Decomposition of human behavior using model of competencies: application to robotic control system

For many years, humans and machines have shared the same physical space. To facilitate their interaction with humans, their social integration and for more rational behavior has been sought that the robots demonstrate human-like behavior. For this it is necessary to understand how human behavior is generated, discuss what tasks are performed and how relate to themselves, for subsequent implementation in robots. In this paper, we propose a model of competencies based on human neuroregulator system for analysis and decomposition of behavior into functional modules. Using this model allow separate and locate the tasks to be implemented in a robot that displays human-like behavior. As an example, we show the application of model to the autonomous movement behavior on unfamiliar environments and its implementation in various simulated and real robots with different physical configurations and physical devices of different nature. The main result of this work has been to build a model of competencies that is being used to build robotic systems capable of displaying behaviors similar to humans and consider the specific characteristics of robots.

Event-based visual servoing with features’ prediction

Event-based visual servoing is a recently presented approach that performs the positioning of a robot using visual information only when it is required. From the basis of the classical image-based visual servoing control law, the scheme proposed in this paper can reduce the processing time at each loop iteration in some specific conditions. The proposed control method enters in action when an event deactivates the classical image-based controller (i.e. when there is no image available to perform the tracking of the visual features). A virtual camera is then moved through a straight line path towards the desired position. The virtual path used to guide the robot improves the behavior of the previous event-based visual servoing proposal.

Model of Competencies for Decomposition of Human Behavior: Application to Control System of Robots

Humans and machines have shared the same physical space for many years. To share the same space, we want the robots to behave like human beings. This will facilitate their social integration, their interaction with humans and create an intelligent behavior. To achieve this goal, we need to understand how human behavior is generated, analyze tasks running our nerves and how they relate to them. Then and only then can we implement these mechanisms in robotic beings. In this study, we propose a model of competencies based on human neuroregulator system for analysis and decomposition of behavior into functional modules. Using this model allow separate and locate the tasks to be implemented in a robot that displays human-like behavior. As an example, we show the application of model to the autonomous movement behavior on unfamiliar environments and its implementation in various simulated and real robots with different physical configurations and physical devices of different nature. The main result of this study has been to build a model of competencies that is being used to build robotic systems capable of displaying behaviors similar to humans and consider the specific characteristics of robots.

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mobile agents in the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency. Offering relevant information to higher level systems, monitoring and making decisions in real time, it must accomplish a set of requirements, such as: time constraints, high availability, robustness, high processing speed and re-configurability. We have built a system able to represent and analyze the motion in video acquired by a multi-camera network and to process multi-source data in parallel on a multi-GPU architecture.

Screen-printed electrode-based electrochemical detector coupled with in-situ ionic-liquid-assisted dispersive liquid–liquid microextraction for determination of 2,4,6-trinitrotoluene

A novel method is reported, whereby screen-printed electrodes (SPELs) are combined with dispersive liquid–liquid microextraction. In-situ ionic liquid (IL) formation was used as an extractant phase in the microextraction technique and proved to be a simple, fast and inexpensive analytical method. This approach uses miniaturized systems both in sample preparation and in the detection stage, helping to develop environmentally friendly analytical methods and portable devices to enable rapid and onsite measurement. The microextraction method is based on a simple metathesis reaction, in which a water-immiscible IL (1-hexyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide, [Hmim][NTf2]) is formed from a water-miscible IL (1-hexyl-3-methylimidazolium chloride, [Hmim][Cl]) and an ion-exchange reagent (lithium bis[(trifluoromethyl)sulfonyl]imide, LiNTf2) in sample solutions. The explosive 2,4,6-trinitrotoluene (TNT) was used as a model analyte to develop the method. The electrochemical behavior of TNT in [Hmim][NTf2] has been studied in SPELs. The extraction method was first optimized by use of a two-step multivariate optimization strategy, using Plackett–Burman and central composite designs. The method was then evaluated under optimum conditions and a good level of linearity was obtained, with a correlation coefficient of 0.9990. Limits of detection and quantification were 7 μg L−1 and 9 μg L−1, respectively. The repeatability of the proposed method was evaluated at two different spiking levels (20 and 50 μg L−1), and coefficients of variation of 7 % and 5 % (n = 5) were obtained. Tap water and industrial wastewater were selected as real-world water samples to assess the applicability of the method.

