Relevant bibliographies by topics / SIZE PREDICTION

Academic literature on the topic 'SIZE PREDICTION'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "SIZE PREDICTION"

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Treme, Gehron, David R. Diduch, Mark J. Billante, Mark D. Miller, and Joseph M. Hart. "Hamstring Graft Size Prediction." American Journal of Sports Medicine 36, no. 11 (August 25, 2008): 2204–9. http://dx.doi.org/10.1177/0363546508319901.

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Background Recently we retrospectively collected clinical data to predict hamstring graft diameter. Prospective data collection will improve and further define prediction of hamstring graft size. Hypothesis Clinical anthropometric data can be used to predict hamstring graft size. Study Design Cohort study (prevalence); Level of evidence, 1. Methods Fifty consecutive patients with anterior cruciate ligament deficiency scheduled for reconstruction using hamstring autograft were prospectively evaluated. Preoperatively we recorded height, weight, body mass index, age, gender, leg length, thigh length, shank length, bilateral thigh circumference, and Tegner score. Intraoperative measurements of both the gracilis and semitendinosus tendons were made, including absolute length before fashioning the graft and final diameter of the quadrupled graft using sizing tubes calibrated to 0.5 mm. Bivariate correlation coefficients (Pearson r) were calculated to identify relationships among clinical data and intraoperatively measured hamstring graft length and diameter. Results Strongest correlations for graft lengths were height and leg length measurements. Shorter persons with shorter leg, thigh, and shank lengths tended to have shorter gracilis and semitendinosus grafts. Likewise, the strongest correlations for graft diameter were weight and thigh circumference. Self-reported activity level and age did not correlate. Gender comparison revealed that women who were shorter, lighter, and had smaller body mass indices were more likely to have smaller graft diameters and shorter graft lengths. Conclusion Patients weighing less than 50 kg, less than 140 cm in height, with less than 37 cm thigh circumference, and with body mass index less than 18 should be considered at high risk for having a quadrupled hamstring graft diameter less than 7 mm. When separated by gender, small graft diameters are most likely in older, short, female subjects with small thigh circumferences or young, skinny, male subjects with small thigh circumferences and low body mass index. Common clinical measurements can be used for preoperative identification of patients at risk for insufficient graft tissue and would be useful for patient counseling and alternative graft source planning.
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Wang, Hsin-Yao, Yu-Hsin Liu, Yi-Ju Tseng, Chia-Ru Chung, Ting-Wei Lin, Jia-Ruei Yu, Yhu-Chering Huang, and Jang-Jih Lu. "Investigating Unfavorable Factors That Impede MALDI-TOF-Based AI in Predicting Antibiotic Resistance." Diagnostics 12, no. 2 (February 5, 2022): 413. http://dx.doi.org/10.3390/diagnostics12020413.

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The combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility testing (AST) of Staphylococcus aureus. Based on the AI predictive probability, cases with probabilities between the low and high cut-offs are defined as being in the “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. In total, 479 S. aureus isolates were collected and analyzed by MALDI-TOF, and AST prediction and standard AST were obtained in a tertiary medical center. The predictions were categorized as correct-prediction group, wrong-prediction group, and grey-zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For methicillin-resistant S. aureus (MRSA), a larger cefoxitin zone size was found in the wrong-prediction group. Multilocus sequence typing of the MRSA isolates in the grey-zone group revealed that uncommon strain types comprised 80%. Of the methicillin-susceptible S. aureus (MSSA) isolates in the grey-zone group, the majority (60%) comprised over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity contribute to suboptimal predictive performance.
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Zhang, Hao, Chong Wang, Zhengyan Chen, Qingyu Kang, Xiaohua Xu, and Tianpeng Gao. "Performance Comparison of Different Particle Size Distribution Models in the Prediction of Soil Particle Size Characteristics." Land 11, no. 11 (November 17, 2022): 2068. http://dx.doi.org/10.3390/land11112068.

