Design issue of a new iCub head sub-system

I am presenting a new design for the head sub-system of the humanoid robot iCub. ICub is a humanoid robot platform that has been conceived as a result of the development of the European Project RobotCub. Mechanical problems of the current head sub-system of iCub robot have been studied in order to figure out improvements for a new design. The feasibility of the new design has been checked through dynamic simulations of the head operations. The successful results have shown plots with curve evolutions of smooth shapes and suitable values of important kinematic and dynamic parameters of the head structure.     

Control of absorption columns in the bioethanol process: Influence of measurement uncertainties

The alcohol lost by evaporation during the bioethanol fermentation process may be collected and recovered using an absorption column. This equipment is also used in the carbonic gas treatment, a by-product from the sugar cane fermentation. In the present work, the development of nonlinear feedforward–feedback controllers, based on neural network inverse models, was proposed and tested to manipulate the absorbent flow rates. The control purposes are: to keep low ethanol concentration in the effluent gas phase from the first absorption column (ethanol recovery column); and to reduce the residual water concentration in the CO₂ gas effluent from the second tower (CO₂ treatment column).Based on simulation studies, the neural network (ANN) controller performance was compared with the conventional PID control scheme application. The best ANN architecture was set up according to the Foresse and Hagan (1997) criterion, while the PID parameters were found from the well-known Cohen–Coon Equations and trial-and-error fine tuning.Initially, performances were evaluated for the system without concentration measurement uncertainties. From these tests, the ANN controller presented the smallest response time and overshoot for regulator and servo problems. Three uncertainty levels were applied afterwards: 5%, 10%, and 15%.The ANN controller outperformed the PID for all uncertainty levels tested for the ethanol recovery column. For the CO₂ treatment column, the ANN controller proceeded successfully under uncertainties of 5% and 10%, while the PID did not deal properly with uncertainties above 5%. The statistical F-test, besides the ITAE, ISE, and IAE performance criteria, were calculated for both controllers applications and then compared. They proved the superiority of the ANN control scheme.Using appropriately the proposed well-controlled absorption columns increases the efficiency of the bioethanol production plant and can also provide carbon credits by avoiding CO₂ emission into the atmosphere.     

Semi-formal transformation of secure business processes into analysis class and use case models: An MDA approach

ContextModel-Driven Development (MDD) is an alternative approach for information systems development. The basic underlying concept of this approach is the definition of abstract models that can be transformed to obtain models near implementation. One fairly widespread proposal in this sphere is that of Model Driven Architecture (MDA). Business process models are abstract models which additionally contain key information about the tasks that are being carried out to achieve the company’s goals, and two notations currently exist for modelling business processes: the Unified Modelling Language (UML), through activity diagrams, and the Business Process Modelling Notation (BPMN).ObjectiveOur research is particularly focused on security requirements, in such a way that security is modelled along with the other aspects that are included in a business process. To this end, in earlier works we have defined a metamodel called secure business process (SBP), which may assist in the process of developing software as a source of highly valuable requirements (including very abstract security requirements), which are transformed into models with a lower abstraction level, such as analysis class diagrams and use case diagrams through the approach presented in this paper.MethodWe have defined all the transformation rules necessary to obtain analysis class diagrams and use case diagrams from SBP, and refined them through the characteristic iterative process of the action-research method.ResultsWe have obtained a set of rules and a checklist that make it possible to automatically obtain a set of UML analysis classes and use cases, starting from SBP models. Our approach has additionally been applied in a real environment in the area of the payment of electrical energy consumption.ConclusionsThe application of our proposal shows that our semi-automatic process can be used to obtain a set of useful artifacts for software development processes.     

Unsupervised classification of dynamic obstacles in urban environments

This paper presents a solution to the problem of unsupervised classification of dynamic obstacles in urban environments. A track‐based model is introduced for the integration of two‐dimensional laser and vision information that provides a robust spatiotemporal synthesis of the sensed moving obstacles and forms the basis for suitable algorithms to perform unsupervised classification by clustering. This work presents various contributions in order to achieve accurate and efficient performance, initially using laser tracks for classification and then incorporating visual tracks to the model. A procedure is proposed for accurate unsupervised classification of dynamic obstacles using a laser stamp representation of the tracks. Laser data are then integrated with visual information through a single‐instance visual stamp representation, which is finally extended using a multiple‐instance framework to robustly deal with challenges associated with perception in real‐world scenarios. The proposed algorithms are extensively validated with a simulated environment. Experiments with a research vehicle in an urban environment demonstrate the performance of the approach with real data. The experimental results reach an accuracy of more than 92% for obstacle classification, finding the clusters that correspond to the main obstacle classes in the data. © 2010 Wiley Periodicals, Inc.     

