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Artykuły w czasopismach na temat "MATERN 5/2 GPR"
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Hossain, SK Safdar, Bamidele Victor Ayodele i Abdulrahman Almithn. "Predictive Modeling of Bioenergy Production from Fountain Grass Using Gaussian Process Regression: Effect of Kernel Functions". Energies 15, nr 15 (31.07.2022): 5570. http://dx.doi.org/10.3390/en15155570.
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Experimental studies have shown that bioethanol production from biomass sources has been reported to be influenced by several process parameters. It is not entirely known, however, how the interaction of these factors affects the concentration of bioethanol production. In this study, the use of Gaussian Process Regression (GPR) in predictive modeling of bioethanol production from fountain grass has been investigated. Parametric analysis showing the interaction effect of time, pH, temperature, and yeast extract on the bioethanol production was examined. The effect of kernel functions on the performance of the GPR in modeling the prediction of bioenergy output was also examined. The study shows that the kernel function, namely, rotational quadratic (RQGPR), squared exponential (SEGPR), Matern 5/2 (MGPR), exponential (EGPR), and the optimizable (Opt.GPR.), had varying effects on the performance of the GPR. Coefficients of determination (R2) of 0.648, 0.670, 0.667, 0.762, and 0.993 were obtained for the RQGPR, SEGPR, MGPR, EGPR, OptGPR, respectively. The OptGPR with R2 of 0.993 and RMSE of 45.13 displayed the best performance. The input parameters analysis revealed that the pH of the fermentation medium significantly influences bioethanol production. A proper understanding of how the various process variables affect bioethanol production will help in the real-time optimization of the process in the eventuality of scale-up.
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Ekanayake, Piyal, Lasini Wickramasinghe i Jeevani W. Jayasinghe. "Development of Crop-Weather Models Using Gaussian Process Regression for the Prediction of Paddy Yield in Sri Lanka". International Journal of Intelligent Systems and Applications 14, nr 4 (8.08.2022): 52–665. http://dx.doi.org/10.5815/ijisa.2022.04.05.
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This research introduces machine learning models using the Gaussian Process Regression (GPR) depicting the association between paddy yield and weather in Sri Lanka. All major regions in the island with most contribution to the total paddy production were considered in this research. The climatic factors of rainfall, relative humidity, minimum temperature, maximum temperature, average wind speed, evaporation, and sunshine hours were considered as input (independent) variables, while the paddy yield was the output (dependent) variable. The collinearity within each pair of independent and dependent variables was determined using Spearman’s and Pearson’s correlation coefficients. Data sets corresponding to the two main annual paddy cultivation seasons since 2009 were trained in MATLAB to develop crop-weather models. The most appropriate Kernel function was chosen from among four types of Kernels viz. Rational Quadratic, Exponential, Squared Exponential, and Matern 5/2 based on their degree of coherence in modeling. This approach exploits the full potential of GPR in developing highly accurate crop-weather models. The performance of the crop-weather models was measured by the Correlation Coefficient, Mean Absolute Percentage Error, Mean Squared Error, Root Mean Squared Error Ratio, Nash Number and the BIAS. All the GPR-based models proposed in this paper are highly accurate in terms of the aforementioned evaluation metrics. Accordingly, when the climatic data are known or projected, the paddy yield and thereby the harvest of Sri Lanka can be predicted precisely by using the proposed crop-weather models.
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Adebayo, Segun, Francis O. Aweda, Isaac A. Ojedokun i James A. Agbolade. "Meteorological data prediction over selected stations in Sub-Sahara Africa: Leveraging on Machine Learning Algorithm". Ruhuna Journal of Science 13, nr 2 (31.12.2022): 129–40. http://dx.doi.org/10.4038/rjs.v13i2.120.
