Asymmetry in morphology by Anne-Marie Di Sciullo 16 editions published between and in English and held by 1, WorldCat member libraries worldwide Di Sciullo argues that the asymmetric property of morphological relations is part of the language facility. She proposes a theory of grammar, Asymmetry Theory, according to which generic operations have specific instantiations in parallel deviations of the computational space.
Asymmetry in grammar by Anne-Marie Di Sciullo 23 editions published between and in English and held by 1, WorldCat member libraries worldwide Annotation. Projections and interface conditions : essays on modularity by Anne-Marie Di Sciullo 18 editions published between and in English and held by 1, WorldCat member libraries worldwide This collection of previously unpublished papers explores the implications of Chomsky's Minimalist framework for the modularity of grammar.
The biolinguistic enterprise : new perspectives on the evolution and nature of the human language faculty 22 editions published between and in English and Hungarian and held by 1, WorldCat member libraries worldwide "This book, by leading scholars, represents some of the main work in progress in biolinguistics. It offers fresh perspectives on language evolution and variation, new developments in theoretical linguistics, and insights on the relations between variation in language and variation in biology;. The authors address the Darwinian questions on the origin and evolution of language from a minimalist perspective, and provide elegant solutions to the evolutionary gap between human language and communication in all other organisms.
They consider language variation in th context of current biological approaches to species diversity--the 'evo-devo revolution'--which brings to light deep homologies between organisms. In dispensing with the classical notion of syntactic parameters, the authors argue that language variation, like biodiversity, is the result of experience and thus not a part of the language faculty in the narrow sense. Written in language accessible to a wide audience, 'The Biolinguistic Enterprise' will appeal to scholars and students of linguistics, cognitive science, biology, and natural language processing"--Publisher's description, p.
Towards a biolinguistic understanding of grammar : essays on interfaces 14 editions published in in English and Swedish and held by 1, WorldCat member libraries worldwide Explores the interaction of grammar with the factors reducing complexity. This book aims to bring about further understanding of the interfaces of the grammar in a broader biolinguistic sense.
It anchors the formal properties of grammar at the interfaces between language and biology, language and experience, bringing about language acquisition. Biolinguistic investigations on the language faculty by Anne-Marie Di Sciullo 11 editions published in in English and held by WorldCat member libraries worldwide The papers assembled in this volume aim to contribute to our understanding of the human capacity for language: the generative procedure that relates sounds and meanings via syntax. Different hypotheses about the properties of this generative procedure are under discussion, and their connection with biology is open to important cross-disciplinary work.
Advances have been made in human-animal studies to differentiate human language from animal communication. Contributions from neurosciences point to the exclusive properties of the human brain for language. Studies in genetically based language impairments also contribute to the understanding of the properties of the language organ. This volume brings together contributions on theoretical and experimental investigations on the Language Faculty.
It will be of interest to scholars and students investigating the properties of the biological basis of language, in terms the modeling of the language faculty, as well as the properties of language variation, language acquisition and language impairments.
On the definition of word by Anne-Marie Di Sciullo Book 24 editions published between and in English and French and held by WorldCat member libraries worldwide. UG and external systems : language, brain, and computation Book 13 editions published in in English and held by WorldCat member libraries worldwide Annotation. Morphology, phonology, acquisition 3 editions published in in English and held by 91 WorldCat member libraries worldwide. Syntax and semantics 3 editions published in in English and held by 88 WorldCat member libraries worldwide.
You can read more about it in our article on the digital transformation of manufacturing. Does this mean it is just a vague idea? No, on the contrary. What we do see indeed though is that most organizations are still in the early stages of preparations for Industry 4. Yet, the vision of Industry 4. Digital transformation, although being academically looked upon and despite the existence of numerous digital transformation frameworks and roadmap strategies, which are developed by numerous people, has no universal definition nor clear industry-wide approach.
The same goes for the implementation of the Industrial Internet of Things. Just like digital transformation and the Industrial Internet of Things, adoption of Industrie 4. However, Industry 4. In essence this means that in Industry 4. That is pretty unique. So, just like digital transformation, Industry 4. Yet, as opposed to digital transformation this vision and reality is far more studied, documented and standardized despite the mentioned need to work in the context of the individual business as well.
