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Machine Learning in Sensors and Imaging
| Machine Learning in Sensors and Imaging |
| Autore | Nam Hyoungsik |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (302 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
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 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910566484703321 |
Nam Hyoungsik
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Plug-in Hybrid Electric Vehicle (PHEV)
| Plug-in Hybrid Electric Vehicle (PHEV) |
| Autore | VAN Mierlo Joeri |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (230 p.) |
| Soggetto non controllato |
hybrid energy storage system
plug-in hybrid electric vehicle Li-ion battery emerging electric machines lithium-ion capacitor electric vehicles (EVs) efficient energy management strategies for hybrid propulsion systems plug-in hybrid attributional electric vehicle energy system energy efficiency modified one-state hysteresis model air quality adaptive neuron-fuzzy inference system (ANFIS) Markov decision process (MDP) simulated annealing Paris Agreement mobility needs interleaved multiport converte dynamic programming state of health estimation strong track filter LCA modelling consequential losses model voltage vector distribution parallel hybrid electric vehicle electricity mix time-delay input convex optimization lifetime model artificial neural network (ANN) Li(Ni1/3Co1/3Mn1/3)O2 battery battery power CO2 capacity degradation regenerative braking open-end winding novel propulsion systems group method of data handling (GMDH) state of charge Well-to-Wheel energy storage systems including wide bandgap (WBG) technology wide bandgap (WBG) technologies marginal lithium polymer battery life-cycle assessment (LCA) energy management dual inverter lithium-ion battery measurements plug-in hybrid electric vehicles (PHEVs) emerging power electronics Q-learning (QL) fuel consumption characteristics Plugin Hybrid electric vehicle Energy Storage systems meta-analysis range-extender engine-on power reinforcement learning (RL) multi-objective genetic algorithm power sharing energy management strategy power distribution hybrid electric vehicles system modelling |
| ISBN | 3-03921-454-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Plug-in Hybrid Electric Vehicle |
| Record Nr. | UNINA-9910367565103321 |
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VAN Mierlo Joeri
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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