Journal articles on the topic 'Depth-average model'
To see the other types of publications on this topic, follow the link: Depth-average model.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Depth-average model.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Zhang, Manfei, Yimeng Wang, Xiao Wang, and Weibo Zhou. "Groundwater Depth Forecasting Using a Coupled Model." Discrete Dynamics in Nature and Society 2021 (February 24, 2021): 1–11. http://dx.doi.org/10.1155/2021/6614195.
Full textAbstract:
Accurate and reliable prediction of groundwater depth is a critical component in water resources management. In this paper, a new method based on coupling wavelet decomposition method (WA), autoregressive moving average (ARMA) model, and BP neural network (BP) model for groundwater depth forecasting applications was proposed. The relative performance of the proposed coupled model (WA-ARMA-BP) was compared to the regular autoregressive integrated moving average (ARIMA) and BP models for annual average groundwater depth forecasting using leave-one-out cross-validation (LOO-CV). The variables used to develop and validate the models were average groundwater depth data recorded from 1981 to 2010 in Jinghui Canal Irrigation District in the northwest of China. It was found that the WA-ARMA-BP model provided more accurate annual average groundwater depth forecasts compared to the ARIMA and BP models. The results of the study indicate the potential of the WA-ARMA-BP model in forecasting nonstationary time series such as groundwater depth.
2
Longo, Alberto, Manuel Pastor, Lorenzo Sanavia, Diego Manzanal, Miguel Martin Stickle, Chuan Lin, Angel Yague, and Saeid Moussavi Tayyebi. "A depth average SPH model includingμ(I) rheology and crushing for rock avalanches." International Journal for Numerical and Analytical Methods in Geomechanics 43, no. 5 (March 19, 2019): 833–57. http://dx.doi.org/10.1002/nag.2912.
Full text
3
Ma, Zhuangzhuang, Zhangsheng Wu, Tongshu Li, Yu Han, Jian Chen, and Liangpei Zhang. "A Simplified Computational Model for the Location of Depth Average Velocity in a Rectangular Irrigation Channel." Applied Sciences 9, no. 16 (August 7, 2019): 3222. http://dx.doi.org/10.3390/app9163222.
Full textAbstract:
Flow measurements in open channels have often utilized velocity-area methods. Thus, estimations of the average velocity in a cross-section of rural canals play an important role in the flow measurement of an irrigation district. This paper derives a model for calculating depth average velocity. This model considers the classical logarithmic formula describing the velocity distribution and flow partitioning theory, which is aimed at finding out a location that represents the depth average velocity (LDAV) along the vertical line from boundary to water surface. Subsequently, the average flow velocity of the whole channel can be further determined by using the velocity-area method in different regions. Moreover, the LDAV has different expressions in different sub-regions according to flow partitioning theory under various aspect ratios. The results are verified by experiments under different experimental conditions, and the formula is highly applicable and has a high theoretical significance and practical value.
4
Li, Zhuoxuan, Meng Tao, Jinde Cao, Xinli Shi, Tao Ma, and Wei Huang. "An Augmented Model of Rutting Data Based on Radial Basis Neural Network." Symmetry 15, no. 1 (December 23, 2022): 33. http://dx.doi.org/10.3390/sym15010033.
Full textAbstract:
The rutting depth is an important index to evaluate the damage degree of the pavement. Therefore, establishing an accurate rutting depth prediction model can guide pavement design and provide the necessary basis for pavement maintenance. However, the sample size of pavement rutting depth data is small, and the sampling is not standardized, which makes it hard to establish a prediction model with high accuracy. Based on the data of RIOHTrack’s asphalt pavement structure, this study builds a reliable data-augmented model. In this paper, different asphalt rutting data augmented models based on Gaussian radial basis neural networks are constructed with the temperature and loading of asphalt pavements as the main features. Experimental results show that the method outperforms classical machine learning methods in data augmentation, with an average root mean square error of 3.95 and an average R-square of 0.957. Finally, the augmented data of rutting depth is constructed for training, and multiple neural network models are used for prediction. Compared with unaugmented data, the prediction accuracy is increased by 50%.
5
Tosto, Sebastiano, Philippe Knauth, and Maria Luisa Di Vona. "Proposed Model of Water Adsorption/Desorption in a PEM Membrane." Defect and Diffusion Forum 297-301 (April 2010): 209–14. http://dx.doi.org/10.4028/www.scientific.net/ddf.297-301.209.
Full textAbstract:
This paper describes the processes of water adsorption and desorption in PE membranes for fuel cells. A simple equation is inferred assuming that the surface of the membrane is uniformly covered by adsorbed molecules to an average depth of some monolayers. The adsorption depth is only controlled by diffusion of adsorbate from the surface towards the bulk through a two-layer or multi-layer mechanisms; so the empty sites formed at the surface can accept further molecules of water. If the diffusion rate is fast enough, cumulative water uptake occurs. The uptake kinetics is described considering the average penetration depth, i.e. neglecting the local concentration spikes below a random number and position of empty sites statistically formed at the surface of the membrane. The model also describes the desorption process, assumed to start at a prefixed time.
6
Zhang, D. J., J. Zhan, C. X. Wang, and G. Q. Zhou. "SHALLOW BATHYMETRY ESTIMATION BASED ON LANDSAT 8 REMOTELY SENSED DATAAT BOHAI SEA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 941–43. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-941-2020.
Full textAbstract:
Abstract. Bathymetry is a key variable in ocean monitoring and measurement research. It becomes more and more important for development of rapid method to invert shallow sea water depth. In this study, a water depth inversion method based on multi-band model is established to analyze the relationship between different bands of Landsat 8 OLI multi-spectral and measured data. The average absolute error of the model is 1.48m at 10–20m water depth and the average relative error is 13.12%. The water depth inversion accuracy under normal conditions are achieved, indicating that the model will have a promising practical application in the future.
7
García-Zurdo, Rubén. "Three-Dimensional Face Shape by Local Fitting to a Single Reference Model." International Journal of Computer Vision and Image Processing 4, no. 1 (January 2014): 17–29. http://dx.doi.org/10.4018/ijcvip.2014010102.
Full textAbstract:
The authors present a simple method to estimate the three-dimensional shape of a face from an input image using a single reference model, based on least squares between the output of the linear-nonlinear (LN) neuronal model applied to blocks from an intensity image and blocks from a depth reference model. The authors present the results obtained by varying the LN model parameters and estimate their best values, which provide an acceptable reconstruction of each subject's depth. The authors show that increasing the light source angle over the horizontal plane in the input image produces slight increases in reconstruction error, but increasing the ambient light proportion produces greater increases in reconstruction error. The authors applied the method to predict each subject's unknown depth using different individual reference models and an average reference model, which provides the best results. As a noise reduction technique, the authors perform a point by point weighted averaging with the average reference model with weights equal to the fractions of the squares of the Laplacian of a Gaussian applied to the prediction and to the reference depth over the sum of both. Finally, the authors present acceptable visual results obtained from external images of faces under arbitrary illumination, having performed an illumination estimation previously.
8
Ravi, V. "Statistical modelling of spatial variability of undrained strength." Canadian Geotechnical Journal 29, no. 5 (October 1, 1992): 721–29. http://dx.doi.org/10.1139/t92-080.
Full textAbstract:
Spatial variability of undrained strength (Cu) has been modelled in several ways in the past. In particular, concepts of time series such as autoregressive moving average models have been used to model the analogous "spatial series" of the values of depth versus undrained strength. It should be noted that the very purpose of such modelling studies is to provide estimates of the values of undrained strength at a given value of depth. In the present paper, the main prerequisite to apply these models, viz. the complete removal of trend present in the spatial series of depth versus Cu, has been focussed. An accurate modelling procedure is recommended which can estimate the values of Cu at a given value of depth better than any other model in this class of models existing in the literature. Sensitivity in the trend patterns of the depth versus Cu data is well taken care of. A computer program has been developed in FORTRAN 77to fit the model in conjunction with a standard nonlinear least-squares routine taken from the literature. One of the advantages of the present model is the speed of convergence of the computer program. Two case studies appearing in the literature have been successfully solved to demonstrate the efficacy of the model developed. Key words : spatial variability, time series analysis, spatial series, nonstationarity, autoregressive moving average models, regression, nonlinear least squares, error sum of squares.
