Single line for assembly just-in-sequence multiple models

Programa Doutoral em Líderes para as Indústrias Tecnológicas

Dielectric relaxation and ferromagnetic resonance in magnetoelectric (Polyvinylidene-fluoride)/ferrite composites

In this work the dielectric properties and ferromagnetic resonance of Polyvinylidene- uoride embedded with 10 wt. % of NiFe2O4 or Ni0.5Zn0.5Fe2O4 nanoparticles are presented. The mechanisms of the dielectric relaxation in these two composites do not differ from each other. For more precise characterization of the dielectric relaxation, a two dimensional distribution of relaxation times was calculated from the temperature dependencies of the complex dielectric permittivity. The results obtained from the 2D distribution and the mean relaxation time are found to be consistent. The dynamics of the dielectric permittivity is described by the Arrhenius law. The energy and attempt time of the dielectric relaxators lie in a narrow energy and time region thus proving that the single type chains of polymer are responsible for a dispersion. The magnetic properties of the composites were investigated using the fer- romagnetic resonance. A single resonance line was observed for both samples. From the temperature dependence (100 K - 310 K) of the resonance eld and linewidth, the origin of the observed line was attributed to the NiFe2O4 and Ni0.5Zn0.5Fe2O4 superparamagnetic nanoparticles. By measuring lms at dif- ferent orientations with respect to the external magnetic eld, the angular dependence of the resonance was observed, indicating the magnetic dipolar in-plane interactions.

In-line rheo-optical characterization during small-scale polymer processing and compounding

As increasingly more sophisticated materials and products are being developed and times-to-market need to be minimized, it is important to make available fast response characterization tools using small amounts of sample, capable of conveying data on the relationships between rheological response, process-induced material structure and product characteristics. For this purpose, a single / twin-screw mini-extrusion system of modular construction, with well-controlled outputs in the range 30-300 g/h, was coupled to a in- house developed rheo-optical slit die able to measure shear viscosity and normal-stress differences, as well as performing rheo-optical experiments, namely small angle light scattering (SALS) and polarized optical microscopy (POM). In addition, the mini-extruder is equipped with ports that allow sample collection, and the extrudate can be further processed into products to be tested later. Here, we present the concept and experimental set-up [1, 2]. As a typical application, we report on the characterization of the processing of a polymer blend and of the properties of extruded sheets. The morphological evolution of a PS/PMMA industrial blend along the extruder, the flow-induced structures developed and the corresponding rheological characteristics are presented, together with the mechanical and structural characteristics of produced sheets. The application of this experimental tool to other material systems will also be discussed.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

In-line rheo-optical microstructural characterization of complex polymer systems

Tese de Doutoramento em Ciência e Engenharia de Polímeros e Compósitos.

Polyethylene glycol 8000+ citrate salts aqueous two-phase systems: Relative hydrophobicity of the equilibrium phases

The Gibbs free energy of transfer of a methylene group, G*(CH2), is reported as a measure of the relative hydrophobicity of the equilibrium phases. Furthermore, G*(CH2) is a characteristic parameter of each tie-line, and for that reason can be used for comparing different tie-lines of a given aqueous two-phase system (ATPS) or even to establish comparisons among different ATPSs. In this work, the partition coefficients of a series of four dinitrophenylated-amino acids were experimentally determined, at 23 °C, in five different tie-lines of PEG8000(sodium or potassium) citrate ATPSs. G*(CH2) values were calculated from the partition coefficients and used to evaluate the relative hydrophobicity of the equilibrium phases. PEG8000potassium citrate ATPSs presented larger relative hydrophobicity than PEG8000sodium citrate ATPSs. Furthermore, the results obtained indicated that the PEG-rich phase (top phase) has higher affinity to participate in hydrophobic hydration interactions than the salt-rich phase (bottom phase).

Integrated management systems as complex adaptive systems

It is a difficult task to avoid the “smart systems” topic when discussing smart prevention and, similarly, it is a difficult task to address smart systems without focusing their ability to learn. Following the same line of thought, in the current reality, it seems a Herculean task (or an irreparable omission) to approach the topic of certified occupational health and safety management systems (OHSMS) without discussing the integrated management systems (IMSs). The available data suggest that seldom are the OHSMS operating as the single management system (MS) in a company so, any statement concerning OHSMS should mainly be interpreted from an integrated perspective. A major distinction between generic systems can be drawn between those that learn, i.e., those systems that have “memory” and those that have not. These former systems are often depicted as adaptive since they take into account past events to deal with novel, similar and future events modifying their structure to enable success in its environment. Often, these systems, present a nonlinear behavior and a huge uncertainty related to the forecasting of some events. This paper seeks to portray, for the first time as we were able to find out, the IMSs as complex adaptive systems (CASs) by listing their properties and dissecting the features that enable them to evolve and self-organize in order to, holistically, fulfil the requirements from different stakeholders and thus thrive by assuring the successful sustainability of a company. Based on the revision of literature carried out, this is the first time that IMSs are pointed out as CASs which may develop fruitful synergies both for the MSs and for CASs communities. By performing a thorough revision of literature and based on some concepts embedded in the “DNA” of the subsystems implementation standards it is intended, specifically, to identify, determine and discuss the properties of a generic IMS that should be considered to classify it as a CAS.