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This study investigated selected meteorological data prediction leveraging on a Machine Learning Algorithm Approach over five selected stations in Nigeria. The algorithm of Machine Learning was explored using weather parameters such as temperature, wind speed, wind direction and relative humidity to predict the rainfall rate. In the results, five Gaussian models (i.e., Rational Quadratic, Squared Exponential, Matern 5/2, Exponential and Optimized GPR) revealed different Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) with prediction speeds ranging from 15000 to 26000 and the training time included 7.936, 1.8923, 2.3701, 3.267 and 282.19, respectively. The predicted response as against the true response for the two models shows a linear graph passing through the origin which confirmed a perfect regression model, where all the points lie on a diagonal line. Therefore, the relationship between MSE, MAE and RMSE for different models revealed that the optimized GPR has a better performance as compared to others. More so, visualizing the relationship between the output variable (rainfall) and each input variable reveals that some input variables (relative humidity, rainfall, pressure, wind speed and direction) have a strong correlation with the output variable (rainfall), with others having a noisy relationship which is not very clear.
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Pustokhina, Inna, Amir Seraj, Hafsan Hafsan, Seyed Mojtaba Mostafavi i S. M. Alizadeh. "Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel Properties". International Journal of Chemical Engineering 2021 (4.06.2021): 1–12. http://dx.doi.org/10.1155/2021/5650499.
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Biodiesel is assumed a renewable and environmentally friendly fuel that possesses the potential to substitute petroleum diesel. The basic purpose of the present study is to design a precise algorithm based on Gaussian Process Regression (GPR) model with several kernel functions, i.e., Rational Quadratic, Squared Exponential, Matern, and Exponential, to estimate biodiesel properties. These properties include kinematic viscosity (KV), pour point (PP), iodine value (IV), and cloud point (CP) as a function of fatty acid composition. In order to develop this model, some variables are assumed, such as molecular weight, carbon number, double bond numbers, monounsaturated fatty acids, polyunsaturated fatty acid, weight percent of saturated acid, and temperature. The performance and efficiency of the GPR model are measured through several statistical criteria and the results are summarized in root mean square error (RMSE) and coefficients of determination ( R 2 ). R 2 and RMSE are sorted as 0.992 & 0.15697, 0.998 & 0.96580, 0.966 & 1.38659, and 0.968 & 1.56068 for four properties such as KV, IV, CP, and PP, respectively. It is worth to mention this point that the kernel function Squared Exponential shows a great performance for IV and PP and kernel functions Exponential and Matern indicate appropriate efficiency for CP and KV properties, respectively. On the other hand, the results of the offered GPR models are compared with those of the previous models, LSSVM-PSO and ANFIS. The outcomes proved the superiority of this model over two former models in point of estimating the biodiesel properties.
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Wang, Shicheng, Wei Li i Issam Alruyemi. "On the Investigation of Effective Factors on Higher Heating Value of Biodiesel: Robust Modeling and Data Assessments". BioMed Research International 2021 (12.07.2021): 1–9. http://dx.doi.org/10.1155/2021/4814888.
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Higher heating value (HHV) is one of the properties of biomass fuels which is essential in investigating their special characteristics and potentialities. In this paper, various techniques based on Gaussian process regression (GPR) were utilized to assess this value for biomass fuels, including several kernel functions, i.e., exponential, Matern, rational quadratic, and squared exponential. An extensive databank was collected from literature. The findings were compared, and the results indicated that Exponential-based model was more accurate, with the coefficient of regression ( R 2 ) of 0.961 and the mean relative error (% MRE) of 3.11 for total data. Compared to former models presented by previous researchers, the model proposed in this study showed a higher ability to predict output values. With various analyses, it can be concluded that the proposed method has a high rate of efficiency in assessing the HHV of various biomass.
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Elbeltagi, Ahmed, Nikul Kumari, Jaydeo Dharpure, Ali Mokhtar, Karam Alsafadi, Manish Kumar, Behrouz Mehdinejadiani i in. "Prediction of Combined Terrestrial Evapotranspiration Index (CTEI) over Large River Basin Based on Machine Learning Approaches". Water 13, nr 4 (20.02.2021): 547. http://dx.doi.org/10.3390/w13040547.
