Relevant bibliographies by topics / Conformal predictions

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Conformal predictions'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Conformal predictions"

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Balasubramanian, Vineeth N., Shayok Chakraborty, and Sethuraman Panchanathan. "Conformal predictions for information fusion." Annals of Mathematics and Artificial Intelligence 74, no. 1-2 (2014): 45–65. http://dx.doi.org/10.1007/s10472-013-9392-4.

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Wang, You, Zhan Wang, Junwei Diao, Xiyang Sun, Zhiyuan Luo, and Guang Li. "Discrimination of Different Species of Dendrobium with an Electronic Nose Using Aggregated Conformal Predictor." Sensors 19, no. 4 (2019): 964. http://dx.doi.org/10.3390/s19040964.

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A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated conformal prediction was applied to make predictions with accuracy and reliability for E-nose detection. This method achieved a classification accuracy close to 80% with an average improvement of 6.2% when compared with the results obtained by using traditional inductive conformal prediction. It also provided reliability assessment to show more comprehensive information for e
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Balasubramanian, Vineeth N. "Conformal predictions in multimedia pattern recognition." ACM SIGMultimedia Records 3, no. 1 (2011): 23–24. http://dx.doi.org/10.1145/2069196.2069202.

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Papadopoulos, H., V. Vovk, and A. Gammerman. "Regression Conformal Prediction with Nearest Neighbours." Journal of Artificial Intelligence Research 40 (April 30, 2011): 815–40. http://dx.doi.org/10.1613/jair.3198.

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In this paper we apply Conformal Prediction (CP) to the k-Nearest Neighbours Regression (k-NNR) algorithm and propose ways of extending the typical nonconformity measure used for regression so far. Unlike traditional regression methods which produce point predictions, Conformal Predictors output predictive regions that satisfy a given confidence level. The regions produced by any Conformal Predictor are automatically valid, however their tightness and therefore usefulness depends on the nonconformity measure used by each CP. In effect a nonconformity measure evaluates how strange a given examp
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Al-Jamel, Ahmed. "Heavy quarkonia properties from a hard-wall confinement potential model with conformal symmetry perturbing effects." Modern Physics Letters A 34, no. 37 (2019): 1950307. http://dx.doi.org/10.1142/s0217732319503073.

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Heavy [Formula: see text] and [Formula: see text] quarkonia are considered as systems confined within a hard-wall potential shaped after a linear combination of a cotangent — with a square co-secant function. Wave functions and energy spectra are then obtained in closed forms in solving by the Nikiforov–Uvarov method the associated radial Schrödinger equation in the presence of a centrifugal term. The interest in this potential is that in one parametrization, it can account for a conformal symmetry of the strong interaction, and in another for its perturbation, a reason for which we here emplo
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WYDRO, TOMASZ, and JOHN F. McCABE. "CONFORMAL THEORY FOR YANG–LEE SINGULARITY OF 2D 3-STATE POTTS MODEL." International Journal of Modern Physics B 19, no. 18 (2005): 3021–35. http://dx.doi.org/10.1142/s0217979205030839.

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We identify a conformal field theory (CFT) that describes the Yang–Lee edge singularity of the 2-dimensional (2D) 3-state Potts model. The identification is based on a comparison of finite-size scaling measurements to predictions for conformal minimal models. The comparison shows that the Yang–Lee edge singularities of the 2D 3-state Potts and the 2D Ising models are in the same universality class.
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LAMBROU, ANTONIS, HARRIS PAPADOPOULOS, EFTHYVOULOS KYRIACOU, et al. "EVALUATION OF THE RISK OF STROKE WITH CONFIDENCE PREDICTIONS BASED ON ULTRASOUND CAROTID IMAGE ANALYSIS." International Journal on Artificial Intelligence Tools 21, no. 04 (2012): 1240016. http://dx.doi.org/10.1142/s0218213012400167.

