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Journal articles on the topic 'Estimations de gradient'
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Liu, Zijian, Chunbo Luo, Peng Ren, Tingwei Wang, and Geyong Min. "Population based optimization via differential evolution and adaptive fractional gradient descent." Filomat 34, no. 15 (2020): 5173–85. http://dx.doi.org/10.2298/fil2015173l.
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We propose a differential evolution algorithm based on adaptive fractional gradient descent (DE-FGD) to address the defects of existing bio-inspired algorithms, such as slow convergence speed and local optimum. The crossover and selection processes of the differential evolution algorithm are discarded and the adaptive fractional gradients are adopted to enhance the global searching capability. For the benchmark functions, our proposed algorithm Specifically, our method has higher searching accuracy than several state of the art bio-inspired algorithms. Furthermore, we apply our method to specific tasks - parameters estimation of system response functions and approximate value functions. Experiment results validate that our proposed algorithm produces accurate estimations and improves searching efficiency
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Håkegård, Jan Erik, Mohammed Ouassou, Nadezda Sokolova, and Aiden Morrison. "Assessment and Validation of Small-Scale Tropospheric Delay Estimations Based on NWP Data." Sensors 24, no. 20 (October 12, 2024): 6579. http://dx.doi.org/10.3390/s24206579.
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This paper investigates the applicability of the Numerical Weather Prediction (NWP) data for characterizing the gradient of zenith wet delay in horizontal direction observed on short baselines over larger territories. A three-year period of data for an area covering Scandinavia and Finland is analyzed, and maximum gradients during the considered period are identified. To assess the quality of the NWP-based estimates, results for a smaller region are compared with the estimates obtained using Global Navigation Satellite System (GNSS) measurements processed by the GipsyX/RTGx software package (version 2.1) from a cluster of GNSS reference stations. Additionally, the NWP data from 7 to 9 August 2023 covering a period that includes a storm with high rain intensities over Southern Norway leading to sustained flooding are processed and analyzed to assess if the gradient of zenith wet delay in the horizontal direction increases significantly during such events. The results show that maximum gradients in the range of 40–50 mm/km are detected. When comparing NWP-based estimates to GNSS-based estimates, the tropospheric delays show a very strong correlation. The tropospheric gradients, however, show a weak correlation, probably due to the uncertainty in the NWP data exceeding the gradient values. The data captured during the storm show that while the tropospheric delay increases significantly it is difficult to see increases in the gradient of zenith wet delay in the horizontal direction using this data source and resolution.
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Kong, Xiangsong, and Dongbin Zheng. "A Knowledge-Informed Simplex Search Method Based on Historical Quasi-Gradient Estimations and Its Application on Quality Control of Medium Voltage Insulators." Processes 9, no. 5 (April 28, 2021): 770. http://dx.doi.org/10.3390/pr9050770.
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Quality control is of great significance for the economical manufacturing and reliable application of medium voltage insulators. With the increasingly stringent quality control requirement, traditional quality control methods in this field face a growing challenge on their efficiency. Therefore, this study aims to achieve quality specifications by optimizing process conditions with the least costs. Thus, a knowledge-informed simplex search method was proposed based on an idea of knowledge-informed optimization to enhance the optimization efficiency. Firstly, a new mathematical quantity, quasi-gradient estimation, was generated following a reconstruction of the simplex search from the essence and the development history of the method. Based on this quantity, the gradient-free method possessed the same gradient property and unified form as the gradient-based methods. Secondly, an implementation of the knowledge-informed simplex search method based on historical quasi-gradient estimations (short for GK-SS) was constructed. The GK-SS-based quality control method utilized the historical quasi-gradient estimations for each simplex generated during the optimization process to improve the method’s search directions’ accuracy in a statistical sense. Finally, this method was applied to the weight control of a kind of post insulator. The experimental simulation results showed that the method is effective and efficient in the quality control of medium voltage insulators.
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Akgul, Volkan, Gokhan Gurbuz, Senol Hakan Kutoglu, and Shuanggen Jin. "Effects of the High-Order Ionospheric Delay on GPS-Based Tropospheric Parameter Estimations in Turkey." Remote Sensing 12, no. 21 (October 31, 2020): 3569. http://dx.doi.org/10.3390/rs12213569.
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The tropospheric delay and gradients can be estimated using Global Positioning System (GPS) observations after removing the ionospheric delay, which has been widely used for atmospheric studies and forecasting. However, high-order ionospheric (HOI) delays are generally ignored in GPS processing to estimate atmospheric parameters. In this study, HOI effects on GPS-estimated tropospheric delay and gradients are investigated from two weeks of GPS data in June 2011 at selected GPS stations in Turkey. Results show that HOI effects are up to 6 mm on zenith tropospheric delay (ZTD), 4 mm on the North-South (NS) gradient and 12 mm on the East-West (EW) gradient during this period, but can reach over 30 mm in slant tropospheric delays. Furthermore, the HOI effects on tropospheric delay and gradient are larger in the daytime than the nighttime. Furthermore, HOI effects on tropospheric delay are further investigated on low and high solar activity days. The HOI effects on GPS estimated tropospheric delay and gradients in high solar activity days are higher than those in low solar activity days.
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Dai, Yaqiao, Renjiao Yi, Chenyang Zhu, Hongjun He, and Kai Xu. "Multi-Resolution Monocular Depth Map Fusion by Self-Supervised Gradient-Based Composition." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (June 26, 2023): 488–96. http://dx.doi.org/10.1609/aaai.v37i1.25123.
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Monocular depth estimation is a challenging problem on which deep neural networks have demonstrated great potential. However, depth maps predicted by existing deep models usually lack fine-grained details due to convolution operations and down-samplings in networks. We find that increasing input resolution is helpful to preserve more local details while the estimation at low resolution is more accurate globally. Therefore, we propose a novel depth map fusion module to combine the advantages of estimations with multi-resolution inputs. Instead of merging the low- and high-resolution estimations equally, we adopt the core idea of Poisson fusion, trying to implant the gradient domain of high-resolution depth into the low-resolution depth. While classic Poisson fusion requires a fusion mask as supervision, we propose a self-supervised framework based on guided image filtering. We demonstrate that this gradient-based composition performs much better at noisy immunity, compared with the state-of-the-art depth map fusion method. Our lightweight depth fusion is one-shot and runs in real-time, making it 80X faster than a state-of-the-art depth fusion method. Quantitative evaluations demonstrate that the proposed method can be integrated into many fully convolutional monocular depth estimation backbones with a significant performance boost, leading to state-of-the-art results of detail enhancement on depth maps. Codes are released at https://github.com/yuinsky/gradient-based-depth-map-fusion.
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Ogunsanwo, F. O., O. T. Olurin, J. D. Ayanda, S. A. Ganiyu, and A. O. Mustapha. "Estimation of ground depth of radioelements sources in Ogun State, Southwestern Nigeria using gradient techniques." Ife Journal of Science 25, no. 2 (August 31, 2023): 319–30. http://dx.doi.org/10.4314/ijs.v25i2.10.
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Radioelement exploration has gained economic interest recently due to its usefulness in the detection and delineation of mineral deposits. In this study, the airborne radiometric data were analysed for depth estimation of the radioelement deposit in Ogun state, Nigeria. Three enhancement gradient techniques, namely; Analytical Signal Amplitude (ASA), Horizontal Gradient Magnitude (HGM) and Local Wave Number (LWN) were employed to estimate the possible depth of radioelements for mineralization. Geosoft's (Oasis Montaj) software and Potential Field's (PF) software were used to conduct the estimations. The result obtained revealed shallow sources of 0.584 km (LWN) and 0.387 km (ASA), and deep-seated sources of 5.950 km (ASA) and 5.880 km (ASA) for uranium and thorium, respectively. The shallow source and deep source for potassium are 0.259 km (ASA) and 2.540 km (ASA), respectively. In this study, the position and depth of the source were automatically estimated using linear equations based on derivatives without the use of any a priori knowledge. The three gradient methods are therefore found suitable in estimating the depth to radioelement anomalous source.
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Goldblatt, Jennifer, Andrew Ward, Mohammed Yusuf, Martin Day, Gauri Godbole, and Stephen Morris-Jones. "Azithromycin susceptibility testing for Salmonella enterica isolates: discordances in results using MIC gradient strips." Journal of Antimicrobial Chemotherapy 75, no. 7 (March 28, 2020): 1820–23. http://dx.doi.org/10.1093/jac/dkaa097.
