Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

Multicast Mobility in Mobile IP Version 6 (MIPv6) : Problem Statement and Brief Survey

Publisher PD

Deep Learning Techniques to Improve the Performance of Olive Oil Classification

The olive oil assessment involves the use of a standardized sensory analysis according to the “panel test” method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for olive oil classification. It is an essential task in an attempt to get the most robust model over time and, both to avoid fraud in the price and to know whether it is suitable for consumption or not. The aim of this paper is to combine chemical techniques and Deep Learning approaches to automatically classify olive oil samples from two different harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples, which were obtained from two olive oil campaigns (2014–2015 and 2015–2016). The data from the two harvests are built from the selection of specific olive oil markers from the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the best results we have configured the parameters of our model according to the nature of the data. The results obtained show that a deep learning approach applied to data obtained from chemical instrumental techniques is a good method when classifying oil samples in their corresponding categories, with higher success rates than those obtained in previous works.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-

Transparent network-assisted flow mobility for multimedia applications in IMS environments

Cellular network operators are striving to solve the problem caused by the increasing volume of traffic over their networks. Given the proliferation of multi-interface devices, offloading part of the traffic to available access networks (e. g., WiFi or 3G access networks, even from other operators) seems to be a promising alternative. Here, we propose an IMS-compatible solution for flow mobility between access networks that exhibits two key features: flow mobility is transparent to both local applications at mobile nodes and their communication peers (e. g., multimedia content servers), and mobility operations are assisted by the network, so the home network supports the terminal in the process of access network discovery, and provides the terminal with policies that meet visited and home operators' roaming agreements while optimizing the use of their networks. The proposed solution has been validated using a real IMS testbed with Ethernet and WiFi access networks, where the mobility of UDP and TCP flows has been tested.The work in this article has been partially granted by the Madrid Community through the MEDIANET project (S-2009/TIC-1468) and by the Celtic UP-TO-US project (TSI-020400-2010-114)Publicad