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Particle size distribution (PSD) is a rich source of information about soil properties, including soil gradation and soil particle size characteristics. This paper compared the PSD prediction ability of three types of mathematical model. We selected nine models that have been proven to accurately predict sample points in previous studies, and we fit 144 pieces of experimental data on 12 texture classes of soil samples from the UNSODA database. We compared the models’ capability for predicting non-sample points, which is important for predicting soil particle size characteristics. Each model’s ability to predict non-sample points of different texture classes of soil was studied using a comprehensive ranking method. The relative differences in the models’ prediction of non-sample points of different texture classes of soil were analyzed using the relative error method. The results showed no considerable correlation between the number of model parameters and the prediction accuracy. For the various texture classes of soil, the Skaggs model and Weipeng model had the highest accuracy in predicting non-sample points, and the Skaggs model had the widest range of application. The Zhongling model and the Weibull model were better in predicting only one texture class of soil, respectively. The Fredlund model, Kolve model, Rosin model, Van Genuchten model and Best model were not as successful as other models. The Weipeng model overestimated the solid particle mass proportion, while the Skaggs model underestimated it when the clay particle content was greater than 20%. Both the Weipeng model and the Skaggs model demonstrated good prediction accuracy when the particle size was within the silt particle size range. The Skaggs model overestimated the particle mass proportion, while the Weipeng model underestimated it when the particle size was within the sand particle size range.
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Rangwala, Murtaza, Jun Liu, Kulbir Singh Ahluwalia, Shayan Ghajar, Harnaik Singh Dhami, Benjamin F. Tracy, Pratap Tokekar, and Ryan K. Williams. "DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets." Agronomy 11, no. 11 (November 5, 2021): 2245. http://dx.doi.org/10.3390/agronomy11112245.

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Effective management of dairy farms requires an accurate prediction of pasture biomass. Generally, estimation of pasture biomass requires site-specific data, or often perfect world assumptions to model prediction systems when field measurements or other sensory inputs are unavailable. However, for small enterprises, regular measurements of site-specific data are often inconceivable. In this study, we approach the estimation of pasture biomass by predicting sward heights across the field. A convolution based sequential architecture is proposed for pasture height predictions using deep learning. We develop a process to create synthetic datasets that simulate the evolution of pasture growth over a period of 30 years. The deep learning based pasture prediction model (DeepPaSTL) is trained on this dataset while learning the spatiotemporal characteristics of pasture growth. The architecture purely learns from the trends in pasture growth through available spatial measurements and is agnostic to any site-specific data, or climatic conditions, such as temperature, precipitation, or soil condition. Our model performs within a 12% error margin even during the periods with the largest pasture growth dynamics. The study demonstrates the potential scalability of the architecture to predict any pasture size through a quantization approach during prediction. Results suggest that the DeepPaSTL model represents a useful tool for predicting pasture growth both for short and long horizon predictions, even with missing or irregular historical measurements.
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Gill, Nasib S., and P. S. Grover. "Software size prediction before coding." ACM SIGSOFT Software Engineering Notes 29, no. 5 (September 2004): 1–4. http://dx.doi.org/10.1145/1022494.1022514.

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van Smeden, Maarten, Karel GM Moons, Joris AH de Groot, Gary S. Collins, Douglas G. Altman, Marinus JC Eijkemans, and Johannes B. Reitsma. "Sample size for binary logistic prediction models: Beyond events per variable criteria." Statistical Methods in Medical Research 28, no. 8 (July 3, 2018): 2455–74. http://dx.doi.org/10.1177/0962280218784726.

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Binary logistic regression is one of the most frequently applied statistical approaches for developing clinical prediction models. Developers of such models often rely on an Events Per Variable criterion (EPV), notably EPV ≥10, to determine the minimal sample size required and the maximum number of candidate predictors that can be examined. We present an extensive simulation study in which we studied the influence of EPV, events fraction, number of candidate predictors, the correlations and distributions of candidate predictor variables, area under the ROC curve, and predictor effects on out-of-sample predictive performance of prediction models. The out-of-sample performance (calibration, discrimination and probability prediction error) of developed prediction models was studied before and after regression shrinkage and variable selection. The results indicate that EPV does not have a strong relation with metrics of predictive performance, and is not an appropriate criterion for (binary) prediction model development studies. We show that out-of-sample predictive performance can better be approximated by considering the number of predictors, the total sample size and the events fraction. We propose that the development of new sample size criteria for prediction models should be based on these three parameters, and provide suggestions for improving sample size determination.
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Di, Yu, Ying Li, and Yan Luo. "Prediction of Implantable Collamer Lens Vault Based on Preoperative Biometric Factors and Lens Parameters." Journal of Refractive Surgery 39, no. 5 (May 2023): 332–39. http://dx.doi.org/10.3928/1081597x-20230207-03.