9
Gao, Yu Fei, and Pei Qi Ge. "Analysis of Grit Cut Depth in Fixed-Abrasive Diamond Wire Saw Slicing Single Crystal Silicon." Solid State Phenomena 175 (June 2011): 72–76. http://dx.doi.org/10.4028/www.scientific.net/ssp.175.72.
Full textAbstract:
A mathematical model to calculate the grit average cut depth in wire sawing single crystal silicon was founded. So the grit average cut depths were calculated theoretically by choosing different process parameters, and influences of process parameters on grit cut depths of slicing silicon crystal were analyzed. Analysis results indicate that the grit average cut depth relates to the silicon mechanical properties, grit shape and size, wire speed and ingot feed speed, etc. And there is a monotone increasing non-linear correlation between grit average cut depth and the ratio i value of ingot feed speed and wire speed, when the i value is lower, the average grit cut depth is lower.
10
Tenzer, R., and V. Gladkikh. "Assessment of Density Variations of Marine Sediments with Ocean and Sediment Depths." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/823296.
Full textAbstract:
We analyze the density distribution of marine sediments using density samples taken from 716 drill sites of the Deep Sea Drilling Project (DSDP). The samples taken within the upper stratigraphic layer exhibit a prevailing trend of the decreasing density with the increasing ocean depth (at a rate of −0.05 g/cm3per 1 km). Our results confirm findings of published studies that the density nonlinearly increases with the increasing sediment depth due to compaction. We further establish a 3D density model of marine sediments and propose theoretical models of the ocean-sediment and sediment-bedrock density contrasts. The sediment density-depth equation approximates density samples with an average uncertainty of about 10% and better represents the density distribution especially at deeper sections of basin sediments than a uniform density model. The analysis of DSDP density data also reveals that the average density of marine sediments is 1.70 g/cm3and the average density of the ocean bedrock is 2.9 g/cm3.
11
Abrahamson, Norman, and Walter Silva. "Summary of the Abrahamson & Silva NGA Ground-Motion Relations." Earthquake Spectra 24, no. 1 (February 2008): 67–97. http://dx.doi.org/10.1193/1.2924360.
Full textAbstract:
Empirical ground-motion models for the rotation-independent average horizontal component from shallow crustal earthquakes are derived using the PEER NGA database. The model is applicable to magnitudes 5–8.5, distances 0–200 km, and spectral periods of 0–10 sec. In place of generic site categories (soil and rock), the site is parameterized by average shear-wave velocity in the top 30 m ( VS30) and the depth to engineering rock (depth to VS=1000 m/s). In addition to magnitude and style-of-faulting, the source term is also dependent on the depth to top-of-rupture: for the same magnitude and rupture distance, buried ruptures lead to larger short-period ground motions than surface ruptures. The hanging-wall effect is included with an improved model that varies smoothly as a function of the source properties (M, dip, depth), and the site location. The standard deviation is magnitude dependent with smaller magnitudes leading to larger standard deviations. The short-period standard deviation model for soil sites is also distant-dependent due to nonlinear site response, with smaller standard deviations at short distances.
12
Socco, Laura Valentina, Cesare Comina, and Farbod Khosro Anjom. "Time-average velocity estimation through surface-wave analysis: Part 1 — S-wave velocity." GEOPHYSICS 82, no. 3 (May 1, 2017): U49—U59. http://dx.doi.org/10.1190/geo2016-0367.1.
Full textAbstract:
In some areas, the estimation of static corrections for land seismic data is a critical step of the processing workflow. It often requires the execution of additional surveys and data analyses. Surface waves (SWs) in seismic records can be processed to extract local dispersion curves (DCs) that can be used to estimate near-surface S-wave velocity models. Here we focus on the direct estimation of time-average S-wave velocity models from SW DCs without the need to invert the data. Time-average velocity directly provides the value of one-way time, given a datum plan depth. The method requires the knowledge of one 1D S-wave velocity model along the seismic line, together with the relevant DC, to estimate a relationship between SW wavelength and investigation depth on the time-average velocity model. This wavelength/depth relationship is then used to estimate all the other time-average S-wave velocity models along the line directly from the DCs by means of a data transformation. This approach removes the need for extensive data inversion and provides a simple method suitable for industrial workflows. We tested the method on synthetic and field data and found that it is possible to retrieve the time-average velocity models with uncertainties less than 10% in sites with laterally varying velocities. The error on one-way times at various depths of the datum plan retrieved by the time-average velocity models is mostly less than 5 ms for synthetic and field data.
13
Xu, Hu, Zhenhua Wang, Wenhao Li, and Qiuliang Wang. "Assessment of Water Measurements in an Irrigation Canal System Based on Experimental Data and the CFD Model." Water 13, no. 21 (November 4, 2021): 3102. http://dx.doi.org/10.3390/w13213102.
Full textAbstract:
Due to their convenience, water measuring structures have become an important means of measuring water in irrigation canal systems However, relevant research on upstream and downstream water-depth monitoring point locations is scarce. Our study aims to determine the functional relationship between the locations of the water-depth monitoring points and the opening width of the sluice. We established 14 trunk-channel and branch-channel hydrodynamic models. The locations of the water-depth monitoring points for the upstream and downstream reaches and their hydraulic characteristics were assessed using a numerical simulation and hydraulic test. The results showed that the locations of the upstream and downstream water-depth monitoring points were, respectively, 16.26 and 15.51 times the width of the sluice. The average error between the calculated flow rate and the simulated value was 14.37%; the average error between the flow rates calculated by the modified and the simulated values was 3.36%. To further verify the accuracy of the modified discharge calculation formula, by comparing the measured values, we reduced the average error of the modified formula by 19.29% compared with the standard formula. This research provides new insights into optimizing water measurements in irrigation canal systems. The results provide an engineering basis for the site selection of water-depth monitoring points that is suitable to be widely applied in the field.
14
Djieto Lordon, Anatole Eugene, Mbohlieu YOSSA, Christopher M. Agyingi, Yves Shandini, and Thierry Stephane Kuisseu. "Geometrical Characterisation of the Mamfe Basin, Cameroon, from the Earth, Gravitational Model (EGM 2008)." Earth Science Research 7, no. 1 (January 13, 2018): 94. http://dx.doi.org/10.5539/esr.v7n1p94.
Full textAbstract:
Gravimetric studies using the ETOPO1-corrected high resolution satellite-based EGM2008 gravity data was used to define the surface extent, depth to basement and shape of the Mamfe basin. The Bouguer anomaly map was produced in Surfer 11.0. The Fast Fourier Transformed data was analyzed by spectral analysis to remove the effect of the regional bodies in the study area. The residual anomaly map obtained was compared with the known geology of the study area, and this showed that the gravity highs correspond to the metamorphic and igneous rocks while the gravity lows match with Cretaceous sediments. Three profiles were drawn on the residual anomaly map along which 2D models of the Mamfe basin were drawn. The modeling was completed in Grav2dc v2.06 software which uses the Talwini’s algorithm and the resulting models gave the depth to basement and the shape of the basement along the profiles. After processing and interpretation, it was deduced that the Mamfe basin has an average length and width of 77.6 km and 29.2 km respectively, an average depth to basement of 5 km and an overall U-shape basement. These dimensions (especially the depth) theoretically create the depth and temperature conditions for petroleum generation.
15
Liu, Jian Kang, Hong Bing Luo, Gu Huang, Xiao Ling Liu, Mei Li, and Jie Liang. "Model of Pollutant Loads and Rainfall Physical Parameters for Urban Surface Runoff." Advanced Materials Research 143-144 (October 2010): 1175–80. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.1175.
