Using internal domain-specific languages to inherit tool support and modularity for model transformations

Software and Systems Modeling - Model-driven engineering (MDE) has proved to be a useful approach to cope with today’s ever-growing complexity in the development of software systems;...     

Computer vision for improved estimates of SO2 emission rates and plume dynamics

Imaging cameras operating at ultraviolet (UV) and infrared (IR) wavelengths can measure sulphur dioxide (SO₂) gas path concentrations or slant column densities. These measurements are useful in a variety of applications including the monitoring of emissions from volcanoes and also emissions from stacks at industrial plants and on ships. The usefulness of these data is increased if the emission rates (or fluxes) of the gases can also be estimated. Here we present an optical flow algorithm that allows rapid and accurate estimates of emission rates using both UV and IR camera imagery sampling at around 1 Hz or higher. Examples are provided from measurements made at Turrialba volcano, Costa Rica, and also at a ship in Hong Kong harbour. Other aspects of the properties of the fluid flow are also introduced, notably the divergence and the vorticity of the two-dimensional wind field. We demonstrate how the divergence can be used in a new method to calculate the emission rate and show how rotational effects observed in volcanic plumes and the resulting entrainment of ambient air affects plume rise and can be observed using vorticity. This is an important aspect for understanding the emplacement of gases and particles into the atmosphere that are subsequently transported by atmospheric winds, sometimes causing pollution episodes at long distances from the source.     

Measure-Valued Variational Models with Applications to Diffusion-Weighted Imaging

We develop a general mathematical framework for variational problems where the unknown function takes values in the space of probability measures on some metric space. We study weak and strong topologies and define a total variation seminorm for functions taking values in a Banach space. The seminorm penalizes jumps and is rotationally invariant under certain conditions. We prove existence of a minimizer for a class of variational problems based on this formulation of total variation and provide an example where uniqueness fails to hold. Employing the Kantorovich–Rubinstein transport norm from the theory of optimal transport, we propose a variational approach for the restoration of orientation distribution function-valued images, as commonly used in diffusion MRI. We demonstrate that the approach is numerically feasible on several data sets.     

An information based feedback control for audio-motor binaural localization

In static scenarios, binaural sound localization is fundamentally limited by front-back ambiguity and distance non-observability. Over the past few years, “active” schemes have been shown to overcome these shortcomings, by combining spatial binaural cues with the motor commands of the sensor. In this context, given a Gaussian prior on the relative position to a source, this paper determines an admissible motion of a binaural head which leads, on average, to the one-step-ahead most informative audio-motor localization. To this aim, a constrained optimization problem is set up, which consists in maximizing the entropy of the next predicted measurement probability density function over a cylindric admissible set. The method is appraised through geometrical arguments, and validated in simulations and on real-life robotic experiments.     

Modeling Firm and Market Dynamics: A Flexible Model Reproducing Existing Stylized Facts on Firm Growth

This paper presents a model of firm and market dynamics that is able to reproduce the empirically observed patterns on firm growth and its statistical characteristics. It goes beyond the existing firm models by reproducing all stylized facts established in the literature. Furthermore, the model is flexible in the sense that various parameter settings are identified that reproduce the stylized facts. We identify and discuss different ranges of the model’s parameters that allow for adapting the model to certain industries and life-cycle stages.     

Special issue: The International Conference on Soft Computing and Machine Intelligence

Neural Computing and Applications -     

Holistic process planning chain for robot machining

Today, machining of large, integral constructed structural parts requires expensive machining centers. In contrast, modern industrial robots are suitable for a wide field of applications and are characterized large working spaces and low capital investment. Therefore, they provide high economical potential for machining applications in aerospace industry, especially for the machining of near to shape pre-products like extruded profiles. However, their constructive characteristics like low stiffness and high sensitivity to vibrations lead to disadvantages compared with conventional machining centers and have to be considered during process planning. Therefore, several methods for offline and online optimization of robot machining processes were developed and integrated in a new process chain for manufacturing of structural fuselage parts. Thereby, the conventional CAD–CAM process planning chain was extended with simulation based analysis and optimization methods and a load-depending trajectory planning. These methods for offline process optimization within this novel process chain are presented in this paper.     

An integrated architecture for the ITER RH control system

The ITER remote handling (RH) system has been divided into 7 major equipment system procurements that deliver complete systems (operator interfaces, equipment controllers, and equipment) according to task oriented functional specifications. Each equipment system itself is an assembly of transporters, power manipulators, telemanipulators, vehicular systems, cameras, and tooling with a need for controllers and operator interfaces.From an operational perspective, the ITER RH systems are bound together by common control rooms, operations team, and maintenance team; and will need to achieve, to a varying degree, synchronization of operations, co-operation on tasks, hand-over of components, and sharing of data and resources. The separately procured RH systems must, therefore, be integrated to form a unified RH system for operation from the RH control rooms.The RH system will contain a heterogeneous mix of specially developed RH systems and off-the-shelf RH equipment and parts. The ITER Organization approach is to define a control system architecture that supports interoperable heterogeneous modules, and to specify a standard set of modules for each system to implement within this architecture. Compatibility with standard parts for selected modules is required to limit the complexity for operations and maintenance. A key requirement for integrating the control system modules is interoperability, and no module should have dependencies on the implementation details of other modules.The RH system is one of the ITER Plant systems that are integrated and coordinated through the hierarchical structure of the ITER CODAC system. It is distinguished from other Plant systems by the man-in-the-loop nature of RH operations and the need for control rooms at a level below the main control room. The RH control system architecture has been designed to also support the central monitoring and coordination of the RH activities.