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Purpose: To establish and validate the accuracy of implantable collamer lens (ICL) vault size prediction formula based on preoperative biometric factors and lens parameters. Methods: This study included 300 patients (300 eyes) with Visian ICL V4c (STAAR Surgical) implantation. They were randomly divided into the formula establishment group and formula validation group. Anterior segment measurements, ICL V4c size and power, and vault 1 week postoperatively were collected from all patients. Multiple linear regression analysis was performed to establish the prediction formula. Mean absolute error (MAE), median absolute error (MedAE), root mean square error (RMSE), and Bland-Altman diagrams were used to evaluate the prediction formula. Results: Anterior chamber depth (ACD) had the greatest influence on vault 1 week after ICL V4c implantation, followed by ICL V4c size and angle-to-angle distance (ATA). The prediction formula was obtained according to the partial regression coefficient, which was vault (mm) = −1.279 + 0.291 × ACD (mm) + 0.210 × ICL V4c size (mm) – 0.144 × ATA (mm) ( R 2 = 0.661). In the formula validation group, the mean predictive vault, MAE, MedAE, and RMSE were 628.10, 135.09, 130.42, and 150.46 µm, respectively. The Bland-Altman diagram showed the predictive vault was in good agreement with the actual vault. Conclusions: A novel ICL V4c vault prediction formula was developed and shown to be an effective method for predicting the vault to reduce surgical complications. [ J Refract Surg . 2023;39(5):332–339.]
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Ajana, Soufiane, Niyazi Acar, Lionel Bretillon, Boris P. Hejblum, Hélène Jacqmin-Gadda, Cécile Delcourt, Niyazi Acar, et al. "Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size." Bioinformatics 35, no. 19 (April 1, 2019): 3628–34. http://dx.doi.org/10.1093/bioinformatics/btz135.

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Abstract Motivation In some prediction analyses, predictors have a natural grouping structure and selecting predictors accounting for this additional information could be more effective for predicting the outcome accurately. Moreover, in a high dimension low sample size framework, obtaining a good predictive model becomes very challenging. The objective of this work was to investigate the benefits of dimension reduction in penalized regression methods, in terms of prediction performance and variable selection consistency, in high dimension low sample size data. Using two real datasets, we compared the performances of lasso, elastic net, group lasso, sparse group lasso, sparse partial least squares (PLS), group PLS and sparse group PLS. Results Considering dimension reduction in penalized regression methods improved the prediction accuracy. The sparse group PLS reached the lowest prediction error while consistently selecting a few predictors from a single group. Availability and implementation R codes for the prediction methods are freely available at https://github.com/SoufianeAjana/Blisar. Supplementary information Supplementary data are available at Bioinformatics online.
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Kumar, Arun, and Mingyue Chen. "Inherent Predictability, Requirements on the Ensemble Size, and Complementarity." Monthly Weather Review 143, no. 8 (August 1, 2015): 3192–203. http://dx.doi.org/10.1175/mwr-d-15-0022.1.

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Abstract Faced with the scenario when prediction skill is low, particularly in conjunction with long-range predictions, a commonly proposed solution is that an increase in ensemble size will rectify the issue of low skill. Although it is well known that an increase in ensemble size does lead to an increase in prediction skill, the general scope of this supposition, however, is that low prediction skill is not a consequence of constraints imposed by inherent predictability limits, but an artifact of small ensemble sizes, and further, increases in ensemble sizes (that are often limited by computational resources) are the major bottlenecks for improving long-range predictions. In proposing that larger ensemble sizes will remedy the issue of low skill, a fact that is not well appreciated is that for scenarios with high inherent predictability, a small ensemble size is sufficient to realize high predictability, while for scenarios with low inherent predictability, much larger ensemble sizes are needed to realize low predictability. In other words, requirements on ensemble size (to realize the inherent predictability) and inherent predictability are complementary variables. A perceived need for larger ensembles, therefore, may also imply the presence of low predictability.
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Gomez, Ana Isabel, Marcos Cruz, and Luis Manuel Cruz-Orive. "VARIANCE PREDICTION FOR POPULATION SIZE ESTIMATION." Image Analysis & Stereology 38, no. 2 (July 18, 2019): 131. http://dx.doi.org/10.5566/ias.1991.