Full textAbstract:
The urban surface runoff pollution is a very complicated process of multimedia, spatiotemporal multidimensional and multi-pollutants. On the basis of monitored pollutant loads of urban surface runoff at a catchment in Futian River watershed in Shenzhen City of China, a regression model of pollutant loads of urban surface runoff with rainfall physical parameters was established, in which the rainfall parameters include rainfall depth (h), average intensity (I), antecedent dry period (D), event duration (T), maximum instantaneous intensity (IMAX) and maximum 5 minutes average intensity (I5min). In the study area, the variable found to be most significant in the prediction of the pollutant load for COD, SS, TN and TP was the rainfall depth. Other rainfall parameters have different contribution to the pollutant loads.
16
Li, Guo, Huadong Zhao, Chengshuai Liu, Jinfeng Wang, and Fan Yang. "City Flood Disaster Scenario Simulation Based on 1D–2D Coupled Rain–Flood Model." Water 14, no. 21 (November 4, 2022): 3548. http://dx.doi.org/10.3390/w14213548.
Full textAbstract:
In order to realize the reproduction and simulation of urban rainstorm and waterlogging scenarios with complex underlying surfaces, based on the 1D–2D coupled models, we constructed an urban storm–flood coupling model considering one-dimensional river channels, two-dimensional ground and underground pipe networks. Luoyang City, located in the western part of Henan Province, China was used as a pilot to realize the construction of a one-dimensional and two-dimensional coupled urban flood model and flood simulation. The coupled model was calibrated and verified by the submerged water depths of 16 survey points in two historical storms flood events. The average relative error of the calibration simulated water depth was 22.65%, and the average absolute error was 13.93 cm; the average relative error of the verified simulated water depth was 15.27%, the average absolute error was 7.54 cm, and the simulation result was good. Finally, 28 rains with different return periods and different durations were designed to simulate and analyze the rainstorm inundation in the downtown area of Luoyang. The result shows that the R2 of rainfall and urban rainstorm inundation is 0.8776, and the R2 of rainfall duration and urban rainstorm inundation is 0.8141. The study results have important practical significance for urban flood prevention, disaster reduction and traffic emergency management.
17
Gao, Yu Fei, and Pei Qi Ge. "Relationship between the Grit Cut Depth and Process Parameters in Electroplated Diamond Wire Sawing KDP Crystal." Applied Mechanics and Materials 101-102 (September 2011): 950–53. http://dx.doi.org/10.4028/www.scientific.net/amm.101-102.950.
Full textAbstract:
A wire-saw cut KDP crystal geometrical model was founded and a mathematical model was established to calculate the grit average cut depth, based on indentation fracture mechanics theory(IFMT). The relationship between the grit cut depth and wire saw process parameter was analyzed theoretically. The research results indicate that there exists an approximate monotone increasing non-linear correlation between grit average cut depth and the ratio i value of crystal feed speed and wire speed. By increasing the wire speed and crystal feed speed accordantly, the value of i can be maintained invariable, however, this way can simultaneously bring higher machined surface quality and machining efficiency.
18
Abtahi, Sayyed Mohammad, Laust Börsting Pedersen, Jochen Kamm, and Thomas Kalscheuer. "A new reference model for 3D inversion of airborne magnetic data in hilly terrain — A case study from northern Sweden." GEOPHYSICS 83, no. 1 (January 2018): B1—B12. http://dx.doi.org/10.1190/geo2016-0331.1.
Full textAbstract:
The inherent nonuniqueness in modeling magnetic data can be partly reduced by adding prior information, either as mathematical constructs or simply as bounds on magnetization obtained from laboratory measurements. If a good prior model can be used as a reference model, then the quality of estimated models through an inverse approach can be greatly improved. But even though data on magnetic properties of rocks might exist, their distribution may often be quite irregular on local and regional scales, so that it is difficult to define representative classes of rock types suitable for constraining geophysical models of magnetization. We have developed a new way of constructing a reference model that varies only laterally and is confined to the part of the terrain that lies above the lowest topography in the area. To obtain this model, several estimated 2D magnetization distributions were constructed by data inversion as a function of the iteration number. Then, a suitable 2D model of the magnetization in the topography was chosen as a starting point for constructing a 3D reference model by modifying it with a vertical decay such that its average source depth was the same for all horizontal positions. The average source depth of the reference model was chosen to satisfy the average source depth obtained from analyzing the radial power spectrum of the area studied. Finally, the measured magnetic data were inverted in three dimensions using the given reference model. For a selected reference model, shallow structures indicated a better overall correlation with large remanent magnetizations measured on rock samples from the area. Throughout the entire model, the direction of magnetization was allowed to vary freely. We found that the Euclidean norm of the estimated model was reduced compared with the case where the magnetization direction was fixed.
19
Saba, V. S., M. A. M. Friedrichs, D. Antoine, R. A. Armstrong, I. Asanuma, M. J. Behrenfeld, A. M. Ciotti, et al. "An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe." Biogeosciences Discussions 7, no. 5 (September 6, 2010): 6749–88. http://dx.doi.org/10.5194/bgd-7-6749-2010.
Full textAbstract:
Abstract. Nearly half of the earth's photosynthetically fixed carbon derives from the oceans. To determine global and region specific rates, we rely on models that estimate marine net primary productivity (NPP) thus it is essential that these models are evaluated to determine their accuracy. Here we assessed the skill of 21 ocean color models by comparing their estimates of depth-integrated NPP to 1156 in situ 14C measurements encompassing ten marine regions including the Sargasso Sea, pelagic North Atlantic, coastal Northeast Atlantic, Black Sea, Mediterranean Sea, Arabian Sea, subtropical North Pacific, Ross Sea, West Antarctic Peninsula, and the Antarctic Polar Frontal Zone. Average model skill, as determined by root-mean square difference calculations, was lowest in the Black and Mediterranean Seas, highest in the pelagic North Atlantic and the Antarctic Polar Frontal Zone, and intermediate in the other six regions. The maximum fraction of model skill that may be attributable to uncertainties in both the input variables and in situ NPP measurements, was nearly 72%. Contrary to prior studies, ocean color models were not highly challenged in extreme conditions of surface chlorophyll-a and sea surface temperature, nor in high-nitrate low-chlorophyll waters. On average, the simplest depth/wavelength integrated models performed no worse than the more complex depth/wavelength resolved models. Water column depth (distance to coastlines) was the primary influence on ocean color model performance such that average skill was significantly higher at depths greater than 250 m, suggesting that ocean color models are more challenged in Case-2 waters (coastal) than in Case-1 (pelagic) waters. Given that in situ chlorophyll-a data was used as input data, algorithm improvement is required to eliminate the poor performance of ocean color models in Case-2 waters that are close to coastlines. Finally, ocean color chlorophyll-a algorithms are challenged by optically complex Case-2 waters, thus using satellite-derived chlorophyll-a to estimate NPP in coastal areas would likely further reduce the skill of ocean color models.
20
Saba, V. S., M. A. M. Friedrichs, D. Antoine, R. A. Armstrong, I. Asanuma, M. J. Behrenfeld, A. M. Ciotti, et al. "An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe." Biogeosciences 8, no. 2 (February 22, 2011): 489–503. http://dx.doi.org/10.5194/bg-8-489-2011.
Full textAbstract:
Abstract. Nearly half of the earth's photosynthetically fixed carbon derives from the oceans. To determine global and region specific rates, we rely on models that estimate marine net primary productivity (NPP) thus it is essential that these models are evaluated to determine their accuracy. Here we assessed the skill of 21 ocean color models by comparing their estimates of depth-integrated NPP to 1156 in situ 14C measurements encompassing ten marine regions including the Sargasso Sea, pelagic North Atlantic, coastal Northeast Atlantic, Black Sea, Mediterranean Sea, Arabian Sea, subtropical North Pacific, Ross Sea, West Antarctic Peninsula, and the Antarctic Polar Frontal Zone. Average model skill, as determined by root-mean square difference calculations, was lowest in the Black and Mediterranean Seas, highest in the pelagic North Atlantic and the Antarctic Polar Frontal Zone, and intermediate in the other six regions. The maximum fraction of model skill that may be attributable to uncertainties in both the input variables and in situ NPP measurements was nearly 72%. On average, the simplest depth/wavelength integrated models performed no worse than the more complex depth/wavelength resolved models. Ocean color models were not highly challenged in extreme conditions of surface chlorophyll-a and sea surface temperature, nor in high-nitrate low-chlorophyll waters. Water column depth was the primary influence on ocean color model performance such that average skill was significantly higher at depths greater than 250 m, suggesting that ocean color models are more challenged in Case-2 waters (coastal) than in Case-1 (pelagic) waters. Given that in situ chlorophyll-a data was used as input data, algorithm improvement is required to eliminate the poor performance of ocean color NPP models in Case-2 waters that are close to coastlines. Finally, ocean color chlorophyll-a algorithms are challenged by optically complex Case-2 waters, thus using satellite-derived chlorophyll-a to estimate NPP in coastal areas would likely further reduce the skill of ocean color models.
