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Deep Learning Applications with Practical Measured Results in Electronics Industries
| Deep Learning Applications with Practical Measured Results in Electronics Industries |
| Autore | Kung Hsu-Yang |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (272 p.) |
| Soggetto non controllato |
faster region-based CNN
visual tracking intelligent tire manufacturing eye-tracking device neural networks A* information measure oral evaluation GSA-BP tire quality assessment humidity sensor rigid body kinematics intelligent surveillance residual networks imaging confocal microscope update mechanism multiple linear regression geometric errors correction data partition Imaging Confocal Microscope image inpainting lateral stage errors dot grid target K-means clustering unsupervised learning recommender system underground mines digital shearography optimization techniques saliency information gated recurrent unit multivariate time series forecasting multivariate temporal convolutional network foreign object data fusion update occasion generative adversarial network CNN compressed sensing background model image compression supervised learning geometric errors UAV nonlinear optimization reinforcement learning convolutional network neuro-fuzzy systems deep learning image restoration neural audio caption hyperspectral image classification neighborhood noise reduction GA MCM uncertainty evaluation binary classification content reconstruction kinematic modelling long short-term memory transfer learning network layer contribution instance segmentation smart grid unmanned aerial vehicle forecasting trajectory planning discrete wavelet transform machine learning computational intelligence tire bubble defects offshore wind multiple constraints human computer interaction Least Squares method |
| ISBN | 3-03928-864-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404080403321 |
Kung Hsu-Yang
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep Learning-Based Machinery Fault Diagnostics
| Deep Learning-Based Machinery Fault Diagnostics |
| Autore | Chen Hongtian |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (290 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
process monitoring
dynamics variable time lag dynamic autoregressive latent variables model sintering process hammerstein output-error systems auxiliary model multi-innovation identification theory fractional-order calculus theory canonical variate analysis disturbance detection power transmission system k-nearest neighbor analysis statistical local analysis intelligent fault diagnosis stacked pruning sparse denoising autoencoder convolutional neural network anti-noise flywheel fault diagnosis belief rule base fuzzy fault tree analysis Bayesian network evidential reasoning aluminum reduction process alumina concentration subspace identification distributed predictive control spatiotemporal feature fusion gated recurrent unit attention mechanism fault diagnosis evidential reasoning rule system modelling information transformation parameter optimization event-triggered control interval type-2 Takagi-Sugeno fuzzy model nonlinear networked systems filter gearbox fault diagnosis convolution fusion state identification PSO wavelet mutation LSSVM data-driven operational optimization case-based reasoning local outlier factor abnormal case removal bearing fault detection deep residual network data augmentation canonical correlation analysis just-in-time learning fault detection high-speed trains autonomous underwater vehicle thruster fault diagnostics fault tolerant control robust optimization ocean currents |
| ISBN | 3-0365-5174-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910619469103321 |
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Chen Hongtian
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| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems
| Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems |
| Autore | Li Chaoshun |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (212 p.) |
| Soggetto topico |
Research & information: general
Physics |
| Soggetto non controllato |
doubly-fed variable-speed pumped storage
Hopf bifurcation stability analysis parameter sensitivity pumped storage unit degradation trend prediction maximal information coefficient light gradient boosting machine variational mode decomposition gated recurrent unit high proportional renewable power system active power change point detection maximum information coefficient cosine similarity anomaly detection thermal-hydraulic characteristics hydraulic oil viscosity hydraulic PTO wave energy converter pumped storage units pressure pulsation noise reduction sparrow search algorithm hybrid system facility agriculture chaotic particle swarms method operation strategy stochastic dynamic programming (SDP) power yield seasonal price reliability cascaded reservoirs doubly-fed variable speed pumped storage power station nonlinear modeling nonlinear pump turbine characteristics pumped storage units (PSUs) successive start-up ‘S’ characteristics low water head conditions multi-objective optimization fractional order PID controller (FOPID) hydropower units comprehensive deterioration index long and short-term neural network ensemble empirical mode decomposition approximate entropy 1D–3D coupling model transition stability sensitivity analysis hydro power |