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Design unbiased estimation of population size by stereological methods is an efficient alternative to automatic computer vision methods, which are generally biased. Moreover, stereological methods offer the possibility of predicting the error variance from a single sample. Here we explore the statistical performance of two alternative variance estimators on a dataset of 26 labelled crowd pictures. The empirical mean square errors of the variance predictors are compared by means of Monte Carlo resampling.
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Dissertations / Theses on the topic "SIZE PREDICTION"

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Pandarum, Krishnavellie. "Size prediction for plus-size women's intimate apparel using a 3D body scanner." Thesis, Nelson Mandela Metropolitan University, 2009. http://hdl.handle.net/10948/1153.

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Garment “fitting” from tailor-made to ready-to-wear clothing, has evolved over time. Ready to-wear and standardized sizes appeared in the middle 19th century. Today garment fitting is one of the most important criteria in the consumer buying decision making process. This is particularly so with “body hugging” garments, such as intimate apparel; or the bra that moulds the form of the wearer to produce “smooth” outer garment silhouettes. The South African bra retailer and manufacturers sizing charts are generally based upon body dimension data collected using traditional anthropometric methods. Professional measurers are not able to capture the hidden areas of the breast such as the inframmatory fold line, the volume, shape and contour of the breast using tape measures, calipers and other measuring devices. Traditional anthropometry also does not have the ability to systemically observe the bottom line of the breast base and extract accurate data on breast volume which are key factors in designing underwire bras and in the pattern making of the bra cup panels. Exploratory retail and consumer studies have indicated that consumers, notably plus size women, experience considerable problems and dissatisfaction with poorly fitting bras. There is therefore clearly a need in South Africa to conduct a 3D anthropometric study, focusing especially on the plus-sized women’s bra market segment, as there is very little or limited studies, to date, conduct for this market segment of the population. This pilot study collected 3D torso body measurement data from a convenient sample of 176 plus sized women, recruited from Playtex (Pty) Ltd. situated in Durban, KwaZulu Natal, South Africa. The study evaluates the 3D breast volume measurement data extraction process, using an expert system developed by [TC]2 integrated into the propriety NX12-3D full body scanner software and that taken using the traditional dress-makers tape-measure. The objective is to establish the relationship between the 3D torso and breast volume data measurement output as extracted by the expert system when compared to the South African bra manufacturers sizing chart, for use in pattern making for bra cup panel designs and in the designing of underwire bras for large breasted or plus size women. The results contained in this dissertation cannot be extrapolated to the larger population of South Africa and is limited to the 176 plus size women selected by Body Mass Index; recruited from KwaZulu Natal, South Africa.
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Wong, Hing-sang Wilfred. "On the prediction of adult shortness and tallness." Click to view the E-thesis via HKUTO, 2003. http://sunzi.lib.hku.hk/hkuto/record/B31971301.

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Olesen, Mark Jørn. "Prediction of drop-size distributions based on ligament breakup." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/nq22488.pdf.

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Gomes, Pimentel Rogerio. "Measurement and Prediction of Droplet Size Distribution in Sprays." Thesis, Université Laval, 2006. http://www.theses.ulaval.ca/2006/23623/23623.pdf.

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Gomes, Pimentel Rogério. "Measurement and prediction of droplet size distribution in sprays." Doctoral thesis, Université Laval, 2006. http://hdl.handle.net/20.500.11794/18194.

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黃慶生 and Hing-sang Wilfred Wong. "On the prediction of adult shortness and tallness." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B31971301.

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ANDO, Hideki, and Yusuke TANAKA. "Register File Size Reduction through Instruction Pre-Execution Incorporating Value Prediction." Institute of Electronics, Information and Communication Engineers, 2010. http://hdl.handle.net/2237/14941.