21
Et al., Prabhu Swamy N. R. "Depth of Penetration and Surface Roughness Analysis of Al6061 cut by Abrasive Water Jet." Psychology and Education Journal 58, no. 1 (January 20, 2021): 5412–17. http://dx.doi.org/10.17762/pae.v58i1.2154.
Full textAbstract:
In this study, model equations to predict average surface roughness value of abrasive water jet cut aluminium 6061 alloy are developed. Model equations are developed considering water jet pressure, abrasive flow rate and traverse speed of the jet. Model equations help in knowing average surface roughness value on the cutting and deformation wear regions. 27 abrasive water jet cutting experiments are conducted on trapezoidal shaped aluminium 6061 block. Depth of penetration values are found for all experimental cutting conditions. Average surface roughness values are found by non-contact surface roughness tester. Surface roughness testing is carried out along the length of depth of penetration. Low and high average surface roughness values are noticed on the cutting and deformation wear regions respectively. Smooth surface finish and rough surface finish with striations are observed on the cutting and deformation wear regions respectively.
22
Khetsuriani, Elguja, Teona Khetsuriani, and Timur Khetsuriani. "Information technologies for the study of the planned and profile model of the flux creep in the transit channel." E3S Web of Conferences 281 (2021): 09013. http://dx.doi.org/10.1051/e3sconf/202128109013.
Full textAbstract:
The article presents the inlet chamber computer models for the Beloyarskyin take structure: a computer model of the depth distribution and a computer model of the water level distribution for determining the hydrodynamic structure of the flux creep in the transit channel. To build a computer model, the depth survey of the inlet chamber was used during hydro meteorological surveys. The water level at the survey time is 15.30 m. The construction of a depth distribution computer model in the inlet chamber at the intake structure for the water supply of the Beloyarsky city was carried out by the finite element method on a personal computer in the Multiphysics software product. The uneven distribution of average velocities on the verticals, both along the channel width and length, is explained by the significant expansion of the inlet chamber in the intermediate sections 2-2, 3-3, 4-4, 5-5 in relation to the input 1-1 and output sections. Deepening the channel in the entrance section 1-1 by an average of 1.3-1.5 m will increase its flow rate to 10, 2m3/s or 10 times with an increase in the average speeds on the verticals by 2.0-2.5 times. Such an increase in the flow rate in the input section will lead to an increase in the average speeds in other sections 2-2, 3-3, etc. Hence, the passage of increased flow through the transit channel after clearing a number of shallow areas will create better conditions for preventing algae spread.
23
Lai, Yong Biao, Meng Shu Wang, and Xin Hua You. "Predicting Model of Ground Deformation Caused by Shield Constructing." Applied Mechanics and Materials 651-653 (September 2014): 1220–23. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1220.
Full textAbstract:
A method of predicting ground deformation caused by shield constructing was proposed with support vector machine. With Matlab language, a predicting model of ground deformation caused by shield constructing based on SVM was designed, 11parameters (soil internal friction angle, cohesive force, buried depth, excavate speed, total thrust, average support pressure of the front two ring pressure tank, average support pressure of the front one ring pressure tank, average soil pressure of pressure tank, shield cutterhead total torque, synchronous grouting quantity, unearthed amount) were chosen for ground deformation influence factors, then an intelligent predicting model of ground deformation caused by shield constructing was built, and the forecast model was used for surface settlement prediction in shield tunneling of Shenzhen metro.
24
Day, Steven M., Robert Graves, Jacobo Bielak, Douglas Dreger, Shawn Larsen, Kim B. Olsen, Arben Pitarka, and Leonardo Ramirez-Guzman. "Model for Basin Effects on Long-Period Response Spectra in Southern California." Earthquake Spectra 24, no. 1 (February 2008): 257–77. http://dx.doi.org/10.1193/1.2857545.
Full textAbstract:
We propose a model for the effect of sedimentary basin depth on long-period response spectra. The model is based on the analysis of 3-D numerical simulations (finite element and finite difference) of long-period (2–10 s) ground motions for a suite of sixty scenario earthquakes (Mw 6.3 to Mw 7.1) within the Los Angeles basin region. We find depth to the 1.5 km/s S-wave velocity isosurface to be a suitable predictor variable, and also present alternative versions of the model based on depths to the 1.0 and 2.5 km/s isosurfaces. The resulting mean basin-depth effect is period dependent, and both smoother (as a function of period and depth) and higher in amplitude than predictions from local 1-D models. The main requirement for the use of the results in construction of attenuation relationships is determining the extent to which the basin effect, as defined and quantified in this study, is already accounted for implicitly in existing attenuation relationships, through (1) departures of the average “rock” site from our idealized reference model, and (2) correlation of basin depth with other predictor variables (such as V s30).
25
Fernandes, T. L., J. W. Wilton, and J. J. Tosh. "Estimates of genetic parameters for ultrasound-measured carcass traits in sheep." Canadian Journal of Animal Science 84, no. 3 (September 1, 2004): 361–65. http://dx.doi.org/10.4141/a03-080.
Full textAbstract:
Data on ultrasound traits (loin depth, average backfat thickness, and loin width) were collected from lambs (n = 3483) across Ontario, born between 1997 and 1999. The data were analysed with a REML procedure in a multiple-trait mixed-animal model to obtain (co)variance component estimates. Analyses of all traits included the additive genetic effect of the lamb, sex of the lamb, contemporary group, and breed group effects. Weight or age was included as a covariate in two separate analyses. Estimates of direct additive heritabilities for loin depth, average backfat thickness, and loin width were 0.29, 0.29 and 0.26 respectively, with genetic correlations of -0.17 between loin depth and average backfat thickness, 0.43 between loin depth and loin width, and 0.23 between loin width and average backfat thickness for the weight constant analysis. When the data were analysed using age in the regression analysis, corresponding estimates of direct additive heritabilities were 0.38, 0.35 and 0.30, and genetic correlations between traits were all positive, 0.29 between loin depth and average backfat thickness, 0.61 between loin depth and loin width, and 0.44 between loin width and average backfat thickness. Results indicate that it is possible to make genetic improvement if selection is based on ultrasound information. Key words: Sheep, genetic parameters, heritability, ultrasound
26
Hong, Kunlong, Hongguang Wang, and Bingbing Yuan. "Inspection-Nerf: Rendering Multi-Type Local Images for Dam Surface Inspection Task Using Climbing Robot and Neural Radiance Field." Buildings 13, no. 1 (January 12, 2023): 213. http://dx.doi.org/10.3390/buildings13010213.
Full textAbstract:
For the surface defects inspection task, operators need to check the defect in local detail images by specifying the location, which only the global 3D model reconstruction can’t satisfy. We explore how to address multi-type (original image, semantic image, and depth image) local detail image synthesis and environment data storage by introducing the advanced neural radiance field (Nerf) method. We use a wall-climbing robot to collect surface RGB-D images, generate the 3D global model and its bounding box, and make the bounding box correspond to the Nerf implicit bound. After this, we proposed the Inspection-Nerf model to make Nerf more suitable for our near view and big surface scene. Our model use hash to encode 3D position and two separate branches to render semantic and color images. And combine the two branches’ sigma values as density to render depth images. Experiments show that our model can render high-quality multi-type images at testing viewpoints. The average peak signal-to-noise ratio (PSNR) equals 33.99, and the average depth error in a limited range (2.5 m) equals 0.027 m. Only labeled 2% images of 2568 collected images, our model can generate semantic masks for all images with 0.957 average recall. It can also compensate for the difficulty of manual labeling through multi-frame fusion. Our model size is 388 MB and can synthesize original and depth images of trajectory viewpoints within about 200 m2 dam surface range and extra defect semantic masks.