In the Industry 4. One such maturity approach looks at the information and actual operations and manufacturing systems perspective with autonomous machines and systems as true Industry 4. In this gradual approach, whereby each stage builds upon the next one and adds more value, we move from data to information to knowledge to wisdom and action from a data perspective.
Indeed, the good old DIKW model. A second maturity approach revolves more around the business as such and corresponds with what you would typically see in any project. What do we want to achieve and what do we have today assess , where do we want to go and what are the missing links to get there called the methodological analysis in Industrie 4. Cyber-physical systems CPS are building blocks in Industry 4. Cyber-physical systems are combinations of intelligent physical components, objects and systems with embedded computing and storage possibilities, which get connected through networks and are the enablers of the smart factory concept of Industry 4.
Simply put, as the term indicates, cyber-physical systems refers to the bridging of digital cyber and physical in an industrial context. This might still seem complex but, then again, cyber-physical systems are complex. So, if you want to understand Industry 4. Cyber-physical systems in the Industry 4. Looking at Industry 4. Cyber-physical systems essentially enable us to make industrial systems capable to communicate and network them, which then adds to existing manufacturing possibilities. They result to new possibilities in areas such as structural health monitoring, track and trace, remote diagnosis, remote services, remote control, condition monitoring, systems health monitoring and so forth.
In the original definitions, going back over a decade, IP addresses where not specifically mentioned in cyber-physical systems. In , Professor Edward A. On his page on the Berkeley website , Professor Lee links to cyberphysicalsystems. For the German Industrie 4. Cyber-physical systems also include dimensions of simulation and twin models, smart analytics, self-awareness self-configuration and more.
Hopefully, the essence of the concept, context and reality of the evolution towards cyber-physical systems has become a bit clearer now. Note: there is a difference between cyber-physical systems and cyber-physical manufacturing systems or cyber-physical production systems CPSS where we move from the technological component to the far more important process and application dimension. Next, we take a deeper look into the Internet of Things and its place in Industry 4.
Before doing so we summarize some key characteristics of cyber-physical systems as they are related with the Internet of Things:. As promised, time for the Internet of Things.championship.comedysportzsanjose.com/xy-azithromycin-vs.php
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The presence of an IP address by definition means that cyber-physical systems, as objects, are connected to the Internet of Things. An IP address also means that the cyber-physical system can be uniquely identified within the network. This is a key characteristic of the Internet of Things as well. Cyber-physical systems are also equipped with sensors, actuators and all the other elements which are part of the Internet of Things. Cyber-physical systems, just like the Internet of Things need connectivity.
The exact connectivity technologies which are needed depend on the context in both. The Internet of Things consists of objects with embedded or attached technologies that enable them to sense data, collect them and send them for a specific purpose. This data as such is just the beginning, the real value starts when analyzing and acting upon them, in the scope of the IoT project goal.
All this applies to cyber-physical systems as well, which are essentially connected objects. There are more similar characteristics but you see how much there is in common already. Moreover, the new capabilities which are enabled by cyber-physical systems, such as structural health monitoring, track and trace and so forth are essentially what we call Internet of Things use cases. In other words: what you can do with the Internet of Things.
Some of them are used in a cross-industry way, beyond manufacturing. Below are two examples of CPS-enabled capabilities we tackled previously and how they really are IoT uses cases. Track and trace possibilities in practice lead to multiple IoT use cases in, among others, healthcare, logistics, warehousing, shipping, mining and even in consumer-oriented Internet of Things use cases.
There are ample applications of the latter with numerous solutions and technologies. You can track and trace your skateboard, your pets, anything really, using IoT. Structural health monitoring is also omnipresent, mainly across industries such as engineering, building maintenance, facility management, etc. With the right sensors and systems you can monitor the structural health of all kinds of objects, from bridges and objects in buildings to the production assets and cyber-physical assets in manufacturing and Industry 4.
