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Industry 4.0: The Industrial Internet of Things by Alasdair Gilchrist
3D printing, additive manufacturing, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, business intelligence, business process, chief data officer, cloud computing, connected car, cyber-physical system, deindustrialization, fault tolerance, global value chain, Google Glasses, hiring and firing, industrial robot, inflight wifi, Infrastructure as a Service, Internet of things, inventory management, job automation, low skilled workers, millennium bug, pattern recognition, peer-to-peer, platform as a service, pre–internet, race to the bottom, RFID, Skype, smart cities, smart grid, smart meter, smart transportation, software as a service, stealth mode startup, supply-chain management, trade route, web application, WebRTC, WebSocket, Y2K
Therefore, it is necessary to identify early in the design process whether the product is to be an IT, network, or a physical system–or a system that has all three, physical, network, and digital processing features. If it has, then it is said to be a cyber-physical system. In some definitions, the networking and communications feature is deemed optional, although that raises the question as to how a CPS differs from an embedded system. Information systems, which are embedded into physical devices, are called “embedded systems”. These embedded systems are found in telecommunication, automation, and transport systems, among many others. Lately, a new term has surfaced, the cyber-physical systems (CPS). This distinguishes between microprocessor based embedded systems and more complex information processing systems that actually integrate with their environment. A precise definition of cyber-physical systems (CPS) is that they are integrations of computation, networking, and physical processes.
Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. Therefore, a cyber-physical system can be just about anything that has integrated computation, networking, and physical processes. A human operator is a cyber-physical system and so is a smart factory. For example, a human operator has physical and cyber components. In this example, the operator has a computational facility—their brain—and they communicate with other humans and the system through HMI (human machine interface) and interact through mechanical interfaces—their hands—to influence their environment. Cyber-physical systems enable the virtual digital world of computers and software to merge through interaction—process management and feedback control—with the physical analogue world, thus leading to an Internet of Things, data, and services.
For example, an Apple iPhone, the Raspberry Pi, and the Arduino with extension shields all provide the tools to create multi-sensor devices that can sense and influence their analogue environment through their interaction with the digital world. The availability of these development kits has accelerated the design process, by allowing the production of proof-of-concept (PoC) models. They have driven innovation in the way we deploy multi-sensor devices into industrial system automation and integrate M2M with cyber-physical systems to create Industrial Internet of Things environments. Cyber Physical Systems (CPS) The Industrial Internet has come about due to the rapid advancements in digital computers in all their formats and vast improvements in digital communications. These disciplines are considered separate domains of knowledge and expertise, with there being a tendency for specialization in one or the other. This results in inter-disciplinary knowledge being required to design and build products that require information processing and networking; for 35 36 Chapter 3 |TheTechnical and Business Innovators of the Industrial Internet example, a device with embedded microprocessor and ZigBee, such as the Raspberry Pi or a smartphone.
Data Mining: Concepts and Techniques: Concepts and Techniques by Jiawei Han, Micheline Kamber, Jian Pei
bioinformatics, business intelligence, business process, Claude Shannon: information theory, cloud computing, computer vision, correlation coefficient, cyber-physical system, database schema, discrete time, distributed generation, finite state, information retrieval, iterative process, knowledge worker, linked data, natural language processing, Netflix Prize, Occam's razor, pattern recognition, performance metric, phenotype, random walk, recommendation engine, RFID, semantic web, sentiment analysis, speech recognition, statistical model, stochastic process, supply-chain management, text mining, thinkpad, Thomas Bayes, web application
Other examples of moving-object data mining include mining periodic patterns for one or a set of moving objects, and mining trajectory patterns, clusters, models, and outliers. Mining Cyber-Physical System Data A cyber-physical system (CPS) typically consists of a large number of interacting physical and information components. CPS systems may be interconnected so as to form large heterogeneous cyber-physical networks. Examples of cyber-physical networks include a patient care system that links a patient monitoring system with a network of patient/medical information and an emergency handling system; a transportation system that links a transportation monitoring network, consisting of many sensors and video cameras, with a traffic information and control system; and a battlefield commander system that links a sensor/reconnaissance network with a battlefield information analysis system. Clearly, cyber-physical systems and networks will be ubiquitous and form a critical component of modern information infrastructure.