27
Tarawneh, Ahmad, Abdullah Alghossoon, Eman Saleh, Ghassan Almasabha, Yasmin Murad, Mahmoud Abu-Rayyan, and Ahmad Aldiabat. "Machine Learning Prediction Model for Shear Capacity of FRP-RC Slender and Deep Beams." Sustainability 14, no. 23 (November 24, 2022): 15609. http://dx.doi.org/10.3390/su142315609.
Full textAbstract:
FPR reinforcing bars have emerged as a promising alternative to steel bars in construction, especially in corrosive environments. Literature includes several shear strength models proposed for FRP-RC members. This study presents a detailed evaluation of design shear models proposed by researchers and design codes. The evaluation was conducted through an extensive surveyed database of 388 FRP-RC beams without shear reinforcement tested in shear. Gene expression programming (GEP) has been utilized in this study to develop accurate design models for the shear capacity of slender and deep FRP-RC beams. Parameters used in the models are concrete compressive strength (f’c), section depth (d), section width (b), modular ratio (n), reinforcement ratio (ρf), shear span-to-depth ratio (a/d). The proposed model for slender beams resulted in an average tested-to-predicted ratio of 0.98 and a standard deviation of 0.21, while the deep beams model resulted in an average tested-to-predicted ratio of 1.03 and a standard deviation of 0.29. For deep beams, the model provided superior accuracy over all models. However, this can be attributed to the fact that the investigated models were not intended for deep beams. The deep beams model provides a simple method compared to the strut-and-tie method.
28
Toure, Ally M., Matthew Rodell, Zong-Liang Yang, Hiroko Beaudoing, Edward Kim, Yongfei Zhang, and Yonghwan Kwon. "Evaluation of the Snow Simulations from the Community Land Model, Version 4 (CLM4)." Journal of Hydrometeorology 17, no. 1 (December 17, 2015): 153–70. http://dx.doi.org/10.1175/jhm-d-14-0165.1.
Full textAbstract:
Abstract This paper evaluates the simulation of snow by the Community Land Model, version 4 (CLM4), the land model component of the Community Earth System Model, version 1.0.4 (CESM1.0.4). CLM4 was run in an offline mode forced with the corrected land-only replay of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land) and the output was evaluated for the period from January 2001 to January 2011 over the Northern Hemisphere poleward of 30°N. Simulated snow-cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the Canadian Meteorological Centre (CMC) daily snow analysis products, snow depth from the National Weather Service Cooperative Observer (COOP) program, and Snowpack Telemetry (SNOTEL) SWE observations. CLM4 SCF was converted into snow-cover extent (SCE) to compare with MODIS SCE. It showed good agreement, with a correlation coefficient of 0.91 and an average bias of −1.54 × 102 km2. Overall, CLM4 agreed well with IMS snow cover, with the percentage of correctly modeled snow–no snow being 94%. CLM4 snow depth and SWE agreed reasonably well with the CMC product, with the average bias (RMSE) of snow depth and SWE being 0.044 m (0.19 m) and −0.010 m (0.04 m), respectively. CLM4 underestimated SNOTEL SWE and COOP snow depth. This study demonstrates the need to improve the CLM4 snow estimates and constitutes a benchmark against which improvement of the model through data assimilation can be measured.
29
Li, Yu, Xinyue Zhao, and Quanhua Zhao. "Snow Depth Inversion in Forest Areas from Sentinel-1 Data Based on Phase Deviation Correction." Remote Sensing 14, no. 23 (November 23, 2022): 5930. http://dx.doi.org/10.3390/rs14235930.
Full textAbstract:
At present, snow depth inversion based on active microwave remote sensing is concerned essentially with areas having a relatively simple underlying surface. The existence of forests reduces the sensitivity of microwaves to snow, which often makes the snow depth inversion results uncertain. This paper presents a snow depth estimation algorithm for forest areas by introducing a forest phase to characterize the effect of forests on backscattering electromagnetic wave. Firstly, the interferogram is generated with the differential interference of two-pass master-slave Synthetic Aperture Radar (SAR) images, and the real phase under snow cover condition is obtained by phase unwrapping. Secondly, the phase models for forest and non-forest areas are constructed. The effects of forest cover are modeled as forest phase in the forest phase model, which is estimated under the assumption of snow depth consistency on both sides of the boundaries between forest and non-forest areas. Finally, snow depth is estimated by the snow phase-depth model. The correctness of the proposed forest snow depth inversion algorithm was verified by taking the Jiagedaqi area of Greater Xing’an Mountains as the study area and sentinel-1 dual polarization images as the data source. Finally, the snow depth distribution of the study area was obtained with a spatial resolution of 30 m on 7 December 2020. The experimental results show that the snow depth values estimated in Jiagedaqi area are mainly between 40–120 cm, and the average snow depth value is 80.27 cm. Taking the snow depth value of 84.69 cm reckoned from hourly accumulated snowfall in Jiagedaqi as the reference snow depth, the results of the estimated snow depth are relatively consistent and well-founded. With the introduction of the forest phase, the average snow depth values estimated in the forest area increase by 5.98 cm, which reduces the underestimation of the snow depth in forest areas.
30
Shaykewich, C. F., G. H. B. Ash, R. L. Raddatz, and D. J. Tomasiewicz. "Field evaluation of a water use model for potatoes." Canadian Journal of Soil Science 78, no. 3 (August 1, 1998): 441–48. http://dx.doi.org/10.4141/s97-088.
Full textAbstract:
A water use model for potatoes (Solanum tuberosum L.) was calibrated and tested. The model requires phenological relationships for estimating emergence, degree of crop cover and rooting depth. These weather-driven crop growth functions were previously calibrated using field data from 1994 and 1995. In this paper, the model was tested using field data from the 1996 growing season at two locations. The 1996 crop growth parameters were estimated fairly accurately. This contributed to reasonably accurate (average bias <3 mm, root mean square error <15 mm) root zone available soil water estimates by the model. Thus, the model could be used in irrigation scheduling. Key words: Evapotranspiration, rooting depth, ground cover
31
Herdiansyah, Sony, Dantje Kardana Natakusumah, and Dhemi Harlan. "FVCOM model simulation of local scouring around bridge pile." MATEC Web of Conferences 270 (2019): 04020. http://dx.doi.org/10.1051/matecconf/201927004020.
Full textAbstract:
Scouring is one of many damages that water can cause. Scouring can occur as a consequence of bridge pile existence. The problem on local scour around single pier will be studied by using FVCOM numerical model. This study objective is to find out how accurate FVCOM model to predict local scour behavior. FVCOM model is based on the finite volume method to solve Navier Stokes, Meyer Peter Muller, and Exner equations. FVCOM computed numerical result then will be verified with computed and measured data in previous numerical (FSUM model) and experimental study. Results from this study show FVCOM model were successfully simulated typical features of local scour around piers such as downflow and wake vortex, but failed to simulate horseshoe vortex. Both computed numerical (FSUM and FVCOM) results are then compared with measured experimental data for its magnitude and time-series of maximum scour depth. FVCOM result shows value 0.99 r-squared correlation and 5.96 percent average error, and FSUM result shows value 0.98 r-squared correlation and 6.82 percent average error. Therefore, it can be deduced that FVCOM successfully predict local scour depth and its time-series and proven that FVCOM is more accurate than FSUM model.
32
Luan, Zhaoqing, Zhongxin Wang, Dandan Yan, Guihua Liu, and Yingying Xu. "The Ecological Response ofCarex lasiocarpaCommunity in the Riparian Wetlands to the Environmental Gradient of Water Depth in Sanjiang Plain, Northeast China." Scientific World Journal 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/402067.
