Publications

Decision support in logistics

Supply Chain Management: An International Journal

Journal publication (2015)

Ilie-Zudor, E.; Ekárt, A.; Kemény, Zs.; Buckingham, C.D.; Welch, P.G.; Monostori, L.: Advanced predictive-analysis-based decision support for collaborative logistics networks, Supply Chain Management: An International Journal, vol. 20, no. 4, pp. 369-388, 2015, (ISSN: 1359-8546) (DOI: 10.1108/SCM-10-2014-0323)

Download : http://www.emeraldinsight.com/doi/full/10.1108/SCM-10-2014-0323

Cloud-based manufacturing (CBM) interoperability in Industry 4.0

Cloud computing (CC) is generating new compute and business models thanks to its service-based nature, which enables collaboration and data exchange at higher level, more flexibility with better efficiency and parallel decreasing costs. Manufacturing environments can also benefit from cloud technology and follow fast changes in market demands. In these new scenarios interoperability has vital importance in the operation and interaction among industrial realizations of the Cyber-physical Systems.

Robust digital twin compositions for Industry 4.0 smart manufacturing systems

Industry 4.0 is an emerging business paradigm that is reaping the benefits of enabling technologies driving intelligent systems and environments. By acquiring, processing and acting upon various kinds of relevant context information, smart automated manufacturing systems can make well-informed decisions to adapt and optimize their production processes at runtime.

Industry 4.0: Mining Physical Defects in Production of Surface-Mount Devices

With the advent of Industry 4.0, production processes have been endowed with intelligent cyber-physical systems generating massive amounts of streaming sensor data. Internet of Things technologies have enabled capturing, managing, and processing production data at a large scale in order to utilize this data as an asset for the optimization of production processes. In this work, we focus on the automatic detection of physical defects in the production of surfacemount devices.

Gaining Insights into Road Traffic Data through Genetic Improvement

We argue that Genetic Improvement can be successfully used for enhancing road trac data mining. Œis would support the relevant decision makers with extending the existing network of devices that sense and control city trac, with the end goal of improving vehicle ƒow and reducing the frequency of road accidents. Our position results from a set of preliminary observations emerging from the analysis of open access road trac data collected in real time by the Birmingham City Council.

Thematic Issue on: Advances in Security and Privacy for Mobile Users in Intelligent Environments

Advances in Security and Privacy for Mobile Users in Intelligent Environments

Call for Papers

Security and privacy are vital challenges for mobile users as they move around denser and more prevalent intelligent environments embedded with different connected smart objects that interact with each other and with mobile users in new ways. The focus of security and privacy is predominantly on protecting the use of personal smart tab and pad sized devices such as smart phones, tablets, and laptops by mobile users, in passive environments.

Cyber-physical systems in manufacturing

One of the most significant advances in the development of computer science, information and communication technologies is represented by the cyber-physical systems (CPS). They are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the Internet.

Data Protection Compliance Regulations and Implications for Smart Factories of the Future

Context-aware systems in intelligent environments digest large amounts of data and personal information to gain situational awareness as a way to assist individuals with their daily activities, enhance their experiences and adapt to their needs and intention, whenever and wherever they are. Large amounts of data drive these environments, motivating the adoption of big data and cloud technologies. A similar digital transformation is taking place in the Factory of the Future and Industry 4.0, two paradigms on creating smart products through smart processes and procedures.

Complementary research and education opportunities — a comparison of learning factory facilities and methodologies at TU Wien and MTA SZTAKI

Typical learning factories are characterized by selective simplification or scaling-down of complex and large-scale production processes, while also safely containing risks in the case of process failures inherent to experimental and didactic activities. The variety of aspects preserved by these scaled-down environments allow different approaches to be taken in research and education. The paper compares two facilities, at TU Wien and at MTA SZTAKI in Budapest, respectively, and highlights differences in their modes of operation, the resulting variations of course-based vs.

CEML: Mixing and moving complex event processing and machine learning to the edge of the network for IoT applications

The Internet of Things (IoT) is a growing field which is expected to generate and collect data everywhere at any time. Highly scalable cloud analytics systems are frequently being used to handle this data explosion. However, the ubiquitous nature of the IoT data imposes new technical and non-technical requirements which are difficult to address with a cloud deployment. To solve these problems, we need a new set of development technologies such as Distributed Data Mining and Ubiquitous Data Mining targeted and optimized towards IoT applications.

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