Abstract
This paper focuses on dynamic performance control of modular multilevel converters (MMC) in high-voltage direct current (HVDC) transmission systems. To achieve this objective, a new mathematical model including six state variables of ac-currents and dc-link voltage of MMC, and circulating currents of converter arms are proposed for MMC in d-q reference frame. In addition, a robust control technique with three sub-control loops is designed to provide the stable operation of MMC. In the overall structure of the proposed controller, three outer, central and inner loops have the duties of 1) making the state variables error zero with changeable convergence rate, 2) adding robustness characteristic to the proposed controller, and 3) generating the appropriate reference values for MMC's currents, respectively. The effectiveness of the proposed control algorithm is investigated via MATLAB simulation. The simulation results highlight the capability of the proposed control algorithm in offering an accurate active and reactive power tracking through the control method of MMC, a stabilized dc-link voltage, capacitor voltage balancing of sub-modules, and minimization of circulating currents of converter arms during dynamic transitions and steady state operation.

Abstract
A traffic incident is defined by an event which provokes a disruption on the normal (free) flow condition of any highway. Such incidents must be caused by a recurrent excessive demand or, in alternative, by a series of possible stochastic occurrences which may suddenly reduce the road capacity (e.g. car accidents, extreme weather changes). This paper proposes a novel binary supervised learning method to classify congestion predictions regarding their causes - CJAMmer. It leverages on heterogeneous and ubiquitous data sources - such as weather, flow counts and traffic incident event logs -To generalize decision models able to understand the road congestion nature. CJAMmer settles on boosted decision trees using the well-known C4.5, as well as a straightforward feature generation process. A real world experiment was used to compare this method against other state-of-The-Art classifiers. The results uncovered the high potential impact of this methodology on industrial scale traffic control systems. © 2016 IEEE.

Abstract
Physical maps of a city or region are important pieces of geographical information for tourists and local citizens. Unfortunately the amount of information that can be presented on a piece of paper is limited. In order to extend the map information we propose an augmented reality (AR) system, ARTourMap, for additional information visualization and interaction. This system provides an abstraction layer to develop applications based on the concept of separated logic map tiles taking advantage of a multi-target system where several regions of the map trigger different superimposed graphics. This allows the map to be folded, to be partially occluded, and to have dematerialized information. To demonstrate the proposed system ARTourMap, three layers were developed: a location-based game with points of interest (POIs), a 3D building visualization and an historical map layer. © 2016 The Eurographics Association.

Abstract
The present study aims (a) to translate and adapt the Igroup Presence Questionnaire (IPQ) to the Portuguese context (semantic equivalence/ conceptual and content validity) and (b) to examine its psychometric properties (reliability and factorial validity). The sample consisted of 478 subjects (285 males and 193 females). The fidelity of the factors varied between 0.53 and 0.83. The confirmatory factor analysis results produced a 14-item version of IPQ-PT, accepting covariance between residual errors of some items of the instrument, as the best structural representation of the data analyzed. The CFA was conducted based on a three-variable model. The fit indexes obtained were X-2/df = 2.647, GFI = .948, CFI = .941, RSMEA = .059, and AIC = 254. These values demonstrate that the proposed Portuguese translation of the IPQ maintains its original validity, demonstrating it to be a robust questionnaire to measure the sense of presence in virtual reality studies. It is therefore recommended for use in presence research when using Portuguese samples.

Abstract

Abstract
Concerned with predicting equipment failures, predictive maintenance has a high impact both at a technical and at a financial level. Most modern equipments have logging systems that allow us to collect a diversity of data regarding their operation and health. Using data mining models for anomaly and novelty detection enables us to explore those datasets, building predictive systems that can detect and issue an alert when a failure starts evolving, avoiding the unknown development up to breakdown. In the present case, we use a failure detection system to predict train door breakdowns before they happen using data from their logging system. We use sensor data from pneumatic valves that control the open and close cycles of a door. Still, the failure of a cycle does not necessarily indicates a breakdown. A cycle might fail due to user interaction. The goal of this study is to detect structural failures in the automatic train door system, not when there is a cycle failure, but when there are sequences of cycle failures. We study three methods for such structural failure detection: outlier detection, anomaly detection and novelty detection, using different windowing strategies. We propose a two-stage approach, where the output of a point-anomaly algorithm is post-processed by a low-pass filter to obtain a subsequence-anomaly detection. The main result of the two-level architecture is a strong impact in the false alarm rate.