A computer implementation of the partition of the unity procedure and its application to arthropod population dynamics. A case study on the European grape berry moth

This paper presents a new procedure to construct families of spatial approximation-prediction functions which depend on several parameters. The method is based on partitions of the unity. In order to find optimal functions in these families we introduce a Vietoris simplicial set associated to an influence radius. We consider error estimators induced by these Vietoris simplicial sets. A computational implementation of these mathematical models has been developed to be applied to any kind of finite discrete data. Our methods provide a useful tool for the analysis of the spatial distribution of species, which is imperative for developing accurate and efficient management of endangered and invasive species, biological conservation programs, etc. We demonstrate the application of our method to the analysis of the spatial distribution of the grape moth Lobesia botrana (Lepidoptera: Tortricidae) in the vineyards of La Rioja obtaining useful results for the management of this pest.     

A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine

We report herein the use of a combined system for the analysis of the spoilage of wine when in contact with air. The system consists of a potentiometric electronic tongue and a humid electronic nose. The potentiometric electronic tongue was built with thick-film serigraphic techniques using commercially available resistances and conductors for hybrid electronic circuits; i.e. Ag, Au, Cu, Ru, AgCl, and C. The humid electronic nose was designed in order to detect vapours that emanate from the wine and are apprehended by a moist environment. The humid nose was constructed using a piece of thin cloth sewn, damped with distilled water, forming five hollows of the right size to introduce the electrodes. In this particular case four electrodes were used for the humid electronic nose: a glass electrode, aluminium (Al), graphite and platinum (Pt) wires and an Ag-AgCl reference electrode. The humid electronic nose together with the potentiometric electronic tongue were used for the evaluation of the evolution in the course of time of wine samples. Additionally to the analysis performed by the tongue and nose, the spoilage of the wines was followed via a simple determination of the titratable (total) acidity.     

Moving objects forecast in image sequences using autoregressive algorithms

The objective of this paper is to present an overall approach to forecasting the future position of the moving objects of an image sequence after processing the images previous to it. The proposed method makes use of classical techniques such as optical flow to extract objects’ trajectories and velocities, and autoregressive algorithms to build the predictive model. Our method can be used in a variety of applications, where videos with stationary cameras are used, moving objects are not deformed and change their position with time. One of these applications is traffic control, which is used in this paper as a case study with different meteorological conditions to compare with.

Applying machine learning techniques for scaling out data quality algorithms in cloud computing environments

Deduplication is the task of identifying the entities in a data set which refer to the same real world object. Over the last decades, this problem has been largely investigated and many techniques have been proposed to improve the efficiency and effectiveness of the deduplication algorithms. As data sets become larger, such algorithms may generate critical bottlenecks regarding memory usage and execution time. In this context, cloud computing environments have been used for scaling out data quality algorithms. In this paper, we investigate the efficacy of different machine learning techniques for scaling out virtual clusters for the execution of deduplication algorithms under predefined time restrictions. We also propose specific heuristics (Best Performing Allocation, Probabilistic Best Performing Allocation, Tunable Allocation, Adaptive Allocation and Sliced Training Data) which, together with the machine learning techniques, are able to tune the virtual cluster estimations as demands fluctuate over time. The experiments we have carried out using multiple scale data sets have provided many insights regarding the adequacy of the considered machine learning algorithms and proposed heuristics for tackling cloud computing provisioning.     

Finding groups with maximum betweenness centrality

In this paper we consider the problem of identifying the most influential (or central) group of nodes (of some predefined size) in a network. Such a group has the largest value of betweenness centrality or one of its variants, for example, the length-scaled or the bounded-distance betweenness centralities. We demonstrate that this problem can be modelled as a mixed integer program (MIP) that can be solved for reasonably sized network instances using off-the-shelf MIP solvers. We also discuss interesting relations between the group betweenness and the bounded-distance betweenness centrality concepts. In particular, we exploit these relations in an algorithmic scheme to identify approximate solutions for the original problem of identifying the most central group of nodes. Furthermore, we generalize our approach for identification of not only the most central groups of nodes, but also central groups of graph elements that consists of either nodes or edges exclusively, or their combination according to some pre-specified criteria. If necessary, additional cohesiveness properties can also be enforced, for example, the targeted group should form a clique or a κ-club. Finally, we conduct extensive computational experiments with different types of real-life and synthetic network instances to show the effectiveness and flexibility of the proposed framework. Even more importantly, our experiments reveal some interesting insights into the properties of influential groups of graph elements modelled using the maximum betweenness centrality concept or one of its variations.