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Advances in Computational Intelligence Applications in the Mining Industry
| Advances in Computational Intelligence Applications in the Mining Industry |
| Autore | Ganguli Rajive |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (324 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
truck dispatching
mining equipment uncertainties orebody uncertainty discrete event simulation Q-learning grinding circuits minerals processing random forest decision trees machine learning knowledge discovery variable importance mineral prospectivity mapping random forest algorithm epithermal gold unstructured data blast impact empirical model mining fragmentation mine worker fatigue random forest model health and safety management stockpiles operational data mine-to-mill geostatistics ore control mine optimization digital twin modes of operation geological uncertainty multivariate statistics partial least squares regression oil sands bitumen extraction bitumen processability mine safety and health accidents narratives natural language processing random forest classification hyperspectral imaging multispectral imaging dimensionality reduction neighbourhood component analysis artificial intelligence mining exploitation masonry buildings damage risk analysis Bayesian network Naive Bayes Bayesian Network Structure Learning (BNSL) rock type mining geology bluetooth beacon classification and regression tree gaussian naïve bayes k-nearest neighbors support vector machine transport route transport time underground mine tactical geometallurgy data analytics in mining ball mill throughput measurement while drilling non-additivity coal petrographic analysis macerals image analysis semantic segmentation convolutional neural networks point cloud scaling fragmentation size analysis structure from motion |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557613103321 |
Ganguli Rajive
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Forest Fire Risk Prediction
| Forest Fire Risk Prediction |
| Autore | Nolan Rachael |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (235 p.) |
| Soggetto topico |
Research & information: general
Biology, life sciences Forestry & related industries |
| Soggetto non controllato |
fire danger rating
fire management fire regime fire size fire weather Portugal critical LFMC threshold forest/grassland fire radiative transfer model remote sensing southwest China acid rain aerosol biomass burning forest fire PM2.5 direct estimation meteorological factor regression moisture content time lag forest fire driving factors forest fire occurrence random forest forest fire management China Cupressus sempervirens fire risk fuels fuel moisture content mass loss calorimeter Seiridium cardinale vulnerability to wildfires disease alien pathogen allochthonous species introduced fungus drying tests humidity diffusion coefficients wildfire prescribed burning modeling drought flammability fuel moisture leaf water potential plant traits climate change MNI fire season fire behavior crown fire fire modeling senescence foliar moisture content canopy bulk density fire danger fire weather patterns RCP FWI system SSR occurrence of forest fire machine learning variable importance prediction accuracy epicormic resprouter eucalyptus fire severity flammability feedbacks temperate forest |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557387103321 |
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Nolan Rachael
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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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UAVs for Vegetation Monitoring
| UAVs for Vegetation Monitoring |
| Autore | de Castro Megías Ana |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (452 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
UAS
UAV vegetation cover multispectral land cover forest Acacia Indonesia tropics vegetation ground cover vegetation indices agro-environmental measures olive groves southern Spain sUAS water stress ornamental container-grown artificial intelligence machine learning deep learning neural network visual recognition precision agriculture canopy cover image analysis crop mapping evapotranspiration (ET) GRAPEX remote sensing Two Source Energy Balance model (TSEB) contextual spatial domain/resolution data aggregation eddy covariance (EC) Fusarium wilt crop disease banana multispectral remote sensing purple rapeseed leaves unmanned aerial vehicle U-Net plant segmentation nitrogen stress Glycine max RGB canopy height close remote sensing growth model curve fitting NDVI solar zenith angle flight altitude time of day operating parameters CNN Faster RCNN SSD Inception v2 patch-based CNN MobileNet v2 detection performance inference time disease detection cotton root rot plant-level single-plant plant-by-plant classification UAV remote sensing crop monitoring RGB imagery multispectral imagery century-old biochar semantic segmentation random forest crop canopy multispectral image chlorophyll content remote sensing technique individual plant segmentation plant detection transfer learning maize tassel tassel branch number convolution neural network VGG16 plant nitrogen estimation vegetation index image segmentation transpiration method comparison oil palm multiple linear regression support vector machine artificial neural network UAV hyperspectral wheat yellow rust disease monitoring texture spatial resolution RGB camera thermal camera drought tolerance forage grass HSV CIELab broad-sense heritability phenotyping gap high throughput field phenotyping UAV digital images winter wheat biomass multiscale textures red-edge spectra least squares support vector machine variable importance drone hyperspectral thermal nutrient deficiency weed detection disease diagnosis plant trails |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557661103321 |
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de Castro Megías Ana
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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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