Abstract

Abstract
It is proposed the multiplexing of optical fiber-based surface plasmon resonance (SPR) sensors deployed in a ladder topology, addressed in wavelength by combining each sensor with specific fiber Bragg gratings (FBGs) and considering intensity interrogation. In each branch of the fiber layout, the FBGs are located after the sensor and the peak optical power reflected by the FBGs is a function of the relative spectral position between the SPR sensor and the FBG resonances, with the former dependent on the refractive index of the surrounding medium. The concept is tested for the multiplexing of two SPR sensors fabricated in an etched region of a single-mode fiber showing intrinsic refractive index sensitivity up to 5000 nm/RIU, which translates into a sensitivity of similar to 829 dB/RIU from the interrogation approach considered. The obtained refractive index resolution is in the order of 10(-4) RIU, and the crosstalk level between the sensors was found negligible.

Abstract
Micro grids represent an emergent vision to address the challenges imposed by recent trends in smart electrical grids, where the large-scale integration of distributed energy production units plays an important role. Nevertheless, the realization of this vision requires the use of advanced intelligent approaches to manage the micro grids elements, such as distributed renewable energy production units, loads and storage devices. Multi-agent systems provide a suitable framework to design and implement such systems, where autonomous agents are endowed with predictive data analytics capabilities, e.g., for the prediction of renewable energy production and consumption, taking advantage of the large amount of data produced in these environments. In this context, this paper presents an agent-based system for the management of micro grids, where predictive capabilities were embedded in agents to provide real-time data analytics. The proposed approach was applied to an experimental case study where a set of predictive models was tested for short and long term forecasting of the energy produced by photovoltaic units.

Abstract

Abstract

Abstract
The era of mass customization of goods forces manufacturing systems to promote agility, flexibility and responsiveness, leading to complex and unpredictable systems. Such challenges have an impact in terms of the system responsiveness and adaptation, production costs, product quality, etc. In order to improve those aspects, some flexible control manufacturing paradigms were proposed offering elasticity to change available skills and provide new services. However, the understanding of when and how to (self-) reconfigure the system aiming to perform a fast changeover, is a crucial issue. This work proposes a self-organizing multi-agent system approach for an efficient and on the fly reconfiguration of services in the manufacturing domain. Besides self-organizing techniques, other dimensions, e.g., "social-based" trust and QoS metrics, are used to ensure a constant QoS in an agile production system. The insertion of intelligent agents facilitates the improvement of strategies that perform the service reconfiguration, and in addition, permits to understand when and how self-reconfiguration takes place in order to allow a continuous improvement of the system performance. Additionally, this work addresses solutions for real industrial applications, being aligned with some characteristics of the Industrie 4.0 initiative, namely the distributed intelligence and self-* methods, e.g. self-adaptation, self-organization and self-configuration.