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Drought is a fundamental physical feature of the climate pattern worldwide. Over the past few decades, a natural disaster has accelerated its occurrence, which has significantly impacted agricultural systems, economies, environments, water resources, and supplies. Therefore, it is essential to develop new techniques that enable comprehensive determination and observations of droughts over large areas with satisfactory spatial and temporal resolution. This study modeled a new drought index called the Combined Terrestrial Evapotranspiration Index (CTEI), developed in the Ganga river basin. For this, five Machine Learning (ML) techniques, derived from artificial intelligence theories, were applied: the Support Vector Machine (SVM) algorithm, decision trees, Matern 5/2 Gaussian process regression, boosted trees, and bagged trees. These techniques were driven by twelve different models generated from input combinations of satellite data and hydrometeorological parameters. The results indicated that the eighth model performed best and was superior among all the models, with the SVM algorithm resulting in an R2 value of 0.82 and the lowest errors in terms of the Root Mean Squared Error (RMSE) (0.33) and Mean Absolute Error (MAE) (0.20), followed by the Matern 5/2 Gaussian model with an R2 value of 0.75 and RMSE and MAE of 0.39 and 0.21 mm/day, respectively. Moreover, among all the five methods, the SVM and Matern 5/2 Gaussian methods were the best-performing ML algorithms in our study of CTEI predictions for the Ganga basin.
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Krueger, Lori E., Jui-Ching Wu, Meng-Fu Bryan Tsou i Lesilee S. Rose. "LET-99 inhibits lateral posterior pulling forces during asymmetric spindle elongation in C. elegans embryos". Journal of Cell Biology 189, nr 3 (26.04.2010): 481–95. http://dx.doi.org/10.1083/jcb.201001115.
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Cortical pulling on astral microtubules positions the mitotic spindle in response to PAR polarity cues and G protein signaling in many systems. In Caenorhabditis elegans single-cell embryos, posterior spindle displacement depends on Gα and its regulators GPR-1/2 and LIN-5. GPR-1/2 and LIN-5 are necessary for cortical pulling forces and become enriched at the posterior cortex, which suggests that higher forces act on the posterior spindle pole compared with the anterior pole. However, the precise distribution of cortical forces and how they are regulated remains to be determined. Using spindle severing, single centrosome assays, and centrosome fragmentation, we show that both the anterior and posterior cortices generate more pulling force than the lateral–posterior region. Lateral inhibition depends on LET-99, which inhibits GPR-1/2 localization to produce a bipolar GPR-1/2 pattern. Thus, rather than two domains of cortical force, there are three. We propose that the attenuation of lateral forces prevents counterproductive pulling, resulting in a higher net force toward the posterior that contributes to spindle elongation and displacement.
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Kiehn, O., i R. M. Harris-Warrick. "Serotonergic stretch receptors induce plateau properties in a crustacean motor neuron by a dual-conductance mechanism". Journal of Neurophysiology 68, nr 2 (1.08.1992): 485–95. http://dx.doi.org/10.1152/jn.1992.68.2.485.
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1. The mechanisms for induction of bistable plateau potential properties by a set of serotonergic/cholinergic peripheral stretch receptor cells [gastropyloric receptor (GPR) cells] were examined in the crab stomatogastric ganglion (STG) with the use of intracellular recording techniques. 2. GPR cell stimulation evoked nicotinic excitatory postsynaptic potentials (EPSPs) and induced plateau potential capability in the dorsal gastric (DG) motor neuron. The plateau potential could be triggered during a GPR train either by the summating nicotinic EPSPs or by brief intracellular current injection. After pharmacological blockade of nicotinic and muscarinic receptors, a slow depolarization in response to GPR stimulation was revealed. Prolonged plateau potentials could still be evoked after this treatment. Local application of serotonin (5-HT; 10 microM to 1 mM) mimicked the noncholinergic plateau inducing effects of GPR stimulation in the DG motor neuron. 3. The synergistic action of acetylcholine (ACh) and 5-HT was examined by stimulating the GPR cells at different frequencies (1-20 Hz). The plateau induction was present down to 2 Hz. The time to onset for triggering a plateau during a GPR train was determined by the co-released ACh. 4. The 5-HT-evoked slow depolarization persisted in tetrodotoxin (TTX; 0.1-1 microM), and the DG motor neuron could still produce a plateau potential on brief depolarization in the absence of the spike-generating mechanism. 