The new capabilities, of which we just mentioned two and which are possible thanks to CPS in the Industry 4. What is a core enabler of smart logistics and so forth? You can perfectly compare this with the Internet of Everything view of connected objects, people, processes and data as the building blocks of smart applications. It is another key similarity between the CPS view of industry 4.
To conclude: in fact, you can call cyber-physical systems the albeit advanced things in the Industrial Internet of Things in manufacturing. A key component of the Industrial Internet of Things is connectivity. According to research, industrial manufacturers still have some catching up to do in regards with connectivity overall. Among others, the adoption of cloud-based services and the connection of legacy systems to digital networks is lagging somewhat behind. Yet, as IIoT strategies are being envisioned and designed, the number of Industrial Internet of Things connections is growing rapidly and changes occur in the types of connectivity solutions that are used.
In research from ABI Research, it is estimated that in there will be 13 million extra new wireline and wirelines connections across the globe. In total this would bring the number of Industrial IoT connections to 66 million. In the years after, this growth continues and even accelerates 18 million new connections per year by Looking at the various types of Industrial IoT connection connection solutions in manufacturing and thus Industry 4. The major part of connections consists of fixed line deployments but wireless is growing and will account for approximately a quarter of all new connections in Research Director Jeff Orr points ou t that with the lowering costs in storage and processing, Industry 4.
With the evolutions in the connectivity options and solutions in that crucial component of Industry 4. RAMI 4. Even if some EU countries use different terms such as intelligent factory, future industry, digital production or smart manufacturing, the European Commission EC is also intervening. An overview of the ongoing acceptance and leverage of Industrie 4.
If you are looking for some examples of Industry 4. Click on a place on the map and read more about the specific case for now only German examples. What are some of the key aspects you need to know about RAMI 4. First, know that there are two documents which laid out the foundations of Industry 4.
The hierarchy dimension consists of 7 aggregation levels , being 1 the connected world, 2 the enterprise, 3 work centers, 4 stations or machines , 5 control devices, 6 field devices sensor and actuators and 7 products. In the pyramid that shows Industry 3. The hierarchy dimension is what we covered several times in our articles on ubiquitous connectivity and digital transformation but in a different scope of hierarchy with smart products and smart factories as part of this connected world.
It also about technologies where we similar decentralizations all across the board IT and especially OT and about the ubiquitous interaction of participants across hierarchy levels, whereby the product is seen as part of the network. The life cycle and value stream dimension, as the term already describes, covers the various data mapping stages across relevant life cycles in RAMI 4. The idea: the more data early on, the more value later on.
The third dimension, the architecture layers, consists of 6 components: business, functional, information a , communication, integration and asset. Bring all three dimensions together and, on top of a nice visual, you have a 3D service-oriented architecture. If you want to know more about the Reference Architectural Model Industrie 4. After this introduction to RAMI 4. These were established in the report in which the Industrie 4.
Despite the fact that there is a difference between horizontal and vertical integration the goal is the same: ecosystem-wide data information between various systems and across all processes, using data transfer standards and creating the basis for an automated supply and value chain.
Horizontal integration refers to the integration of IT systems for and across the various production and business planning processes. In-between these various processes there are flows of materials, energy and information. Moreover, they concern both the internal as external partners, suppliers, customers but also other ecosystem members, from logistics to innovation flows and stakeholders.
In other words: horizontal integration is about digitization across the full value and supply chain, whereby data exchanges and connected information systems take center stage. As you can imagine this is not a small task. For starters, within organizations there are still quite some disconnected IT systems. This is a challenge for all organizations, industrial or not.
If you start looking at seamless integration and data exchange with suppliers, customers and other external stakeholders, the picture becomes even more complex. Also keep in mind the life cycle and value stream dimension of RAMI 4. Nevertheless, it is critical for Industry 4. The benefits and drivers for this need for horizontally connected information systems are pretty comparable to those we find in information management, as are the disadvantages if systems are not integrated.
Ask any organization in any industry. These hierarchical level are respectively the field level interfacing with the production process via sensors and actuators , the control level regulation of both machines and systems , the process line level or actual production process level that needs to be monitored and controlled , the operations level production planning, quality management and so forth and the enterprise planning level order management and processing, the bigger overall production planning etc.