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Yao, Juncheng. "Characterization and Prediction of Water Droplet Size in Oil-Water Flow." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1470741069.

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Moshgbar, Mojgan. "Prediction and real-time compensation of liner wear in cone crushers." Thesis, Loughborough University, 1996. https://dspace.lboro.ac.uk/2134/27362.

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In the comminution industry, cone crushers are widely used for secondary and subsequent stages of size reduction. For a given crusher, the achieved size reduction is governed by the closed-side setting. Hadfield Steel is commonly used to line the crushing members to minimize wear. Yet, liner wear caused by some rock types can still be excessive. Enlargement of discharge opening induced by wear of liners produces a drift in product size which, if unchecked, can lead to high volumes of re-circulating load. Alteration of closed-side setting is now commonly achieved via hydraulic means. However, compensation of liner wear still involves plant shut down and loss of production.
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Tanaka, Yusuke, and Hideki Ando. "Reducing register file size through instruction pre-execution enhanced by value prediction." IEEE, 2009. http://hdl.handle.net/2237/13892.

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Books on the topic "SIZE PREDICTION"

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Conway, Thomas Michael. A computer programme for the prediction of hydrocyclone performance, parameters, and product-size distributions. Randburg, South Africa: Mintek (Ore-Dressing Division), printed and published by the Council for Mineral technology, 1985.

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K, De Groh Kim, and NASA Glenn Research Center, eds. The dependence of atomic oxygen undercutting of protected polyimide Kapton® H upon defect size. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Snyder, Aaron. The dependence of atomic oxygen undercutting of protected polyimide Kapton® H upon defect size. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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J, Vaz Roy, and Klabunde Thomas, eds. Antitargets: Prediction and prevention of drug side effects. Weinheim: Wiley-VCH, 2008.

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J, Vaz Roy, and Klabunde Thomas, eds. Antitargets: Prediction and prevention of drug side effects. Weinheim: Wiley-VCH, 2008.

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Canada. Natural Resources Canada. Canadian Forest Service. Great Lakes Forestry Centre. Predicting canopy closure for habitat modeling. Ottawa: Natural Resources Canada., 1995.

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Bernreuter, D. L. Development of site specific response spectra. Washington, DC: Division of Engineering Safety, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1987.

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Bernreuter, D. L. Development of site specific response spectra. Washington, DC: Division of Engineering Safety, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1987.

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Verhagen, Philip. Case studies in archaeological predictive modelling [sic. [Leiden]: Leiden University Press, 2007.

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Verhagen, Philip. Case studies in archaeological predictive modelling [sic. [Leiden]: Leiden University Press, 2007.

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Book chapters on the topic "SIZE PREDICTION"

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Saaty, Thomas L., and Luis G. Vargas. "Family Size in Rural India." In Prediction, Projection and Forecasting, 95–110. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7952-0_6.

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Liu, Wendi, Léan E. Garland, Jesus Ochoa, and Michael J. Pyrcz. "A Geostatistical Heterogeneity Metric for Spatial Feature Engineering." In Springer Proceedings in Earth and Environmental Sciences, 3–19. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_1.

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AbstractHeterogeneity is a vital spatial feature for subsurface resource recovery predictions, such as mining grade tonnage functions, hydrocarbon recovery factor, and water aquifer draw-down predictions. Feature engineering presents the opportunity to integrate heterogeneity information, but traditional heterogeneity engineered features like Dykstra-Parsons and Lorenz coefficients ignore the spatial context; therefore, are not sufficient to quantify the heterogeneity over multiple scales of spatial intervals to inform predictive machine learning models. We propose a novel use of dispersion variance as a spatial-engineered feature that accounts for heterogeneity within the spatial context, including spatial continuity and sample data and model volume support size to improve predictive machine-learning-based models, e.g., for pre-drill prediction and uncertainty quantification. Dispersion variance is a generalized form of variance that accounts for volume support size and can be calculated from the semivariogram-based spatial continuity model. We demonstrate dispersion variance as a useful predictor feature for the case of hydrocarbon recovery prediction, with the ability to quantify the spatial variation over the support size of the production well drainage radius, given the spatial continuity from the variogram and trajectory of the well. We include a synthetic example based on geostatistical models and flow simulation to show the sensitivity of dispersion variance to production. Then we demonstrate the dispersion variance as an informative predictor feature for production forecasting with a field case study in the Duvernay formation.
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Lacerda, Anisio, Adriano Veloso, Rodrygo L. T. Santos, and Nivio Ziviani. "Context-Aware Deal Size Prediction." In String Processing and Information Retrieval, 256–67. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11918-2_25.

