نوع المادة: كتاب

الموضوعات

energy harvesting; thermoelectric generator; railroad vehicle; rolling stock; axle bearing; TiO_2?x nanoparticle; high dielectric constant; triboelectric nanogenerators; oxygen vacancy; ferroelectric; PVDF; piezoelectric; mechanical fatigue resistance; remnant polarization; PVDF; sensor; triboelectric nanogenerator; triboelectric nanogenerator; nanoimprinting; nanostructures; microstructures; femtosecond laser; energy harvesting; triboelectric nanogenerator; superhydrophobic surface; spray method; mechanical energy; triboelectric nanogenerator; energy harvesting; hybrid energy; water wave energy; thermoelectric; layer-by-layer; graphene; carbon nanotube; thin film; thermoelectric; graphene; carbon nanotubes; power factor; polymers; energy harvesting; organic composites; mesoporous composite polymer; metal oxidation; gapless; triboelectric nanogenerator; high deformability; energy-harvesting metamaterial; wireless chemical sensor; metamaterial sensor; triboelectric generator; shock absorber; suspension system; advanced driver assistance technology; IoT technology; frictional force; n/a

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038976226

ملف خارجي

نوع المادة: كتاب

الموضوعات

data scarce basins; runoff series; data forward prediction; ensemble empirical mode decomposition (EEMD); stopping criteria; method of tracking energy differences (MTED); deep learning; convolutional neural networks; superpixel; urban water bodies; high-resolution remote-sensing images; monthly streamflow forecasting; artificial neural network; ensemble technique; phase space reconstruction; empirical wavelet transform; hybrid neural network; flood forecasting; self-organizing map; bat algorithm; particle swarm optimization; flood routing; Muskingum model; machine learning methods; St. Venant equations; rating curve method; nonlinear Muskingum model; hydrograph predictions; flood routing; Muskingum model; hydrologic models; improved bat algorithm; Wilson flood; Karahan flood; flood susceptibility modeling; ANFIS; cultural algorithm; bees algorithm; invasive weed optimization; Haraz watershed; ANN-based models; flood inundation map; self-organizing map (SOM); recurrent nonlinear autoregressive with exogenous inputs (RNARX); ensemble technique; artificial neural networks; uncertainty; streamflow predictions; sensitivity; flood forecasting; extreme learning machine (ELM); backtracking search optimization algorithm (BSA); the upper Yangtze River; deep learning; LSTM network; water level forecast; the Three Gorges Dam; Dongting Lake; Muskingum model; wolf pack algorithm; parameters; optimization; flood routing; flash-flood; precipitation-runoff; forecasting; lag analysis; random forest; machine learning; flood prediction; flood forecasting; hydrologic model; rainfall–runoff, hybrid &; ensemble machine learning; artificial neural network; support vector machine; natural hazards &; disasters; adaptive neuro-fuzzy inference system (ANFIS); decision tree; survey; classification and regression trees (CART), data science; big data; artificial intelligence; soft computing; extreme event management; time series prediction; LSTM; rainfall-runoff; flood events; flood forecasting; data assimilation; particle filter algorithm; micro-model; Lower Yellow River; ANN; hydrometeorology; flood forecasting; real-time; postprocessing; machine learning; early flood warning systems; hydroinformatics; database; flood forecast; Google Maps

