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Relevant bibliographies by topics / Semi-random models

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Academic literature on the topic 'Semi-random models'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Semi-random models"

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Dikta, Gerhard. "Asymptotically efficient estimation under semi-parametric random censorship models." Journal of Multivariate Analysis 124 (February 2014): 10–24. http://dx.doi.org/10.1016/j.jmva.2013.10.002.

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Xia, Lirong. "Semi-random Impossibilities of Condorcet Criterion." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (June 26, 2023): 5867–75. http://dx.doi.org/10.1609/aaai.v37i5.25727.

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The Condorcet criterion (CC) is a classical and well-accepted criterion for voting. Unfortunately, it is incompatible with many other desiderata including participation (PAR), half-way monotonicity (HM), Maskin monotonicity (MM), and strategy-proofness (SP). Such incompatibilities are often known as impossibility theorems, and are proved by worst-case analysis. Previous work has investigated the likelihood for these impossibilities to occur under certain models, which are often criticized of being unrealistic. We strengthen previous work by proving the first set of semi-random impossibilities for voting rules to satisfy CC and the more general, group versions of the four desiderata: for any sufficiently large number of voters n, any size of the group 1

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Ohlssen, D. I., L. D. Sharples, and D. J. Spiegelhalter. "Flexible random-effects models using Bayesian semi-parametric models: applications to institutional comparisons." Statistics in Medicine 26, no. 9 (2007): 2088–112. http://dx.doi.org/10.1002/sim.2666.

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Caraballo, Tomás, Renato Colucci, and Xiaoying Han. "Semi-Kolmogorov models for predation with indirect effects in random environments." Discrete and Continuous Dynamical Systems - Series B 21, no. 7 (August 2016): 2129–43. http://dx.doi.org/10.3934/dcdsb.2016040.

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Abd El-Monsef, Mohamed, Elhoussainy Rady, and Ayat Sobhy. "WEIBULL SEMIPARAMETRIC REGRESSION MODELS UNDER RANDOM CENSORSHIP." JOURNAL OF ADVANCES IN MATHEMATICS 11, no. 8 (December 22, 2015): 5577–82. http://dx.doi.org/10.24297/jam.v11i8.1209.

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Semiparametric regression is concerned with the flexible combination of non-linear functional relationships in regression analysis. The main advantage of the semiparametric regression models is that any application benefits from regression analysis can also benefit from the semiparametric regression. In this paper, we derived a consistent estimator of parametric portion and nonparametric portion in Weibull semi-parametric regression models under random censorship.

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Rushton, Philip A., Farid Taheri, and David C. Stredulinsky. "Fatigue Response and Characterization of 350WT Steel Under Semi-Random Loading." Journal of Pressure Vessel Technology 129, no. 3 (March 9, 2006): 525–34. http://dx.doi.org/10.1115/1.2748835.

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Novel data obtained through experimental investigation into the fatigue response of 350WT steel, subjected to semi-random loading, comprised of various combinations of intermittent tensile overloads and compressive underloads are presented. An effective model for predicting the fatigue response is also introduced. For that, the capabilities of some of the currently available models are investigated and then an exponential delay model, being capable of accounting for the effects of not only overload ratio, but also stress ratio and overload/underload ratio is introduced. Since most variable amplitude models are based on a constant amplitude model, efforts were also expended to identify a constant amplitude fatigue crack growth model that would be easy to use, requiring the calibration of few (if any) empirical curve-fitting parameters. The integrity of a selected model is examined and results are presented.

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Soullard, Yann, Martin Saveski, and Thierry Artières. "Joint semi-supervised learning of Hidden Conditional Random Fields and Hidden Markov Models." Pattern Recognition Letters 37 (February 2014): 161–71. http://dx.doi.org/10.1016/j.patrec.2013.03.028.

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Zhang, Yuanqing, and Dianmei Shen. "Estimation of semi-parametric varying-coefficient spatial panel data models with random-effects." Journal of Statistical Planning and Inference 159 (April 2015): 64–80. http://dx.doi.org/10.1016/j.jspi.2014.11.001.

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Loeys, Tom, Catherine Legrand, Antonio Schettino, and Gilles Pourtois. "Semi-parametric proportional hazards models with crossed random effects for psychometric response times." British Journal of Mathematical and Statistical Psychology 67, no. 2 (August 13, 2013): 304–27. http://dx.doi.org/10.1111/bmsp.12020.