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Song, Dan, Ruofeng Tong, Jian Chang, Tongtong Wang, Jiang Du, Min Tang, and Jian J. Zhang. "Clothes Size Prediction from Dressed-Human Silhouettes." In Next Generation Computer Animation Techniques, 86–98. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69487-0_7.

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Burns, Ethan, and Wheeler Ruml. "Iterative-Deepening Search with On-Line Tree Size Prediction." In Lecture Notes in Computer Science, 1–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34413-8_1.

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Rokade, Siddhartha, and Rakesh Kumar. "Road Crash Prediction Model for Medium Size Indian Cities." In Advances in Intelligent Systems and Computing, 655–69. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0589-4_61.

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Sharma, Saurabh, Debayan Dasgupta, Sujit Nath, and Dipankar Bhanja. "Prediction of Droplet Size Distribution For Viscoelastic Liquid Sheet." In Lecture Notes in Mechanical Engineering, 243–50. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7711-6_26.

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Rzepka, Rafal, Koichi Muramoto, and Kenji Araki. "Limiting Context by Using the Web to Minimize Conceptual Jump Size." In Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence, 318–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-44958-1_25.

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Zhang, Hao, Fengfeng Tan, and Zhan Ma. "Hierarchical Block Size Decision for H.264/AVC Intra Prediction." In Lecture Notes in Computer Science, 88–97. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03731-8_9.

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Farbaniec, L., H. Couque, and G. Dirras. "Prediction of Ductile Fracture Through Small-Size Notched Tensile Specimens." In Fracture, Fatigue, Failure and Damage Evolution, Volume 8, 67–72. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-21611-9_9.

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Conference papers on the topic "SIZE PREDICTION"

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Martinez, S. Martinez, T. Harrold, M. Tilita, P. Rouillé, and M. Gonzalez Quijano. "Novel Application of Centroid Analysis to Understand Reservoir Size and Connectivity." In Third EAGE Workshop on Pore Pressure Prediction. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202038014.

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Sanchez, L. A., and T. E. Shoup. "Computer Prediction of Anthropometric Frame Size in Children." In ASME 1996 Design Engineering Technical Conferences and Computers in Engineering Conference. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-detc/cie-1436.

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Abstract In the design and improvement of medical equipment and consumer products, it is often important to be able to make predictions of anthropometric measurements of the human frame in order to size devices for optimum use. Properly sized equipment and products are easier to use, have far more functional utility and present improved market opportunities. The prediction process for anthropometric size is especially difficult when the human frame is that of a child, since for this segment of the human population, the size varies greatly with time. The most frequently used independent variable used in the prediction of frame size for children is the chronological age in years. It is the purpose of this paper to discuss the most often used prediction formulas for human growth and to present a new formulation for anthropometric size made possible by the use of an optimization algorithm using the digital computer. It is believed that this is the first time this particular formulation has been presented.
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Sigg, Stephan, Monty Beuster, Daniel Rohr, and Michael Beigl. "Search space size and context prediction." In 2008 Fifth International Conference on Networked Sensing Systems (INSS). IEEE, 2008. http://dx.doi.org/10.1109/inss.2008.4610879.

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Rahman, Foyzur, Daryl Posnett, Israel Herraiz, and Premkumar Devanbu. "Sample size vs. bias in defect prediction." In the 2013 9th Joint Meeting. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2491411.2491418.

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Kupavskii, Andrey, Liudmila Ostroumova, Alexey Umnov, Svyatoslav Usachev, Pavel Serdyukov, Gleb Gusev, and Andrey Kustarev. "Prediction of retweet cascade size over time." In the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398634.

