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  1. 1021
    by Rodrigues, Clarence C.
    Published 2012
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  2. 1022
    by Park, Kyung S. (Kyung Soo)
    Published 1987
    Table of Contents: ...7.5 HUMAN ERROR DATA BANKS7.6 TASK CRITICALITY ANALYSIS; 7.7 TECHNIQUE FOR HUMAN ERROR RATE PREDICTION (THERP); 7.8 OPERATOR-ACTION TREE METHODOLOGY; 7.9 FAULT-TREE ANALYSIS; 7.10 FAULT-TREE ANALYSIS OF INTERMITTENT EVENTS; 7.11 DIGITAL SIMULATION FOR HUMAN RELIABILITY PREDICTION; Chapter 8. NEW HORIZONS; 8.1 HUMAN ERROR AND LEARNING; 8.2 HUMAN ERROR IN INDUSTRIAL QUALITY INSPECTION; 8.3 HUMAN ERROR IN MAINTENANCE; 8.4 APPLICATION OF OPTIMAL CONTROL MODELS TO HUMAN RELIABILITY ANALYSIS; 8.5 PROGRAMMING ERROR AND SOFTWARE RELIABILITY; 8.6 FUZZY SETS AND APPLICATIONS...
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  3. 1023
  4. 1024
  5. 1025
  6. 1026
  7. 1027
  8. 1028
  9. 1029
    Published 2015
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  10. 1030
    Published 1988
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    Conference Proceeding eBook
  11. 1031
  12. 1032
  13. 1033
  14. 1034
    by Massaro, Alessandro, 1974-
    Published 2022
    Table of Contents: ...Preface -- About the Author -- Chapter 1 Introduction: state of the art and technology innovation -- 1.1 State of the art of flexible technologies in industry -- 1.1.1 Sensors and actuators layer: I/O layer -- 1.1.2 Agent/ Firmware layer: user interface layer -- 1.1.3 Gateway and ESB layer -- 1.1.4 IoT middleware -- 1.1.5 Processing layer -- 1.1.6 Application layer -- 1.1.7 File transfer protocols -- 1.2 State of the art of scientific approaches oriented on process control and automatisms -- 1.2.1 Architectures integrating AI -- 1.2.2 AI supervised and unsupersived algorithms -- 1.2.3 AI image processing -- 1.2.4 Production process mapping -- 1.2.5 Technologies of Industry 4.0 and Industry 5.0: interconnection and main limits -- 1.2.6 Infrared thermography in monitoring process -- 1.2.7 Key parameters in supply chain and AI improving manufacturing processes -- 1.3 Intelligent automatic systems in industries -- 1.4 Technological approaches to transform the production in auto-adaptive control and actuation systems -- 1.5 Basic concepts of artificial intelligence -- 1.6 Knowledge upgrading in industries -- Chapter 2 Introduction: information technology infrastructures supporting Industry 5.0 facilities -- 2.1 Production process simulation and object design approaches -- 2.1.1 Object design of a data mining algorithm: block functions and parameter setting -- 2.1.2 Example 1: BPM modeling of wheat storage process for pasta production -- 2.1.3 Example 2: block diagram design of a servo valve control and actuation system -- 2.1.4 Example 3: block diagram of a liquid production system -- 2.1.5 Example 4: UML design of a PLC system -- 2.1.6 Example 5: electronic logic timing diagram -- 2.1.7 Example 6: AR system in kitchen production process -- 2.1.8 Example 7: intelligent canned food production line -- 2.2 Electronic logic design oriented on information infrastructure of Industry 5.0 -- 2.3 Predictive Maintenance: artificial intelligence failures predictions and information infrastructure layout in temperature monitoring process -- 2.4 Defect estimation and prediction by artificial neural network -- 2.5 Defect clustering and classification: combined use of K-Means algorithm with infrared thermography for predictive maintenance -- 2.6 Facilities of a prototype network implementing advanced technology: example of an advanced platform suitable for Industry 5.0 integrating predictive maintenance -- 2.7 Predictive maintenance approaches -- 2.7.1 Preventive maintenance and predictive maintenance operations in the railway industry -- 2.8 Examples of advanced infrastructures implementing AI -- 2.9 Examples of telemedicine platforms integrating advanced facilities -- 2.9.1 Advanced Telecardiology platform -- 2.9.2 Advanced Teleoncology Platform -- 2.9.3 Multipurpose E-Health platform -- Chapter 3 Introduction: human-machine interfaces -- 3.1 Mechatronic machine interface architectures integrating sensor systems -- 3.1.1 Multiple mechatronic boards managing different production stages -- 3.1.2 Mechatronic boards managing components processing -- 3.2 Machine to Machine M2M interfaces: new concepts of Industry 5.0 -- 3.3 Production line command and actuation interfaces in upgraded systems -- 3.3.1 PLC, PAC, IPC and improvements -- 3.3.2 SCADA systems for centralization of data production -- 3.4 McCulloch-Pitts neurons and logic port for automatic decision-making setting thresholds -- 3.5 PLC I/O ports interfacing with AI engine -- 3.6 Human Machine Interface for data transfer and AI data processing -- 3.7 Example of interface configuration of temperature control -- 3.8 AI interfaces oriented on cybersecurity attack detection -- 3.9 AI interfaces oriented on database security -- 3.10 Cybersecurity platform and AI control interface -- Chapter 4 Introduction: IoT solutions in industry -- 4.1 Cloud computing IoT -- 4.1.1 IoT agent -- 4.1.2 IoT gateway in smart environments -- 4.1.3 Basic elements of smart industry environment controlling production -- 4.1.3.1 Feedback control: basic concepts -- 4.1.4 Augmented reality hardware and cloud computing processing -- 4.1.5 Real time control and actuation -- 4.1.6 Localisation technologies in an industrial environment -- 4.1.7 GPU processing units -- 4.1.7.1 Performance of GPUs by processing binary matrices -- 4.2 IoT and external artificial intelligence engines -- 4.2.1 Artificial engines and server location: artificial intelligence and adaptive production -- 4.2.2 IoT Security systems in working environment and implementation aspects -- 4.2.3 Example of energy power control and actuation: energy routing and priority load management for energy efficiency -- 4.2.4 Online configurators: cloud DSS -- 4.3 Blockchain and IoT data storage systems -- 4.3.1 Blockchain implementation rules -- 4.3.2 Blockchain and IoT production traceability -- 4.4 Mechatronic machine interface architectures integrating sensor systems -- 4.5 Multiple mechatronic board managing different production stages -- Chapter 5 Introduction: advanced robotics -- 5.1 Collaborative robotics in industry and protocols IoT agent -- 5.1.1 Data protocols -- 5.1.2 Basic concepts of robotic arms and control improvement -- 5.1.3 Collaborative exoskeletons communication system protocols -- 5.1.4 Advanced robotics and intelligent automation in manufacturing: logic conditions and PLC programming -- 5.2 Artificial intelligence in advanced robotics and auto-adaptive movement -- 5.2.1 General technological aspects about auto-adaptive motion in advanced robotics -- 5.2.1.1 Main aspects of electrostatic actuators -- 5.2.1.2 MEMS electrostatic actuators -- 5.2.1.3 Piezoelectric actuators -- 5.2.1.4 DC motor actuation -- 5.2.1.5 Intelligent control integrating AI: speed regulation -- 5.2.2 Improvement of collaborative exoskeletons by auto-adaptive solutions implementing artificial intelligence -- 5.3 Human-robot self-learning collaboration in industrial applications and electronic aspects -- 5.3.1 DC-DC converter -- 5.3.2 Voltage-source inverter -- 5.3.3 Current-source inverter -- 5.3.4 DC voltage source -- 5.3.5 Capacitor and Reactor effects on signal control -- 5.3.6 Human-robot system and learning approaches -- 5.3.6.1 Example of PID implementation self-adapting gains -- 5.3.7 Unsupervised learning approaches -- 5.3.8 Soft robotics for intelligent collaborative robotics -- 5.4 Robotics in additive manufacturing -- 5.4.1 Additive manufacturing in industrial production and spray technique -- 5.4.2 Artificial intelligence applications in additive manufacturing -- 5.4.3 Advanced electronic for design-to-product transformation: laser texturing manufacturing and artificial intelligence -- Chapter 6: Introduction: advanced opto-electronic and micro-nano sensors -- 6.1 Nanotechnology laboratories in industries -- 6.1.1 Facilities for micro-nanosensors fabrication and characterization -- 6.2 Micro and nano-sensors as preliminary prototypes for industry research -- 6.2.1 Nanocomposite optoelectronic sensors and optoelectronic circuits for pressure sensors -- 6.2.1.1 Optical fiber nanocomposite tip -- 6.2.2 Plasmonic probes -- 6.2.3 Nanocomposite pressure sensor -- 6.2.4 Nanocomposite sensor for liquid detection systems and fluid loss systems -- 6.2.4.1 Nanocomposite sensor for liquid detection systems based on pillar type layout -- 6.2.4.2 Micro and nano sensors in production processes monitoring: leakage monitoring -- 6.2.5 Examples of digital MEMS/NEMS sensors: technological aspects and applications -- 6.2.5.1 Thin film MEMS -- 6.2.5.2 Nanoprobes for medical imaging -- 6.2.5.3 Diamond thin film devices: sensing improvements -- 6.3 Multi-sensor systems and big data synchronization of micro/nano probes -- Chapter 7: Introduction: image vision advances -- 7.1 Defects classification by artificial intelligence and data processor units -- 7.1.1 Artificial intelligence algorithms and automatism for defects classification: the case of study of tires production -- 7.1.2 Welding classification and non-destructive suitable for the quality check -- 7.1.2.1 Watershed image segmentation and automatic welding defect classification -- 7.1.3 Encoding and decoding circuits in artificial intelligence data processing -- 7.1.4 Electronic logic port implementations: pixel matrix logic condition -- 7.2 Image vision architectures and electronic design -- 7.2.1 Infrared thermography monitoring industrial processes -- 7.2.1.1 Welding image vision processing and architecture design: radiometric...
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  15. 1035
  16. 1036
  17. 1037
    by Peckol, James K.
    Published 2021
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  18. 1038
  19. 1039
  20. 1040