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Conformal Predictors (CPs) are Machine Learning algorithms that can provide reliable confidence measures to their predictions. In this work, we make use of the Conformal Prediction framework for the assessment of stroke risk based on ultrasound images of atherosclerotic carotid plaques. For this application, images were recorded from 137 asymptomatic and 137 symptomatic plaques (symptoms are Stroke, Transient Ischaemic Attack (TIA), and Amaurosis Fugax (AF)). Two feature sets were extracted from the plaques; the first based on morphological image analysis and the second based on image texture
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HENKEL, MALTE. "AGEING, DYNAMICAL SCALING AND CONFORMAL INVARIANCE." International Journal of Modern Physics A 19, no. 14 (2004): 2207–15. http://dx.doi.org/10.1142/s0217751x04018440.

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Building on an analogy with conformal invariance, local scale transformations consistent with dynamical scaling are constructed. Two types of local scale invariance are found which act as dynamical space–time symmetries of certain nonlocal free field theories. The scaling form of two-point functions is completely fixed by the requirement of local scale invariance. These predictions are confirmed through tests in the 3D ANNNI model at its Lifshitz point and in ageing phenomena of simple ferromagnets, here studied through the kinetic Ising model with Glauber dynamics.
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Leib, S. J. "Noise Predictions for Rectangular Jets Using a Conformal Mapping Method." AIAA Journal 51, no. 3 (2013): 721–37. http://dx.doi.org/10.2514/1.j052190.

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Belitsky, A. V., and D. Müller. "Predictions from conformal algebra for the deeply virtual Compton scattering." Physics Letters B 417, no. 1-2 (1998): 129–40. http://dx.doi.org/10.1016/s0370-2693(97)01390-7.

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Dissertations / Theses on the topic "Conformal predictions"

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Carrión, Brännström Robin. "Aggregating predictions using Non-Disclosed Conformal Prediction." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385098.

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When data are stored in different locations and pooling of such data is not allowed, there is an informational loss when doing predictive modeling. In this thesis, a new method called Non-Disclosed Conformal Prediction (NDCP) is adapted into a regression setting, such that predictions and prediction intervals can be aggregated from different data sources without interchanging any data. The method is built upon the Conformal Prediction framework, which produces predictions with confidence measures on top of any machine learning method. The method is evaluated on regression benchmark data sets u
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van, Miltenburg Jelle. "Conformal survival predictions at a user-controlled time point : The introduction of time point specialized Conformal Random Survival Forests." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232060.

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The goal of this research is to expand the field of conformal predictions using Random Survival Forests. The standard Conformal Random Survival Forest can predict with a fixed certainty whether something will survive up until a certain time point. This research is the first to show that there is little practical use in the standard Conformal Random Survival Forest algorithm. It turns out that the confidence guarantees of the conformal prediction framework are violated if the Standard algorithm makes predictions for a user-controlled fixed time point. To solve this challenge, this thesis propos
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Ivina, Olga. "Conformal prediction of air pollution concentrations for the Barcelona Metropolitan Region." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/108341.

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This thesis is aimed to introduce a newly developed machine learning method, conformal predictors, for air pollution assessment. For the given area of study, the Barcelona Metropolitan Region (BMR), several conformal prediction models have been developed. These models use the specification which is called ridge regression confidence machine (RRCM). The conformal predictors that have been developed for the purposes of the present study are ridge regression models, and they always provide valid predictions. Instead of a point prediction, a conformal predictor outputs a prediction set, which is u
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Laxhammar, Rikard. "Conformal anomaly detection : Detecting abnormal trajectories in surveillance applications." Doctoral thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8762.

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Human operators of modern surveillance systems are confronted with an increasing amount of trajectory data from moving objects, such as people, vehicles, vessels, and aircraft. A large majority of these trajectories reflect routine traffic and are uninteresting. Nevertheless, some objects are engaged in dangerous, illegal or otherwise interesting activities, which may manifest themselves as unusual and abnormal trajectories. These anomalous trajectories can be difficult to detect by human operators due to cognitive limitations. In this thesis, we study algorithms for the automated detection of
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Löfström, Tuwe. "On Effectively Creating Ensembles of Classifiers : Studies on Creation Strategies, Diversity and Predicting with Confidence." Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-116683.

