Probabilistic movement primitives under unknown system dynamics

Physical interaction requires robots to accurately follow kinematic trajectories while modulating the interaction forces to accomplish tasks and to be safe to the environment. However, current approaches rely on accurate physical models or iterative learning approaches. We present a versatile approach for physical interaction tasks, based on Movement Primitives (MPs) that can learn physical interaction tasks solely by demonstrations, without explicitly modeling the robot or the environment. We base our approach on the Probabilistic Movement Primitives (ProMPs), which utilizes the variance of the demonstrations to provide better generalization of the encoded skill, combine skills, and derive a controller that follows exactly the encoded trajectory distribution. However, the ProMP controller requires the system dynamics to be known. We present a reformulation of the ProMPs that allows accurate reproduction of the skill without modeling the system dynamics and, further, we extent our approach to incorporate external sensors, as for example, force/torque sensors. Our approach learns physical interaction tasks solely from demonstrations and online adapts the movement to force–torque sensor input. We derive a variable-stiffness controller in closed form that reproduces the trajectory distribution and the interaction forces present in the demonstrations. We evaluate our approach in simulated and real-robot tasks.     

Generating a Visual Language of Performance-Driven Configurations for the Principal Façade of a Prototype Sustainable House

A performance-driven application of shape grammars is presented. A parametric shape grammar that generates a language of pattern designs for the principal façade of a prototype house—featuring a 5 × 20 matrix of electrochromic windows—based equally on performance and aesthetic criteria is described. The adjustment of the chromatism and light transmittance of each individual windowpane on the façade enables the adjustment of solar radiation at the house interior. The novel aspect of the grammar is that it encodes performance constraints of interior daylight illuminance and associates them to visual, symmetry principles of two-dimensional pattern generation. Twelve parametric rules account for the generation of the façade pattern language and five subclasses account for the symmetry of the patterns in the language.     

Behavioral adaptation of information systems through goal models

Customizing software to perfectly fit individual needs is becoming increasingly important in information systems engineering. Users want to be able to customize software behavior through reference to terms familiar to their diverse needs and experience. We present a requirements-driven approach to behavioral customization of software systems. Goal models are constructed to represent alternative behaviors that users can exhibit to achieve their goals. Customization information is then added to restrict the space of possibilities to those that fit specific users, contexts, or situations. Meanwhile, elements of the goal models are mapped to units of source code. This way, customization preferences posed at the requirements level are directly translated into system customizations. Our approach, which we apply to an on-line shopping cart system and an automated teller machine simulator, does not assume adoption of a particular development methodology, platform, or variability implementation technique and keeps the reasoning computation overhead from interfering with the execution of the configured application.     

A sparse nonparametric hierarchical Bayesian approach towards inductive transfer for preference modeling

In this paper, we present a novel methodology for preference learning based on the concept of inductive transfer. Specifically, we introduce a nonparametric hierarchical Bayesian multitask learning approach, based on the notion that human subjects may cluster together forming groups of individuals with similar preference rationale (but not identical preferences). Our approach is facilitated by the utilization of a Dirichlet process prior, which allows for the automatic inference of the most appropriate number of subject groups (clusters), as well as the employment of the automatic relevance determination (ARD) mechanism, giving rise to a sparse nature for our model, which significantly enhances its computational efficiency. We explore the efficacy of our novel approach by applying it to both a synthetic experiment and a real-world music recommendation application. As we show, our approach offers a significant enhancement in the effectiveness of knowledge transfer in statistical preference learning applications, being capable of correctly inferring the actual number of human subject groups in a modeled dataset, and limiting knowledge transfer only to subjects belonging to the same group (wherein knowledge transferability is more likely).     

Customized kernel execution on reconfigurable hardware for embedded applications

To conserve space and power as well as to harness high performance in embedded systems, high utilization of the hardware is required. This can be facilitated through dynamic adaptation of the silicon resources in reconfigurable systems in order to realize various customized kernels as execution proceeds. Fortunately, the encountered reconfiguration overheads can be estimated. Therefore, if the scheduling of time-consuming kernels considers also the reconfiguration overheads, an overall performance gain can be obtained. We present our policy, experiments, and performance results of customizing and reconfiguring Field-Programmable Gate Arrays (FPGAs) for embedded kernels. Experiments involving EEMBC (EDN Embedded Microprocessor Benchmarking Consortium) and MiBench embedded benchmark kernels show high performance using our main policy, when considering reconfiguration overheads. Our policy reduces the required reconfigurations by more than 50% as compared to brute-force solutions, and performs within 25% of the ideal execution time while conserving 60% of the FPGA resources. Alternative strategies to reduce the reconfiguration overhead are also presented and evaluated.     