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Shafiq, Zubair, and Alex Liu. "Cascade size prediction in online social networks." In 2017 IFIP Networking Conference (IFIP Networking) and Workshops. IEEE, 2017. http://dx.doi.org/10.23919/ifipnetworking.2017.8264864.

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Xiong, Jian, and Hongliang Li. "Fast and efficient prediction unit size selection for HEVC intra prediction." In 2012 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS 2012). IEEE, 2012. http://dx.doi.org/10.1109/ispacs.2012.6473512.

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Deshwal, Aryan, Janardhan Rao Doppa, and Dan Roth. "Learning and Inference for Structured Prediction: A Unifying Perspective." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/878.

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In a structured prediction problem, one needs to learn a predictor that, given a structured input, produces a structured object, such as a sequence, tree, or clustering output. Prototypical structured prediction tasks include part-of-speech tagging (predicting POS tag sequence for an input sentence) and semantic segmentation of images (predicting semantic labels for pixels of an input image). Unlike simple classification problems, here there is a need to assign values to multiple output variables accounting for the dependencies between them. Consequently, the prediction step itself (aka ``inference" or ``decoding") is computationally-expensive, and so is the learning process, that typically requires making predictions as part of it. The key learning and inference challenge is due to the exponential size of the structured output space and depend on its complexity. In this paper, we present a unifying perspective of the different frameworks that address structured prediction problems and compare them in terms of their strengths and weaknesses. We also discuss important research directions including integration of deep learning advances into structured prediction, and learning from weakly supervised signals and active querying to overcome the challenges of building structured predictors from small amount of labeled data.
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Maggi, Filippo, Alessio Bandera, and Luigi DeLuca. "Approaching Solid Propellant Heterogeneity for Agglomerate Size Prediction." In 46th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-6751.

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Bailini, Alessandro, and Paolo M. Ossi. "Cluster size prediction in pulsed laser deposited films." In SPIE Proceedings, edited by Dan C. Dumitras, Maria Dinescu, and Vitally I. Konov. SPIE, 2007. http://dx.doi.org/10.1117/12.729583.

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Reports on the topic "SIZE PREDICTION"

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Bitz, Cecilia M. Modeling the Floe-Size Distribution to Improve the Prediction of Sea Ice in the Marginal Seas. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada628070.

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Eilerts. L52026 Improved Prediction of Burnthrough for In-Service Welding. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 2002. http://dx.doi.org/10.55274/r0011153.

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Experiments were conducted to evaluate the effect of pressure on burnthrough risk.� The results indicate that hoop stress has a significant effect for thin-wall pipe.� The experimental data was used to develop and evaluate an alternative burnthrough prediction approach that accounts for pressure in the pipe.� The approach that was developed assumes that the volume of heated metal under the arc behaves similar to an area of metal loss caused by corrosion pitting.� An equivalent pit size is determined from the pipe diameter and wall thickness and the calculated weld penetration.� The predicted burst pressure (i.e., the pressure limit) is then determined using RSTRENG.� While this approach was shown to be relatively accurate for the experimental welds that were made, it does not consider the removal of heat by the contents and it was evaluated for a limited range of conditions.
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Chang, Ke-Vin. Ultrasound Imaging for Size Prediction of the Autograft for Anterior Cruciate Ligament Reconstruction: a Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2022. http://dx.doi.org/10.37766/inplasy2022.1.0114.

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Judge, K. J. Installation and use of a quantimet 720 image analyzer for particle characterization. Natural Resources Canada/CMSS/Information Management, 1989. http://dx.doi.org/10.4095/331777.

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The size and shape of particles, in a dust, can affect the explosion hazards posed by the dust. Dust explosion work at the Canadian Explosive Atmospheres Laboratory (CEAL) now involves the use of an image analyzer. The analyzer is being used to characterize the shape and size of grains involved in explosion tests at this laboratory. It is hoped that this information will indicate the relationships between these parameters and that a more comprehensive prediction of dust explosion phenomena will result. The system configuration, operating procedures and supporting hardware and software are detailed in this report.
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Vecherin, Sergey, Stephen Ketcham, Aaron Meyer, Kyle Dunn, Jacob Desmond, and Michael Parker. Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45300.

