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Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.
star image --- image denoising --- reinforcement learning --- maximum likelihood estimation --- mixed Poisson–Gaussian likelihood --- machine learning-based classification --- non-uniform foundation --- stochastic analysis --- vehicle–pavement–foundation interaction --- forest growing stem volume --- coniferous plantations --- variable selection --- texture feature --- random forest --- red-edge band --- on-shelf availability --- semi-supervised learning --- deep learning --- image classification --- machine learning --- explainable artificial intelligence --- wildfire --- risk assessment --- Naïve bayes --- transmission-line corridors --- image encryption --- compressive sensing --- plaintext related --- chaotic system --- convolutional neural network --- color prior model --- object detection --- piston error detection --- segmented telescope --- BP artificial neural network --- modulation transfer function --- computer vision --- intelligent vehicles --- extrinsic camera calibration --- structure from motion --- convex optimization --- temperature estimation --- BLDC --- electric machine protection --- touchscreen --- capacitive --- display --- SNR --- stylus --- laser cutting --- quality monitoring --- artificial neural network --- burr formation --- cut interruption --- fiber laser --- semi-supervised --- fuzzy --- noisy --- real-world --- plankton --- marine --- activity recognition --- wearable sensors --- imbalanced activities --- sampling methods --- path planning --- Q-learning --- neural network --- YOLO algorithm --- robot arm --- target reaching --- obstacle avoidance
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Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.
Technology: general issues --- History of engineering & technology --- star image --- image denoising --- reinforcement learning --- maximum likelihood estimation --- mixed Poisson–Gaussian likelihood --- machine learning-based classification --- non-uniform foundation --- stochastic analysis --- vehicle–pavement–foundation interaction --- forest growing stem volume --- coniferous plantations --- variable selection --- texture feature --- random forest --- red-edge band --- on-shelf availability --- semi-supervised learning --- deep learning --- image classification --- machine learning --- explainable artificial intelligence --- wildfire --- risk assessment --- Naïve bayes --- transmission-line corridors --- image encryption --- compressive sensing --- plaintext related --- chaotic system --- convolutional neural network --- color prior model --- object detection --- piston error detection --- segmented telescope --- BP artificial neural network --- modulation transfer function --- computer vision --- intelligent vehicles --- extrinsic camera calibration --- structure from motion --- convex optimization --- temperature estimation --- BLDC --- electric machine protection --- touchscreen --- capacitive --- display --- SNR --- stylus --- laser cutting --- quality monitoring --- artificial neural network --- burr formation --- cut interruption --- fiber laser --- semi-supervised --- fuzzy --- noisy --- real-world --- plankton --- marine --- activity recognition --- wearable sensors --- imbalanced activities --- sampling methods --- path planning --- Q-learning --- neural network --- YOLO algorithm --- robot arm --- target reaching --- obstacle avoidance
Choose an application
Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.
Technology: general issues --- History of engineering & technology --- star image --- image denoising --- reinforcement learning --- maximum likelihood estimation --- mixed Poisson–Gaussian likelihood --- machine learning-based classification --- non-uniform foundation --- stochastic analysis --- vehicle–pavement–foundation interaction --- forest growing stem volume --- coniferous plantations --- variable selection --- texture feature --- random forest --- red-edge band --- on-shelf availability --- semi-supervised learning --- deep learning --- image classification --- machine learning --- explainable artificial intelligence --- wildfire --- risk assessment --- Naïve bayes --- transmission-line corridors --- image encryption --- compressive sensing --- plaintext related --- chaotic system --- convolutional neural network --- color prior model --- object detection --- piston error detection --- segmented telescope --- BP artificial neural network --- modulation transfer function --- computer vision --- intelligent vehicles --- extrinsic camera calibration --- structure from motion --- convex optimization --- temperature estimation --- BLDC --- electric machine protection --- touchscreen --- capacitive --- display --- SNR --- stylus --- laser cutting --- quality monitoring --- artificial neural network --- burr formation --- cut interruption --- fiber laser --- semi-supervised --- fuzzy --- noisy --- real-world --- plankton --- marine --- activity recognition --- wearable sensors --- imbalanced activities --- sampling methods --- path planning --- Q-learning --- neural network --- YOLO algorithm --- robot arm --- target reaching --- obstacle avoidance
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Currently, the transition from using the combustion engine to electrified vehicles is a matter of time and drives the demand for compact, high-energy-density rechargeable lithium ion batteries as well as for large stationary batteries to buffer solar and wind energy. The future challenges, e.g., the decarbonization of the CO2-intensive transportation sector, will push the need for such batteries even more. The cost of lithium ion batteries has become competitive in the last few years, and lithium ion batteries are expected to dominate the battery market in the next decade. However, despite remarkable progress, there is still a strong need for improvements in the performance of lithium ion batteries. Further improvements are not only expected in the field of electrochemistry but can also be readily achieved by improved manufacturing methods, diagnostic algorithms, lifetime prediction methods, the implementation of artificial intelligence, and digital twins. Therefore, this Special Issue addresses the progress in battery and energy storage development by covering areas that have been less focused on, such as digitalization, advanced cell production, modeling, and prediction aspects in concordance with progress in new materials and pack design solutions.