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Abstract Background Azithromycin resistance is emerging in typhoidal Salmonella. Confirmation of azithromycin MIC is the most frequent antibiotic susceptibility request made to the Gastrointestinal Bacteria Reference Unit (GBRU) laboratory in England by local diagnostic laboratories. Objectives (i) Determine concordance between local diagnostic and reference laboratory estimations of azithromycin MIC by gradient strip in Salmonella enterica serovars Typhi and Paratyphi. (ii) Consider causes of variation. Methods Isolates from patients with enteric fever attending a central London hospital between May 2011 and April 2019 were tested for azithromycin susceptibility using gradient strips, according to EUCAST methodology. Matched local diagnostic and reference laboratory estimations of azithromycin and ciprofloxacin (as a comparator) MICs were included; concordance in estimations was examined. Results Local diagnostic laboratory readings overestimated azithromycin MIC values compared with the reference laboratory, resulting in poor concordance in susceptibility/resistance attribution (concordant susceptibility interpretation in 8/19, κ = 0). In contrast, ciprofloxacin MIC estimation demonstrated superior concordance (concordant susceptibility interpretation in 16/17, κ = 0.85). None of the isolates was resistant to azithromycin at the reference laboratory and no known genes associated with azithromycin resistance were detected in any isolate using WGS. Conclusions Overestimation of azithromycin resistance is likely to be due to difficulty in interpreting the point of intersection of the ‘trailing edge’ with the gradient strip, used to determine MIC. We advise local diagnostic laboratories to review their experience and consider adopting a ‘second reader’ system to mitigate this.
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Grünewald, Thomas, and Michael Lehning. "Altitudinal dependency of snow amounts in two small alpine catchments: can catchment-wide snow amounts be estimated via single snow or precipitation stations?" Annals of Glaciology 52, no. 58 (2011): 153–58. http://dx.doi.org/10.3189/172756411797252248.
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AbstarctThe spatial distribution and the local amount of snow in mountainous regions strongly depend on the spatial characteristics of snowfall, snow deposition and snow redistribution. Uniform altitudinal gradients can only represent a part of these influences but are without alternative for use in larger-scale models. How well altitudinal gradients represent the true snow distribution has not been assessed. We analyse altitudinal characteristics of snow stored in two high-alpine catchments in Switzerland. Peak winter snow depths were monitored using high-resolution airborne laser scanning technology. These snow depths were transferred to snow water equivalent by applying simple density estimations. From these data, altitudinal gradients were calculated for the total catchment areas and for selected subareas characterized by different accumulation patterns. These gradients were then compared with gradients resulting from automated snow depth measurements obtained from several snow stations on different height levels located in the catchments, and with estimations from climatological precipitation gradients. The analysis showed that neither precipitation gradients nor flat-field stations estimate catchment-wide snow amounts accurately. While the climatological gradient showed different trends for different areas and years, the snow stations tended to overestimate mean snow amounts.
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Zonia, Laura, and Dennis Bray. "Swimming patterns and dynamics of simulated Escherichia coli bacteria." Journal of The Royal Society Interface 6, no. 40 (February 25, 2009): 1035–46. http://dx.doi.org/10.1098/rsif.2008.0397.
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A spatially and temporally realistic simulation of Escherichia coli chemotaxis was used to investigate the swimming patterns of wild-type and mutant bacteria within a rectangular arena in response to chemoattractant gradients. Swimming dynamics were analysed during long time series with phase-space trajectories, power spectra and estimations of fractal dimensions (FDs). Cell movement displayed complex trajectories in the phase space owing to interaction of multiple attractors that captured runs and tumbles. Deletion of enzymes responsible for adaptation (CheR and CheB) restricted the pattern of bacterial swimming in the absence of a gradient. In the presence of a gradient, there was a strong increase in trajectories arising from runs and attenuation of those arising from tumbles. Similar dynamics were observed for mutants lacking CheY, which are unable to tumble. The deletion of CheR, CheB and CheY also caused significant shifts in chemotaxis spectral frequencies. Rescaled range analysis and estimation of FD suggest that wild-type bacteria display characteristics of fractional Brownian motion with positive correlation between past and future events. These results reveal an underlying order in bacterial swimming dynamics, which enables a chemotactic search strategy conforming to a fractal walk.
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Wen, Jiawei, Zhe Ouyang, Donghu Nie, and Cong Ren. "Fast Parameter Estimation of Linear Frequency Modulation Signals in Marine Environments Based on Gradient Optimization Strategy." Journal of Marine Science and Engineering 12, no. 12 (December 1, 2024): 2195. https://doi.org/10.3390/jmse12122195.
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Multi-buoy sonar systems achieve target localization by receiving broadband Linear Frequency Modulation signals emitted from the transmitter. Accurate estimations of the parameters of Linear Frequency Modulation signals significantly enhance the localization accuracy. Linear Frequency Modulation signals can be focused into the fractional domain through Fractional Fourier Transform, but this increases the computational complexity. In marine environments, the low signal-to-noise ratio and multipath effects degrade the parameter estimation accuracy further. To address these issues, this paper proposes a fast estimation algorithm based on the Fractional Fourier Transform and a Gradient Subtraction-Average-Based Optimizer. This algorithm leverages the impulsive characteristics of Linear Frequency Modulation signals after Fractional Fourier Transform transformation, using the Fractional Fourier Transform as the fitness function. The Gradient Subtraction-Average-Based Optimizer algorithm includes three enhancement strategies: chaotic mapping initialization, a Golden Sine Algorithm, and an adaptive t-distribution variational operator. The simulation results demonstrate that the Gradient Subtraction-Average-Based Optimizer algorithm improves the issues of low diversity in the search agents, imbalanced global and local search capabilities, and susceptibility to local optima. A comparative analysis and statistical testing confirm that under a low signal-to-noise ratio and multipath effect conditions, the Gradient Subtraction-Average-Based Optimizer algorithm not only ensures real-time parameter estimation but also improves the estimation accuracy. The results of the parameter estimation provide reliable data support for subsequent target localization.
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Zhao, Chen, Xia Zhao, Zhao Li, and Qiong Zhang. "XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway." Sustainability 14, no. 11 (June 2, 2022): 6829. http://dx.doi.org/10.3390/su14116829.
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This study is conducted on a real live highway to investigate the driver’s performance in estimating the speed and distance of vehicles behind the target lane during lane changes. Data on the participants’ estimated and actual data on the rear car were collected in the experiment. Ridge regression is used to analyze the effects of both the driver’s features, as well as the relative and absolute motion characteristics between the target vehicle and the subject vehicle, on the driver’s estimation outcomes. Finally, a mixed algorithm of extreme gradient boosting (XGBoost) and deep neural network (DNN) was proposed in this paper for establishing driver’s speed estimation and distance prediction models. Compared with other machine learning models, the XGBoost-DNN prediction model performs more accurate prediction performance in both classification scenarios. It is worth mentioning that the XGBoost-DNN mixed model exhibits a prediction accuracy approximately two percentage points higher than that of the XGBoost model. In the two-classification scenarios, the accuracy estimations of XGBoost-DNN speed and distance prediction models are 91.03% and 92.46%, respectively. In the three-classification scenarios, the accuracy estimations of XGBoost-DNN speed and distance prediction models are 87.18% and 87.59%, respectively. This study can provide a theoretical basis for the development of warning rules for lane-change warning systems as well as insights for understanding lane-change decision failures.
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Su, Hua, Xin Yang, Wenfang Lu, and Xiao-Hai Yan. "Estimating Subsurface Thermohaline Structure of the Global Ocean Using Surface Remote Sensing Observations." Remote Sensing 11, no. 13 (July 5, 2019): 1598. http://dx.doi.org/10.3390/rs11131598.