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038975489

ملف خارجي

نوع المادة: كتاب

الموضوعات

evapotranspiration; Northeast China; MS–PT algorithm; spatial-temporal variations; controlling factors; potential evapotranspiration; vegetation remote sensing; reflectance model; spectra; leaf; copper; PROSPECT; leaf area density; terrestrial LiDAR; tree canopy; vertical structure; voxel; spatial representativeness; heterogeneity; validation; land-surface temperature products (LSTs); observations; HiWATER; remote sensing; spatiotemporal representative; cost-efficient, sampling design; heterogeneity; validation; FY-3C/MERSI; GLASS; Land surface temperature; Land surface emissivity; GPP; SIF; MuSyQ-GPP algorithm; BEPS; vegetation phenology; Tibetan Plateau; MODIS; NDVI; start of growing season (SOS); end of growing season (EOS); GLASS LAI time series; forest disturbance; disturbance index; latent heat; machine learning algorithms; plant functional type; high-resolution freeze/thaw; AMSR2; MODIS; LAI; ZY-3 MUX; GF-1 WFV; HJ-1 CCD; maize; PROSPECT-5B+SAILH (PROSAIL) model; spatial heterogeneity; variability; evapotranspiration; land surface variables; probability density function; HiWATER; spectral; albedometer; interference filter; photoelectric detector; validation; land surface albedo; multi-scale validation; rugged terrain; MRT-based model; MCD43A3 C6; precipitation; statistics methods; China; Tibetan Plateau; South China’s; drought; SPI; TMI data; crop-growing regions; downward shortwave radiation; machine learning; gradient boosting regression tree; AVHRR; CMA; BRDF; aerosol; MODIS; sunphotometer; arid/semiarid; solar-induced chlorophyll fluorescence; fluorescence quantum efficiency in dark-adapted conditions (FQE); SCOPE; Fraunhofer Line Discrimination (FLD); gross primary productivity (GPP); longwave upwelling radiation (LWUP); Visible Infrared Imaging Radiometer Suite (VIIRS); surface radiation budget; hybrid method; remote sensing; leaf age; leaf spectral properties; leaf area index; Cunninghamia; Chinese fir; canopy reflectance; NIR; EVI2; geometric optical radiative transfer (GORT) model; land surface albedo; snow-free albedo; rugged terrain; topographic effects; black-sky albedo (BSA); GPP; NPP; MODIS; validation; phenology; RADARSAT-2; rice; Synthetic Aperture Radar (SAR); decision tree; forest canopy height; aboveground biomass; ICESat GLAS; Landsat; random forest model; anisotropic reflectance; BRDF; rugged terrain; solo slope; composite slope; surface solar irradiance; geostationary satellite; polar orbiting satellite; LUT method; SURFRAD; downward shortwave radiation; daily average value; Antarctica; sinusoidal method; cloud fraction; interpolation; boreal forest; GPP; spatiotemporal distribution and variation; meteorological factors; phenological parameters; multisource data fusion; aerosol retrieval; urban scale; vegetation dust-retention; multiple ecological factors; geographical detector model; snow cover; passive microwave; FY-3C/MWRI; algorithmic assessment; China; land surface temperature; satellite observations; flux measurements; latitudinal pattern; land cover change; fractional vegetation cover (FVC); multi-data set; northern China; spatio-temporal; inter-annual variation; uncertainty; standard error of the mean; downscaling; GPP; spatial heterogeneity; remote sensing; subpixel information; LiDAR; point cloud; leaf; gap fraction; 3D reconstruction; biodiversity; remote sensing; species richness; metric comparison; metric integration; leaf area index; MODIS products; Landsat; high resolution; homogeneous and pure pixel filter; pixel unmixing; vertical vegetation stratification; gross primary production (GPP); light use efficiency; dense forest; MODIS; VPM; temperature profiles; humidity profiles; n/a; geometric-optical model; thermal radiation directionality; quantitative remote sensing inversion; scale effects; comprehensive field experiment

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038972709

ملف خارجي

نوع المادة: كتاب

الموضوعات

evapotranspiration; Northeast China; MS–PT algorithm; spatial-temporal variations; controlling factors; potential evapotranspiration; vegetation remote sensing; reflectance model; spectra; leaf; copper; PROSPECT; leaf area density; terrestrial LiDAR; tree canopy; vertical structure; voxel; spatial representativeness; heterogeneity; validation; land-surface temperature products (LSTs); observations; HiWATER; remote sensing; spatiotemporal representative; cost-efficient, sampling design; heterogeneity; validation; FY-3C/MERSI; GLASS; Land surface temperature; Land surface emissivity; GPP; SIF; MuSyQ-GPP algorithm; BEPS; vegetation phenology; Tibetan Plateau; MODIS; NDVI; start of growing season (SOS); end of growing season (EOS); GLASS LAI time series; forest disturbance; disturbance index; latent heat; machine learning algorithms; plant functional type; high-resolution freeze/thaw; AMSR2; MODIS; LAI; ZY-3 MUX; GF-1 WFV; HJ-1 CCD; maize; PROSPECT-5B+SAILH (PROSAIL) model; spatial heterogeneity; variability; evapotranspiration; land surface variables; probability density function; HiWATER; spectral; albedometer; interference filter; photoelectric detector; validation; land surface albedo; multi-scale validation; rugged terrain; MRT-based model; MCD43A3 C6; precipitation; statistics methods; China; Tibetan Plateau; South China’s; drought; SPI; TMI data; crop-growing regions; downward shortwave radiation; machine learning; gradient boosting regression tree; AVHRR; CMA; BRDF; aerosol; MODIS; sunphotometer; arid/semiarid; solar-induced chlorophyll fluorescence; fluorescence quantum efficiency in dark-adapted conditions (FQE); SCOPE; Fraunhofer Line Discrimination (FLD); gross primary productivity (GPP); longwave upwelling radiation (LWUP); Visible Infrared Imaging Radiometer Suite (VIIRS); surface radiation budget; hybrid method; remote sensing; leaf age; leaf spectral properties; leaf area index; Cunninghamia; Chinese fir; canopy reflectance; NIR; EVI2; geometric optical radiative transfer (GORT) model; land surface albedo; snow-free albedo; rugged terrain; topographic effects; black-sky albedo (BSA); GPP; NPP; MODIS; validation; phenology; RADARSAT-2; rice; Synthetic Aperture Radar (SAR); decision tree; forest canopy height; aboveground biomass; ICESat GLAS; Landsat; random forest model; anisotropic reflectance; BRDF; rugged terrain; solo slope; composite slope; surface solar irradiance; geostationary satellite; polar orbiting satellite; LUT method; SURFRAD; downward shortwave radiation; daily average value; Antarctica; sinusoidal method; cloud fraction; interpolation; boreal forest; GPP; spatiotemporal distribution and variation; meteorological factors; phenological parameters; multisource data fusion; aerosol retrieval; urban scale; vegetation dust-retention; multiple ecological factors; geographical detector model; snow cover; passive microwave; FY-3C/MWRI; algorithmic assessment; China; land surface temperature; satellite observations; flux measurements; latitudinal pattern; land cover change; fractional vegetation cover (FVC); multi-data set; northern China; spatio-temporal; inter-annual variation; uncertainty; standard error of the mean; downscaling; GPP; spatial heterogeneity; remote sensing; subpixel information; LiDAR; point cloud; leaf; gap fraction; 3D reconstruction; biodiversity; remote sensing; species richness; metric comparison; metric integration; leaf area index; MODIS products; Landsat; high resolution; homogeneous and pure pixel filter; pixel unmixing; vertical vegetation stratification; gross primary production (GPP); light use efficiency; dense forest; MODIS; VPM; temperature profiles; humidity profiles; n/a; geometric-optical model; thermal radiation directionality; quantitative remote sensing inversion; scale effects; comprehensive field experiment