Full textAbstract:
The response ofCarex lasiocarpain riparian wetlands in Sanjiang Plain to the environmental gradient of water depth was analyzed by using the Gaussian Model based on the biomass and average height data, and the ecological water-depth amplitude ofCarex lasiocarpawas derived. The results indicated that the optimum ecological water-depth amplitude ofCarex lasiocarpabased on biomass was [13.45 cm, 29.78 cm], while the optimum ecological water-depth amplitude ofCarex lasiocarpabased on average height was [2.31 cm, 40.11 cm]. The intersection of the ecological water-depth amplitudes based on biomass and height confirmed that the optimum ecological water-depth amplitude ofCarex lasiocarpawas [13.45 cm, 29.78 cm] and the optimist growing water-depth ofCarex lasiocarpawas 21.4 cm. The TWINSPAN, a polythetic and divisive classification tool, was used to classify the wetland ecological series into 6 associations. Result of TWINSPAN matrix classification reflected an obvious environmental gradient in these associations: water-depth gradient. The relation of biodiversity ofCarex lasiocarpacommunity and water depth was determined by calculating the diversity index of each association.
33
Sun, Yimeng, Xi Chen, Xi Chen, and Liu Yang. "Modeling Groundwater-Fed Irrigation and Its Impact on Streamflow and Groundwater Depth in an Agricultural Area of Huaihe River Basin, China." Water 13, no. 16 (August 15, 2021): 2220. http://dx.doi.org/10.3390/w13162220.
Full textAbstract:
The amount of water taken from groundwater for agricultural irrigation is often not observed, while hydrological models have been extensively proposed to investigate the irrigation dynamics and impacts in agricultural areas. In this work, we propose an agro-hydrological model that integrates agricultural irrigation with the traditional Xin’anjiang (XAJ) hydrological model. In particular, the proposed model incorporates the FAO guidelines on crop evapotranspiration into hydrological routing of water balance and flow fluxes in unsaturated and saturated zones. The model was used to calibrate the groundwater irrigation amounts in terms of both the observed river discharge and the groundwater depth in the Xuanwu plain area of the Huaihe River Basin in China. The calibration and sensitivity analyses were performed by the shuffled complex evolution (SCE-UA) method. This method can be applied to a single-objective optimization of model parameters, based on either the river discharge or the groundwater depth, or to a multi-objective optimization of model parameters based on both of these objectives. The results show that the multi-objective calibration is more efficient than the single-objective method for capturing dynamics of the river discharge and the groundwater depth. The estimated means of the annual groundwater withdrawal for wheat and maize irrigations were found to be about 140.5 mm and 13.7 mm, respectively. The correlation between the groundwater withdrawal and the change in groundwater depth during crop growing seasons demonstrated that the groundwater withdrawal is the dominant factor for the groundwater depth change in the river basin, particularly in the winter wheat season. Moreover, model simulations show that the combined effects of the reduced precipitation and the increased groundwater withdrawal would lead to a decrease of the average annual runoff and an increase of the average groundwater depth. These estimates can greatly help in understanding the irregular changes in the groundwater withdrawal and offer a quantitative basis for studying future groundwater demands in this area.
34
Xu, Fan, Li Shen, Zhiying Wang, Bo Su, Hui Guo, and Wei Chen. "Using Heuristic Value Prediction and Dynamic Task Granularity Resizing to Improve Software Speculation." Scientific World Journal 2014 (2014): 1–18. http://dx.doi.org/10.1155/2014/478013.
Full textAbstract:
Exploiting potential thread-level parallelism (TLP) is becoming the key factor to improving performance of programs on multicore or many-core systems. Among various kinds of parallel execution models, the software-based speculative parallel model has become a research focus due to its low cost, high efficiency, flexibility, and scalability. The performance of the guest program under the software-based speculative parallel execution model is closely related to the speculation accuracy, the control overhead, and the rollback overhead of the model. In this paper, we first analyzed the conventional speculative parallel model and presented an analytic model of its expectation of the overall overhead, then optimized the conventional model based on the analytic model, and finally proposed a novel speculative parallel model named HEUSPEC. The HEUSPEC model includes three key techniques, namely, the heuristic value prediction, the value based correctness checking, and the dynamic task granularity resizing. We have implemented the runtime system of the model in ANSI C language. The experiment results show that when the speedup of the HEUSPEC model can reach 2.20 on the average (15% higher than conventional model) when depth is equal to 3 and 4.51 on the average (12% higher than conventional model) when speculative depth is equal to 7. Besides, it shows good scalability and lower memory cost.
35
Pittman, R., and B. Hu. "ESTIMATION OF SOIL BULK DENSITY AND CARBON USING MULTI-SOURCE REMOTELY SENSED DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 541–48. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-541-2020.
Full textAbstract:
Abstract. Bulk density and soil carbon models were fitted for soil samples collected during field campaigns in 2018 and 2019 for the Kapuskasing region of the District of Cochrane in Ontario, Canada. Prediction maps for bulk density and soil carbon were generated for the 0–15 cm depth mineral soil layer. The application of multi-source remotely sensed data as environmental covariates for model predictors was implemented. Environmental covariates were obtained from multispectral satellite imagery, LiDAR (light detection and ranging) retrievals and airborne geomagnetic surveys, as well from a digital elevation model (DEM) for topographic covariates. Two covariates derived from LiDAR, canopy height model (CHM) and gap fraction, were of high variable importance when fitting models for average bulk density; gap fraction had the highest to second highest variable importance for average bulk density when considered among a full set of 76, or reduced sets of 12 or 5 separate predictors respectively. Environmental covariates corresponding to vegetation cover, specifically reflectance from multispectral imagery or LiDAR data, had the highest variable importance when compared with other categories of soil formation factors. Random forest (RF) models were generated, with RF models based upon just 12 predictors obtaining reasonable results with coefficients of determinations (R2) greater than 0.7 for the standard derivation of bulk density, standard deviation of total carbon and average total carbon for the 0–15 cm depth layer.
36
Christiansen, Thomas L., and Marcel A. J. Somers. "Reconstruction of Stress and Composition Profiles from X-Ray Diffraction Experiments — How to Avoid Ghost Stresses?" Materials Science Forum 443-444 (January 2004): 91–94. http://dx.doi.org/10.4028/www.scientific.net/msf.443-444.91.
Full textAbstract:
On evaluating lattice strain-depth or stress-depth profiles with X-ray diffraction, the variation of the information depth while combining various tilt angles, in combination with lattice spacing gradients leads to artefacts, so-called ghost or fictitious stresses. X-ray diffraction lattice-strain analysis was simulated for a model stress-depth profile combined with a composition-depth profile. Two principally different methods were investigated for the reconstruction of the actual stress and composition profiles from the simulated data: - considering the stress/strain determined at a specific depth as a weighted average over the actual stress/strain depth profile - considering the lattice spacing determined at a specific depth, for a specific value for as a weighted average over the actual lattice spacing profile for this direction. On the basis of the results it is possible to propose a preferred method for the evaluation of stress/strain and composition profiles, while minimising the risk for ghost stresses.
37
Tian, Chenyang, Minglei Guan, Yaxin Cheng, Wei Zhang, Dejin Zhang, and Jinfeng Yang. "A Method to Construct Depth Datum Geodesic Height Model for GNSS Bathymetric Survey." Journal of Marine Science and Engineering 11, no. 1 (December 27, 2022): 30. http://dx.doi.org/10.3390/jmse11010030.
Full textAbstract:
Water depth measurement requires the establishment of one or more tidal stations in the survey area for synchronous water level observation, and finally the water depth is estimated to the depth datum. The non-tidal observation measuring has high efficiency and avoids the water level correction error caused by tidal observation in traditional sounding. Therefore, non-tidal observation measuring has become an effective water depth measurement method in offshore and inland water. However, datum conversion in non-tide operation is mostly based on the polynomial fitting method. The accuracy of this method is influenced by the distribution of datum control points, topographic relief and operation ranges. In this paper, we present a method to construct a depth datum geodesic height model, which can directly obtain a bathymetric database of depth data in a GNSS bathymetric survey. The model incorporates the continuous depth datum and the mean sea level of geodetic height in the same area. Through the numerical simulation of tidal wave motion in regional water, the tidal model is obtained. Based on the grid model, the tidal level is extracted from the tidal model for harmonic analysis, and a continuous depth datum model is established. Mean sea level geodetic height is from the CNES-CLS2015 Average Sea Surface Model. In this paper, the model is confirmed in the South Yellow Sea area. The results show that the accuracy of the depth datum model, and the depth datum geodetic height model meets the accuracy requirements of the datum.