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Kazemi, Iraj, and Reyhaneh Rikhtehgaran. "Topics on Dynamic Panel Data Models with Random Effects Using Semi-Parametric Bayesian Approach." Communications in Statistics - Theory and Methods 43, no. 8 (April 2014): 1630–48. http://dx.doi.org/10.1080/03610926.2012.673672.

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Dissertations / Theses on the topic "Semi-random models"

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Wang, Chao. "Exploiting non-redundant local patterns and probabilistic models for analyzing structured and semi-structured data." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1199284713.

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Dillon, Joshua V. "Stochastic m-estimators: controlling accuracy-cost tradeoffs in machine learning." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42913.

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m-Estimation represents a broad class of estimators, including least-squares and maximum likelihood, and is a widely used tool for statistical inference. Its successful application however, often requires negotiating physical resources for desired levels of accuracy. These limiting factors, which we abstractly refer as costs, may be computational, such as time-limited cluster access for parameter learning, or they may be financial, such as purchasing human-labeled training data under a fixed budget. This thesis explores these accuracy- cost tradeoffs by proposing a family of estimators that maximizes a stochastic variation of the traditional m-estimator. Such "stochastic m-estimators" (SMEs) are constructed by stitching together different m-estimators, at random. Each such instantiation resolves the accuracy-cost tradeoff differently, and taken together they span a continuous spectrum of accuracy-cost tradeoff resolutions. We prove the consistency of the estimators and provide formulas for their asymptotic variance and statistical robustness. We also assess their cost for two concerns typical to machine learning: computational complexity and labeling expense. For the sake of concreteness, we discuss experimental results in the context of a variety of discriminative and generative Markov random fields, including Boltzmann machines, conditional random fields, model mixtures, etc. The theoretical and experimental studies demonstrate the effectiveness of the estimators when computational resources are insufficient or when obtaining additional labeled samples is necessary. We also demonstrate that in some cases the stochastic m-estimator is associated with robustness thereby increasing its statistical accuracy and representing a win-win.

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Sokolovska, Nataliya. "Contributions to the estimation of probabilistic discriminative models: semi-supervised learning and feature selection." Phd thesis, Télécom ParisTech, 2010. http://pastel.archives-ouvertes.fr/pastel-00006257.

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Dans cette thèse nous étudions l'estimation de modèles probabilistes discriminants, surtout des aspects d'apprentissage semi-supervisé et de sélection de caractéristiques. Le but de l'apprentissage semi-supervisé est d'améliorer l'efficacité de l'apprentissage supervisé en utilisant des données non-étiquetées. Cet objectif est difficile à atteindre dans les cas des modèles discriminants. Les modèles probabilistes discriminants permettent de manipuler des représentations linguistiques riches, sous la forme de vecteurs de caractéristiques de très grande taille. Travailler en grande dimension pose des problèmes, en particulier computationnels, qui sont exacerbés dans le cadre de modèles de séquences tels que les champs aléatoires conditionnels (CRF). Notre contribution est double. Nous introduisons une méthode originale et simple pour intégrer des données non étiquetées dans une fonction objectif semi-supervisée. Nous démontrons alors que l'estimateur semi-supervisé correspondant est asymptotiquement optimal. Le cas de la régression logistique est illustré par des résultats d'expèriences. Dans cette étude, nous proposons un algorithme d'estimation pour les CRF qui réalise une sélection de modèle, par le truchement d'une pénalisation $L_1$. Nous présentons également les résultats d'expériences menées sur des tâches de traitement des langues (le chunking et la détection des entités nommées), en analysant les performances en généralisation et les caractéristiques sélectionnées. Nous proposons finalement diverses pistes pour améliorer l'efficacité computationelle de cette technique.

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Mahdaviani, Maryam. "Semi-supervised and active training of conditional random fields for activity recognition." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/346.