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Retrieving multi-temporal and large-scale thermohaline structure information of the interior of the global ocean based on surface satellite observations is important for understanding the complex and multidimensional dynamic processes within the ocean. This study proposes a new ensemble learning algorithm, extreme gradient boosting (XGBoost), for retrieving subsurface thermohaline anomalies, including the subsurface temperature anomaly (STA) and the subsurface salinity anomaly (SSA), in the upper 2000 m of the global ocean. The model combines surface satellite observations and in situ Argo data for estimation, and uses root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), and R2 as accuracy evaluations. The results show that the proposed XGBoost model can easily retrieve subsurface thermohaline anomalies and outperforms the gradient boosting decision tree (GBDT) model. The XGBoost model had good performance with average R2 values of 0.69 and 0.54, and average NRMSE values of 0.035 and 0.042, for STA and SSA estimations, respectively. The thermohaline anomaly patterns presented obvious seasonal variation signals in the upper layers (the upper 500 m); however, these signals became weaker as the depth increased. The model performance fluctuated, with the best performance in October (autumn) for both STA and SSA, and the lowest accuracy occurred in January (winter) for STA and April (spring) for SSA. The STA estimation error mainly occurred in the El Niño-Southern Oscillation (ENSO) region in the upper ocean and the boundary of the ocean basins in the deeper ocean; meanwhile, the SSA estimation error presented a relatively even distribution. The wind speed anomalies, including the u and v components, contributed more to the XGBoost model for both STA and SSA estimations than the other surface parameters; however, its importance at deeper layers decreased and the contributions of the other parameters increased. This study provides an effective remote sensing technique for subsurface thermohaline estimations and further promotes long-term remote sensing reconstructions of internal ocean parameters.
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Fu, Hu Dai, Hua Wang, and Jin Gang Gao. "Multicircle-Finding Probabilistic Hough Transform Based on Incorporating Gradient Estimations." Advanced Materials Research 422 (December 2011): 24–28. http://dx.doi.org/10.4028/www.scientific.net/amr.422.24.
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It present a new Probabilistic Hough Transform algorithm to detect circles in this paper. The algorithm can reduce the generation of redundant evidence in two ways. Firstly, it uses point-pairs to define circles by applying gradient information. Consequently the sampling complexity was decreased from three dimensions to two dimensions. Secondly, not all the pairs are eligible to vote, because the transformation is conditional. The evidence is gathered in a very sparse parameter space, so that peak recovery is performed readily. The result is proved that the detection accuracy increases and the memory resources decreases. Illustrative examples demonstrate the detection accuracy of the algorithm.
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Goulermas, J. Y., and P. Liatsis. "Incorporating Gradient Estimations in a Circle-Finding Probabilistic Hough Transform." Pattern Analysis & Applications 2, no. 3 (August 17, 1999): 239–50. http://dx.doi.org/10.1007/s100440050032.
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Hui, Shyamal Kumar, Abimbola Abolarinwa, and Sujit Bhattacharyya. "Gradient Estimations for Nonlinear Elliptic Equations on Weighted Riemannian Manifolds." Lobachevskii Journal of Mathematics 44, no. 4 (April 2023): 1341–49. http://dx.doi.org/10.1134/s1995080223040121.
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Lv, Shao-Gao. "Refined Generalization Bounds of Gradient Learning over Reproducing Kernel Hilbert Spaces." Neural Computation 27, no. 6 (June 2015): 1294–320. http://dx.doi.org/10.1162/neco_a_00739.
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Gradient learning (GL), initially proposed by Mukherjee and Zhou ( 2006 ) has been proved to be a powerful tool for conducting variable selection and dimensional reduction simultaneously. This approach presents a nonparametric version of a gradient estimator with positive definite kernels without estimating the true function itself, so that the proposed version has wide applicability and allows for complex effects between predictors. In terms of theory, however, existing generalization bounds for GL depend on capacity-independent techniques, and the capacity of kernel classes cannot be characterized completely. Thus, this letter considers GL estimators that minimize the empirical convex risk. We prove generalization bounds for such estimators with rates that are faster than previous results. Moreover, we provide a novel upper bound for Rademacher chaos complexity of order two, which also plays an important role in general pairwise-type estimations, including ranking and score problems.
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Aguirre, Ana, Miren del Río, and Sonia Condés. "Productivity Estimations for Monospecific and Mixed Pine Forests along the Iberian Peninsula Aridity Gradient." Forests 10, no. 5 (May 18, 2019): 430. http://dx.doi.org/10.3390/f10050430.
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National Forest Inventories (NFIs) are the primary source of information to fulfill international requirements, such as growing stock volume. However, NFI cycles may be “out of phase” in terms of the information required, so prediction techniques are needed. To disentangle the effects of climate and competition on stand productivity and to estimate the volume of stocks at national scale, it is important to recognize that growth and competition are species-specific and vary along climatic gradients. In this study, we estimate the productivity of five pine species (Pinus sylvestris, Pinus pinea, Pinus halepensis, Pinus nigra and Pinus pinaster), growing in monospecific stands or in mixtures along an aridity gradient in the Iberian Peninsula, based on Spanish NFI data. We study the stand volume growth efficiency (VGE), since it allows the comparison of volume growth in monospecific and mixed stands. The results reveal the importance of considering the aridity when assessing VGE. Moreover, it was found that, in general, admixture among pine species leads to modifications in the VGE, which can vary from negative to positive effects depending on species composition, and that this is always influenced by the aridity. Finally, we provide simple growth efficiency models for the studied pines species which are valid for both monospecific and mixed stands along the aridity gradient of the Iberian Peninsula.
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Martínez-Comesaña, Miguel, Lara Febrero-Garrido, Enrique Granada-Álvarez, Javier Martínez-Torres, and Sandra Martínez-Mariño. "Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models." Applied Sciences 10, no. 24 (December 16, 2020): 8968. http://dx.doi.org/10.3390/app10248968.
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The Heat Loss Coefficient (HLC) characterizes the envelope efficiency of a building under in-use conditions, and it represents one of the main causes of the performance gap between the building design and its real operation. Accurate estimations of the HLC contribute to optimizing the energy consumption of a building. In this context, the application of black-box models in building energy analysis has been consolidated in recent years. The aim of this paper is to estimate the HLC of an existing building through the prediction of building thermal demands using a methodology based on Machine Learning (ML) models. Specifically, three different ML methods are applied to a public library in the northwest of Spain and compared; eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network. Furthermore, the accuracy of the results is measured, on the one hand, using both CV(RMSE) and Normalized Mean Biased Error (NMBE), as advised by AHSRAE, for thermal demand predictions and, on the other, an absolute error for HLC estimations. The main novelty of this paper lies in the estimation of the HLC of a building considering thermal demand predictions reducing the requirement for monitoring. The results show that the most accurate model is capable of estimating the HLC of the building with an absolute error between 4 and 6%.
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Wisaksono, Anggoro. "Novel Direct Downhole Enthalpy Estimation at Geothermal Well in Cubadak, West Sumatra, Indonesia." Formosa Journal of Applied Sciences 1, no. 3 (August 30, 2022): 165–76. http://dx.doi.org/10.55927/fjas.v1i3.882.
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Temperature, pressure and enthalpy data are very important and valuable, both during geothermal drilling and in well operation. Fibre optic distributed temperature sensors (DTS) that offer flexible and robust solutions for geothermal applications and temperature logging, an established method for well temperature measurement. All temperature measurement methods will be combined with reservoir enthalpy estimation using T-p-x state space correlation for H2O-NaCl geothermal brine. A geothermal brine enthalpy estimation model has been developed, based on T-p-x state space correlation using C programming language. This was able to provide estimations of pressure, mass fraction, density and enthalpy, solely based on measured temperature limited to below 300oC. The model can be applied to various geothermal fields with sodium chloride dominated waters. The study treated the mixture of geothermal brine and drilling fluid heated in the well for certain periods as a simulation of geothermal brine at the reservoir. The enthalpy monitoring graph proved useful for monitoring the enthalpy in the reservoir. The results from the model are used for early electrical power estimations of the Cubadak geothermal working area site, based on current geothermal gradient extrapolation of CBD-1 well.
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Tyoh, Andrew A., Etim Daniel Uko, Olatunji S. Ayanninuola, and Onengiyeofori A. Davies. "Effects of Near-Surface Air Temperature on Sub-Surface Geothermal Gradient and Heat Flow in Bornu-Chad Basin, Nigeria." International Journal of Terrestrial Heat Flow and Applications 4, no. 1 (March 26, 2021): 95–102. http://dx.doi.org/10.31214/ijthfa.v4i1.68.