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An ensemble is a composite model, combining the predictions from several other models. Ensembles are known to be more accurate than single models. Diversity has been identified as an important factor in explaining the success of ensembles. In the context of classification, diversity has not been well defined, and several heuristic diversity measures have been proposed. The focus of this thesis is on how to create effective ensembles in the context of classification. Even though several effective ensemble algorithms have been proposed, there are still several open questions regarding the role d
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Chavarria, Pablo C. "Reduction of Confidence Interval Length for Small-Normal Data Sets Utilizing Bootstrap and Conformal Prediction Methods." Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10840988.

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<p> It is of common practice to evoke a t-confidence interval for estimating the mean of a small data set with an assumed Normal distribution. These t-intervals are known to be wide to account for the lack of information. This thesis will focus on exploring ways to reduce the length of the interval, while preserving the level of confidence. Simulated small normal data sets will be used to analyze a combination of Bootstrapping and Conformal Prediction methods, while investigating measures of spread, such as standard deviation, kurtosis, excess CS kurtosis, skewness, etc. to create a criterion
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Öhrn, Håkan. "General image classifier for fluorescence microscopy using transfer learning." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388633.

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Modern microscopy and automation technologies enable experiments which can produce millions of images each day. The valuable information is often sparse, and requires clever methods to find useful data. In this thesis a general image classification tool for fluorescence microscopy images was developed usingfeatures extracted from a general Convolutional Neural Network (CNN) trained on natural images. The user selects interesting regions in a microscopy image and then, through an iterative process, using active learning, continually builds a training data set to train a classifier that finds si
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Geylan, Gökçe. "Training Machine Learning-based QSAR models with Conformal Prediction on Experimental Data from DNA-Encoded Chemical Libraries." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447354.

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DNA-encoded chemical libraries (DEL) allows an exhaustive chemical space sampling with a large-scale data consisting of compounds produced through combinatorial synthesis. This novel technology was utilized in the early drug discovery stages for robust hit identification and lead optimization. In this project, the aim was to build a Machine Learning- based QSAR model with conformal prediction for hit identification on two different target proteins, the DEL was assayed on. An initial investigation was conducted on a pilot project with 1000 compounds and the analyses and the conclusions drawn fr
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Lindh, Martin. "Computational Modelling in Drug Discovery : Application of Structure-Based Drug Design, Conformal Prediction and Evaluation of Virtual Screening." Doctoral thesis, Uppsala universitet, Avdelningen för organisk farmaceutisk kemi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-328505.

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Structure-based drug design and virtual screening are areas of computational medicinal chemistry that use 3D models of target proteins. It is important to develop better methods in this field with the aim of increasing the speed and quality of early stage drug discovery. The first part of this thesis focuses on the application of structure-based drug design in the search for inhibitors for the protein 1-deoxy-D-xylulose-5-phosphate reductoisomerase (DXR), one of the enzymes in the DOXP/MEP synthetic pathway. This pathway is found in many bacteria (such as Mycobacterium tuberculosis) and in the
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Pradhan, Manoj Kumar. "Conformal Thermal Models for Optimal Loading and Elapsed Life Estimation of Power Transformers." Thesis, Indian Institute of Science, 2004. http://hdl.handle.net/2005/97.

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Power and Generator Transformers are important and expensive elements of a power system. Inadvertent failure of Power Transformers would cause long interruption in power supply with consequent loss of reliability and revenue to the supply utilities. The mineral oil impregnated paper, OIP, is an insulation of choice in large power transformers in view of its excellent dielectric and other properties, besides being relatively inexpensive. During the normal working regime of the transformer, the insulation thereof is subjected to various stresses, the more important among them are, electrical,
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Books on the topic "Conformal predictions"

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Gammerman, Alexander, Zhiyuan Luo, Jesús Vega, and Vladimir Vovk, eds. Conformal and Probabilistic Prediction with Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33395-3.