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Automated human activity recognition has attracted increasing attention in the past decade. However, the application of machine learning and probabilistic methods for activity recognition problems has been studied only in the past couple of years. For the first time, this thesis explores the application of semi-supervised and active learning in activity recognition. We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs),a probabilistic graphical model. In real-world applications such as activity recognition, unlabeled sensor traces are relatively easy to obtain whereas labeled examples are expensive and tedious to collect. Furthermore, the ability to automatically select a small subset of discriminatory features from a large pool can be advantageous in terms of computational speed as well as accuracy. We introduce the semi-supervised virtual evidence boosting (sVEB)algorithm for training CRFs — a semi-supervised extension to the recently developed virtual evidence boosting (VEB) method for feature selection and parameter learning. sVEB takes advantage of the unlabeled data via mini-mum entropy regularization. The objective function combines the unlabeled conditional entropy with labeled conditional pseudo-likelihood. The sVEB algorithm reduces the overall system cost as well as the human labeling cost required during training, which are both important considerations in building real world inference systems. Moreover, we propose an active learning algorithm for training CRFs is based on virtual evidence boosting and uses entropy measures. Active virtual evidence boosting (aVEB) queries the user for most informative examples, efficiently builds up labeled training examples and incorporates unlabeled data as in sVEB. aVEB not only reduces computational complexity of training CRFs as in sVEB, but also outputs more accurate classification results for the same fraction of labeled data. Ina set of experiments we illustrate that our algorithms, sVEB and aVEB, benefit from both the use of unlabeled data and automatic feature selection, and outperform other semi-supervised and active training approaches. The proposed methods could also be extended and employed for other classification problems in relational data.

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Lamborn, Peter C. "January : search based On social insect behavior /." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd801.pdf.

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"Fitting random effects in semi-parametric regression model with application to horse racing." 2003. http://library.cuhk.edu.hk/record=b5891579.

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Cheung Man-Yuen.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references (leaves 35-40).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Rank Regression --- p.1
Chapter 1.2 --- Clustering --- p.2
Chapter 1.3 --- Modeling of the ranked data --- p.3
Chapter 1.4 --- Application in Horse Racing data --- p.4
Chapter 2 --- Semi-Parametric Regression Model --- p.7
Chapter 2.1 --- Review --- p.7
Chapter 2.2 --- Parameter Estimation --- p.9
Chapter 3 --- Random Effects --- p.11
Chapter 3.1 --- Definition --- p.11
Chapter 3.1.1 --- A Simple Estimation Algorithm --- p.13
Chapter 3.2 --- Metropolis-Hastings Algorithm for Simulating Random Effects --- p.14
Chapter 3.3 --- EM Algorithms for Maximizing the Likelihood --- p.16
Chapter 3.3.1 --- Stochastic EM Algorithm --- p.17
Chapter 3.3.2 --- MCEM Algorithm --- p.18
Chapter 4 --- Application --- p.20
Chapter 4.1 --- Fundamental Variables and Variable Selection --- p.21
Chapter 4.2 --- Simulation Results --- p.23
Chapter 4.3 --- Betting Strategies and Comparisons --- p.25
Chapter 5 --- Conclusions and Further Studies --- p.29
Appendix --- p.31
Bibliograply --- p.35

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Books on the topic "Semi-random models"

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Peschanskiy, Aleksey. Semi-Markov models of prevention of unreliable single-channel service system with losses. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1870597.

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The monograph examines various maintenance strategies of a single-channel system with losses and unreliable recoverable service device under the assumption of a general type of random variables describing random processes occurring in the system. The apparatus for constructing models of the functioning of the system are semi-Markov processes with a measurable phase space of states and phase enlargement algorithms. Stationary probabilistic and economic indicators of the system are explicitly determined and the tasks of optimal frequency of maintenance of the device are solved. For researchers, engineers and specialists in the field of metamathematic theory of reliability, system analysis. It can be useful for graduate students and students of relevant specialties of technical universities and universities.

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Eynard, Bertrand. Random matrices and loop equations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0007.

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This chapter is an introduction to algebraic methods in random matrix theory (RMT). In the first section, the random matrix ensembles are introduced and it is shown that going beyond the usual Wigner ensembles can be very useful, in particular by allowing eigenvalues to lie on some paths in the complex plane rather than on the real axis. As a detailed example, the Plancherel model is considered from the point of RMT. The second section is devoted to the saddle-point approximation, also called the Coulomb gas method. This leads to a system of algebraic equations, the solution of which leads to an algebraic curve called the ‘spectral curve’ which determines the large N expansion of all observables in a geometric way. Finally, the third section introduces the ‘loop equations’ (i.e., Schwinger–Dyson equations associated with matrix models), which can be solved recursively (i.e., order by order in a semi-classical expansion) by a universal recursion: the ‘topological recursion’.