■ Mining social and information networks: Mining social and information networks and link analysis are critical tasks because such networks are ubiquitous and complex. The development of scalable and effective knowledge discovery methods and applications for large numbers of network data is essential, as outlined in Section 13.1.2. ■ Mining spatiotemporal, moving-objects, and cyber-physical systems: Cyber-physical systems as well as spatiotemporal data are mounting rapidly due to the popular use of cellular phones, GPS, sensors, and other wireless equipment. As outlined in Section 13.1.3, there are many challenging research issues realizing real-time and effective knowledge discovery with such data. ■ Mining multimedia, text, and web data: As outlined in Section 13.1.3, mining such kinds of data is a recent focus in data mining research.
seecustomer relationship management crossover operation 426 cross-validation 370–371, 386 k-fold 370 leave-one-out 371 in number of clusters determination 487 stratified 371 cube gradient analysis 321 cube shells 192, 211 computing 211 cube space discovery-driven exploration 231–234 multidimensional data analysis in 227–234 prediction mining in 227 subspaces 228–229 cuboid trees 205 cuboids 137 apex 111, 138, 158 base 111, 137–138, 158 child 193 individual 190 lattice of 139, 156, 179, 188–189, 234, 290 sparse 190 subset selection 160see alsodata cubes curse of dimensionality 158, 179 customer relationship management (CRM) 619 customer retention analysis 610 CVQE. seeConstrained Vector Quantization Error algorithm cyber-physical systems (CPS) 596, 623–624 D data antimonotonicity 300 archeology 6 biological sequence 586, 590–591 complexity 32 conversion to knowledge 2 cyber-physical system 596 for data mining 8 data warehouse 13–15 database 9–10 discrimination 16 dredging 6 generalizing 150 graph 14 growth 2 linearly inseparable 413–415 linearly separated 409 multimedia 14, 596 multiple sources 15, 32 multivariate 556 networked 14 overfitting 330 relational 10 sample 219 similarity and dissimilarity measures 65–78 skewed 47, 271 spatial 14, 595 spatiotemporal 595–596 specializing 150 statistical descriptions 44–56 streams 598 symbolic sequence 586, 588–589 temporal 14 text 14, 596–597 time-series 586, 587 “tombs” 5 training 18 transactional 13–14 types of 33 web 597–598 data auditing tools 92 data characterization 15, 166 attribute-oriented induction 167–172 data mining query 167–168 example 16 methods 16 output 16 data classification.
Apple II, augmented reality, autonomous vehicles, bioinformatics, Build a better mousetrap, business process, cloud computing, computer vision, cyber-physical system, distributed generation, game design, Grace Hopper, Richard Feynman, Richard Feynman, Silicon Valley, skunkworks, Skype, smart transportation, speech recognition, statistical model, stealth mode startup, Steve Jobs, Steve Wozniak, the market place, Yogi Berra
But it soon became mine, because I was the only one who had patience to learn how to use it. I was making just the kind of things eleven-year-olds would make: video games, space invaders, keyboard races, etc. Stern: Jumping forward, could you define the technology or your inventions in technical terms, and then define them in layperson’s terms? Greiner: I like to build integrated robot systems, or “cyber-physical systems,” that are able to negotiate unstructured environments using dynamic sensing and onboard intelligence. Robotics encompasses many of the other disciplines, like artificial intelligence, dynamic sensing, and electrical engineering. You’d be hard-pressed to come up with an area that robotics doesn’t use. Stern: Could you define it in one sentence in very simple terms? Greiner: I guess I’d have to say that I build practical robot systems.