Abstract
Standard Work is a very useful tool of the Lean Production paradigm to specify standards and to establish the best methods and sequences for each process and for each worker, helping to reduce wastes. Despite its usefulness, this tool is often underused, neglected and misunderstood. Apart from that, there is a lack of literature about Standard Work. It is possible to find many papers on Lean Production in general and on some Lean tools, but there are few that focus on or describe in detail this particular tool. The main purpose of this paper is to prove the validity and show the importance and the applicability of Standard Work. The main aspects and ideas to understand this tool are described, as well as the benefits of applying it in a real world manufacturing environment. In this paper, an application example is given on a production unit of an elevators company, following the Action-Research methodology. The main findings are that Standard Work is effectively a good tool to normalize work procedures, allowing increased flexibility and production, and decreased wastes and assembly errors.

Abstract
Stress detection from speech is a less explored field than Automatic Emotion Recognition and it is still not clear which features are better stress discriminants. The project VOCE aims at doing speech classification as stressed or not-stressed in real-time, using acoustic-prosodic features only. We therefore look for the best discriminating feature subsets from a set of 6125 features extracted with openSMILE toolkit plus 160 Teager Energy Operator (TEO) features. We use a Mutual Information (MI) filter and a branch and bound wrapper heuristic with an SVM classifier to perform feature selection. Since many feature sets are selected, we analyse them in terms of chosen features and classifier performance concerning also true positive and false positive rates. The results show that the best feature types for our application case are Audio Spectral, MFCC, PCM and TEO. We reached results as high as 70.4% for generalisation accuracy.

Abstract
This paper addresses the problem of bottom following by the MARES Autonomous Underwater Vehicle, and presents derivation of a controller able to cope with the peculiarities of such problem. In specific, the main requirement for the controller is the existence of no overshoot both in the depth and pitch outputs of the system. The existence of such time-domain requirements motivates the use of Eigenstructure Assignment techniques in the formulation of the controller. Simulation results obtained with a dynamic model of the MARES AUV are presented and discussed, indicating the validity of the proposed approach.

Abstract

Abstract
Recently, Serious Games (SG) achieved a recognized position as a learning tool in several contexts. SG provide constructive learning environments in which errors can be made without real life penalties and where students get instant feedback from their actions when facing challenges. These challenges should be in accordance with the intended learning goals and they should adapt and/or be repeated according to the learner's level. This aspect is decisive in the acquisition of knowledge, experience and professional skills through the simulation of different situations and contexts. The effectiveness of competences' training is directly related to the success in their acquisition but, above all, it is related to the ability to apply them to successfully perform a given task. However, an optimal game design methodology for competence training is yet to be created. This article presents a study that identifies the most appropriate game categories to develop specific skills and competences. It considers a taxonomy with eight game categories (Action, Strategy, Playing, Sports, Management Simulation, Adventure, Puzzle and Quiz) that were matched with the Education Competences and Educational Competency Wheel. Analysing 116 serious games allowed identifying which categories were more efficient in the training of a specific competence and therefore should be reused in the same scope.

Abstract
In this work, two all-fiber loop mirrors using a clover microstructured fiber for the simultaneous measurement of temperature and strain are presented. The sensing heads are formed by a short piece of clover microstructured fiber with 35 mm and 89 mm length respectively. The geometry of the fiber allowed observing different interferences created by the microstructured fiber core section. Different sensitivities to temperature and strain were obtained and, using a matrix method, it is possible to discriminate both physical parameters. Resolutions of +/- 2 degrees C and +/- 11 mu epsilon for the first structure and +/- 2.3 degrees C and +/- 18 mu epsilon for the second one, for temperature and strain, respectively, were attained.