| ISBN | 3-0365-5838-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637780603321 |
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Li Chaoshun
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Process Modeling in Pyrometallurgical Engineering
| Process Modeling in Pyrometallurgical Engineering |
| Autore | Saxen Henrik |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (642 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
steelmaking
oxygen consumption GPR prediction model secondary refining water model mixing time slag entrapment stainless steel slag heating time Cr2O3 spinel crystal size processing maps nickel-based alloy flow behavior arrhenius equation hearth drainage PCA analysis tool pattern tapholes blast furnace coke carbon solution loss numerical simulation pellet pile Discrete Element Method porosity distribution angle of repose coordination number bubble motion interfacial phenomena entrainment moving path arsenopyrite arsenic removal mechanism roasting arsenate dust ash arsenic recovery titanium distribution ratio thermodynamic model ion-molecule coexistence theory LF refining slags electric arc furnace simulation process model COREX raceway zone gas flow COREX melter gasifier mixed charging burden layer structure burden pile width DEM burden distribution particle flow validation tire cord steel TiN inclusion solidification segregation models hot rolling TOU electricity pricing hot rolling planning genetic algorithm C-H2 smelting reduction furnace double-row side nozzles dimensional analysis multiple linear regression ironmaking blast furnace coke bed trickle flow molten slag liquid iron SPH charging system mathematical model radar data main trough transient fluid of hot metal and molten slag wall shear stress conjugate heat transfer refractory shape rolling flat rolling wire rod temperature distribution machine learning artificial intelligence neural network BOS reactor copper smelting SKS Shuikoushan process oxygen bottom blown gated recurrent unit support vector data description time sequence prediction fault detection and identification Lignite microwave and ultrasound modification structural characterization 3D molecular model structural simulation coke combustion rate charcoal combustion rate iron ore sintering process biomass quasi-particle quasi-particle structure monomer blended fuel quasi-particle fuel apparent activation energy coupling effect dynamic model basic oxygen furnace computational fluid dynamics CFD-DEM coalescence settling funneling flow horizontal single belt casting process (HSBC) computational fluid dynamics (CFD) double impingement feeding system supersonic coherent jet decarburization steel refining EAF CFD mass transfer coefficient physical modeling mathematical modeling kinetic models natural gas fuel injection combustion RAFT roll design flat-rolled wire strain inhomogeneity normal pressure macroscopic shear bands numerical model dual gas injection slag eye electrical energy consumption Electric Arc Furnace scrap melting statistical modeling raceway evolution raceway size flow pattern Eulerian multiphase flow blast furnace hearth dead man iron and slag flow lining wear hearth drainage Industry 4.0 copper smelter nickel-copper smelter radiometric sensors Peirce-smith converting matte-slag chemistry discrete event simulation adaptive finite differences |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557580503321 |
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Saxen Henrik
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Wearable Sensors Applied in Movement Analysis
| Wearable Sensors Applied in Movement Analysis |
| Autore | Buisseret Fabien |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (154 p.) |
| Soggetto topico | Medical equipment & techniques |
| Soggetto non controllato |
inertial measurement unit
movement analysis long-track speed skating validity IMU principal component analysis wearable scoring carving balance assessment data augmentation gated recurrent unit human activity recognition one-dimensional convolutional neural network intermittent claudication vascular rehabilitation 6 min walking test functional walking TUG kinematics fall risk logistic regression elderly inertial sensor artificial intelligence supervised machine learning head rotation test neck pain cerebral palsy dystonia choreoathetosis machine learning home-based wearable device MLP gesture recognition flex sensor model search neural network inertial measurement unit-IMU movement complexity sample entropy trunk flexion low back pain lifting technique camera system ward clustering method K-means clustering method ensemble clustering method Bayesian neural network pain self-efficacy questionnaire |
| ISBN | 3-0365-5859-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637794103321 |
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Buisseret Fabien
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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