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A study of the effect of near-surface temperature on fields of subsurface geothermal gradient and heat flow has been carried out in the Bornu-Chad Basin, Nigeria, using corrected Bottom-Hole Temperatures (BHTc) lithologic-log data from 9 oil wells. The geothermal gradient using only BHTs ranges from 15.9oCkm-1 to 38.2oCkm-1 with an average of 26.9+/-3.5oCkm-1, while that computed with mean annual temperature and BHTs ranges from 28.2oCkm-1 to 51.5oCkm-1with an average of 37.5+/-2.5oCkm-1. The geothermal gradient using the mean annual temperature and BHTs in the Bornu-Chad is higher than using only BHTs by 7.0oCkm-1. Heatflow ranges from a minimum of 61 mWm-2 to a maximum of 114mWm-2 with an average of 68+/-5.89mWm-2. The isotherm maps exhibit an increasing SW-NE trend. An average heat flow of 68+/-5.9mWm-2 deduced from Bornu-Chad basin is normal for a continental passive margin with age of about 100My. Geothermal gradient results show a distinct and direct relationship with near-surface conditions. There are indications that surface heat flow is controlled by lithology, geothermal gradient and near-surface solar radiation conditions in the Bornu-Chad basin. Consequently, it is recommended that the mean surface temperature be used in geothermal gradients and heatflows estimations. The knowledge of geothermal properties is very important in the search for geothermal energy in the area of study.
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Crăciun, Ioana, Dorian Popa, Florina Serdean, and Lucian Tudose. "On Approximate Aesthetic Curves." Symmetry 12, no. 9 (August 21, 2020): 1394. http://dx.doi.org/10.3390/sym12091394.
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Symmetry plays an essential role for generating aesthetic forms. The curve is the basic element used by designers to obtain aesthetic forms. A curve with a linear logarithmic curvature graph gradient is called aesthetic curve. The aesthetic value of a curve increases when its gradient is close to a straight line. We introduce the notions of approximate aesthetic curves and approximate neutral curves and obtain estimations between the curvature of an approximate aesthetic/neutral curve and an aesthetic curve.
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Haslund, Lars Emil, Alexander Cuculiza Henriksen, Billy Yat Shun Yiu, Ali Salari, Marie Sand Traberg, Lasse Thurmann Jørgensen, Borislav Gueorguiev Tomov, Michael Bachmann Nielsen, and Jørgen Arendt Jensen. "Precision of in vivo pressure gradient estimations using synthetic aperture ultrasound." Ultrasonics 149 (May 2025): 107574. https://doi.org/10.1016/j.ultras.2025.107574.
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Huang, Tianbao, Guanglong Ou, Hui Xu, Xiaoli Zhang, Yong Wu, Zihao Liu, Fuyan Zou, Chen Zhang, and Can Xu. "Comparing Algorithms for Estimation of Aboveground Biomass in Pinus yunnanensis." Forests 14, no. 9 (August 28, 2023): 1742. http://dx.doi.org/10.3390/f14091742.
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Comparing algorithms are crucial for enhancing the accuracy of remote sensing estimations of forest biomass in regions with high heterogeneity. Herein, Sentinel 2A, Sentinel 1A, Landsat 8 OLI, and Digital Elevation Model (DEM) were selected as data sources. A total of 12 algorithms, including 7 types of learners, were utilized for estimating the aboveground biomass (AGB) of Pinus yunnanensis forest. The results showed that: (1) The optimal algorithm (Extreme Gradient Boosting, XGBoost) was selected as the meta-model (referred to as XGBoost-stacking) of the stacking ensemble algorithm, which integrated 11 other algorithms. The R2 value was improved by 0.12 up to 0.61, and RMSE was decreased by 4.53 Mg/ha down to 39.34 Mg/ha compared to the XGBoost. All algorithms consistently showed severe underestimation of AGB in the Pinus yunnanensis forest of Yunnan Province when AGB exceeded 100 Mg/ha. (2) XGBoost-Stacking, XGBoost, BRNN (Bayesian Regularized Neural Network), RF (Random Forest), and QRF (Quantile Random Forest) have good sensitivity to forest AGB. QRNN (Quantile Regression Neural Network), GP (Gaussian Process), and EN (Elastic Network) have more outlier data and their robustness was poor. SVM-RBF (Radial Basis Function Kernel Support Vector Machine), k-NN (K Nearest Neighbors), and SGB (Stochastic Gradient Boosting) algorithms have good robustness, but their sensitivity was poor, and QRF algorithms and BRNN algorithm can estimate low values with higher accuracy. In conclusion, the XGBoost-stacking, XGBoost, and BRNN algorithms have shown promising application prospects in remote sensing estimation of forest biomass. This study could provide a reference for selecting the suitable algorithm for forest AGB estimation.
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Adiperdana, Budi, and Risdiana. "μSR Spectrum Reconstruction Using Monte Carlo Approach: A Preliminary Study." Materials Science Forum 966 (August 2019): 483–88. http://dx.doi.org/10.4028/www.scientific.net/msf.966.483.
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A possible method to reconstruct μSR spectra using Monte Carlo approach presented. Three issues carefully addressed for the simulations, that is automatic muon sites estimations, movement of muon due to gradient electrostatic potential and thermal fluctuation. All minima within the unit cell need to be included for more realistic theoretical μSR spectra. The optimum scale of gradient potential and thermal fluctuation needed to achieve a realistic result. Additional μSR spectra can be revealed in comparison with the simulation at lower thermal fluctuation.
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Barella-Ortiz, A., J. Polcher, A. Tuzet, and K. Laval. "Potential evaporation estimation through an unstressed surface-energy balance and its sensitivity to climate change." Hydrology and Earth System Sciences 17, no. 11 (November 22, 2013): 4625–39. http://dx.doi.org/10.5194/hess-17-4625-2013.
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Abstract. Potential evaporation (ETP) is a basic input for many hydrological and agronomic models, as well as a key variable in most actual evaporation estimations. It has been approached through several diffusive and energy balance methods, out of which the Penman–Monteith equation is recommended as the standard one. In order to deal with the diffusive approach, ETP must be estimated at a sub-diurnal frequency, as currently done in land surface models (LSMs). This study presents an improved method, developed in the ORCHIDEE LSM, which consists of estimating ETP through an unstressed surface-energy balance (USEB method). The results confirm the quality of the estimation which is currently implemented in the model (Milly, 1992). The ETP underlying the reference evaporation proposed by the Food and Agriculture Organization, FAO, (computed at a daily time step) has also been analysed and compared. First, a comparison for a reference period under current climate conditions shows that USEB and FAO's ETP estimations differ, especially in arid areas. However, they produce similar values when the FAO's assumption of neutral stability conditions is relaxed, by replacing FAO's aerodynamic resistance by that of the model's. Furthermore, if the vapour pressure deficit (VPD) estimated for the FAO's equation, is substituted by ORCHIDEE's VPD or its humidity gradient, the agreement between the daily mean estimates of ETP is further improved. In a second step, ETP's sensitivity to climate change is assessed by comparing trends in these formulations for the 21st century. It is found that the USEB method shows a higher sensitivity than the FAO's. Both VPD and the model's humidity gradient, as well as the aerodynamic resistance have been identified as key parameters in governing ETP trends. Finally, the sensitivity study is extended to two empirical approximations based on net radiation and mass transfer (Priestley–Taylor and Rohwer, respectively). The sensitivity of these ETP estimates is compared to the one provided by USEB to test if simplified equations are able to reproduce the impact of climate change on ETP.
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M S*, Sruthi, Sushmitha Magudeswaren, Soniya Tamilarasu, and Sushmitha Muralitharen. "Diabetes Prediction and Analysis using Machine Learning Methods." International Journal of Innovative Technology and Exploring Engineering 9, no. 6 (April 30, 2020): 568–70. http://dx.doi.org/10.35940/ijitee.e2689.049620.
