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  1. Technologies and enablers
  2. Information and communication technologies
Mapped projects Mapped results, demos etc. Unfold list

Kanban
    Information and communication technologies View Taxon in all lists
    Kanban
      Digital manufacturing platforms Description

      See https://www.effra.eu/digital-manufacturing-platforms

      IoT - Internet of Things Description

      The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.  (from https://en.wikipedia.org/wiki/Internet_of_things)

      Human Machine Interfaces
      Kanban
        Augmented reality Description

        https://en.wikipedia.org/wiki/Augmented_reality

        Virtual reality Description

        https://en.wikipedia.org/wiki/Virtual_reality

        Advanced and ubiquitous human machine interaction Description

        Advanced machine interaction with humans through ubiquity of mobile devices will enable users to receive relevant production and enterprise-specific information regardless of their geographical location and tailored to the context and the skills/responsibilities they own. Interactions with ICT infrastructures and equipment will be natural language-like

        Data visualisation Description

        https://en.wikipedia.org/wiki/Data_visualization ; http://www.visual-analytics.eu/faq

      Data collection, storage, analytics, processing and AI
      Kanban
        Data acquisition Description

        Data acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. Data acquisition systems, abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing. The components of data acquisition systems include:

        • Sensors, to convert physical parameters to electrical signals.
        • Signal conditioning circuitry, to convert sensor signals into a form that can be converted to digital values.
        • Analog-to-digital converters, to convert conditioned sensor signals to digital values.

         
        Data acquisition applications are usually controlled by software programs developed using various general purpose programming languages
        So, as a summary, Data acquisition is in itself a vast group of protocols, technologies, sensors, hardware and software…

        (from https://en.wikipedia.org/wiki/Data_acquisition)

        Data storage Description
        Kanban

        Data storage is the recording (storing) of information (data) in a storage medium. DNA and RNA, handwriting, phonographic recording, magnetic tape, and optical discs are all examples of storage media. (from https://en.wikipedia.org/wiki/Database)

          ICT solutions for next generation data storage and information mining
          Kanban
            Non-relational database (NoSQL) Description

            https://en.wikipedia.org/wiki/NoSQL

          Dataspaces Description

          Dataspaces are an abstraction in data management that aim to overcome some of the problems encountered in data integration system. The aim is to reduce the effort required to set up a data integration system by relying on existing matching and mapping generation techniques, and to improve the system in "pay-as-you-go" fashion as it is used. (From https://en.wikipedia.org/wiki/Dataspaces)

          Relational databases Description

          https://en.wikipedia.org/wiki/Relational_database_management_system

        Data processing Description
        Kanban

        Data processing is, generally, the collection and manipulation of items of data to produce meaningful information (https://en.wikipedia.org/wiki/Data_processing)

          Cloud computing, edge computing
          Kanban
            Cloud computing Description

            https://en.wikipedia.org/wiki/Cloud_computing

            Cloud computing can be deployed as private cloud, public cloud, hybrid cloud

            Digital Manufacturing Platforms can be ran into IaaS, PaaS or SaaS. 
            Considerations need to be made to security measures in the cloud (kubernetes, container security), identity & access, or carefully considering the security measures by the respective cloud services providers. 

             

            Edge computing Description

            https://en.wikipedia.org/wiki/Edge_computing

            https://ecconsortium.eu/

            European Edge Computing Consortium is working on a reference architecture in line with RAMI. 

            A security architecture is being worked upon. 

        Data analytics
        Data modelling Description

        Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques. (from https://en.wikipedia.org/wiki/Data_modeling)

        Cognitive and artificial intelligence (AI) technologies - machine learning Description
        Kanban

        In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is used to describe machines that mimic "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving"  (from https://en.wikipedia.org/wiki/Artificial_intelligence)

          Fuzzy logic Description

          Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 inclusive. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false (from https://en.wikipedia.org/wiki/Fuzzy_logic)

          Neural networks Description

          Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks and astrocytes that constitute animal brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs.[4] Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. (from https://en.wikipedia.org/wiki/Artificial_neural_network)

          Genetic algorithms Description

          In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. (https://en.wikipedia.org/wiki/Genetic_algorithm)

