An engineering approach for the estimation of slewing bearing stiffness in wind turbine generators

The stiffness of yaw and pitch slewing bearings has a critical influence on the structural behaviour of wind turbine generators. Thus, it is commonly required by designers for their simulations to estimate deformations and select the most suitable bearing for their working conditions in preliminary design stages. In this work, a design of experiments was carried out via finite element analysis to obtain the stiffness curves of all of the standard four‐point contact slewing bearings from the catalogues of manufacturers under radial, axial, and tilting loads. From these results, a set of simple formulas to calculate the ring deformations were adjusted. Combining them with contact deformation results obtained in previous work by the authors, a complete and efficient tool for slewing bearing stiffness estimation has been developed.     

A new marker design for a robust marker tracking system against occlusions

Marker systems are a widely used optical tracking method that does not support occlusions. Thus, this paper proposes a new marker design to overcome the problem of marker occlusions. It is highly adaptable, because it can be used by any marker tracking system that uses its central area to codify the digital identification. Our proposal takes advantage of an untapped frame to place some textures that will be tracked during marker occlusion. In addition, these textures are customizable, which lets users make their own designs. Two tracking methods are combined to offer a robust tracking, updating the six degrees of freedom of the camera in real time. The first one is a fast technique based on temporal coherence, whereas the second one is a robust technique based on appearance, which is used as a recovery mode. Copyright © 2012 John Wiley & Sons, Ltd.     

Evolution of Gaussian Process kernels for machine translation post-editing effort estimation

In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is the estimation of the human effort needed to improve a text that has been translated using a machine translation method. Recent advances in this area have shown that Gaussian Processes can be effective in post-editing effort prediction. However, Gaussian Processes require a kernel function to be defined, the choice of which highly influences the quality of the prediction. On the other hand, the extraction of features from the text can be very labor-intensive, although recent advances in sentence embedding have shown that this process can be automated. In this paper, we use a Genetic Programming algorithm to evolve kernels for Gaussian Processes to predict post-editing effort based on sentence embeddings. We show that the combination of evolutionary optimization and Gaussian Processes removes the need for a-priori specification of the kernel choice, and, by using a multi-objective variant of the Genetic Programming approach, kernels that are suitable for predicting several metrics can be learned. We also investigate the effect that the choice of the sentence embedding method has on the kernel learning process.

     

Equipment modelling of CVD and etching

This paper presents results of equipment simulation giving applications of the fluid-dynamic solution to three commonly used reactors in the semiconductor industry — Tungsten CVD, Aluminum plasma etching, and Poly LPCVD. The results obtained include the determination of three-dimensional profiles for pressure, temperature, velocity, concentration of different chemical species, etch rates, deposition rates and uniformity over the wafer. This simulation technique is an important tool for equipment and process optimization by both the equipment suppliers and process engineers, because it allows a better visualization of the variables that control the process and allows a fast estimation of how changes on the process parameters or hardware configuration influence the results.     

Towards a standard methodology to evaluate internal cluster validity indices

The evaluation and comparison of internal cluster validity indices is a critical problem in the clustering area. The methodology used in most of the evaluations assumes that the clustering algorithms work correctly. We propose an alternative methodology that does not make this often false assumption. We compared 7 internal cluster validity indices with both methodologies and concluded that the results obtained with the proposed methodology are more representative of the actual capabilities of the compared indices.     

Molecular dynamics study of the influence of functionalization on the elastic properties of single and multiwall carbon nanotubes

The effect of carboxylation on axial Young’s modulus of carbon nanotubes is investigated using a molecular dynamics (MD) approach. COMPASS force field is used to model the interatomic interactions in single wall (SWCNT) and multiwall carbon (MWCNT) with different amounts of –COOH groups attached to their surfaces. The results of the MD simulations show how an increase of the number of carboxylic groups on the CNT surface leads to a decrease on the Young moduli of the CNTs. The decrease of MWCNT Young’s modulus is found to be lower than in the case of SWCNT.     

Warm Forming of Mg Sheets: From Incremental to Electromagnetic Forming

Magnesium alloys are generating interest in the automotive and aeronautic industries due to their low density and potential to reduce gross vehicular weight. However, the formability of these alloys is poor and they are very difficult to be formed at room temperature due to their strong basal texture in rolled form. In this paper, the potential of magnesium alloy sheets to be processed at warm conditions is studied for four different forming technologies: incremental forming (IF), deep drawing (DD), hydroforming (HF), and electromagnetic forming (EMF). Forming mechanisms and process window are experimentally characterized by monitoring different process parameters. Special focus is made on the influence of the forming temperature and the strain rate. Thus, experiments at temperatures from room to 523 K (250 °C) and a wide range of strain rates, between 10^?3 up to 10³ s^?1 according to each process nature and scope, are conducted. It is observed that, even the inherent forming rate range of each process vary considerably, increasing forming temperature increases formability for all of these forming processes. In the other hand, an opposing effect of the strain rate is observed between the quasi-static processes (IF, DD, and HF) and the high speed process (EMF). Thus, a detrimental effect on formability is observed when increasing strain rate for quasi-static processes, while a mild increase is observed for EMF.     

Web usage and content mining to extract knowledge for modelling the users of the Bidasoa Turismo website and to adapt it

The tourism industry has experienced a shift from offline to online travellers and this has made the use of intelligent systems in the tourism sector crucial. These information systems should provide tourism consumers and service providers with the most relevant information, more decision support, greater mobility and the most enjoyable travel experiences. As a consequence, Destination Marketing Organizations (DMOs) not only have to respond by adopting new technologies, but also by interpreting and using the knowledge created by the use of these techniques. This work presents the design of a general and non-invasive web mining system, built using the minimum information stored in a web server (the content of the website and the information from the log files stored in Common Log Format (CLF)) and its application to the Bidasoa Turismo (BTw) website. The proposed system combines web usage and content mining techniques with the three following main objectives: generating user navigation profiles to be used for link prediction; enriching the profiles with semantic information to diversify them, which provides the DMO with a tool to introduce links that will match the users taste; and moreover, obtaining global and language-dependent user interest profiles, which provides the DMO staff with important information for future web designs, and allows them to design future marketing campaigns for specific targets. The system performed successfully, obtaining profiles which fit in more than 60% of cases with the real user navigation sequences and in more than 90% of cases with the user interests. Moreover the automatically extracted semantic structure of the website and the interest profiles were validated by the BTw DMO staff, who found the knowledge provided to be very useful for the future.     

Structure–Activity Relationship Studies of Amino Acid Substitutions in Radiolabeled Neurotensin Conjugates

Radiolabeled derivatives of the peptide neurotensin (NT) and its binding sequence NT(8–13) have been studied as potential imaging probes and therapeutics for NT‐1‐receptor‐positive cancer. However, a direct comparison of reported NT analogues, even if radiolabeled with the same radionuclide, is difficult because different techniques and models have been used for preclinical evaluations. In an effort to identify a suitable derivative of NT(8–13) for radiotracer development, we herein report a side‐by‐side in vitro comparison of radiometallated NT derivatives bearing some of the most commonly reported amino acid substitutions in their sequence. Performed investigations include cell internalization experiments, determinations of receptor affinity, measurements of the distribution coefficient, and blood serum stability studies. Of the [¹⁷⁷Lu]‐1,4,7,10‐tetraazacyclododecane‐1,4,7,10‐tetraacetic acid (DOTA)‐labeled examples studied, analogues of NT(8–13) containing a short hydrophilic tetraethylene glycol (PEG₄) spacer between the peptide and the radiometal complex, and a minimum number of substitutions of amino acid residues, exhibited the most promising properties in vitro.