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 | ||
MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing |
Autore | Lee Saro |
Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
Descrizione fisica | 1 electronic resource (438 p.) |
Soggetto non controllato |
artificial neural network
model switching sensitivity analysis neural networks logit boost Qaidam Basin land subsidence land use/land cover (LULC) naïve Bayes multilayer perceptron convolutional neural networks single-class data descriptors logistic regression feature selection mapping particulate matter 10 (PM10) Bayes net gray-level co-occurrence matrix multi-scale Logistic Model Trees classification Panax notoginseng large scene coarse particle grayscale aerial image Gaofen-2 environmental variables variable selection spatial predictive models weights of evidence landslide prediction random forest boosted regression tree convolutional network Vietnam model validation colorization data mining techniques spatial predictions SCAI unmanned aerial vehicle high-resolution texture spatial sparse recovery landslide susceptibility map machine learning reproducible research constrained spatial smoothing support vector machine random forest regression model assessment information gain ALS point cloud bagging ensemble one-class classifiers leaf area index (LAI) landslide susceptibility landsat image ionospheric delay constraints spatial spline regression remote sensing image segmentation panchromatic Sentinel-2 remote sensing optical remote sensing materia medica resource GIS precise weighting change detection TRMM traffic CO crop training sample size convergence time object detection gully erosion deep learning classification-based learning transfer learning landslide traffic CO prediction hybrid model winter wheat spatial distribution logistic alternating direction method of multipliers hybrid structure convolutional neural networks geoherb predictive accuracy real-time precise point positioning spectral bands |
ISBN | 3-03921-216-8 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910367564103321 |
Lee Saro | ||
MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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