Research & information: general --- battery energy storage --- renewable energy --- distribution network --- genetic algorithm --- particle swarm optimization --- electrolyte --- additive --- interface --- pseudocapacitance --- intercalation --- energy storage --- secondary battery --- sodium-ion --- lithium-ion battery --- traction battery --- waterjet-based recycling --- direct recycling --- life cycle assessment --- global warming potential --- electro-thermal model --- smart cell --- intelligent battery --- neural network --- temperature prediction --- DRT by time domain data --- pulse evaluation --- relaxation voltage --- online diagnosis --- degradation mechanisms --- EIS --- lead batteries --- safety concept --- safety battery --- battery monitoring --- electronic battery sensor --- failure modes --- failure distribution --- failure rates --- field battery investigation --- safe supply --- power supply system --- zinc ion batteries --- stationary energy storage --- polymer binder --- solvent --- doctor blade coating --- manganese dioxide --- mixing ratio --- electrochemical impedance spectroscopy --- SEM+EDX --- electrode fabrication --- lithium ion battery --- AC current injection --- bi-directional control --- charger --- lithium-ion battery cell --- volumetric expansion --- mechanical degradation --- state of charge dependency --- cell thickness --- mechanical aging --- non-uniform volume change --- solar photovoltaic energy --- redox flow battery --- residential load --- renewable energy integration --- battery sizing --- battery efficiency --- lithium battery --- temperature dependency --- ether based electrolyte --- insitu deposited lithium-metal electrode --- Coulombic efficiency --- lithium deposition morphology --- Li-ion battery --- thermal runaway --- model --- post-mortem analysis --- ecofriendly electrolyte for lithium-ion batteries --- increased thermal stability of electrolytes --- enhanced electrolyte safety based on high flash point --- tributylacetylcitrate --- acetyltributylcitrate --- electric vehicle battery --- disassembly --- disassembly planner design --- disassembly strategy optimization --- battery management system --- state monitoring --- state-of-charge --- digital twin --- battery model --- Doyle-Fuller-Newman model --- equivalent circuit model --- parameter estimation --- lithium-ion batteries --- temperature estimation --- sensorless temperature measurement --- artificial intelligence --- artificial neural network --- lithium-ion cells --- battery thermal management systems --- CFD simulations --- liquid cooling
Choose an application
Currently, the transition from using the combustion engine to electrified vehicles is a matter of time and drives the demand for compact, high-energy-density rechargeable lithium ion batteries as well as for large stationary batteries to buffer solar and wind energy. The future challenges, e.g., the decarbonization of the CO2-intensive transportation sector, will push the need for such batteries even more. The cost of lithium ion batteries has become competitive in the last few years, and lithium ion batteries are expected to dominate the battery market in the next decade. However, despite remarkable progress, there is still a strong need for improvements in the performance of lithium ion batteries. Further improvements are not only expected in the field of electrochemistry but can also be readily achieved by improved manufacturing methods, diagnostic algorithms, lifetime prediction methods, the implementation of artificial intelligence, and digital twins. Therefore, this Special Issue addresses the progress in battery and energy storage development by covering areas that have been less focused on, such as digitalization, advanced cell production, modeling, and prediction aspects in concordance with progress in new materials and pack design solutions.