Connected component labelling on the BLITZEN massively parallel processor

This paper presents the implementation of two connected component labelling algorithms on the BLITZEN massively parallel processor that was developed recently for NASA. The topology of BLITZEN is a two-dimensional mesh that can be dynamically configured to also support diagonal data transfers. It is shown that an algorithm based on Levialdi's connected component shrinking process performs much better than a straightforward algorithm for connected component labelling.     

The new external ion beam analysis setup at the Demokritos Tandem accelerator and first applications in cultural heritage

At the 5.5 MV Tandem VdG accelerator of the Institute of Nuclear Physics of N.C.S.R. “Demokritos”, Athens, Greece, an external ion-beam set-up has been recently developed and installed. The aim of this development was to integrate the analytical capabilities of the PIXE, RBS and PIGE ion beam techniques in one experimental set-up, so that to attain a complete elemental and near surface structural characterization of samples in an almost non-destructive way and without any limitation concerning their size or conductive state. A careful 3D mechanical drawing optimized the set-up experimental parameters achieving probe dimensions at the millimeter range (1 mm²) and fulfilling the special requirements imposed for optimum performance of the aforementioned techniques, including the possibility to use heavier, than protons, ion beams. For the digital pulse processing of the X-ray, γ-ray and charged particle detector signals, novel hardware and software tools were developed based on a custom FPGA configuration.The first applications were focused in the quality control of materials that have been intentionally contaminated with a particular tracer-element (“tagged” materials). The tagged materials which were developed and tested are technologically authentic replicas of ancient attic ceramics with black glazed decoration. Analytical diagnostic studies were carried out for a few representative paintings of contemporary Greek painters in order to identify and document materials/pigments and techniques and eventually to prevent trade of fakes. Finally, ancient glass beads were also examined with respect to the sodium concentration and its in-depth homogeneity.     

The potential of WHR/batch and cullet preheating for energy efficiency in the EU ETS glass industry and the related energy incentives

The European Emissions Trading Scheme (EU ETS) covers approximately 45% of European greenhouse gases (GHGs), 11,000 stationary installations as well as aircraft operators. The EU ETS particularly affects the energy-intensive industries while it imposes a significant risk of “carbon leakage,” i.e., the risk of EU industry departing to countries with weaker restraints on GHG emissions. The EU glass industry, being capital intensive and also requiring long investment cycles, is the world’s largest glass producer with a market share close to one third of global production. Therefore, it is of significant importance to view the position of the EU ETS glass industry in terms of energy conservation possibilities. The present paper utilizes a vertical approach to provide information on both ETS market evolution and specific technical information to support technological innovation to the glass industry. EU ETS glass industry is analyzed regarding the balance between allocated European Union emission allowances (EUAs), verified CO₂ emissions, and potential shortfall in allowances so as to determine the situation of glass industries and the extent of urge for energy-saving activities towards the strengthening of their position within the requirements of the EU ETS phase III. The replication potential of waste heat recovery (WHR) through batch preheating is specifically addressed since it is considered a promising technology according to the latest Best Available Techniques (BAT) reference document for the glass industry under Directive IED 2010/75/EU. A case study for a container glass furnace based on simulation results is presented investigating the impact of different operating and design configurations on specific energy consumption and CO₂ emissions.     

Parallel generation of adaptive multiresolution structures for image processing

In the paper we present an algorithm for creating region-adjacency-graph (RAG) pyramids on TurboNet, an experimental parallel computer system. Each level of these hierarchies of irregular tessellations is generated by independent stochastic processes that adapt the structure of the pyramid to the content of the image. RAGs can be used in multiresolution image analysis to extract connected components from labeled images. The implementation of the algorithm is discussed and performance results are presented for three different communication techniques which are supported by the TurboNet's hybrid architecture. The results indicate that efficient communications are vital to good performance of the algorithm. © 1997 by John Wiley & Sons, Ltd.     

First measurements of carbon tetrachloride and tetrachloroethene in the atmosphere of Athens,Greece

Levels of carbon tetrachloride and tetrachloroethene were determined for the first time in Athens in the summer of 2000. The mean CCl4 and C2Cl4 concentrations were 0.060 and 0.18 ppb, respectively. CCl4 did not exhibit significant variation throughout the measurement period or with wind direction. C2Cl4 under strong eastern winds had less than half the concentrations observed during other meteorological conditions. No diurnal variation was observed for either compound.