38
Westermann, S., B. Elberling, S. Højlund Pedersen, M. Stendel, B. U. Hansen, and G. E. Liston. "Future permafrost conditions along environmental gradients in Zackenberg, Greenland." Cryosphere 9, no. 2 (April 17, 2015): 719–35. http://dx.doi.org/10.5194/tc-9-719-2015.
Full textAbstract:
Abstract. The future development of ground temperatures in permafrost areas is determined by a number of factors varying on different spatial and temporal scales. For sound projections of impacts of permafrost thaw, scaling procedures are of paramount importance. We present numerical simulations of present and future ground temperatures at 10 m resolution for a 4 km long transect across the lower Zackenberg valley in northeast Greenland. The results are based on stepwise downscaling of future projections derived from general circulation model using observational data, snow redistribution modeling, remote sensing data and a ground thermal model. A comparison to in situ measurements of thaw depths at two CALM sites and near-surface ground temperatures at 17 sites suggests agreement within 0.10 m for the maximum thaw depth and 1 °C for annual average ground temperature. Until 2100, modeled ground temperatures at 10 m depth warm by about 5 °C and the active layer thickness increases by about 30%, in conjunction with a warming of average near-surface summer soil temperatures by 2 °C. While ground temperatures at 10 m depth remain below 0 °C until 2100 in all model grid cells, positive annual average temperatures are modeled at 1 m depth for a few years and grid cells at the end of this century. The ensemble of all 10 m model grid cells highlights the significant spatial variability of the ground thermal regime which is not accessible in traditional coarse-scale modeling approaches.
39
Wang, Hao-Nan, Li-Xin Zheng, Shu-Wan Pan, Tan Yan, and Qiu-Ling Su. "Image Recognition of Pediatric Pneumonia Based on Fusion of Texture Features and Depth Features." Computational and Mathematical Methods in Medicine 2022 (August 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/1973508.
Full textAbstract:
Pneumonia is one of the diseases that seriously endangers human health, and it is also the leading cause of death of children under the age of five in China. The most commonly used imaging examination method for radiologists is mainly based on chest X-ray images. Still, imaging errors often result during imaging examinations due to objective factors such as visual fatigue and lack of experience. Therefore, this paper proposes a feature fusion model, FC-VGG, based on the fusion of texture features (local binary pattern LBP and directional gradient histogram HOG) and depth features. The model improves model performance by adding detailed information in texture features to the convolutional neural network while making the model more suitable for clinical use. We input the X-ray image with texture features into the modified VGG16 model, C-VGG, and then add the Add fusion method to C-VGG for feature fusion so that FC-VGG is obtained, so FC-VGG has texture features detailed information and abstract information of deep features. Through experiments, our model has achieved 92.19% accuracy in recognizing children’s pneumonia images, 93.44% average precision, 92.19% average recall, and 92.81% average F1 coefficient, and the model performance exceeds existing deep learning models and traditional feature recognition algorithms.
40
Liu, Y., X. Gao, G. Wang, T. Zhang, and J. Wang. "A METHOD OF WATER DEPTH INVERSION IN COASTAL AREA CONSIDERING TEMPERATURE INFORMATION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2021 (June 17, 2021): 23–28. http://dx.doi.org/10.5194/isprs-annals-v-3-2021-23-2021.
Full textAbstract:
Abstract. The remote sensing method for water depth inversion is fast, flexible, and low in cost, which has become an important means of method for water depth detection. This paper takes the coastal area where is around Gulangyu Island as the research area. Based on the spectral reflectance, sea surface temperature (SST) and measured water depth data, a nonlinear inversion model of water depth is established by using BP neural network. Combined with the tide data, the water depth and underwater topography in coastal area is obtained. The average relative error is 0.27. The root mean square error is 1.92. The results show that the participation of sea surface temperature in the model construction can improve the inversion error of offshore water depth to a certain extent, and can help improve the accuracy of the model.
41
Li, Shiwen, Yunhe Liu, and Jianping Li. "A Mantle Plume Beneath South China Revealed by Electrical Conductivity Obtained from Three-Dimensional Inversion of Geomagnetic Data." Sensors 23, no. 3 (January 21, 2023): 1249. http://dx.doi.org/10.3390/s23031249.
Full textAbstract:
A three-dimensional electrical conductivity model of the mantle beneath South China is presented using the geomagnetic depth sounding method in this paper. The data misfit term in the inversion function is measured by the L1-norm to suppress the instability caused by large noises contained in the observed data. To properly correct the ocean effect in responses at coastal observatories, a high-resolution (1° × 1°) heterogeneous and fixed shell is included in inversion. The most striking feature of the obtained model is a continuous high-conductivity anomaly that is centered on ~(112° E, 27° N) in the mantle. The average conductivity of the anomaly appears to be two to four times higher than that of the global average models at the most sensitive depths (410–900 km) of geomagnetic depth sounding. Further analysis combining laboratory-measured conductivity models with the observed conductivity model shows that the anomaly implies excess temperature in the mantle. This suggests the existence of a mantle plume, corresponding to the Hainan plume, that originates in the lower mantle, passes through the mantle transition zone, and enters the upper mantle. Our electrical conductivity model provides convincing evidence for the mantle plume beneath South China.
42
Joy, Helen K., and Manjunath R. Kounte. "DECISION ALGORITHM FOR INTRA PREDICTION IN HIGH-EFFICIENCY VIDEO CODING (HEVC)." Journal of Southwest Jiaotong University 57, no. 5 (October 30, 2022): 180–93. http://dx.doi.org/10.35741/issn.0258-2724.57.5.15.
Full textAbstract:
Prediction in HEVC exploits the redundant information in the fame to improve compression efficiency. The computational complexity of prediction is comparatively high as it recursively calculates the depth by comparing the rate-distortion optimization cost (RDO) exhaustively. The deep learning technology has shown a good mark in this area compared to traditional signal processing because of its content-based analysis and learning ability. This paper proposes a deep depth decision algorithm to predict the depth of the coding tree unit (CTU) and store it as a 16-element vector, and this model is pipelined to the HEVC encoder to compare the time taken and bit rate of encoding. The comparison chart clearly shows the reduction in computational time and enhancement in bitrate while encoding. The dataset used here is generated for the model with 110000 frames of the various resolutions, split into test, training, and validation, and trained on a depth decision model. The trained model interfaced with the HEVC encoder is compared with the normal encoder. The evaluation is done for quality check for the proposed model with BD-PSNR and BD-Bitrate shows a dip of 0.6 in BD-PSNR and increment of 6.7 in BD-Bitrate. When pipelined with the original HEVC, the RDO cost shows an improvement over existing techniques. The average encoding time is reduced by about 72% by the pipelined deep depth decision algorithm that points to the reduction in computational complexity. An average time saving of 88.49% is achieved with a deep depth decision algorithm-based encoder compared to the existing techniques.
43
Hu, Yanxing, Tao Che, Liyun Dai, and Lin Xiao. "Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere." Remote Sensing 13, no. 7 (March 25, 2021): 1250. http://dx.doi.org/10.3390/rs13071250.
Full textAbstract:
In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.
44
Zhang, Xiao Yu, and Hui Yan Zhang. "Research on the Marine Corrosion Model of Ferrous Metal Based on PLS and Grey Relational Analysis." Applied Mechanics and Materials 128-129 (October 2011): 237–42. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.237.