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Book chapters on the topic "Semi-random models"

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Korolyuk, Vladimir S. "Semi-Markov Random Walks." In Semi-Markov Models and Applications, 61–75. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4613-3288-6_4.

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Dikta, Gerhard. "Semi-parametric Random Censorship Models." In From Statistics to Mathematical Finance, 43–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50986-0_3.

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Vannucci, Giulia, Anna Gottard, Leonardo Grilli, and Carla Rampichini. "Random effects regression trees for the analysis of INVALSI data." In Proceedings e report, 29–34. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.07.

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Mixed or multilevel models exploit random effects to deal with hierarchical data, where statistical units are clustered in groups and cannot be assumed as independent. Sometimes, the assumption of linear dependence of a response on a set of explanatory variables is not plausible, and model specification becomes a challenging task. Regression trees can be helpful to capture non-linear effects of the predictors. This method was extended to clustered data by modelling the fixed effects with a decision tree while accounting for the random effects with a linear mixed model in a separate step (Hajjem & Larocque, 2011; Sela & Simonoff, 2012). Random effect regression trees are shown to be less sensitive to parametric assumptions and provide improved predictive power compared to linear models with random effects and regression trees without random effects. We propose a new random effect model, called Tree embedded linear mixed model, where the regression function is piecewise-linear, consisting in the sum of a tree component and a linear component. This model can deal with both non-linear and interaction effects and cluster mean dependencies. The proposal is the mixed effect version of the semi-linear regression trees (Vannucci, 2019; Vannucci & Gottard, 2019). Model fitting is obtained by an iterative two-stage estimation procedure, where both the fixed and the random effects are jointly estimated. The proposed model allows a decomposition of the effect of a given predictor within and between clusters. We will show via a simulation study and an application to INVALSI data that these extensions improve the predictive performance of the model in the presence of quasi-linear relationships, avoiding overfitting, and facilitating interpretability.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "General Elements of Genomic Selection and Statistical Learning." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 1–34. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_1.

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AbstractNowadays, huge data quantities are collected and analyzed for delivering deep insights into biological processes and human behavior. This chapter assesses the use of big data for prediction and estimation through statistical machine learning and its applications in agriculture and genetics in general, and specifically, for genome-based prediction and selection. First, we point out the importance of data and how the use of data is reshaping our way of living. We also provide the key elements of genomic selection and its potential for plant improvement. In addition, we analyze elements of modeling with machine learning methods applied to genomic selection and stress their importance as a predictive methodology. Two cultures of model building are analyzed and discussed: prediction and inference; by understanding modeling building, researchers will be able to select the best model/method for each circumstance. Within this context, we explain the differences between nonparametric models (predictors are constructed according to information derived from data) and parametric models (all the predictors take predetermined forms with the response) as well their type of effects: fixed, random, and mixed. Basic elements of linear algebra are provided to facilitate understanding the contents of the book. This chapter also contains examples of the different types of data using supervised, unsupervised, and semi-supervised learning methods.

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Roussas, George G., and Debasis Bhattacharya. "Asymptotic Behavior of the Log-Likelihood Function in Stochastic Processes when Based on a Random Number of Random Variables." In Semi-Markov Models and Applications, 119–47. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4613-3288-6_7.

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Balkanski, Eric, Yuri Faenza, and Mathieu Kubik. "The Simultaneous Semi-random Model for TSP." In Integer Programming and Combinatorial Optimization, 43–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06901-7_4.

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Kinne, Marko, Ronald Schneider, and Sebastian Thöns. "Reconstructing Stress Resultants in Wind Turbine Towers Based on Strain Measurements." In Lecture Notes in Mechanical Engineering, 224–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77256-7_18.

