Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Robotics and control are both research and application domains that have been frequently engineered through the use of interdisciplinary approaches like cybernetics. Cognition is a particular concept of this approach, abstracted from the context of living organisms to that of artificial devices, and is concerned with knowledge acquisition and understanding through thought, experience, and the senses. Cognitive robotics and control refer to knowledge processing as much as knowledge generation from problem understanding, leading to special forms of architectures that enable systems to behave in an autonomous way. The main aim of this book is to highlight emerging applications and address recent breakthroughs in the domain of cognitive robotics and control and related areas. Procedures, algorithms, architectures, and implementations for reasoning, problem solving, or decision making are considered in the domain of robotics and control.

History of engineering & technology --- initial trajectory --- trajectory optimization --- Bézier surface --- robotics --- open FPGAs --- robot control --- surgical robotics --- human-machine interaction --- autonomous guidance --- low-cost platform --- FPGA --- S-curve --- motion control --- SoC --- telemanipulation --- haptics --- machine learning --- gesture recognition --- upper limb rehabilitation robot --- particle swam optimization (PSO) --- artificial bee colony (ABC) --- Ziegler Nichols --- Maximum sensitivity --- ontology --- robot task planning --- knowledge base --- knowledge representation --- industrial collaborative robots --- shared robotic tasks --- physical human-robot interaction --- human intention recognition --- time series classification --- initial trajectory --- trajectory optimization --- Bézier surface --- robotics --- open FPGAs --- robot control --- surgical robotics --- human-machine interaction --- autonomous guidance --- low-cost platform --- FPGA --- S-curve --- motion control --- SoC --- telemanipulation --- haptics --- machine learning --- gesture recognition --- upper limb rehabilitation robot --- particle swam optimization (PSO) --- artificial bee colony (ABC) --- Ziegler Nichols --- Maximum sensitivity --- ontology --- robot task planning --- knowledge base --- knowledge representation --- industrial collaborative robots --- shared robotic tasks --- physical human-robot interaction --- human intention recognition --- time series classification

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Robotics and control are both research and application domains that have been frequently engineered through the use of interdisciplinary approaches like cybernetics. Cognition is a particular concept of this approach, abstracted from the context of living organisms to that of artificial devices, and is concerned with knowledge acquisition and understanding through thought, experience, and the senses. Cognitive robotics and control refer to knowledge processing as much as knowledge generation from problem understanding, leading to special forms of architectures that enable systems to behave in an autonomous way. The main aim of this book is to highlight emerging applications and address recent breakthroughs in the domain of cognitive robotics and control and related areas. Procedures, algorithms, architectures, and implementations for reasoning, problem solving, or decision making are considered in the domain of robotics and control.

initial trajectory --- trajectory optimization --- Bézier surface --- robotics --- open FPGAs --- robot control --- surgical robotics --- human–machine interaction --- autonomous guidance --- low-cost platform --- FPGA --- S-curve --- motion control --- SoC --- telemanipulation --- haptics --- machine learning --- gesture recognition --- upper limb rehabilitation robot --- particle swam optimization (PSO) --- artificial bee colony (ABC) --- Ziegler Nichols --- Maximum sensitivity --- ontology --- robot task planning --- knowledge base --- knowledge representation --- industrial collaborative robots --- shared robotic tasks --- physical human–robot interaction --- human intention recognition --- time series classification --- n/a --- Bézier surface --- human-machine interaction --- physical human-robot interaction

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes.

spatial-temporal data --- pasting process --- process image --- convolutional neural network --- Industry 4.0 --- auto machine learning --- failure mode effects analysis --- risk priority number --- rolling bearing --- condition monitoring --- classification --- OPTICS --- statistical process control --- control chart pattern --- disruptions --- disruption management --- fault diagnosis --- construction industry --- plaster production --- neural networks --- decision support systems --- expert systems --- failure mode and effects analysis (FMEA) --- discriminant analysis --- non-intrusive load monitoring --- load identification --- membrane --- data reconciliation --- real-time --- online --- monitoring --- Six Sigma --- multivariate data analysis --- latent variables models --- PCA --- PLS --- high-dimensional data --- statistical process monitoring --- artificial generation of variability --- data augmentation --- quality prediction --- continuous casting --- multiscale --- time series classification --- imbalanced data --- combustion --- optical sensors --- spectroscopy measurements --- signal detection --- digital processing --- principal component analysis --- curve resolution --- data mining --- semiconductor manufacturing --- quality control --- yield improvement --- fault detection --- process control --- multi-phase residual recursive model --- multi-mode model --- process monitoring --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.

