Active learning based laboratory towards engineering education 4.0

Universities have a relevant and essential key role to ensure knowledge and development of competencies in the current fourth industrial revolution called Industry 4.0. The Industry 4.0 promotes a set of digital technologies to allow the convergence between the information technology and the operation technology towards smarter factories. Under such new framework, multiple initiatives are being carried out worldwide as response of such evolution, particularly, from the engineering education point of view. In this regard, this paper introduces the initiative that is being carried out at the Technical University of Catalonia, Spain, called Industry 4.0 Technologies Laboratory, I4Tech Lab. The I4Tech laboratory represents a technological environment for the academic, research and industrial promotion of related technologies. First, in this work, some of the main aspects considered in the definition of the so called engineering education 4.0 are discussed. Next, the proposed laboratory architecture, objectives as well as considered technologies are explained. Finally, the basis of the proposed academic method supported by an active learning approach is presented.Postprint (published version

Challenges of Internet of Things and Big Data Integration

The Internet of Things anticipates the conjunction of physical gadgets to the In-ternet and their access to wireless sensor data which makes it expedient to restrain the physical world. Big Data convergence has put multifarious new opportunities ahead of business ventures to get into a new market or enhance their operations in the current market. considering the existing techniques and technologies, it is probably safe to say that the best solution is to use big data tools to provide an analytical solution to the Internet of Things. Based on the current technology deployment and adoption trends, it is envisioned that the Internet of Things is the technology of the future, while to-day's real-world devices can provide real and valuable analytics, and people in the real world use many IoT devices. Despite all the advertisements that companies offer in connection with the Internet of Things, you as a liable consumer, have the right to be suspicious about IoT advertise-ments. The primary question is: What is the promise of the Internet of things con-cerning reality and what are the prospects for the future.Comment: Proceedings of the International Conference on International Conference on Emerging Technologies in Computing 2018 (iCETiC '18), 23rd -24th August, 2018, at London Metropolitan University, London, UK, Published by Springer-Verla

Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems, cyber risk at the edge

The Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture

Thickness-Dependent Differential Reflectance Spectra of Monolayer and Few-Layer MoS2, MoSe2, WS2 and WSe2

The research field of two dimensional (2D) materials strongly relies on optical microscopy characterization tools to identify atomically thin materials and to determine their number of layers. Moreover, optical microscopy-based techniques opened the door to study the optical properties of these nanomaterials. We presented a comprehensive study of the differential reflectance spectra of 2D semiconducting transition metal dichalcogenides (TMDCs), MoS2, MoSe2, WS2, and WSe2, with thickness ranging from one layer up to six layers. We analyzed the thickness-dependent energy of the different excitonic features, indicating the change in the band structure of the different TMDC materials with the number of layers. Our work provided a route to employ differential reflectance spectroscopy for determining the number of layers of MoS2, MoSe2, WS2, and WSe2.Comment: Main text (3 Figures) and Supp. Info. (23 Figures

The Scorecard on Development, 19602016: China and the Global Economic Rebound

This report looks at the rate of progress of economic, health, and social indicators, including per capita GDP, mortality, life expectancy, and education for all countries with available data. It examines the twenty-first century rebound for the majority of low- and middle-income countries after an unusual long-term decline in the rate of progress on most of these indicators in the last two decades of the twentieth century. It discuss the role of China in the twenty-first-century rebound, and the possible role of major policy changes that took place in many low- and middle-income countries, as well as other policy and institutional influences.The report is the latest edition of a series that began in 2001; the last version was published in 2011. The first two editions of the Scorecard (2001 and 2005) documented a then ongoing, historic long-term economic failure that occurred in the 1980s and '90s, before most low- and middle-income countries began to experience an economic rebound

Learning for Advanced Motion Control

Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatronic systems. First, a preliminary analysis reveals the potential performance improvement of ILC prior to its actual implementation. Second, a frequency domain approach is presented, where fast learning is achieved through noncausal model inversion, and safe and robust learning is achieved by employing a contraction mapping theorem in conjunction with nonparametric frequency response functions. The approach is demonstrated on a desktop printer. Finally, a detailed analysis of industrial motion systems leads to several shortcomings that obstruct the widespread implementation of ILC algorithms. An overview of recently developed algorithms, including extensions using machine learning algorithms, is outlined that are aimed to facilitate broad industrial deployment.Comment: 8 pages, 15 figures, IEEE 16th International Workshop on Advanced Motion Control, 202