible Modelica compiler, the JModelica compiler, and an extension that supports also Optimica. A Modelica library for paper machine dryer section modeling, DryLib, has been developed. The classes in the library enable structured and hierarchical modeling of dryer sections at the application user level, while offering extensibility for the expert user. Based on DryLib, a parameter optimization problem, a model reduction problem, and an optimization-based control problem have been formulated and solved. A start-up optimization problem for a plate reactor has been formulated in Optimica, and solved by means of the Optimica compiler. In addition, the robustness properties of the start-up trajectories have been evaluated by means of Monte-Carlo simulation. In many control systems, it is necessary to consider interaction with a user. In this thesis, a manual control scheme for an unstable inverted pendulum system, where the inputs are bounded, is presented. The proposed controller is based on the notion of reachability sets and guarantees semi global stability for all references. An inverted pendulum on a two wheels robot has been developed. A distributed control system, including sensor processing algorithms and a stabilizing control scheme has been implemented on three on-board embedded processors
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