Typical solutions and technologies in this vertical integration include PLCs which control manufacturing processes and sit on the control level, SCADA which enables various production process level and supervisory tasks and is de facto commonly used in industrial control systems, MES or manufacturing execution systems for the management level and intelligent ERP for the enterprise level, which is the highest level in this hierarchical picture.
As mentioned previously, the MES manufacturing execution system plays a central role in the first stages of Industry 4. As mentioned previously the opportunities which are offered by Industry 4. As the people behind Industrie 4. Although that is easier said than done for many companies reaching these stages and goals is a virtually impossible task, certainly now, one of the reasons why they mainly focus on a staged approach or smaller steps as you can read in our article on industrial transformation , it is the true goal: new business models based on data, new ecosystems and new ways to service customers, meet demands in novel ways and create new revenue streams.
These more aspirational goals of industrial transformation mainly revolve around the service dimension of the so-called automation pyramid. Below is a nice example of such an automation pyramid, courtesy of the people at invilution. Indeed, it looks like the vertical integration image above. What we do have now is the growing importance of the Internet of Things.
And Industry 4. And, yes, it also looks a bit like the DIKW pyramid , a model that has existed forever to show the path from data to information to knowledge to wisdom and in some depictions to action , in the end it is all very much related. That automation pyramid is really just a depiction of the implementation of Industry 4. The automation pyramid for the implementation of Industry 4. Do also think about the layers of network models such as OSI and others when looking at them as obviously there is a technological dimension and IT and IoT people will — of course — recognize a lot too.
Just as other aspects of Industry 4. The first layer of the automation pyramid concerns sensors and actuators. The first layer essentially consists of product and manufacturing assets and components which become information carriers as they can be addressed, localized and identified through sensors and are connected. Built upon that connected layer of sensors, actuators and essentially data sits a layer of services and systems that enables the new ways in which the value chain is organized and managed. Here we meet applications such as energy monitoring and the monitoring and management of systems and conditions of assets such as machines, buildings, infrastructure and so forth.
In other words: mainly monitoring and managing, albeit it with the next step in mind: we do monitor for a reason — to enhance, understand and build new capabilities. Initially these maintenance, tracking and other applications are often focusing on internal operations but of course some can become additional revenue sources when deployed and offered in a customer ecosystem context, for example by offering maintenance contracts that could bring in new revenues or be offered as a service with the equipment you sell, while lowering costs for yourself service and support and your customers less downtime.
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These could range from applications enabling consumers to tailor the goods they order and sell advanced services to come up with new revenue streams, certainly when developing services within ecosystems of data and possible partners. But again, it looks easier than it is in practice of course. Going from less paper and legacy systems to simply connecting assets and leveraging IoT, bridging IT and OT integration challenges in the first layer and being able to monitor and manage whatever needs to be monitored and managed, from energy to structures and beyond is already a huge step for many.
There has been an awful lot of academic work into those design principles so you might find other terms and potentially four instead of six design principles. In essence they are relatively simple — and should allow to explain what Industry 4. These relatively well-known Industry 4. In order to move to intelligent manufacturing, smart factories, or connected industries, you need to bridge things such as real things, people, standards, work processes man and machine and more. And to bridge all that you need data and networks. They must all inter-operate and inter-connect.
You need to bridge IT and OT, you need to have assets such as machines that can connect and communicate thanks to sensors and other equipment and you need to connect people, data, machines and so on. This is indeed mainly about the Internet of Things and, in a broader perspective an Internet of Services, Internet of People, Services and Things, Internet of Everything, whatever name you prefer.
Interoperability is also about collaboration, the ability to have many really many standards talk to each other so data from various sources can be leveraged why we use Industrial IoT gateways, IoT platforms and talk about IT and OT integration, which goes beyond technology and is about human collaboration too, namely IT and OT teams. Interoperability means connected devices, connected communication technologies, connected people, connected data, people connected and collaborating with machines, machines working with machines, an interoperable unified and holistic information, security and data layer and so forth.