A systematic study on the influence of carbon on the behavior of hard-to-ionize elements in inductively coupled plasma–mass spectrometry

A systematic study on the influence of carbon on the signal of a large number of hard-to-ionize elements (i.e. B, Be, P, S, Zn, As, Se, Pd, Cd, Sb, I, Te, Os, Ir, Pt, Au, and Hg) in inductively coupled plasma–mass spectrometry has been carried out. To this end, carbon matrix effects have been evaluated considering different plasma parameters (i.e. nebulizer gas flow rate, r.f. power and sample uptake rate), sample introduction systems, concentration and type of carbon matrix (i.e. glycerol, citric acid, potassium citrate and ammonium carbonate) and type of mass spectrometer (i.e. quadrupole filter vs. double-focusing sector field mass spectrometer). Experimental results show that P, As, Se, Sb, Te, I, Au and Hg sensitivities are always higher for carbon-containing solutions than those obtained without carbon. The other hard-to-ionize elements (Be, B, S, Zn, Pd, Cd, Os, Ir and Pt) show no matrix effect, signal enhancement or signal suppression depending on the experimental conditions selected. The matrix effects caused by the presence of carbon are explained by changes in the plasma characteristics and the corresponding changes in ion distribution in the plasma (as reflected in the signal behavior plot, i.e. the signal intensity as a function of the nebulizer gas flow rate). However, the matrix effects for P, As, Se, Sb, Te, I, Au and Hg are also related to an increase in analyte ion population caused as a result of charge transfer reactions involving carbon-containing charged species in the plasma. The predominant specie is C+, but other species such as CO+, CO2+, C2+ and ArC+ could also play a role. Theoretical data suggest that B, Be, S, Pd, Cd, Os, Ir and Pt could also be involved in carbon based charge transfer reactions, but no experimental evidence substantiating this view has been found.

A GIS-based methodology to quantitatively define an Adjacent Protected Area in a shallow karst cavity: The case of Altamira cave

Different types of land use are usually present in the areas adjacent to many shallow karst cavities. Over time, the increasing amount of potentially harmful matter and energy, of mainly anthropic origin or influence, that reaches the interior of a shallow karst cavity can modify the hypogeal ecosystem and increase the risk of damage to the Palaeolithic rock art often preserved within the cavity. This study proposes a new Protected Area status based on the geological processes that control these matter and energy fluxes into the Altamira cave karst system. Analysis of the geological characteristics of the shallow karst system shows that direct and lateral infiltration, internal water circulation, ventilation, gas exchange and transmission of vibrations are the processes that control these matter and energy fluxes into the cave. This study applies a comprehensive methodological approach based on Geographic Information Systems (GIS) to establish the area of influence of each transfer process. The stratigraphic and structural characteristics of the interior of the cave were determined using 3D Laser Scanning topography combined with classical field work, data gathering, cartography and a porosity–permeability analysis of host rock samples. As a result, it was possible to determine the hydrogeological behavior of the cave. In addition, by mapping and modeling the surface parameters it was possible to identify the main features restricting hydrological behavior and hence direct and lateral infiltration into the cave. These surface parameters included the shape of the drainage network and a geomorphological and structural characterization via digital terrain models. Geological and geomorphological maps and models integrated into the GIS environment defined the areas involved in gas exchange and ventilation processes. Likewise, areas that could potentially transmit vibrations directly into the cave were identified. This study shows that it is possible to define a Protected Area by quantifying the area of influence related to each transfer process. The combined maximum area of influence of all the processes will result in the new Protected Area. This area will thus encompass all the processes that account for most of the matter and energy carried into the cave and will fulfill the criteria used to define the Protected Area. This methodology is based on the spatial quantification of processes and entities of geological origin and can therefore be applied to any shallow karst system that requires protection.

Logic-Based Outer-Approximation Algorithm for Solving Discrete-Continuous Dynamic Optimization Problems

We present an extension of the logic outer-approximation algorithm for dealing with disjunctive discrete-continuous optimal control problems whose dynamic behavior is modeled in terms of differential-algebraic equations. Although the proposed algorithm can be applied to a wide variety of discrete-continuous optimal control problems, we are mainly interested in problems where disjunctions are also present. Disjunctions are included to take into account only certain parts of the underlying model which become relevant under some processing conditions. By doing so the numerical robustness of the optimization algorithm improves since those parts of the model that are not active are discarded leading to a reduced size problem and avoiding potential model singularities. We test the proposed algorithm using three examples of different complex dynamic behavior. In all the case studies the number of iterations and the computational effort required to obtain the optimal solutions is modest and the solutions are relatively easy to find.