Technology: general issues --- smart grid --- nontechnical losses --- electricity theft detection --- synthetic minority oversampling technique --- K-means cluster --- random forest --- smart grids --- smart energy system --- smart meter --- GDPR --- data privacy --- ethics --- multi-label learning --- Non-intrusive Load Monitoring --- appliance recognition --- fryze power theory --- V-I trajectory --- Convolutional Neural Network --- distance similarity matrix --- activation current --- electric vehicle --- synthetic data --- exponential distribution --- Poisson distribution --- Gaussian mixture models --- mathematical modeling --- machine learning --- simulation --- Non-Intrusive Load Monitoring (NILM) --- NILM datasets --- power signature --- electric load simulation --- data-driven approaches --- smart meters --- text convolutional neural networks (TextCNN) --- time-series classification --- data annotation --- non-intrusive load monitoring --- semi-automatic labeling --- appliance load signatures --- ambient influences --- device classification accuracy --- NILM --- signature --- load disaggregation --- transients --- pulse generator --- smart metering --- smart power grids --- power consumption data --- energy data processing --- user-centric applications of energy data --- convolutional neural network --- energy consumption --- energy data analytics --- energy disaggregation --- real-time --- smart meter data --- transient load signature --- attention mechanism --- deep neural network --- electrical energy --- load scheduling --- satisfaction --- Shapley Value --- solar photovoltaics --- review --- deep learning --- deep neural networks --- smart grid --- nontechnical losses --- electricity theft detection --- synthetic minority oversampling technique --- K-means cluster --- random forest --- smart grids --- smart energy system --- smart meter --- GDPR --- data privacy --- ethics --- multi-label learning --- Non-intrusive Load Monitoring --- appliance recognition --- fryze power theory --- V-I trajectory --- Convolutional Neural Network --- distance similarity matrix --- activation current --- electric vehicle --- synthetic data --- exponential distribution --- Poisson distribution --- Gaussian mixture models --- mathematical modeling --- machine learning --- simulation --- Non-Intrusive Load Monitoring (NILM) --- NILM datasets --- power signature --- electric load simulation --- data-driven approaches --- smart meters --- text convolutional neural networks (TextCNN) --- time-series classification --- data annotation --- non-intrusive load monitoring --- semi-automatic labeling --- appliance load signatures --- ambient influences --- device classification accuracy --- NILM --- signature --- load disaggregation --- transients --- pulse generator --- smart metering --- smart power grids --- power consumption data --- energy data processing --- user-centric applications of energy data --- convolutional neural network --- energy consumption --- energy data analytics --- energy disaggregation --- real-time --- smart meter data --- transient load signature --- attention mechanism --- deep neural network --- electrical energy --- load scheduling --- satisfaction --- Shapley Value --- solar photovoltaics --- review --- deep learning --- deep neural networks

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.

Technology: general issues --- smart grid --- nontechnical losses --- electricity theft detection --- synthetic minority oversampling technique --- K-means cluster --- random forest --- smart grids --- smart energy system --- smart meter --- GDPR --- data privacy --- ethics --- multi-label learning --- Non-intrusive Load Monitoring --- appliance recognition --- fryze power theory --- V-I trajectory --- Convolutional Neural Network --- distance similarity matrix --- activation current --- electric vehicle --- synthetic data --- exponential distribution --- Poisson distribution --- Gaussian mixture models --- mathematical modeling --- machine learning --- simulation --- Non-Intrusive Load Monitoring (NILM) --- NILM datasets --- power signature --- electric load simulation --- data-driven approaches --- smart meters --- text convolutional neural networks (TextCNN) --- time-series classification --- data annotation --- non-intrusive load monitoring --- semi-automatic labeling --- appliance load signatures --- ambient influences --- device classification accuracy --- NILM --- signature --- load disaggregation --- transients --- pulse generator --- smart metering --- smart power grids --- power consumption data --- energy data processing --- user-centric applications of energy data --- convolutional neural network --- energy consumption --- energy data analytics --- energy disaggregation --- real-time --- smart meter data --- transient load signature --- attention mechanism --- deep neural network --- electrical energy --- load scheduling --- satisfaction --- Shapley Value --- solar photovoltaics --- review --- deep learning --- deep neural networks --- n/a