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Different computational procedures and gadgets are open for data examination. At the present time, took the advantages of those open developments to improve the adequacy of the estimate model for the desire for a Type-2 Diabetic Patient. We intend to inquire about how diabetes scenes are impacted by patients' characteristics and estimations. The capable gauge model is required for clinical researchers. Until generally, Type II diabetes was evaluated uncommon in children. The contamination is, nonetheless, creating among youths in peoples with high paces of Type II diabetes in adults. This work presents the adequacy of Gradient Boosted Classifier which is obscure in past current works. It is related to two AI figuring’s, for instance, Neural Networks, Random Forest. These estimations are applied to the Pima Indians Diabetes Database (PIDD) which is sourced from the UCI AI storage facility. The models made are surveyed by standard techniques, for instance, AUC, Recall, and Accuracy. As obvious, Gradient helped classifier clobbers other two classifiers in all introduction qualities.
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Huang, Cheng-Hung, and Bo-Yi Li. "A Non-linear Inverse Problem in Estimating the Reaction Rate Function for an Annular-Bed Reactor." International Journal of Chemical Reactor Engineering 12, no. 1 (January 1, 2014): 271–83. http://dx.doi.org/10.1515/ijcre-2013-0142.
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Abstract The conjugate gradient method, or iterative regularization method, based inverse algorithm is utilized in this work to predict the unknown concentration-dependent reaction rate function for an annular-bed reactor (ABR) using interior measurements of concentration distributions. Since no prior information on the functional form of unknown reaction rate is available, it can be classified as function estimation for the inverse calculation. The validity and accuracy of this inverse ABR problem are examined using the simulated exact and inexact concentration measurements in the numerical experiments. Results show that the estimation of the concentration-dependent reaction rate function can be obtained within a very short CPU time on an Intel Xeon Core 2 2.00 GHz personal computer, and reliable estimations can still be obtained when measurement errors are considered.
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Yoon, Jeong-Hyun, Se-Won Kim, Ji-Sung Jo, and Ju-Mi Park. "A Comparative Study of Machine Learning Models for Predicting Vessel Dwell Time Estimation at a Terminal in the Busan New Port." Journal of Marine Science and Engineering 11, no. 10 (September 22, 2023): 1846. http://dx.doi.org/10.3390/jmse11101846.
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Container shipping plays a pivotal role in global trade, and understanding the duration that vessels spend in ports is crucial for efficient voyage planning by shipping companies. However, these companies often rely solely on one-way communication for required arrival times provided by terminals. This reliance on fixed schedules can lead to vessels arriving punctually, only to face berths that are still occupied, resulting in unnecessary waiting times. Regrettably, limited attention has been given to these issues from the perspective of shipping companies. This study addresses this gap by focusing on the estimation of dwell times for container vessels at a terminal in the Port of Busan using various machine learning techniques. The estimations were compared against the terminal’s operational reference. To compile the dataset, a 41-month history of terminal berth schedules and vessel particulars data were utilized and preprocessed for effective training. Outliers were removed, and dimensions were reduced. Six regression machine learning algorithms, namely adaptive learning, gradient boosting, light gradient boosting, extreme gradient boosting, categorical boosting and random forest, were employed, and their parameters were fine-tuned for optimal performance on the validation dataset. The results indicated that all models exhibited superior performance compared to the terminal’s operating reference model.
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Pugh, Ashley J., Christopher D. Wickens, Nathan Herdener, Benjamin A. Clegg, and C. A. P. Smith. "Effect of Visualization on Spatial Trajectory Prediction under Uncertainty." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 297–301. http://dx.doi.org/10.1177/1541931213601555.
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Spatial predictions of uncertain trajectories are challenging, but are often associated with overconfidence. This study explored how a visualization influenced prediction of uncertain spatial trajectories (e.g., unknown path of a downed aircraft or future path of a hurricane). Mean and variance estimates were compared for participants provided with a gradient-shaded “cone of uncertainty” visualization and those who were not provided with a visualization. Participants exhibited less error in mean estimations when a visualization was present, but performed worse than controls once the visualization was removed. For variance estimations, participants provided with a visualization did not retain any advantage in their estimations once the visualization was removed. Combined these findings suggest that visualizations may support some aspects of spatial predictions under uncertainty, but they can be associated with costs for the underlying knowledge being developed.
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Boueke, Moritz, Johannes Hoffmann, Mark Ellrichmann, Robert Bergholz, and Gerhard Schmidt. "Adaptive Algorithm for Fast 3D Characterization of Magnetic Sensors." Sensors 25, no. 4 (February 7, 2025): 995. https://doi.org/10.3390/s25040995.
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Magnetic sensors are highly relevant in clinical and industrial applications such as localization tasks and geological investigations. The spatial behavior of these sensors is of great interest for accurate forward modeling and the consequential possibilities for sophisticated applications, e.g., solutions to inverse problems. In this contribution, we present a novel characterization approach using adaptive system identification approaches. We utilize a gradient-based algorithm for estimating impulse and corresponding frequency responses for a directivity analysis in 1D, 2D, and 3D. For this, we built a triaxial Helmholtz coil setup to generate a 3D directive field. This is controlled by an algorithm that exploits similarities in sensor behavior with respect to small differences in excitation field angles. We found advantages for a controlled adaptation, with faster convergence and a smaller system distance between estimations and measurements with a proposed control based on the contraction–expansion approach (CEA). With runtimes averaging less than 1.5 s per direction for full impulse response estimation, this proof of concept shows the potential of the proposed algorithm for enabling a feasible frequency and directivity characterization method.
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Gordovskyy, M., S. Shelyag, P. K. Browning, and V. G. Lozitsky. "Analysis of unresolved photospheric magnetic field structure using Fe I 6301 and 6302 lines." Astronomy & Astrophysics 619 (November 2018): A164. http://dx.doi.org/10.1051/0004-6361/201833421.
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Context.Early magnetographic observations indicated that the magnetic field in the solar photosphere has an unresolved small-scale structure. Near-infrared and optical data with extremely high spatial resolution show that these structures have scales of a few tens of kilometres, which are not resolved in the majority of solar observations. Aims.The goal of this study is to establish the effect of the unresolved photospheric magnetic field structure on Stokes profiles observed with relatively low spatial resolution. Ultimately, we aim to develop methods for fast estimation of the photospheric magnetic filling factor and line-of-sight gradient of the photospheric magnetic field, which can be applied to large observational data sets. Methods.We exploit 3D magnetohydrodynamic models of magneto-convection developed using the MURAM code. Corresponding profiles of Fe I 6301.5 and 6302.5 Å spectral lines are calculated using the NICOLE radiative transfer code. The resulting I and V Stokes [x, y, λ] cubes with a reduced spatial resolution of 150 km are used to calculate magnetic field values as they would be obtained in observations with the Solar Optical Telescope (SOT) onboard Hinode or the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamic Observatory (SDO) mission. Results. Three different methods of magnetic filling factor estimation are considered: the magnetic line ratio method, the Stokes V width method, and a simple statistical method. We find that the statistical method and the Stokes V width method are sufficiently reliable for fast filling factor estimations. Furthermore, we find that the Stokes I ± V bisector splitting gradient can be used for fast estimation of the line-of-sight gradient of the photospheric magnetic field.
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Javed, Muhammad Yaqoob, Iqbal Ahmed Khurshid, Aamer Bilal Asghar, Syed Tahir Hussain Rizvi, Kamal Shahid, and Krzysztof Ejsmont. "An Efficient Estimation of Wind Turbine Output Power Using Neural Networks." Energies 15, no. 14 (July 18, 2022): 5210. http://dx.doi.org/10.3390/en15145210.
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Wind energy is a valuable source of electric power as its motion can be converted into mechanical energy, and ultimately electricity. The significant variability of wind speed calls for highly robust estimation methods. In this study, the mechanical power of wind turbines (WTs) is successfully estimated using input variables such as wind speed, angular speed of WT rotor, blade pitch, and power coefficient (Cp). The feed-forward backpropagation neural networks (FFBPNNs) and recurrent neural networks (RNNs) are incorporated to perform the estimations of wind turbine output power. The estimations are performed based on diverse parameters including the number of hidden layers, learning rates, and activation functions. The networks are trained using a scaled conjugate gradient (SCG) algorithm and evaluated in terms of the root mean square error (RMSE) and mean absolute percentage error (MAPE) indices. FFBPNN shows better results in terms of RMSE (0.49%) and MAPE (1.33%) using two and three hidden layers, respectively. The study indicates the significance of optimal selection of input parameters and effects of changing several hidden layers, activation functions, and learning rates to achieve the best performance of FFBPNN and RNN.