An evolutionary multi-objective optimization system for earthworks

Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

Comparison of single and multi-objective evolutionary algorithms for robust link-state routing

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

An immunity based configuration for multilayer single featured biometric authentication algorithms

Immune systems have been used in the last years to inspire approaches for several computational problems. This paper focus on behavioural biometric authentication algorithms’ accuracy enhancement by using them more than once and with different thresholds in order to first simulate the protection provided by the skin and then look for known outside entities, like lymphocytes do. The paper describes the principles that support the application of this approach to Keystroke Dynamics, an authentication biometric technology that decides on the legitimacy of a user based on his typing pattern captured on he enters the username and/or the password and, as a proof of concept, the accuracy levels of one keystroke dynamics algorithm when applied to five legitimate users of a system both in the traditional and in the immune inspired approaches are calculated and the obtained results are compared.

Implementation of a lean six sigma project in a production line

Companies from the motorcycles components branch are dealing with a dynamic environment, resulting from the introduction of new products and the increase of market demand. This dynamic environment requires frequent changes in production lines and requires flexibility in the processes, which can cause reductions in the level of quality and productivity. This paper presents a Lean Six Sigma improvement project performed in a production line of the company's machining sector, in order to eliminate losses that cause low productivity, affecting the fulfillment of the production plan and customer satisfaction. The use of Lean methodology following the DMAIC stages allowed analyzing the factors that influence the line productivity loss. The major problems and causes that contribute to a reduction on productivity and that were identified in this study are the lack of standardization in the setup activities and the excessive stoppages for adjustment of the processes that caused an increase of defects. Control charts, Pareto analysis and cause-and-effect diagrams were used to analyze the problem. On the improvement stage, the changes were based on the reconfiguration of the line layout as well as the modernization of the process. Overall, the project justified an investment in new equipment, the defective product units were reduced by 84% and an increase of 29% of line capacity was noticed.

Mapping numbers onto space in preschool children and adults

Tese de Doutoramento em Psicologia - Especialidade em Psicologia Experimental e Ciências Cognitivas

Structural, mechanical and piezoelectric properties of polycrystalline AlN films sputtered on titanium bottom electrodes

Polycrystalline AlN coatings deposited on Ti-electrodes films were sputtered by using nitrogen both as reactive gas and sputtering gas, in order to obtain high purity coatings with appropriate properties to be further integrated into wear resistance coatings as a piezoelectric monitoring wear sensor. The chemical composition, the structure and the morphology of the films were investigated by X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy and atomic force microscopy techniques. These measurements show the formation of highly (101), (102) and (103) oriented AlN films with good piezoelectric and mechanical properties suitable for applications in electronic devices. Through the use of lower nitrogen flow a densification of the AlN coating occurs in the microstructure, with an improvement of the crystallinity along with the increase of the hardness. Thermal stability of aluminum nitride coatings at high temperature was also examined. It was found an improvement of the piezoelectric properties of the highly (10x) oriented AlN films which became c-axis (002) oriented after annealing. The mechanical behavior after heat treatment shows an important enhancement of the surface hardness and Young’s modulus, which decrease rapidly with the increase of the indentation depth until approach constant values close to the substrate properties after annealing. Thus, thermal annealing energy promotes not only the rearrangement of Al–N network, but also the occurrence of a nitriding process of unsaturated Al atoms which cause a surface hardening of the film.

Search for s-channel single top-quark production in proton-proton collisions at s√=8 TeV with the ATLAS detector

This Letter presents a search at the LHC for s-channel single top-quark production in proton-proton collisions at a centre-of-mass energy of 8 TeV. The analyzed data set was recorded by the ATLAS detector and corresponds to an integrated luminosity of 20.3 fb−1. Selected events contain one charged lepton, large missing transverse momentum and exactly two b-tagged jets. A multivariate event classifier based on boosted decision trees is developed to discriminate s-channel single top-quark events from the main background contributions. The signal extraction is based on a binned maximum-likelihood fit of the output classifier distribution. The analysis leads to an upper limit on the s-channel single top-quark production cross-section of 14.6 pb at the 95% confidence level. The fit gives a cross-section of σs=5.0±4.3 pb, consistent with the Standard Model expectation.

Search for dark matter in events with heavy quarks and missing transverse momentum in pp collisions with the ATLAS detector

This article reports on a search for dark matter pair production in association with bottom or top quarks in 20.3fb−1 of pp collisions collected at s√=8 TeV by the ATLAS detector at the LHC. Events with large missing transverse momentum are selected when produced in association with high-momentum jets of which one or more are identified as jets containing b-quarks. Final states with top quarks are selected by requiring a high jet multiplicity and in some cases a single lepton. The data are found to be consistent with the Standard Model expectations and limits are set on the mass scale of effective field theories that describe scalar and tensor interactions between dark matter and Standard Model particles. Limits on the dark-matter--nucleon cross-section for spin-independent and spin-dependent interactions are also provided. These limits are particularly strong for low-mass dark matter. Using a simplified model, constraints are set on the mass of dark matter and of a coloured mediator suitable to explain a possible signal of annihilating dark matter.