5. In normal TTX-containing saline, the 5-HT-evoked depolarization was accompanied by a weak and variable decrease in apparent input conductance. After substituting one-half of the extracellular sodium with either Trisma-HCl or choline, the decrease in apparent input conductance became more pronounced. This decrease was converted to an increase in apparent input conductance when extracellular Ca2+ was replaced with Mg2+. 6. Under voltage-clamp conditions, local application of 5-HT caused a slow inward current of prolonged duration in DG. The current versus voltage relationship had an inverted U-shape with no apparent reversal potential in the entire voltage range investigated (-90 to -5 mV). The 5-HT-induced changes in input conductance showed a complex voltage dependence, with a conductance decrease from moderately depolarized voltages. 7. Extracellular Cs+ (2-4 mM) caused the DG to hyperpolarize 2-4 mV from rest, whereas lowering extracellular Ca2+ caused it to depolarize 7-15 mV. The combined action of low extracellular Ca2+ and 2-4 mM Cs+ caused an almost complete block of the slow 5-HT-evoked depolarization.(ABSTRACT TRUNCATED AT 400 WORDS)
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Zhang, B., i R. M. Harris-Warrick. "Multiple receptors mediate the modulatory effects of serotonergic neurons in a small neural network." Journal of Experimental Biology 190, nr 1 (1.05.1994): 55–77. http://dx.doi.org/10.1242/jeb.190.1.55.
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The gastropyloric receptor (GPR) cells are a set of cholinergic/serotonergic mechanosensory neurons that modulate the activity of neural networks in the crab stomatogastric ganglion (STG). Stimulation of these cells evokes a variety of slow modulatory responses in different STG neurons that are mimicked by exogenously applied serotonin (5-HT); these responses include tonic inhibition, tonic excitation and induction of rhythmic bursting. We used pharmacological agonists and antagonists to show that these three classes of modulatory response in the STG neurons are mediated by distinct 5-HT receptor subtypes. GPR stimulation or application of 5-HT or 2-me-5HT (a vertebrate 5-HT3 agonist) inhibited the pyloric constrictor (PY) neurons; these actions were selectively antagonized by gramine. GPR stimulation or application of 5-HT induced rhythmic bursting in the electrically coupled anterior burster (AB) and pyloric dilator (PD) neurons; these effects were antagonized by the 5-HT1c/2 antagonist cinanserin and by atropine at concentrations that do not block muscarinic cholinergic receptors in the crab STG. The 5-HT agonists 5-CT (5-HT1) and alpha-me-5HT (5-HT2) also induced AB/PD bursting, which was blocked by cinanserin, but not by atropine. GPR stimulation or application of 5-HT and 5-CT evoked tonic excitation of the lateral pyloric (LP) neuron. These effects were blocked by cinanserin. Several other 5-HT agonists and nearly all the vertebrate 5-HT antagonists we tested had little or no effect on the crab pyloric 5-HT receptors. These results provide further evidence that the modulatory sensory GPR neuron uses serotonin to evoke multiple modulatory responses via multiple 5-HT receptors. However, the 5-HT receptors in the crab STG neurons are not pharmacologically similar to vertebrate 5-HT receptors.
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Nugroho, Untoro, i Norick Cahya Jonathan Susanto. "Comparison of GPR (Ground Penetrating Radar) Penetration Results Using the GCB-400 Antenna on Flexible and Rigid Pavement". IOP Conference Series: Earth and Environmental Science 1203, nr 1 (1.06.2023): 012022. http://dx.doi.org/10.1088/1755-1315/1203/1/012022.
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Abstract GPR (Ground Penetrating Radar) has been known as a tool used in non-destructive testing or in other words GPR can find out the conditions below the surface without damaging the layer above it. The GPR method is generally applied to roads namely flexible pavements and rigid pavements. This method has non-destructive properties because it utilizes electromagnetic waves in its operation, such as detecting the presence of objects or constituent layers at a certain depth. The penetration depth that can be achieved by GPR is influenced by three physical parameters, namely conductivity, relative dielectric permittivity (RDP), and magnetic permeability. In this study, GPR used the GCB-400 antenna model produced by the Swedish company Geoscanners. The research was conducted on roads with 2 different types of materials, namely asphalt (flexible pavement) and concrete (rigid pavement). Asphalt and concrete have different relative dielectric permittivity (RDP), where asphalt has an RDP of 5 and concrete has an RDP of 5.5. The results showed that there were significant differences between roads with asphalt and roads with concrete materials. On roads with asphalt material, GPR can reach a depth of more than 3 meters. On roads with concrete materials, GPR can only reach a depth of less than 2 meters.