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To make a prediction for seismic signal propagation, one needs to specify physical properties and subsurface ground structure of the site. This information is frequently unknown or estimated with significant uncertainty. This paper describes a methodology for probabilistic seismic ensemble prediction for vertically stratified soils and short ranges with no in situ site characterization. Instead of specifying viscoelastic site properties, the methodology operates with probability distribution functions of these properties taking into account analytical and empirical relationships among viscoelastic variables. This yields ensemble realizations of signal arrivals at specified locations where statistical properties of the signals can be estimated. Such ensemble predictions can be useful for preliminary site characterization, for military applications, and risk analysis for remote or inaccessible locations for which no data can be acquired. Comparison with experiments revealed that measured signals are not always within the predicted ranges of variability. Variance-based global sensitivity analysis has shown that the most significant parameters for signal amplitude predictions in the developed stochastic model are the uncertainty in the shear quality factor and the Poisson ratio above the water table depth.
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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, December 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&R activities, thus promoting the most cost-effective alternative in LCCA.
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Baete, Christophe. PR-405-153600-R01 Validation of the AC Corrosion Criteria Based on Real-World Pipeline Measurements. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 2019. http://dx.doi.org/10.55274/r0011592.

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This report refers to the activities performed in the frame of PRCI project on the refinement of the AC corrosion criteria by evaluating real-world pipeline AC corrosion cases and applying an improved AC corrosion prediction model. The diagrams of AC corrosion likelihood assessment in the standard ISO18086:2015 was used as a starting point. Correlations between field data (especially those provided through dig reports from AC corrosion anomalies) and the simulation results for different CP polarization levels, AC induced voltage, soil conditions (texture, soil resistivity and moisture) and coating defect properties (size and thickness) were investigated. The final goal is achieving a further refinement of the proposed AC corrosion criteria in previous project (EC-6-2) based on long-term AC corrosion behavior. This document has a related webinar.
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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.
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Fluhr, Robert, and Volker Brendel. Harnessing the genetic diversity engendered by alternative gene splicing. United States Department of Agriculture, December 2005. http://dx.doi.org/10.32747/2005.7696517.bard.

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Our original objectives were to assess the unexplored dimension of alternative splicing as a source of genetic variation. In particular, we sought to initially establish an alternative splicing database for Arabidopsis, the only plant for which a near-complete genome has been assembled. Our goal was to then use the database, in part, to advance plant gene prediction programs that are currently a limiting factor in annotating genomic sequence data and thus will facilitate the exploitation of the ever increasing quantity of raw genomic data accumulating for plants. Additionally, the database was to be used to generate probes for establishing high-throughput alternative transcriptome analysis in the form of a splicing-specific oligonucleotide microarray. We achieved the first goal and established a database and web site termed Alternative Splicing In Plants (ASIP, http://www.plantgdb.org/ASIP/). We also thoroughly reviewed the extent of alternative splicing in plants (Arabidopsis and rice) and proposed mechanisms for transcript processing. We noted that the repertoire of plant alternative splicing differs from that encountered in animals. For example, intron retention turned out to be the major type. This surprising development was proven by direct RNA isolation techniques. We further analyzed EST databases available from many plants and developed a process to assess their alternative splicing rate. Our results show that the lager genome-sized plant species have enhanced rates of alternative splicing. We did advance gene prediction accuracy in plants by incorporating scoring for non-canonical introns. Our data and programs are now being used in the continuing annotation of plant genomes of agronomic importance, including corn, soybean, and tomato. Based on the gene annotation data developed in the early part of the project, it turned out that specific probes for different exons could not be scaled up to a large array because no uniform hybridization conditions could be found. Therefore, we modified our original objective to design and produce an oligonucleotide microarray for probing alternative splicing and realized that it may be reasonable to investigate the extent of alternative splicing using novel commercial whole genome arrays. This possibility was directly examined by establishing algorithms for the analysis of such arrays. The predictive value of the algorithms was then shown by isolation and verification of alternative splicing predictions from the published whole genome array databases. The BARD-funded work provides a significant advance in understanding the extent and possible roles of alternative splicing in plants as well as a foundation for advances in computational gene prediction.
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Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200434-2.

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This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.
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