Inter-operating and inter-connecting and in more than one sense connected with vertical and horizontal integration. Information transparency or virtualization might be a bit harder to explain to a friend as it is not about the transparency of information. Without interoperability, information transparency and virtualization are not possible as the information needs to be put in context and systems are context-aware, combining information from other sources too. In the cyber-physical lingo of Industry 4. Finally do note that we speak about context-aware information.
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This essentially means two things: 1 information is not data, remember the DIKW model so analytics and moving from data to information and so forth is key here and 2 context-aware also means that the information can differ, depending on not just the actual context in which it is gathered and enriched but also in the context of its scope which can mean real-time information and so forth. The easier way to explain it to a friend is probably to say that there is a virtual copy for pretty much everything.
As mentioned earlier one of the core goals of Industry 4. Only then the agility and flexibility needed to be able to deal with uncertainties, respond to demands of personalization, the concept of the smart factory and its place in an inter-connected ecosystem, the required data analytics and the various logistics can be enhanced, meeting the need for speed. We tackled this aspect of autonomy and semi- autonomous decisions and intelligence more in depth in our article on Logistics 4.
Decentralization is not just a given in Industry 4. In fact, the IoT de facto is a decentralized given as such. We are talking about a distributed reality. However, in the scope of Industry 4. Decentralized and autonomous decisions are not just key in the technologies and cyber-physical systems of Industry 4. The end of the discussion on decentralization and autonomy is far from over, certainly from the human and decision-making perspective.
In Industry 4. However, in practice this is not always achievable, let alone desirable. If you strive towards more autonomy on the machine and cyber-physical system level you do so for increased efficiency and to meet the demands of an increasingly real-time economy. Advanced analytics, the IoT and the information and production systems in a smart manufacturing environment in its broader context of collaboration and ecosystems already are all about the development of real-time capabilities.
Flexibility, predictive maintenance, being able to quickly replace assets in case of failures and the IoT all are important in this perspective which also touches the previously mentioned design principles and the data to decision aspects tackled previously. Moreover, a real-time capability is essential for the last two design principles, service orientation and modularity.
The service orientation is related with the as-a-service economy, the Internet of Services and the obvious fact that manufacturing needs to be more tailored to the demand of customers for services and products with value added services e. Yet, the service orientation is also related with the need for manufacturers and other industries to develop new services that are de facto based upon data, turned into intelligence, and seek new service-based revenue models. Moreover, technical assistance and, more specifically maintenance, is a core principle as IoT and data analytics simply allow the transformation of services and maintenance.
There are plenty of companies who changed their service models by simply adding levels of intelligence and connectivity with IoT to the equipment they sell. And here we also meet Human-Machine Interaction. Finally, the service aspect is also related with the development of new as-a-service-models based upon data but also based upon the evolution towards a Machines as a Service model. Modularity means many things, depending on how you look at it: the various individual modules within the broad smart factory environment or simply as the end result when it becomes agility and flexibility.
You could say that modularity has everything to do with a shift from rigid systems, inflexible models and linear manufacturing and planning to an environment where changing demands from customers, partners in the overall supply chain, regulators, market conditions and all other possible elements causing the need for transformation and flexibility are put in the center.
The modules are locally controlled without hierarchy.
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Previously in this overview of Industry 4. Most of them are really umbrella terms for several technologies.
We already tackled horizontal and vertical integration, cyber-physical systems and the Industrial Internet of Things as really vast realities with many technologies and components before on this page and elsewhere. We also have literally dozens of articles on other evolutions in the mentioned convergence and application of nine digital industrial technologies as BCG calls them. Those that are less typical with typical ones being the integration of IT and OT, additive manufacturing, industrial robots and so forth are probably the ones you are looking at today: IoT, Big Data, the cloud, maybe 3D-printing etc.
So, what technologies are really key to Industry 4. It depends but the Internet of Things is clearly critical as it is what makes most so-called Industry 4. Security is also an inherent part of the Industrie 4. In fact, most of the mentioned technologies are essential as they are inevitably connected and interdependent.
So, where do we start? The best way to start is by looking at your goals and challenges and at the capabilities you need on your Industry 4. Big Data, analytics, the cloud and the fog , AI and simulation, to name a few, are about the adaptability, flexibility, modularity, scalability and rapid deployment and integration capabilities that we want to see with Industry 4.