Capillary microreactors based on hierarchical SiO2 monoliths incorporating noble metal nanoparticles for the Preferential Oxidation of CO

Novel hierarchical SiO2 monolithic microreactors loaded with either Pd or Pt nanoparticles have been prepared in fused silica capillaries and tested in the Preferential Oxidation of CO (PrOx) reaction. Pd and Pt nanoparticles were prepared by the reduction by solvent method and the support used was a mesoporous SiO2 monolith prepared by a well-established sol–gel methodology. Comparison of the activity with an equivalent powder catalyst indicated that the microreactors show an enhanced catalytic behavior (both in terms of CO conversion and selectivity) due to the superior mass and heat transfer processes that take place inside the microchannel. TOF values at low CO conversions have been found to be ∼2.5 times higher in the microreactors than in the powder catalyst and the residence time seems to have a noticeable influence over the selectivity of the catalysts designed for this reaction. The Pd and Pt flexible microreactors developed in this work have proven to be effective for the CO oxidation reaction both in the presence and absence of H2, standing out as a very interesting and suitable option for the development of CO purification systems of small dimensions for portable and on-board applications.

Algorithm for the detection of outliers based on the theory of rough sets

Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented.

The effect of carbon nanofillers on the performance of electromechanical polyaniline-based composite actuators

Different types of crystalline carbon nanomaterials were used to reinforce polyaniline for use in electromechanical bilayer bending actuators. The objective is to analyze how the different graphitic structures of the nanocarbons affect and improve the in situ polymerized polyaniline composites and their subsequent actuator behavior. The nanocarbons investigated were multiwalled carbon nanotubes, nitrogen-doped carbon nanotubes, helical-ribbon carbon nanofibers and graphene oxide, each one presenting different shape and structural characteristics. Films of nanocarbon-PAni composite were tested in a liquid electrolyte cell system. Experimental design was used to select the type of nanocarbon filler and composite loadings, and yielded a good balance of electromechanical properties. Raman spectroscopy suggests good interaction between PAni and the nanocarbon fillers. Electron microscopy showed that graphene oxide dispersed the best, followed by multiwall carbon nanotubes, while nitrogen-doped nanotube composites showed dispersion problems and thus poor performance. Multiwall carbon nanotube composite actuators showed the best performance based on the combination of bending angle, bending velocity and maximum working cycles, while graphene oxide attained similarly good performance due to its best dispersion. This parallel testing of a broad set of nanocarbon fillers on PAni-composite actuators is unprecedented to the best of our knowledge and shows that the type and properties of the carbon nanomaterial are critical to the performance of electromechanical devices with other conditions remaining equal.

NOx storage and reduction over copper-based catalysts. Part 2: Ce0.8M0.2Oδ supports (M = Zr, La, Ce, Pr or Nd)

5% copper catalysts with Ce0.8M0.2Oδ supports (M = Zr, La, Ce, Pr or Nd) have been studied by rapid-scan operando DRIFTS for NOx Storage and Reduction (NSR) with high frequency (30 s) CO, H2 and 50%CO + 50%H2 micropulses. In the absence of reductant pulses, below 200–250 °C NOx was stored on the catalysts as nitrite and nitro groups, and above this temperature nitrates were the main species identified. The thermal stability of the NOx species stored on the catalysts depended on the acid/basic character of the dopant (M more acidic = NOx stored less stable ⇒ Zr4+ < none < Nd3+ < Pr3+ < La3+ ⇐ M more basic = NOx stored more stable). Catalysts regeneration was more efficient with H2 than with CO, and the CO + H2 mixture presented an intermediate behavior, but with smaller differences among the series of catalyst than observed using CO alone. N2 is the main NOx reduction product upon H2 regeneration. The highest NOx removal in NSR experiments performed at 400 °C with CO + H2 pulses was achieved with the catalyst with the most basic dopant (CuO/Ce0.8La0.2Oδ) while the poorest performing catalyst was that with the most acidic dopant (CuO/Ce0.8Zr0.2Oδ). The poor performance of CuO/Ce0.8Zr0.2Oδ in NSR experiments with CO pulses was attributed to its lower oxidation capacity compared to the other catalysts.

Synthesis of denim waste-based adsorbents and their application in water defluoridation

This study evaluates the application of denim fiber scraps as a precursor for the synthesis of adsorbents for water treatment via pyrolysis and their application in water defluoridation. The best pyrolysis conditions for the synthesis of this novel adsorbent have been identified and a metal doping route with different salts of Al3 +, La3 + and Fe3 + was proposed to improve its fluoride adsorption behavior. Different spectroscopic and microscopic techniques (i.e., FTIR, XPS, XRF, SEM) were used to characterize the precursor and adsorbents, and to analyze the surface interactions involved in the fluoride removal mechanism. Experimental results showed that these adsorbents were effective for fluoride adsorption showing uptakes up to 4.25 mg/g. The Si-O–metal–F interactions appear to be highly relevant for the fluoride removal. This study highlights the potential of denim textile waste as a raw material for the production of added-value products, thus minimizing their associated disposal cost. It also shows the performance of denim textile waste as a precursor of adsorbents for addressing relevant environmental concerns such as fluoride pollution.