IIoT Data Ness: From Streaming to Added Value

In the emerging Industry 4.0 paradigm, the internet of things has been an innovation driver, allowing for environment visibility and control through sensor data analysis. However the data is of such volume and velocity that data quality cannot be assured by conventional architectures. It has been argued that the quality and observability of data are key to a project’s success, allowing users to interact with data more effectively and rapidly. In order for a project to become successful in this context, it is of imperative importance to incorporate data quality mechanisms in order to extract the most value out of data. If this goal is achieved one can expect enormous advantages that could lead to financial and innovation gains for the industry. To cope with this reality, this work presents a data mesh oriented methodology based on the state-of-the-art data management tools that exist to design a solution which leverages data quality in the Industrial Internet of Things (IIoT) space, through data contextualization. In order to achieve this goal, practices such as FAIR data principles and data observability concepts were incorporated into the solution. The result of this work allowed for the creation of an architecture that focuses on data and metadata management to elevate data context, ownership and quality.O conceito de Internet of Things (IoT) é um dos principais fatores de sucesso para a nova Indústria 4.0. Através de análise de dados sobre os valores que os sensores coletam no seu ambiente, é possível a construção uma plataforma capaz de identificar condições de sucesso e eventuais problemas antes que estes ocorram, resultando em ganho monetário relevante para as empresas. No entanto, este caso de uso não é de fácil implementação, devido à elevada quantidade e velocidade de dados proveniente de um ambiente de IIoT (Industrial Internet of Things)

A new approach to the development and maintenance of industrial sequence logic

This thesis is concerned with sequence logic as found in industrial control systems, with the focus being on process and manufacturing control systems. At its core is the assertion that there is a need for a better approach to the development of industrial sequence logic to satisfy the life-cycle requirements, and that many of the ingredients required to deliver such an approach are now available. The needs are discussed by considering the business case for automation and deficiencies with traditional approaches. A set of requirements is then derived for an integrated development environment to address the business needs throughout the control system life-cycle. The strengths and weaknesses of relevant control system technology and standards are reviewed and their bias towards implementation described. Mathematical models, graphical methods and software tools are then assessed with respect to the requirements for an integrated development environment. A solution to the requirements, called Synect is then introduced. Synect combines a methodology using familiar graphical notations with Petri net modelling supported by a set of software tools. Its key features are justified with reference to the requirements. A set of case studies forms the basis of an evaluation against business needs by comparing the Synect methodology with current approaches. The industrial relevance and exploitation are then briefly described. The thesis ends with a review of the key conclusions along with contributions to knowledge and suggestions for further research

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

MAPPA. Methodologies applied to archaeological potential Predictivity

The fruitful cooperation over the years between the university teaching staff of Univerità di Pisa (Pisa University), the officials of the Soprintendenza per i Beni Archeologici della Toscana (Superintendency for Archaeological Heritage of Tuscany), the officials of the Soprintendenza per i Beni Architettonici, Paesaggistici, Artistici ed Etnoantropologici per le Province di Pisa e Livorno (Superintendency for Architectural, Landscape and Ethno-anthropological Heritage for the Provinces of Pisa and Livorno), and the Comune di Pisa (Municipality of Pisa) has favoured a great deal of research on issues regarding archaeological heritage and the reconstruction of the environmental and landscape context in which Pisa has evolved throughout the centuries of its history. The desire to merge this remarkable know-how into an organic framework and, above all, to make it easily accessible, not only to the scientific community and professional categories involved, but to everyone, together with the wish to provide Pisa with a Map of archaeological potential (the research, protection and urban planning tool capable of converging the heritage protection needs of the remains of the past with the development requirements of the future) led to the development of the MAPPA project – Methodologies applied to archaeological potential predictivity - funded by Regione Toscana in 2010. The two-year project started on 1 July 2011 and will end on 30 June 2013. The first year of research was dedicated to achieving the first objective, that is, to retrieving the results of archaeological investigations from the archives of Superintendencies and University and from the pages of scientific publications, and to making them easily accessible; these results have often never been published or have often been published incompletely and very slowly. For this reason, a webGIS (“MappaGIS” that may freely accessed at http://mappaproject.arch.unipi.it/?page_id=452) was created and will be followed by a MOD (Mappa Open Data archaeological archive), the first Italian archive of open archaeological data, in line with European directives regarding access to Public Administration data and recently implemented by the Italian government also (the beta version of the archive can be viewed at http://mappaproject.arch.unipi.it/?page_id=454). Details are given in this first volume about the operational decisions that led to the creation of the webGIS: the software used, the system architecture, the organisation of information and its structuring into various information layers. But not only. The creation of the webGIS also gave us the opportunity to focus on a series of considerations alongside the work carried out by the MAPPA Laboratory researchers. We took the decision to publish these considerations with a view to promoting debate within the scientific community and, more in general, within the professional categories involved (e.g. public administrators, university researchers, archaeology professionals). This allowed us to overcome the critical aspects that emerged, such as the need to update the archaeological excavation documentation and data archiving systems in order to adjust them to the new standards provided by IT development; most of all, the need for greater and more rapid spreading of information, without which research cannot truly progress. Indeed, it is by comparing and connecting new data in every possible and, at times, unexpected way that research can truly thrive

Object Detection and Tracking in Cooperative Multi-Robot Transportation

Contemporary manufacturing systems imply the utilization of autonomous robotic systems, mainly for the execution of manipulation and transportation tasks. With a goal to reduce transportation and manipulation time, improve efficiency, and achieve flexibility of intelligent manufacturing systems, two or more intelligent mobile robots can be exploited. Such multi-robot systems require coordination and some level of communication between heterogeneous or homogeneous robotic systems. In this paper, we propose the utilization of two heterogeneous robotic systems, original intelligent mobile robots RAICO (Robot with Artificial Intelligence based COgnition) and DOMINO (Deep learning-based Omnidirectional Mobile robot with Intelligent cOntrol), for transportation tasks within a laboratory model of a manufacturing environment. In order to reach an adequate cooperation level and avoid collision while moving along predefined paths, our own developed intelligent mobile robots RAICO and DOMINO will communicate their current poses, and object detection and tracking system is developed. A stereo vision system equipped with two parallelly placed industrial-grade cameras is used for image acquisition, while convolutional neural networks are utilized for object detection, classification, and tracking. The proposed object detection and tracking system enables real-time tracking of another mobile robot within the same manufacturing environment. Furthermore, continuous information about mobile robot poses and the size of the bounding box generated by the convolutional neural network in the process of detection of another mobile robot is used for estimation of object movement and collision avoidance. Mobile robot localization through time is performed based on kinematic models of two intelligent mobile robots, and conducted experiments within a laboratory model of manufacturing environment confirm the applicability of the proposed framework for object detection and collision avoidance