These capabilities come back in many of the Industry 4. Do note that several consulting firms and analysts zoom in on other digital technologies as enablers of Industry 4. Mobile devices and technologies are just one example. More advanced interfaces in the relationship between human and machine are another or better: new interfaces in the relationship between human and technologies as machines makes us overlooks the critical software dimension in a world where software as they say is eating that world. Think artificial intelligence agents and bots or in another context phenomena such as Robotic Process Automation or RPA.
We looked at the strategic dimension of Industry 4. If you want to have a more value-oriented and purpose-driven view at the technological journey, you might want to check out the so-called digital compass which McKinsey made a few years ago, especially the value drivers in it. For the many organizations who are still in the beginning of their Industry 4. So, these are really some main areas where, in the scope of that compass, you could create more value towards one or more stakeholders at the same time.
The second part of the compass shows the Industry 4.
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As an example: in order to better utilize your assets, remote monitoring and control and predictive maintenance can help you achieve that goal. Although companies such as McKinsey and many others are absolute leaders in Industry 4. In that sense we say the exact same thing regarding Industry 4. While that might sound like common business sense it is often forgotten. The reality of Industry 4. Yet, technologies and Industry 4.
And that requires a different approach for each organization, even if there are many common lessons and strategies we can learn from.
Yet, there is never a one size fits all. Take a look again at the digital compass of McKinsey, for example. You might want to improve customer service; the dark green part of the compass. The labor part, for example, is also key in customer service. And so is the inventories piece. And what about time to market and quality? Or the flexibility in utilizing your assets. They all play a role in better servicing the customer.
Take them for what they are: instruments and compasses to think about dealing with complexity and offering ideas or approaches which we can leverage in our own, increasingly digital, reality of challenges and opportunities, in which industrial transformation and technologies fit. On a level of manufacturing and supply chain management technology in the sense of operational technologies and traditional manufacturing technologies it is clear that a lot is happening as well, enabled by information technology IP, analytics and AI, for instance , IoT, scientific research and innovations and technological innovations on virtually all levels.
Who would have ever though that a company like Airbus one day would have an aviation data platform and commercialize it? The thing with all the technological evolutions in Industry 4. That — secure — backbone is poised to integrate IoT, cognitive artificial intelligence and machine learning and even blockchain technology and by a fifth of the largest Global manufacturers would depend on it.
Next generation presses and tooling equipment, evolutions in Manufacturing Execution Systems, new CAD and CAM applications, shaping materials, digital platforms and industry clouds, smart assets, customer-driven design possibilities through virtual reality, crowdsourcing and product virtualization, the list of what is changing and yet to come is long.
However, as per usual not everything will be relevant for everyone. More about the mentioned predictions and the evolutions in operational technology and industrial technologies used in manufacturing, in combination with IT via the button below. Although we often mentioned the traditional image of the automation pyramid just as we like to keep referring to the DIKW model to explain things with its nicely and easy looking hierarchical levels taking center stage in the vertical integration of Industry 4.
While for many companies Industry 4. Just as the borders between the various technologies and the various levels of our traditional automation pyramid are blurring, so are the data, communication and system silos. With the move to the cloud, the increasing importance of IoT and the need to connect systems with the proper newer technologies which we see in Industry 4. It comes with many challenges, on the level of value creation, connected data and security, to name a few. However, one needs to start somewhere. And often one starts at the edge, of the business and of the technology stack.
From a perspective of the vendors of software alone this is also a challenge for many. As said, for many organizations blockchain is still something they never heard about or know from the virtual currency perspective alone. With edge computing, and again AI, other applications in the broader picture of manufacturing operations is becoming one of many uncertainties about the long-term future of SCADA, MES and so on. Think about the previously mentioned design principles of Industry 4.
They all play in the radically changing architectures of the various solutions that exist. A quick look at some of the evolutions in the top 3 levels of our classic automation pyramid which is starting to look like a mesh as systems start speaking the same languages of industrial protocols and IIoT.
Related Projections and Interface Conditions: Essays on Modularity
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