Rozprawy doktorskie na temat "MATERN 5/2 GPR"
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BOBAN, AMMU. "DYNAMIC ANALYSIS OF CONFINED GEOMATERIAL SUBJECTED TO VIBRATORY LOADING". Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20018.
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The reinforcement using single-layer geogrid on a confined geomaterial is an effective
way of solving practical problems. With the use of multi-layered reinforcement, this study
aims to offer a superior alternative design for supporting heavy loads on geomaterial. The
location of reinforcement plays a crucial role in the overall strength. The experimental
investigations were conducted on poorly graded sand (SP) whose angle of internal friction
and cohesion are 36.6º and 4.4 kPa, respectively. The effect of single-layer geogrid
reinforcement placed at different depths of the geomaterial was evaluated. Further, the
bearing capacity of the geomaterial was compared for single, double and triple-layered
geogrid reinforcement. A laboratory Digital Static Cone Penetration Test (DSCP) was
performed to assess the load-displacement behaviour of unreinforced and reinforced
geomaterial. The result shows that reinforced geomaterial achieved higher resistance
compared to unreinforced systems. An optimum combination of placement depths of
double-layered reinforcement is proposed.
Additionally, the dynamic response of the confined geomaterial subjected to vibratory
load has been investigated using a numerical program supported by experimental
findings. An accelerometer has been used to report the acceleration, velocity, and
displacement of confined geomaterial fill along the depth at varied frequencies of
vibratory load. Further, the experimental findings were used in the numerical program to
obtain the shear modulus and damping of confined geomaterial. The stress-strain response
shows compounded effects with an increase in frequency and modulus of elasticity. It has
been observed that displacement is amplified by 10-90 % for a frequency range of 5-75
iv
Hz. The shear stress-strain results showed that the shear modulus is magnified by 50 %
for varied input parameters considered in the study. The damping of the confined
geomaterial has been found to be 0.5-5 % for varied unit weight inputs (16 - 22 kN/m3
).
The results are compared with the outputs obtained by numerical simulation and
experimental analysis for estimating the dynamic properties of the confined geomaterial
subjected to vibratory load.
Further, the study utilized various numerical simulation and experimental data to train
and evaluate different models to generate predictions. These predictions were essential
for the research. The models employed included ensemble boosted tree, squared
exponential Gaussian Process Regression (GPR), Matern 5/2 GPR, exponential GPR, and
decision tree architectures (fine and medium). These models greatly facilitated the
analysis of the collected data and enabled accurate result predictions. Among the
examined models, the Matern 5/2 GPR model exhibited exceptional accuracy with an R2
value of 0.99, demonstrating its remarkable predictive capability. The outcomes highlight
the proficiency of the Matern 5/2 GPR and Boosted Tree models in forecasting
displacement patterns and enhancing the comprehension of the relationship between
displacement and depth. The outcomes of the present study can effectively be adopted by
engineers and partitioners for estimating the dynamic properties of the confined
geomaterial in construction practices.
Części książek na temat "MATERN 5/2 GPR"
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Laletsang, Kebabonye, i Baram Montshiwa. "Case Study". W Advances in Geospatial Technologies, 213–29. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3440-2.ch014.