Full textAbstract:
The marine corrosion of metal materials is a complex chemical process which is affected by multiple factors that are nonlinear interconnected. This paper aims at integrating PLS and Grey theory in the model building of corrosion time， rate，maximum corrosion depth and average corrosion depth based on small sample data measured in a certain marine environment. The result shows that the time of data，temperature，salinity，dissolved oxygen，ph value and humidity are strongly correlated with the maximum corrosion depth in the full immersion zone. The fitting effects of PLS modeling based on grey relational analysis are better than the PLS model and the GM (1, 1) model in both precision and stability.
45
Chen, Hanxin, Mingming Liu, Zhenyu Hu, Menglong Li, and Sen Li. "Mechanical structural health prognosis with nonlinear mixed frequency ultrasonic signal analysis." E3S Web of Conferences 268 (2021): 01075. http://dx.doi.org/10.1051/e3sconf/202126801075.
Full textAbstract:
In order to detect the early fatigue crack of mechanical components simply, this paper puts forward the ultrasonic testing technology of different side collinear mixing. Firstly, based on the nonlinear ultrasonic theory, the method of calculating the difference frequency and sum frequency nonlinear coefficients of mixing ultrasonic is deduced. Then, the ram-5000 SINAP ultrasonic system is used to detect the aluminum alloy specimens with five different depth fatigue cracks, and the corresponding spectrum diagram is drawn. From the experimental results, we get that the crack depth is positively correlated with the nonlinear coefficients of difference frequency and sum frequency within a certain crack depth. Finally, by analyzing and fitting the experimental data, the prediction models of the difference frequency and sum frequency nonlinear coefficients on the crack depth are established. Through the analysis and combination of the above two prediction models, the prediction model of the mixing relative nonlinear coefficient is established, and the average error of the three prediction models is compared. The results show that the mixing relative nonlinear model has better results. The research work in this paper makes a useful exploration for crack detection and crack depth prediction.
46
Zhao, Hai Jing, Ya Dong Jin, Huang Feng Yan, and Fu Xin Chai. "Model Test of the Riverbed Deformation for the Flood Control Project of Chengde Reaches of Luanhe River." Applied Mechanics and Materials 641-642 (September 2014): 191–95. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.191.
Full textAbstract:
The flow field variation and the riverbed deformation of Chengde reaches of Luanhe river before and after the construction of the embankment and dam engineering were studied by the river model test. The results showed that the dike layout was reasonable, the designed erosion control measures can resist the local scour, and the dam reduced the scouring. So the maximum erosion depth near the left and right dike was decreased by 3.0m and the average depth reduction was 0.9m. Under the dam project conditions, the deep scour pit at the dike toe with the maximum scouring depth of 4.0 - 5.0m was shaped. Such erosion control measures as the fuseplugspillway form is suggested to reduce the erosion significantly.
47
Pozdniakov, S. P., S. O. Grinevskyi, E. A. Dedulina, and E. S. Koreko. "Sensitivity of the results of modeling of seasonal ground freezing to selection of parameterization of the snow cover thermal conductivity." Ice and Snow 59, no. 1 (March 20, 2019): 67–80. http://dx.doi.org/10.15356/2076-6734-2019-1-67-80.
Full textAbstract:
The relationship between the results of calculations of the dynamics of the temperature regime of the in freezing and thawing soil profile with the heating effect of the snow cover is considered. To analyze this connection, two coupled models are used: the model of formation and degradation of snow cover in winter and the model of heat transfer and soil moisture transport in underlying vadoze zone profile. Parametrization of the influence of the snow cover, which at each calculated moment of time has the current average density and depth, on the dynamics of the temperatures of the soil profile is due to the use of its specific thermal resistance, which depends on its current depth and the thermal conductivity coefficient. The coefficient of thermal conductivity of the snow cover is related with its density using six different published empirical relationships. Modeling of heat transfer in freezing and thawing soil is carried out on the example of the field site for monitoring the thermal regime located on the territory of the Zvenigorod Biological Station of Moscow State University. It is shown that the well-known relationships give similar curves for the dynamics of the depth of seasonal freezing, including the degradation of the seasonal freezing layer in the spring period, with the same dynamics of the snow cover. However, the maximum penetration depth of the zero isotherm differs significantly for different snow conductivity-snow density relationships. The tested six relationships were divided into three groups. Minimal freezing is provided by the Sturm model and the effective medium model. The average and rather poorly differentiating freezing from each other is given by the Pavlov, Osokin et al. and Jordan relationships. The greatest value of the freezing depth is obtained with using Pavlov’s relationship with a temperature correction.
48
Pekárová, Pavla, Andrej Tall, Ján Pekár, Justína Vitková, and Pavol Miklánek. "Groundwater Temperature Modelling at the Water Table with a Simple Heat Conduction Model." Hydrology 9, no. 10 (October 19, 2022): 185. http://dx.doi.org/10.3390/hydrology9100185.
Full textAbstract:
This study aimed at the analysis and modelling of the groundwater temperature at the water table in different regions of Slovakia. In the first part, the analysis of the long-term trends of air and soil/ground temperature to a depth of 10 m is presented. The average annual soil/groundwater temperatures at different depths were the same but lower than the annual average air temperature by about 0.8 °C. The long-term trend analysis of the air temperature and soil temperature at a depth of up to 10 m in Slovakia showed that the air and soil/ground water temperature have risen by 0.6 and 0.5 °C, respectively, per decade over the past 30 years. The second part of the study aimed at modelling the daily groundwater temperatures at depths of 0.6–15 m below the surface. The simple groundwater temperature model was constructed based on a one-dimensional differential Fourier heat conduction equation. The given model can be used to estimate future groundwater temperature trends using regional air temperature projections calculated for different greenhouse gas emission scenarios.
49
Zeng, Xianming, Lihui Wang, Xuecheng Zou, Liyuan Yin, and Jian Cheng. "The Evolution Characteristics of Soil Heat Storage of the Sidewalls in Subway Stations with Years." E3S Web of Conferences 356 (2022): 02022. http://dx.doi.org/10.1051/e3sconf/202235602022.
Full textAbstract:
The long-term evolution characteristics of the heat reservoir of soil have been analyzed by 15 years of simulation test with periodic indoor and outdoor air temperature conditions. A scale model test of the soil in the subway station sidewalls and software ANSYS fluid-structure coupling heat transfer model are built in this study, which are complementary and mutual authentication. In 1∼15 years, the results show that the maximal temperature rise of soil at 2 m buried depth is 3.9 °C, at 7 m buried depth is 1.6 °C, and at 12 m buried depth is 1.5 °C. On the sidewalls surface the average maximal endothermic heat flow density is 6.8 W/m2 in summer, and the average maximum exothermic heat flow density is 11.3 W/m2 in winter. It provides theoretical reference for the reasonable use of heat storage of the soil in the sidewalls of subway stations.
50
Li, Jingxian, Xuexiang Yu, and Ya Liang. "A prediction model of mining subsidence in thick loose layer based on probability integral model." Earth Sciences Research Journal 24, no. 3 (October 12, 2020): 367–72. http://dx.doi.org/10.15446/esrj.v24n3.90111.
Full textAbstract:
The probability integral method is the most commonly used mining subsidence prediction model, but it is only applicable to ordinary geological mining conditions. When the loose layer in the geological mining conditions where the mining face is located is too thick, many inaccurate phenomena will occur when the movement deformation value is predicted by the probability integral method. The most obvious one is the problem that the predicted value converges too fast compared with the measured value in the edge of the sinking basin. In 2012, Wang and Deng proposed a modified model of probability integral method for the marginal errors in the model of probability integral method and verified the feasibility of the method through examples. In this paper, the method is applied to the prediction of surface movement under thick and loose layers after modified. Through practical application, it is found that due to the angle between the working face and the horizontal direction, the average mining depth in the strike direction is different from the average mining depth in the inclined direction, and the main influence radius of the two main sections are often. Therefore, based on this problem, this paper divides the main influence radius into trend and tendency and adjusts the parameters in the model to find the rules of the parameters. The original method uses a dynamic scale factor to adjust the predicted shape of the graph by adjusting the sinking coefficient. This study is aimed to set the scale factor to 0.5 and fix the value of the sinking factor, and propose to adjust the integral range and then adjust the shape of the graph to make it more in line with the actual measurement situation.