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Conformal Prediction for Reliable Machine Learning. Elsevier, 2014. http://dx.doi.org/10.1016/c2012-0-00234-7.

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Vovk, Vladimir, Vineeth Balasubramanian, and Shen-Shyang Ho. Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications. Elsevier Science & Technology Books, 2014.

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Divers, John. De Re Modality in the Late Twentieth Century. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198786436.003.0010.

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Quine’s (in)famous sceptical critique of de re modality is expounded in the pair of 1953 classic papers ‘Reference and Modality’ and ‘Three Grades of Modal Involvement’. Here, I position the salient, and non-sceptical, treatments of de re modality in the later part of the twentieth century—those due to Kripke, Lewis, and Fine—in relation to that prior sceptical critique. My theses are: (a) that that Kripke, Lewis, and Fine all undertake non-sceptical defences of de re modal predication that conform to the Smullyan language-independence strategy; and (b) none does so in a way that falsifies Qui
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Book chapters on the topic "Conformal predictions"

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Linusson, Henrik, Ulf Johansson, Henrik Boström, and Tuve Löfström. "Reliable Confidence Predictions Using Conformal Prediction." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31753-3_7.

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Balasubramanian, Vineeth N., Aaron Baker, Matthew Yanez, Shayok Chakraborty, and Sethuraman Panchanathan. "PyCP: An Open-Source Conformal Predictions Toolkit." In IFIP Advances in Information and Communication Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41142-7_37.

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Bortolussi, Luca, Francesca Cairoli, Nicola Paoletti, and Scott D. Stoller. "Conformal Predictions for Hybrid System State Classification." In From Reactive Systems to Cyber-Physical Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31514-6_13.

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Pereira, Telma, Sandra Cardoso, Dina Silva, Alexandre de Mendonça, Manuela Guerreiro, and Sara C. Madeira. "Towards Trustworthy Predictions of Conversion from Mild Cognitive Impairment to Dementia: A Conformal Prediction Approach." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60816-7_19.

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Carlsson, Lars, Martin Eklund, and Ulf Norinder. "Aggregated Conformal Prediction." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44722-2_25.

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Vovk, Vladimir, and Dusko Pavlovic. "Universal Probability-Free Conformal Prediction." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33395-3_3.

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Fedorova, Valentina, Alex Gammerman, Ilia Nouretdinov, and Vladimir Vovk. "Conformal Prediction under Hypergraphical Models." In IFIP Advances in Information and Communication Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41142-7_38.

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Vovk, Vladimir, Ivan Petej, and Valentina Fedorova. "From Conformal to Probabilistic Prediction." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44722-2_24.

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Nouretdinov, Ilia. "Conformal Prediction under Probabilistic Input." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44722-2_31.

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Vovk, Vladimir, Valentina Fedorova, Ilia Nouretdinov, and Alexander Gammerman. "Criteria of Efficiency for Conformal Prediction." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33395-3_2.

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Conference papers on the topic "Conformal predictions"

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Dall'agata, Gianguido, A. Ceresolea, R. D'Auria, and S. Ferrara. "Supergravity Predictions on Conformal Field Theories." In Quantum aspects of gauge theories, supersymmetry and unification. Sissa Medialab, 2000. http://dx.doi.org/10.22323/1.004.0013.

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Balasubramanian, Vineeth, Shayok Chakraborty, Sethuraman Panchanathan, and Jieping Ye. "Kernel Learning for Efficiency Maximization in the Conformal Predictions Framework." In 2010 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2010. http://dx.doi.org/10.1109/icmla.2010.42.