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038972761

ملف خارجي

نوع المادة: كتاب

الموضوعات

air-steam gasification; equilibrium model; tar; energy exchange; exergy efficiency; bio-electro-Fenton microbial fuel cells (Bio-E-Fenton MFCs); wastewater; photo catalyst; degradation; calcination; chemical oxygen demand (COD); MFC; hydrodynamic boundary layer; recirculation mode; shear rate; voltage; charge transfer resistance; biodiesel; direct transesterification; Rhodotorula glutinis; single cell oil; biogas; tri-reforming process; syngas; methane and carbon dioxide conversion; hydrogen/carbon monoxide ratio; first-law/second-law efficiency; biodiesel; esterification; liquid lipase; superabsorbent polymer; response surface methodology; waste wood; torrefaction; energy yield; mass yield; CHO index; gross calorific value; Van Krevelen diagram; anaerobic digestion; biogas production; wastewater treatment; membrane bioreactors; anaerobic digestion; methane; carbon dioxide; small-scale biogas plants; developing countries; SOFC; validation; simulation; exergy; syngas; Chlorella; coal-fired flue-gas; screening; biodiesel property; mixotrophic cultivation; thermophilic anaerobic digestion; corn stover; prairie cord grass; unbleached paper; digester performance; process stability; synergistic effects; microbial community; Methanothermobacter; biochemical methane potential; redox potential reduction; direct interspecies electron transfer; electroactive biofilm; Nejayote; granular activated carbon; Jerusalem artichoke; lignocellulose; acid pretreatment; nitric acid; alkali pretreatment; enzymatic hydrolysis; ethanol fermentation; waste biomass; Vietnam; solid biofuel; calorific value; mechanical durability; fatty acid methyl ester; catalyst; viscosity; iodine value; acidity index; sewage sludge; pyrolytic oil; Taguchi method; thermogravimetric analysis; synergistic effect; combined pretreatment; ball mill; ethanol organosolv; herbaceous biomass; lignin recovery; Annona muricata; biodiesel production; seed oil; soursop; two-step process; response surface methodology; RSM; second-generation biodiesel; stone fruit; optimisation; biodiesel testing; transesterification; lignocellulosic biomass; Miscanthus; mechanical pretreatment; organosolv pretreatment; microbial biofuel; metabolic engineering; alkanes; alcohols; acetone; electrochemical hydrogenation; isopropanol; membrane contamination; polymer electrolyte membrane; relative humidity; diesel; Carica papaya; engine performance; biodiesel; characterisation; porosity; thermophoretic force; biomass fuel; non-premixed combustion; counter-flow structure; mathematical modeling; emulsification; liquefaction; bio-oils; co-surfactant; surfactant; diesel; biogas; Clostridiales; hydrogen-producing bacteria; bioreactors; anaerobic fermentation; anaerobic digestion; microbial community composition

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038975960

ملف خارجي