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Wardhani, Kartina Diah Kusuma, and Memen Akbar. "Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost)." Jurnal Online Informatika 7, no. 2 (December 29, 2022): 244–50. http://dx.doi.org/10.15575/join.v7i2.970.
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One of the uses of medical data from diabetes patients is to produce models that can be used by medical personnel to predict and identify diabetes in patients. Various techniques are used to be able to provide a diabetes model as early as possible based on the symptoms experienced by diabetic patients, including using machine learning. The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost). XGBoost is an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations. Errors made by the previous model are tried to be corrected by the next model by adding some weight to the model. The diabetes prediction model using XGBoost is shown in the form of a tree, with the accuracy of the model produced in this study of 98.71%
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Bernard, Olivier, Olivier Alata, and Marc Francaux. "On the modeling of breath-by-breath oxygen uptake kinetics at the onset of high-intensity exercises: simulated annealing vs. GRG2 method." Journal of Applied Physiology 100, no. 3 (March 2006): 1049–58. http://dx.doi.org/10.1152/japplphysiol.00712.2005.
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Modeling in the time domain, the non-steady-state O2 uptake on-kinetics of high-intensity exercises with empirical models is commonly performed with gradient-descent-based methods. However, these procedures may impair the confidence of the parameter estimation when the modeling functions are not continuously differentiable and when the estimation corresponds to an ill-posed problem. To cope with these problems, an implementation of simulated annealing (SA) methods was compared with the GRG2 algorithm (a gradient-descent method known for its robustness). Forty simulated V̇o2 on-responses were generated to mimic the real time course for transitions from light- to high-intensity exercises, with a signal-to-noise ratio equal to 20 dB. They were modeled twice with a discontinuous double-exponential function using both estimation methods. GRG2 significantly biased two estimated kinetic parameters of the first exponential (the time delay td1 and the time constant τ1) and impaired the precision (i.e., standard deviation) of the baseline A0, td1, and τ1 compared with SA. SA significantly improved the precision of the three parameters of the second exponential (the asymptotic increment A2, the time delay td2, and the time constant τ2). Nevertheless, td2 was significantly biased by both procedures, and the large confidence intervals of the whole second component parameters limit their interpretation. To compare both algorithms on experimental data, 26 subjects each performed two transitions from 80 W to 80% maximal O2 uptake on a cycle ergometer and O2 uptake was measured breath by breath. More than 88% of the kinetic parameter estimations done with the SA algorithm produced the lowest residual sum of squares between the experimental data points and the model. Repeatability coefficients were better with GRG2 for A1 although better with SA for A2 and τ2. Our results demonstrate that the implementation of SA improves significantly the estimation of most of these kinetic parameters, but a large inaccuracy remains in estimating the parameter values of the second exponential.
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Ding, Xiaorong, and Zhan Shen. "O18 UNCERTAINTY QUANTIFICATION OF WEARABLE CUFFLESS BLOOD PRESSURE MEASUREMENT." Journal of Hypertension 42, Suppl 3 (September 2024): e9. http://dx.doi.org/10.1097/01.hjh.0001062540.27030.d4.
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Background and Objective: Due to aleatoric and epistemic uncertainty, most cuffless blood pressure (BP) estimation models struggle to provide reliable and accurate BP estimations. The purpose of this study is to quantify the uncertainty of wearable cuffless BP estimation so as to reduce the impact of uncertainty on the accuracy of model estimation, and in the meanwhile to provide an estimated uncertainty interval (UI) in addition to the point estimation. Methods: We developed a gradient boosting regression tree (GBRT) model to estimate ambulatory BP and the estimation UI with eight noninvasive features extracted from wearable photoplethysmogram (PPG) and electrocardiogram (ECG) signals. We validated the proposed method with the Microsoft Aurora dataset that was originally collected for the Aurora-BP study. We identified 483 subjects (247 males) with wearable watches collecting PPG, ECG, and other signals, while ambulatory BP was monitored hourly using an oscillometric BP device. We trained the model with quantile loss on 60% of subjects (2954 samples), then calibrated the estimated UI with conformal predication with 24% (2148 samples) of subjects and tested the model with 16% (1658 samples) of the subjects. Results: The mean absolute difference (MAD) in systolic BP (SBP) and diastolic BP (DBP) estimated by the GBRT model were 13.96 mmHg and 9.89 mmHg, respectively. Then, with implementation of conformal prediction with error rates of ϵ=0.05, for the test set samples, the percentage of the estimated UI covering the reference BP during the daytime and nighttime phases can reach 91.67% and 95.89%, respectively. Conclusions: The combination of the GBRT model with conformal predication can quantify the uncertainty in cuffless BP estimations. In addition, the estimated uncertainty quantification interval together with the point estimation is potentially more reliable than single-point estimation, which could benefit better diagnosis and treatment of hypertension.
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Rosenhek, Raphael, Julia Mascherbauer, Leopold Huber, Heinrich Schima, and Gerald Maurer. "Doppler gradient estimations across tunnel obstructions: An in vitro study including flow visualization." Journal of the American College of Cardiology 41, no. 6 (March 2003): 446. http://dx.doi.org/10.1016/s0735-1097(03)81359-1.
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Fekri, Majid, and M. K. Yau. "A Study of Rain Forecast Error Structure Based on Radar Observations over a Continental-Scale Spatial Domain." Monthly Weather Review 144, no. 8 (July 25, 2016): 2871–87. http://dx.doi.org/10.1175/mwr-d-15-0191.1.
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Abstract This study examines the univariate error covariances of hourly rainfall accumulations using two different NWP models and a mosaic of radar reflectivity over a continental-scale domain. The study focuses on two main areas. The focus of the first part of the paper is on the ensemble-based and the innovation-based error variance and correlation estimations. An ensemble of forecasts and a set of observations provide the basis for estimating the errors in two different ways. The results indicate that both ensemble- and innovation-based methods lead to comparable variance estimations, while the local error correlation estimates have larger differences due to the sensitivity of calculations to the gradient of the variance field. The second part of the paper uses innovations for identifying the errors. The focus of this part is on a prognostic method for estimating the error statistics from the background based on the Bayesian inference technique. The case study shows that the predictive model produces a similar result regarding the magnitude and the dispersion of variance in comparison with the innovation and ensemble-based variances. This study represents a step toward estimating local error variances and local error correlations to construct a nonhomogeneous and precipitation-dependent error covariance matrix of rainfall. These results will be used in a future paper in the design of a 2D-VAR Assimilation Method for Blending Extrapolated Radars (AMBER) with NWP precipitation forecast to form a precipitation nowcasting model.
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Lin, Nan, Xiaofan Shao, Huizhi Wu, Ranzhe Jiang, and Menghong Wu. "Heavy Metal Concentration Estimation for Different Farmland Soils Based on Projection Pursuit and LightGBM with Hyperspectral Images." Sensors 24, no. 10 (May 20, 2024): 3251. http://dx.doi.org/10.3390/s24103251.
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Heavy metal pollution in farmland soil threatens soil environmental quality. It is an important task to quickly grasp the status of heavy metal pollution in farmland soil in a region. Hyperspectral remote sensing technology has been widely used in soil heavy metal concentration monitoring. How to improve the accuracy and reliability of its estimation model is a hot topic. This study analyzed 440 soil samples from Sihe Town and the surrounding agricultural areas in Yushu City, Jilin Province. Considering the differences between different types of soils, a local regression model of heavy metal concentrations (As and Cu) was established based on projection pursuit (PP) and light gradient boosting machine (LightGBM) algorithms. Based on the estimations, a spatial distribution map of soil heavy metals in the region was drawn. The findings of this study showed that considering the differences between different soils to construct a local regression estimation model of soil heavy metal concentration improved the estimation accuracy. Specifically, the relative percent difference (RPD) of As and Cu element estimations in black soil increased the most, by 0.30 and 0.26, respectively. The regional spatial distribution map of heavy metal concentration derived from local regression showed high spatial variability. The number of characteristic bands screened by the PP method accounted for 10–13% of the total spectral bands, effectively reducing the model complexity. Compared with the traditional machine model, the LightGBM model showed better estimation ability, and the highest determination coefficients (R2) of different soil validation sets reached 0.73 (As) and 0.75 (Cu), respectively. In this study, the constructed PP–LightGBM estimation model takes into account the differences in soil types, which effectively improves the accuracy and reliability of hyperspectral image estimation of soil heavy metal concentration and provides a reference for drawing large-scale spatial distributions of heavy metals from hyperspectral images and mastering soil environmental quality.