          Heuristics Description

          A heuristic function, also called simply a heuristic, is a function that ranks alternatives in search algorithms at each branching step based on available information to decide which branch to follow. (from https://en.wikipedia.org/wiki/Heuristic_(computer_science))

      System modelling, simulation and forecasting Description

      Simulation (often referred to as digital twins) is the imitation of the operation of a real-world process or system. The act of simulating something first requires that a model be developed; this model represents the key characteristics, behaviors and functions of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time. (from https://en.wikipedia.org/wiki/Simulation)

      Programming Frameworks – Software Development Kits (SDKs)
      Kanban
        FIWARE Description
        Kanban

        See https://www.fiware.org/

          Smart Industry Context Information Management and Persistence
          Kanban
            The Orion Context Broker Generic Enabler Description

            The Orion Context Broker Generic Enabler is the core and mandatory component of any “Powered by FIWARE” platform or solution. It enables to manage context information in a highly decentralized and large-scale manner. It provides the FIWARE NGSIv2 API which is a simple yet powerful Restful API enabling to perform updates, queries or subscribe to changes on context information.

            The STH Comet Generic Enabler Description

            The STH Comet Generic Enabler brings the means for storing a short-term history of context data (typically months) on MongoDB.

            The Cygnus Generic Enabler Description

            The Cygnus Generic Enabler brings the means for managing the history of context that is created as a stream of data which can be injected into multiple data sinks, including some popular databases like PostgreSQL, MySQL, MongoDB or AWS DynamoDB as well as BigData platforms like Hadoop, Storm, Spark or Flink.

          Smart Industry NGSI Agents Framework to Real World
          Kanban
            The IDAS Generic Enabler
            The Fast RTPS Incubated Generic Enabler
            The OpenMTC Incubated Generic Enabler
          Smart Industry Information Processing
          Kanban
            The Wirecloud Generic Enabler
            The Knowage Generic Enabler
            The Kurento Generic Enabler
            The Cosmos Generic Enabler
            The FogFlow Incubated Generic Enabler
            The AEON Incubated Generic Enabler
            The Domibus Incubated Generic Enabler
            PROTON
            PERSEO
            CEPHEUS
            XML3D
            Augmented Reality (FIWARE)
            Quantum Leap
          Smart Industry Context Data/API Management, Publication and Monetization
          Kanban
            The CKAN extensions Generic Enabler
            The Keyrock Identity Management Generic Enabler
            The Wilma proxy Generic Enabler
            The AuthZForce PDP/PAP Generic Enabler
            The Biz Framework Generic Enabler
      Programming Languages
      Operating systems
      ICT Architectures
      Kanban
        Collaborative and decentralized application architectures and development tools
Key content (13)
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ENISA Good Practices for Security of Internet of Things in the context of Smart Manufacturing

Type: | Updated at: 24-10-2020

ENISA - Industry 4.0 - Cybersecurity Challenges and Recommendations

Type: | Updated at: 24-10-2020

NISTIR 8183 - Cybersecurity Framework Version 1.1 Manufacturing Profile

Type: | Updated at: 23-10-2020

NISTIR 8259 - Foundational Cybersecurity Activities for IoT Device Manufacturers

Type: | Updated at: 23-10-2020

ETSI EN 303 645

Type: | Updated at: 23-10-2020

CIF Seminar - "Cybersecurity Act"

Type: / | Updated at: 16-11-2020

The Industrial Internet Security Framework (IISF)

Type: | Updated at: 23-10-2020

Cloud Security Alliance (CSA) – Consensus Assessment Initiative Questionnaire (CAIQ)

Type: | Updated at: 23-10-2020

Artificial Intelligence (AI) in Security Aspects of Industrie 4.0

Type: | Updated at: 22-10-2020

Cybersecurity Regulatory Framework in Germany/EU and USA (GER/ENG/CHN)

Type: | Updated at: 22-10-2020

Industrie 4.0 Security Guidelines

Type: | Updated at: 22-10-2020

Specification - Details of the Asset Administration Shell - Part 1

Type: | Updated at: 22-10-2020


See section 'Overview Metamodel Asset Administration Shell for Security, page 155

Access control for Industrie 4.0 components for application by manufacturers, operators and integrators

Type: | Updated at: 22-10-2020

© 2021 Chris Decubber BV