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Pelham, T. G., G. Hilton, E. Mellios, C. Railton, and R. Lewis. "Conformal antenna array modelling, FDTD predictions and measurements for Dual Circular Patch in Variable Geometry Conformal Antenna Array Test Rig." In 2016 10th European Conference on Antennas and Propagation (EuCAP). IEEE, 2016. http://dx.doi.org/10.1109/eucap.2016.7481683.

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Daniewicz, S. R., J. A. Collins, and Donald R. Houser. "The Stress Intensity Factor and Stiffness for a Cracked Spur Gear Tooth." In ASME 1992 Design Technical Conferences. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/detc1992-0014.

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Abstract The stiffness of a spur gear tooth mesh controls load sharing in an operating gearset as well as vibratory properties which further dictate fatigue resistance and gear noise. A spur gear mesh consisting of a pinion with a single cracked tooth and an uncracked gear is considered. Expressions are presented which allow the determination of stress intensity factors for small through face width fatigue cracks in spur gear teeth. Predictions of tooth pair stiffness for a cracked pinion tooth and uncracked gear tooth pair are made using an analytical model. The model is based on elastic ener
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Ortolani, Andrea, Giacomo Persico, Jernej Drofelnik, Adrian Jackson, and M. Sergio Campobasso. "High-Fidelity Calculation of Floating Offshore Wind Turbines Under Pitching Motion." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15552.

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Abstract The unsteady aerodynamics of floating offshore wind turbine (FOWT) rotors is more complex than that of fixed–bottom turbine rotors, and the uncertainty of low-fidelity aerodynamic predictions, such as those of the blade element momentum theory (BEMT) codes, is higher for the former rotors. Navier-Stokes CFD can improve the understanding of FOWT rotor and wake aerodynamics, and help improve lower-fidelity models. To highlight this potential, blade–resolved analyses of the in-house compressible CFD COSA code and the commercial incompressible CFD code FLUENT were used to investigate the
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Yavuzkurt, Savas, and Melaku Habte. "Effect of Computational Grid on Performance of Two-Equation Models of Turbulence for Film Cooling Applications." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50153.

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The effect of the computational grid on the performance of two-equation models of turbulence in predicting film cooling effectiveness has been investigated. The four turbulence models are: the standard, RNG, and realizable k-ε, and the standard k-ω models in the CFD code FLUENT. The geometry and flow-field were taken from an experimental study on discrete-hole film-cooling on a flat plate and are simulated in FLUENT. Some of the parameters are: Pitch-to-diameter ratio of 3.04, injection tube length-to-diameter ratio of 4.6. Two blowing ratios of M = 0.5 and 1.5 were considered. In earlier stud
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Beganovic, Dorian, and Evgueni Smirnov. "Ensemble Cross-Conformal Prediction." In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. http://dx.doi.org/10.1109/icdmw.2018.00128.

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Papadopoulos, Harris, Volodya Vovk, and Alex Gammermam. "Conformal Prediction with Neural Networks." In 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007). IEEE, 2007. http://dx.doi.org/10.1109/ictai.2007.47.

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Johansson, Ulf, Henrik Bostrom, and Tuve Lofstrom. "Conformal Prediction Using Decision Trees." In 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.85.

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Gauraha, Niharika, and Ola Spjuth. "Synergy Conformal Prediction for Regression." In 10th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010229402120221.

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Reports on the topic "Conformal predictions"

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Frydman, Roman, and Joshua Stillwagon. Market Participants Neither Commit Predictable Errors nor Conform to REH: Evidence from Survey Data of Inflation Forecasts. Institute for New Economic Thinking Working Paper Series, 2021. http://dx.doi.org/10.36687/inetwp163.

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We develop a novel characterization of participants’ forecasts with a mixture of normal variables arising from a Markov component. Using this characterization, we formulate five behavioral specifications, including four implied by the diagnostic expectations approach, as well as three implied by REH, and derive several new predictions for Coibion and Gorodnichenko.s regression of forecast errors on forecast revisions. Predictions of all eight specifications are inconsistent with the observed instability of individual CG regressions’ coefficients, based on inflation forecasts from 24 profession
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