Abstract
Dams require continuous security and monitoring programs, integrated with visual inspection and testing in dam surveillance programs. New approaches for dam monitoring focus on multi-sensor integration, taking into account emerging technologies such as GNSS, optic fiber, TLS, InSAR techniques, GBInSAR, GPR, that can be used as complementary data in dam monitoring, eliciting causes of dam deformation that cannot be assessed with traditional techniques. This paper presents a Multi-temporal InSAR (MT-InSAR) monitoring of La Viñuela dam (Málaga, Spain), a 96 m height earth-fill dam built from 1982 to 1989. The presented MT-InSAR monitoring system comprises three C-band radar (~5,7 cm wavelength) datasets from the European satellites ERS-1/2 (1992-2000), Envisat (2003-2008), and Sentinel-1A/B (2014-2018). ERS-1/2 and Envisat datasets were processed using StaMPS. In the case of Sentinel-1A/B, two different algorithms were applied, SARPROZ and ISCE-SALSIT, allowing the comparison of the estimated LOS velocity pattern. The obtained results confirm that LaViñuela dam is deforming since its construction, as an earth-fill dam. Maximum deformation rates were measured in the initial period (1992-2000), being around -7 mm/yr (LOS direction) on the coronation of the dam. In the period covered by the Envisat dataset (2003-2008), the average deforming pattern was lower, of the order of -4 mm/yr. Sentinel-1A/B monitoring confirms that the deformation is still active in the period 2014-2018 in the central-upper part of the dam, with maximums of velocity reaching -6 mm/yr. SARPROZ and ISCE-SALSIT algorithms provide similar results. It was concluded that MT-InSAR techniques can support the development of new and more effective means of monitoring and analyzing the health of dams complementing actual dam surveillance systems. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.

Abstract
Synthetic Aperture Radar Interferometry (InSAR) is a remote sensing technique very effective for the measurement of small displacements of the Earth's surface over large areas at a very low cost in comparison with conventional geodetic techniques. Advanced InSAR time series (Multi-Temporal InSAR or MT-InSAR) algorithms for monitoring and investigating surface displacement on Earth are based on conventional radar interferometry. These techniques allow us to measure deformation with uncertainties of one millimeter per year, interpreting time series of interferometric phases at coherent point scatterers (PS) without the need for human or special equipment presence. By applying InSAR processing techniques to a series of radar images over the same region, it is possible to monitor large areas and detect vertical displacements of ground, and infrastructures on the ground, and therefore identify abnormal or excessive movements indicating potential problems requiring detailed ground investigation. In this paper, we apply the PS-InSAR technique to a dataset of ERS-1/2 and Envisat radar images covering the period 1993-2010, to monitor the northern sector of the Valencia basin (Valencia city and its surroundings). Some subsiding areas were detected, with rates up to -5 mm/yr, whose causes are being investigated.

Abstract
The present paper addresses the use of evolutionary based algorithms for off-line fractional-order controller tuning. In particular, a linearized model of a motor-rotor propulsion device was assumed whose representativeness is supported by laboratorial measurements. Initially, the controller was calibrated, using the devised linear model, by a procedure that uses a cost function defined as the linear combination between the sum of the squared error and the sum of the absolute error. In this work, it was shown that this process can be improved by using an evolutionary based algorithm in order to find the best controller parameters. This strategy allows a more automatic tuning procedure isolating it from the user intervention. Moreover, the results achieved by this process, lead to an improved rotational speed regulation.

Abstract
The present paper addresses the use of evolutionary based algorithms for off-line fractional-order controller tuning. In particular, a linearized model of a motor-rotor propulsion device was assumed whose representativeness is supported by laboratorial measurements. Initially, the controller was calibrated, using the devised linear model, by a procedure that uses a cost function defined as the linear combination between the sum of the squared error and the sum of the absolute error. In this work, it was shown that this process can be improved by using an evolutionary based algorithm in order to find the best controller parameters. This strategy allows a more automatic tuning procedure isolating it from the user intervention. Moreover, the results achieved by this process, lead to an improved rotational speed regulation. © 2018 IEEE.

Abstract
This paper describes the development of a multi-agent system (MAS) to support the implementation of zero-defect manufacturing strategies in multi-stage production systems. The MAS infrastructure, combined with on-line inspection tools, data analytics and knowledge generation, constitutes a suitable approach to integrate process and quality control in multi-stage environments. This will allow the early detection of product defects, the adaptation to operating condition changes and the optimisation of manufacturing processes. This type of integrated management structure is aligned with a zero-defect manufacturing production model which is of paramount importance in the actual state-of-the-art manufacturing paradigms. As a proof of concept, the devised manufacturing supervision model was deployed into an experimental multi-stage system that run a set of several tests on electrical motors. The agent-based solution was implemented using the JADE framework and the exchange of information structured by proper data models and industrial based Internet-of-Things and Machine-to-Machine technologies, such as OPC-UA, REST and JSON. The obtained results demonstrate the suitability of the devised integrated management model as a vehicle to achieve dynamic and continuous system improvement in multi-stage manufacturing environments.

Abstract
Multi-stage manufacturing, typical in important industrial sectors, is inherently a complex process. The application of the zero defect manufacturing (ZDM) philosophy, together with recent technological advances in cyber-physical systems (CPS), presents significant challenges and opportunities for the implementation of new methodologies towards the continuous system improvement. This paper introduces the main principles of a multi-agent CPS aiming the application of ZDM in multi-stage production systems, which is being developed under the EU H2020 GOOD MAN project. In particular, this paper describes the MAS architecture that allows the distributed data collection and the balancing of the data analysis for monitoring and adaptation among cloud and edge layers, to enable the earlier detection of process and product variability, and the generation of new optimized knowledge by correlating the aggregated data.