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Ströbel, Marco, Julia Pross-Brakhage, Mike Kopp, and Kai Peter Birke. "Impedance Based Temperature Estimation of Lithium Ion Cells Using Artificial Neural Networks." Batteries 7, no. 4 (December 12, 2021): 85. http://dx.doi.org/10.3390/batteries7040085.
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Tracking the cell temperature is critical for battery safety and cell durability. It is not feasible to equip every cell with a temperature sensor in large battery systems such as those in electric vehicles. Apart from this, temperature sensors are usually mounted on the cell surface and do not detect the core temperature, which can mean detecting an offset due to the temperature gradient. Many sensorless methods require great computational effort for solving partial differential equations or require error-prone parameterization. This paper presents a sensorless temperature estimation method for lithium ion cells using data from electrochemical impedance spectroscopy in combination with artificial neural networks (ANNs). By training an ANN with data of 28 cells and estimating the cell temperatures of eight more cells of the same cell type, the neural network (a simple feed forward ANN with only one hidden layer) was able to achieve an estimation accuracy of ΔT= 1 K (10 ∘C <T< 60 ∘C) with low computational effort. The temperature estimations were investigated for different cell types at various states of charge (SoCs) with different superimposed direct currents. Our method is easy to use and can be completely automated, since there is no significant offset in monitoring temperature. In addition, the prospect of using the above mentioned approach to estimate additional battery states such as SoC and state of health (SoH) is discussed.
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R, Priya, and Sundar Raj M. "Mapping Gene Expression and Transcription Inhibition to Cancer Treatment Cost Estimation." Knowledge Transactions on Applied Machine Learning 02, no. 06 (November 20, 2024): 14–20. https://doi.org/10.59567/ktaml.v2.06.03.
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The escalating costs of cancer treatments present a significant challenge for healthcare systems globally. Traditional methods of estimating cancer treatment costs often fail to incorporate patient-specific biological factors. Recent advancements in predictive oncology have emphasized the need for machine learning techniques to predict treatment costs based on biological attributes. Transcription inhibition plays a pivotal role in regulating gene expression and cancer cell proliferation, yet the relationship between transcription inhibition and treatment budgets remains under- explored. This study aims to address this gap by creating a machine learning (ML) framework that combines transcription inhibition dynamics with cancer treatment budgeting, with the goal of improving the accuracy of cost estimations. Our framework involves generating synthetic data representing transcription inhibition attributes, training multiple regression models, and evaluating them based on metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and the R² score. The machine learning models applied include Linear Regression, Random Forest, Gradient Boosting, Support Vector Regressor, and Neural Network Regressor. Random Forest and Gradient Boosting models showed promising results, outperforming other models in budget prediction. This approach offers potential applications in precision oncology, with implications for optimizing resource allocation and supporting cost-effective clinical decision-making.
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Boonruangkan, Nunthakarn, and Pattrawut Chansangiam. "Gradient Iterative Method with Optimal Convergent Factor for Solving a Generalized Sylvester Matrix Equation with Applications to Diffusion Equations." Symmetry 12, no. 10 (October 20, 2020): 1732. http://dx.doi.org/10.3390/sym12101732.
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We introduce a gradient iterative scheme with an optimal convergent factor for solving a generalized Sylvester matrix equation ∑i=1pAiXBi=F, where Ai,Bi and F are conformable rectangular matrices. The iterative scheme is derived from the gradients of the squared norm-errors of the associated subsystems for the equation. The convergence analysis reveals that the sequence of approximated solutions converge to the exact solution for any initial value if and only if the convergent factor is chosen properly in terms of the spectral radius of the associated iteration matrix. We also discuss the convergent rate and error estimations. Moreover, we determine the fastest convergent factor so that the associated iteration matrix has the smallest spectral radius. Furthermore, we provide numerical examples to illustrate the capability and efficiency of this method. Finally, we apply the proposed scheme to discretized equations for boundary value problems involving convection and diffusion.
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Holmes, Emily, Barry Hughes, and Gunnar Jansson. "Haptic Perception of Texture Gradients." Perception 27, no. 8 (August 1998): 993–1008. http://dx.doi.org/10.1068/p270993.
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To pick up 3-D aspects of pictures is arguably the most difficult problem concerning tactile pictorial perception by the blind. The aim of the experiments reported was to examine the potential utility of texture gradients in this context. Since there is no theoretical basis for predicting absolute values of 3-D properties from 2-D patterns read by the finger pads, the abilities of participants to perceive gradients lying between known maxima and minima were assessed. Experiment 1 involved blindfolded sighted participants making verbal magnitude estimations of texture-gradient magnitudes corresponding to plane surfaces at different slants. In experiment 2 the participants' task was to orient a surface at a slant corresponding to the texture gradients depicted tactually, and experiment 3 required early-blind participants to attempt the same task. The results revealed that participants can scale the magnitudes of texture gradients with high precision and that they can also accurately produce surface slants from depictions, providing the extreme conditions are clearly defined and there are opportunities for learning. Texture gradients appear as informative to the blind as they do to the sighted. To what extent these data can be generalised to other gradients and textures or to other projections of 3-D scenes remains to be investigated.
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Kamp, Jesper Nørlem, Christoph Häni, Tavs Nyord, Anders Feilberg, and Lise Lotte Sørensen. "Calculation of NH3 Emissions, Evaluation of Backward Lagrangian Stochastic Dispersion Model and Aerodynamic Gradient Method." Atmosphere 12, no. 1 (January 12, 2021): 102. http://dx.doi.org/10.3390/atmos12010102.
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Two campaigns measuring ammonia (NH3) emissions with different measurement techniques were performed on a large grass field (26 ha) after the application of liquid animal manure. The aim was to compare emissions from a confined area estimated from either (i) concentration measurements, both point and line-integrated measurements, combined with backward Lagrangian stochastic (bLS) dispersion modeling or by (ii) estimation of the vertical flux by the aerodynamic gradient method (AGM) with and without footprint correction approximated by the bLS model estimates of the flux footprint. The objective of the comparison is to establish the best practice to derive NH3 emissions from a large field. NH3 emissions derived from bLS agreed well when comparing point and line-integrated measurements. Simple point measurements combined with bLS yield good emission estimations for the confined area. Without footprint correction, the AGM underestimates the emissions by up to 9% compared to the footprint-corrected AGM results. The sensitivity of the measurement methods makes it possible to quantify NH3 emissions with diurnal patterns even five days after a field application of liquid animal manure under wet conditions. The bLS model proves to be a strong tool to determine the NH3 emissions from point concentration measurements inside a large field after a slurry application.
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Martínez-Macias, Karla Janeth, Aldo Rafael Martínez-Sifuentes, Selenne Yuridia Márquez-Guerrero, Arturo Reyes-González, Pablo Preciado-Rangel, Pablo Yescas-Coronado, and Ramón Trucíos-Caciano. "Machine-Learning Approaches in N Estimations of Fig Cultivations Based on Satellite-Born Vegetation Indices." Nitrogen 5, no. 3 (July 10, 2024): 598–609. http://dx.doi.org/10.3390/nitrogen5030040.
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Nitrogen is one of the most important macronutrients for crops, and, in conjunction with artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices through remote sensing. Various indices were calculated and those with a correlation of ≥0.7 were selected for subsequent use in random forest, gradient boosting, and artificial neural networks to determine their relationship with nitrogen levels measured in the laboratory. Random forest showed no relationship, yielding an R2 of zero; and gradient boosting and the classical method were similar with 0.7; whereas artificial neural networks yielded the best results with an R2 of 0.93. Thus, estimating nitrogen levels using this algorithm is reliable, by feeding it with data from the Modified Chlorophyll Absorption Ratio Index, Transformed Chlorophyll Absorption Reflectance Index, Modified Chlorophyll Absorption Ratio Index/Optimized Soil Adjusted Vegetation Index, and Transformed Chlorophyll Absorption Ratio Index/Optimized Soil Adjusted Vegetation Index
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Chaibi, Mohamed, EL Mahjoub Benghoulam, Lhoussaine Tarik, Mohamed Berrada, and Abdellah El Hmaidi. "An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction." Energies 14, no. 21 (November 5, 2021): 7367. http://dx.doi.org/10.3390/en14217367.