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A geophysical survey was carried out in 2012 to map the subsurface geology at a quarry and the village to determine if an alleged damage to houses could have been caused by blasting activity in the quarry. The survey included seismic refraction and reflection recorded with the same spread at source and receiver spacing of 2 m, 3 m, and 5 m. The signal source was a 28-lb. sledge hammer stacked 10 times to increase the S/N. Ground penetrating radar (GPR) was also recorded at a trace spacing of 0.3 m to provide detailed images of the subsurface near the damaged buildings using a 50 MHz unshielded antenna. The seismic refraction and reflection profiles show that the village is underlain by 0-4 m of loose sand, 4-10 m of fractured calcrete, and up to 50 m of fractured silcrete, with the groundwater table at 10-15 m. The GPR images show that the damaged houses sit on thick sand, which cushions them against severe ground vibration. The houses were found to have suffered more damage on the superstructure and this is possibly due to ground vibration amplification at low frequencies of 30-40 Hz.
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Zhou, Chenxi, i Gang Shen. "Tracking Underground Metro Cars with Low-Cost Acceleration Sensors". W Advances in Transdisciplinary Engineering. IOS Press, 2021. http://dx.doi.org/10.3233/atde210103.
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Subways and other rapid transit systems are marked symbols of the modern metropolis. As a transdisciplinary service, accurately and safely positioning and tracking the metro trains helps the passengers to plan their travels and provides the operators with auxiliary information about the trains to enhance the metro system’s resilience. However, many general-purpose positioning technologies, such as Global Navigation Satellite Systems (GNSS) and Wi-Fi signals, do not apply to the situations of underground metro trains. In this paper, we propose a two-stage framework for automatic real-time tracking of metro cars implemented only with low-cost accelerometers, saving the expense for complicated infrastructures. In the off-line stage, reference maps are developed for station-to-station track sections using the onboard acceleration data. To handle the missing data and uncalibrated consumer-grade sensors, Gaussian process regression (GPR) is adopted to denoise and interpolate the online acceleration readings, followed by the application of the Kalman filter algorithm to track the cars in real-time with the help of the reference map. We tested the proposed system in Wuhan Metro Line 2, and the results showed that our system achieved an error below 5% in positioning.
Streszczenia konferencji na temat "MATERN 5/2 GPR"
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Pal, Basudha, Vikas R. Bhat i Anitha H. "The SIUQRD and Matern 5/2 GPR Models describing the Covid-19 Pandemic in India". W 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2021. http://dx.doi.org/10.1109/icecct52121.2021.9616706.
Pełny tekst źródłaRaporty organizacyjne na temat "MATERN 5/2 GPR"
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Gupta, Kanika, Sung Soo Park, Antonio Bobet i Tommy Nantung. Improved Reliability of FWD Test Results and Correlations with Resilient Modulus. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317370.
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Resilient modulus (MR) is a key factor in the Mechanistic Empirical Pavement Design Guide (MEPDG) which was adopted by INDOT in January 2009. The resilient modulus can be determined in new pavement projects from subgrade soil samples collected at the site. However, for a pavement rehabilitation project, it becomes difficult to obtain soil information, and coring for samples may not be feasible because of traffic. The literature is rich with correlations between the MR of the subgrade obtained in the laboratory with that estimated from FWD tests in the field. However, the review conducted shows very contradictory and inconsistent findings, thus existing correlations seem to apply only to the cases investigated, i.e., they are ad hoc correlations and cannot be generalized. To improve the interpretation of the FWD data and enhance the reliability of the results in Indiana, FWD and GPR tests were performed at five different road construction projects at the same locations where soil samples were collected and tested in the laboratory for resilient modulus. The selected sites included roads with rigid pavement and treated subgrade and flexible pavement with untreated subgrade. The study showed that (1) FWD backcalculation is greatly affected by pavement thickness; (2) GPR can provide actual thicknesses and can identify discrepancies between as-built and design pavement thickness; (3) for flexible pavements, a one-to-one correlation exists between FWD modulus and laboratory resilient modulus values for untreated subgrade soils; (4) MODTAG or ELMOD codes can both be used to estimate the resilient modulus of the subgrade in flexible pavements; (5) for rigid pavements, results of FWD backcalculation analysis using ELMOD or MODTAG greatly overestimate the resilient modulus of the soil, with backcalculated moduli 1.3 to 6 times higher than laboratory results; (6) ELMOD is recommended for routine analysis of FWD data, while MODTAG is recommended for research or to evaluate the quality of the data, when needed.