battery energy storage --- renewable energy --- distribution network --- genetic algorithm --- particle swarm optimization --- electrolyte --- additive --- interface --- pseudocapacitance --- intercalation --- energy storage --- secondary battery --- sodium-ion --- lithium-ion battery --- traction battery --- waterjet-based recycling --- direct recycling --- life cycle assessment --- global warming potential --- electro-thermal model --- smart cell --- intelligent battery --- neural network --- temperature prediction --- DRT by time domain data --- pulse evaluation --- relaxation voltage --- online diagnosis --- degradation mechanisms --- EIS --- lead batteries --- safety concept --- safety battery --- battery monitoring --- electronic battery sensor --- failure modes --- failure distribution --- failure rates --- field battery investigation --- safe supply --- power supply system --- zinc ion batteries --- stationary energy storage --- polymer binder --- solvent --- doctor blade coating --- manganese dioxide --- mixing ratio --- electrochemical impedance spectroscopy --- SEM+EDX --- electrode fabrication --- lithium ion battery --- AC current injection --- bi-directional control --- charger --- lithium-ion battery cell --- volumetric expansion --- mechanical degradation --- state of charge dependency --- cell thickness --- mechanical aging --- non-uniform volume change --- solar photovoltaic energy --- redox flow battery --- residential load --- renewable energy integration --- battery sizing --- battery efficiency --- lithium battery --- temperature dependency --- ether based electrolyte --- insitu deposited lithium-metal electrode --- Coulombic efficiency --- lithium deposition morphology --- Li-ion battery --- thermal runaway --- model --- post-mortem analysis --- ecofriendly electrolyte for lithium-ion batteries --- increased thermal stability of electrolytes --- enhanced electrolyte safety based on high flash point --- tributylacetylcitrate --- acetyltributylcitrate --- electric vehicle battery --- disassembly --- disassembly planner design --- disassembly strategy optimization --- battery management system --- state monitoring --- state-of-charge --- digital twin --- battery model --- Doyle-Fuller-Newman model --- equivalent circuit model --- parameter estimation --- lithium-ion batteries --- temperature estimation --- sensorless temperature measurement --- artificial intelligence --- artificial neural network --- lithium-ion cells --- battery thermal management systems --- CFD simulations --- liquid cooling
Choose an application
Currently, the transition from using the combustion engine to electrified vehicles is a matter of time and drives the demand for compact, high-energy-density rechargeable lithium ion batteries as well as for large stationary batteries to buffer solar and wind energy. The future challenges, e.g., the decarbonization of the CO2-intensive transportation sector, will push the need for such batteries even more. The cost of lithium ion batteries has become competitive in the last few years, and lithium ion batteries are expected to dominate the battery market in the next decade. However, despite remarkable progress, there is still a strong need for improvements in the performance of lithium ion batteries. Further improvements are not only expected in the field of electrochemistry but can also be readily achieved by improved manufacturing methods, diagnostic algorithms, lifetime prediction methods, the implementation of artificial intelligence, and digital twins. Therefore, this Special Issue addresses the progress in battery and energy storage development by covering areas that have been less focused on, such as digitalization, advanced cell production, modeling, and prediction aspects in concordance with progress in new materials and pack design solutions.
Research & information: general --- battery energy storage --- renewable energy --- distribution network --- genetic algorithm --- particle swarm optimization --- electrolyte --- additive --- interface --- pseudocapacitance --- intercalation --- energy storage --- secondary battery --- sodium-ion --- lithium-ion battery --- traction battery --- waterjet-based recycling --- direct recycling --- life cycle assessment --- global warming potential --- electro-thermal model --- smart cell --- intelligent battery --- neural network --- temperature prediction --- DRT by time domain data --- pulse evaluation --- relaxation voltage --- online diagnosis --- degradation mechanisms --- EIS --- lead batteries --- safety concept --- safety battery --- battery monitoring --- electronic battery sensor --- failure modes --- failure distribution --- failure rates --- field battery investigation --- safe supply --- power supply system --- zinc ion batteries --- stationary energy storage --- polymer binder --- solvent --- doctor blade coating --- manganese dioxide --- mixing ratio --- electrochemical impedance spectroscopy --- SEM+EDX --- electrode fabrication --- lithium ion battery --- AC current injection --- bi-directional control --- charger --- lithium-ion battery cell --- volumetric expansion --- mechanical degradation --- state of charge dependency --- cell thickness --- mechanical aging --- non-uniform volume change --- solar photovoltaic energy --- redox flow battery --- residential load --- renewable energy integration --- battery sizing --- battery efficiency --- lithium battery --- temperature dependency --- ether based electrolyte --- insitu deposited lithium-metal electrode --- Coulombic efficiency --- lithium deposition morphology --- Li-ion battery --- thermal runaway --- model --- post-mortem analysis --- ecofriendly electrolyte for lithium-ion batteries --- increased thermal stability of electrolytes --- enhanced electrolyte safety based on high flash point --- tributylacetylcitrate --- acetyltributylcitrate --- electric vehicle battery --- disassembly --- disassembly planner design --- disassembly strategy optimization --- battery management system --- state monitoring --- state-of-charge --- digital twin --- battery model --- Doyle-Fuller-Newman model --- equivalent circuit model --- parameter estimation --- lithium-ion batteries --- temperature estimation --- sensorless temperature measurement --- artificial intelligence --- artificial neural network --- lithium-ion cells --- battery thermal management systems --- CFD simulations --- liquid cooling
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