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Machine learning (ML) models are commonly used in solar modeling due to their high predictive accuracy. However, the predictions of these models are difficult to explain and trust. This paper aims to demonstrate the utility of two interpretation techniques to explain and improve the predictions of ML models. We compared first the predictive performance of Light Gradient Boosting (LightGBM) with three benchmark models, including multilayer perceptron (MLP), multiple linear regression (MLR), and support-vector regression (SVR), for estimating the global solar radiation (H) in the city of Fez, Morocco. Then, the predictions of the most accurate model were explained by two model-agnostic explanation techniques: permutation feature importance (PFI) and Shapley additive explanations (SHAP). The results indicated that LightGBM (R2 = 0.9377, RMSE = 0.4827 kWh/m2, MAE = 0.3614 kWh/m2) provides similar predictive accuracy as SVR, and outperformed MLP and MLR in the testing stage. Both PFI and SHAP methods showed that extraterrestrial solar radiation (H0) and sunshine duration fraction (SF) are the two most important parameters that affect H estimation. Moreover, the SHAP method established how each feature influences the LightGBM estimations. The predictive accuracy of the LightGBM model was further improved slightly after re-examination of features, where the model combining H0, SF, and RH was better than the model with all features.
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Li, Yunfei, Hongda Zeng, Jingfeng Xiong, and Guofang Miao. "Influence of Topography on UAV LiDAR-Based LAI Estimation in Subtropical Mountainous Secondary Broadleaf Forests." Forests 15, no. 1 (December 20, 2023): 17. http://dx.doi.org/10.3390/f15010017.
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The leaf area index (LAI) serves as a crucial metric in quantifying the structure and density of vegetation canopies, playing an instrumental role in determining vegetation productivity, nutrient and water utilization, and carbon balance dynamics. In subtropical montane forests, the pronounced spatial heterogeneity combined with undulating terrain introduces significant challenges for the optical remote sensing inversion accuracy of LAI, thereby complicating the process of ground validation data collection. The emergence of UAV LiDAR offers an innovative monitoring methodology for canopy LAI inversion in these terrains. This study assesses the implications of altitudinal variations on the attributes of UAV LiDAR point clouds, such as point density, beam footprint, and off-nadir scan angle, and their subsequent ramifications for LAI estimation accuracy. Our findings underscore that with increased altitude, both the average off-nadir scan angle and point density exhibit an ascending trend, while the beam footprint showcases a distinct negative correlation, with a correlation coefficient (R) reaching 0.7. In contrast to parallel flight paths, LAI estimates derived from intersecting flight paths demonstrate superior precision, denoted by R2 = 0.70, RMSE = 0.75, and bias = 0.42. Notably, LAI estimation discrepancies intensify from upper slope positions to middle positions and further to lower ones, amplifying with the steepness of the gradient. Alterations in point cloud attributes induced by the terrain, particularly the off-nadir scan angle and beam footprint, emerge as critical influencers on the precision of LAI estimations. Strategies encompassing refined flight path intervals or multi-directional point cloud data acquisition are proposed to bolster the accuracy of canopy structural parameter estimations in montane landscapes.
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van Splunder, I. P., T. Stijnen, and J. W. Wladimiroff. "Fetal pressure gradient estimations across the ductus venosus in early pregnancy using Doppler ultrasonography." Ultrasound in Obstetrics and Gynecology 6, no. 5 (November 1, 1995): 334–39. http://dx.doi.org/10.1046/j.1469-0705.1995.06050334.x.
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Niinemets, Ülo, Aljona Lukjanova, Ashley D. Sparrow, and Matthew H. Turnbull. "Light-acclimation of cladode photosynthetic potentials in Casuarina glauca: trade-offs between physiological and structural investments." Functional Plant Biology 32, no. 7 (2005): 571. http://dx.doi.org/10.1071/fp05037.
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Many arid and saline habitat species possess sparse canopies with cylindrical foliage that is considered relatively invariable along environmental gradients. However, even in sparse canopies strong gradients of light develop between the canopy top and bottom. We studied structural and photosynthetic acclimation to within-canopy light gradient in Casuarina glauca Sieb. ex Spreng., the photosynthetic organs of which are cylindrical cladodes. Seasonal average integrated quantum flux density (Qint) varied 25-fold between the canopy top and the canopy bottom. Cladode cross-sectional shape was unaffected by irradiance, but cladode dry mass per unit total area (MA) varied 2-fold within the canopy light gradient. This resulted primarily from light-dependent changes in cladode thickness (volume to total area ratio,V / AT) and to a lesser extent from changes in cladode density (D, MA = DV / AT). Nitrogen content, and the volume of mesophyll per unit surface area increased with increasing Qint and V / AT, resulting in positive scaling of foliage photosynthetic potential (capacity of photosynthetic electron transport and maximum Rubisco carboxylase activity per unit area) with light. However, nitrogen content per unit dry mass and the volume fraction of mesophyll decreased with increasing irradiance. This was explained by greater fractional investment in mechanical tissues in cladodes with greater volume to surface area ratio. This trade-off between photosynthetic and support investments reduced the cladode photosynthetic plasticity. Our study demonstrates a significant acclimation potential of species with cylindrical foliage that should be included in larger-scale carbon balance estimations of arid and saline communities.
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Barella-Ortiz, A., J. Polcher, A. Tuzet, and K. Laval. "Potential evaporation estimation through an unstressed surface energy balance and its sensitivity to climate change." Hydrology and Earth System Sciences Discussions 10, no. 6 (June 26, 2013): 8197–231. http://dx.doi.org/10.5194/hessd-10-8197-2013.
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Abstract. Potential evaporation (ETP) is a basic input for hydrological and agronomic models, as well as a key variable in most actual evaporation estimations. It has been approached through several diffusive and energy balance methods, out of which the Penman–Monteith equation is recommended as the standard one. In order to deal with the diffusive approach, ETP must be estimated at a sub-diurnal frequency, as currently done in land surface models (LSM). This study presents an improved method, developed in the ORCHIDEE LSM, which consists in estimating ETP through an unstressed surface energy balance (USEB method). The results confirm the quality of the estimation which is currently implemented in the model (Milly, 1992). ETP has also been estimated using a reference equation (computed at a daily time step) provided by the Food and Agriculture Organization (FAO). First, a comparison for a reference period under current climate conditions, shows that both formulations differ, specially in arid areas. However, they supply similar values when FAO's assumption of neutral stability conditions is relaxed, by replacing FAO's aerodynamic resistance by the model's one. Furthermore, if the vapour pressure deficit (VPD) estimated for FAO's equation, is substituted by ORCHIDEE's VPD or its humidity gradient, the daily mean estimate is further improved. In a second step, ETP's sensitivity to climate change is assessed comparing trends in both formulations for the 21st Century. It is found that the USEB method shows a higher sensitivity. Both VPD and the model's humidity gradient, as well as the aerodynamic resistance have been identified as key parameters in governing ETP trends. Finally, the sensitivity study is extended to three empirical approximations based on temperature, net radiation and mass transfer (Hargreaves, Priestley–Taylor and Rohwer, respectively). The sensitivity of these methods is compared to the USEB method's one to test if simplified equations are able to reproduce the impact of climate change.
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Zhou, Xianmei, Shanliang Zhu, Wentao Jia, and Hengkai Yao. "Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model." Atmosphere 15, no. 9 (August 29, 2024): 1043. http://dx.doi.org/10.3390/atmos15091043.
Full textAbstract:
Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and the El Niño phenomenon. This paper proposes an improved double-output residual neural network (DO-ResNet) model to concurrently estimate the subsurface temperature (ST) and subsurface salinity (SS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), sea surface wind (SSW), and geographical information (including longitude and latitude). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE), and coefficient of determination (R2) were employed to evaluate the model’s performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R2 values for estimating ST (SS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of ST and SS across different depths with Argo data, demonstrating the model’s ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model demonstrates strong adaptability to seasonal variations. In conclusion, this research introduces a novel artificial intelligence technique for estimating ST and SS in the tropical Western Pacific Ocean.