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AbstractSupport structures of offshore wind turbines are subject to cyclic stresses generated by different time-variant random loadings such as wind, waves, and currents in combination with the excitation by the rotor. In the design phase, the cyclic demand on wind turbine support structure is calculated and forecasted with semi or fully probabilistic engineering models. In some cases, additional cyclic stresses may be induced by construction deviations, unbalanced rotor masses and structural dynamic phenomena such as, for example, the Sommerfeld effect. Both, the significant uncertainties in the design and a validation of absence of unforeseen adverse dynamic phenomena necessitate the employment of measurement systems on the support structures. The quality of the measurements of the cyclic demand on the support structures depends on (a) the precision of the measurement system consisting of sensors, amplifier and data normalization and (b) algorithms for analyzing and converting data to structural health information. This paper presents the probabilistic modelling and analysis of uncertainties in strain measurements performed for the purposes of reconstructing stress resultants in wind turbine towers. It is shown how the uncertainties in the strain measurements affect the uncertainty in the individual components of the reconstructed forces and moments. The analysis identifies the components of the vector of stress resultants that can be reconstructed with sufficient precision.

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Liang, Qingzhu, Changhong Peng, Hang Zhang, and Jianchao Lu. "A Multi-state Degradation Model for Reliability Assessment of Multi-component Nuclear Safety Systems Considering Degradation Dependency and Random Shocks." In Springer Proceedings in Physics, 297–311. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1023-6_28.

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AbstractThe degradation (e.g., wear, stress corrosion cracking, and fatigue) of nuclear safety systems is an inherently irreversible process, which will lead to system failure when the accumulated damage reaches a threshold level, resulting in catastrophic consequences. Therefore, it is essential to understand and model the degradation behavior of nuclear safety systems to predict and prevent potential failures and thus effectively avoid subsequent losses. This paper proposes a multi-state degradation model for multi-component nuclear safety systems, considering the dependency among the degradation processes and the effect of random shocks. The degradation processes of the system were modeled by the Semi-Markov process. The arrival of random shocks obeys a Poisson process. The transfer kernel function of the holistic model was derived, based on which the Monte Carlo algorithm for estimation of the system reliability was developed. Based on a simple case, the correctness of the proposed model is verified. The model is applied to the reliability analysis of one sub-system of the residual heat removal system of a nuclear power plant.

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Pfahringer, Bernhard. "Semi-random Model Tree Ensembles: An Effective and Scalable Regression Method." In AI 2011: Advances in Artificial Intelligence, 231–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25832-9_24.

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Rao, R. Prabhakar, Brajendra C. Sutradhar, and V. N. Pandit. "Longitudinal Mixed Models with t Random Effects for Repeated Count and Binary Data." In Advances and Challenges in Parametric and Semi-parametric Analysis for Correlated Data, 41–71. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31260-6_2.

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Conference papers on the topic "Semi-random models"

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Goutsias, John, and Jerry M. Mendel. "Semi-Markov Random Field Models For Texture Synthesis." In Pattern Recognition and Acoustical Imaging, edited by Leonard A. Ferrari. SPIE, 1987. http://dx.doi.org/10.1117/12.940284.

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Kolla, Alexandra, Konstantin Makarychev, and Yury Makarychev. "How to Play Unique Games Against a Semi-random Adversary: Study of Semi-random Models of Unique Games." In 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2011. http://dx.doi.org/10.1109/focs.2011.78.

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Jian Xue, Lili Che, and Yunxin Zhao. "Semi-tied covariance matrices for acoustic models based on random forests of phonetic decision trees." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960622.

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Bitner-Gregersen, Elzbieta M., and Alessandro Toffoli. "A Semi-Empirical Wave Crest Distribution of Random Directional Wave Fields." In ASME 2012 31st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/omae2012-83844.

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Laboratory data of random directional wave fields have been used to investigate the combined effect of higher order nonlinearity and directional spreading on the wave crest distribution. Different sea states with a variety of combination of steepness and directional spreading have been considered, ranging from long to short crested wave fields. The analysis is also supported by numerical simulations. A 2-parameter Weibull distribution has been fitted to the experimental data and the related parameters have been parameterized as a function of a general version of the Benjamin-Feir Index for directional sea states recently presented by Mori et al. [1]. Long-term distributions of the one-dimensional and two-dimensional Benjamin-Feir Index have been studied based on the hindcast data from the North Atlantic and probabilistic models to describe them are proposed.

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Izadparast, Amir H., and John M. Niedzwecki. "Application of Semi-Empirical Probability Distributions in Wave-Structure Interaction Problems." In ASME 2012 Fluids Engineering Division Summer Meeting collocated with the ASME 2012 Heat Transfer Summer Conference and the ASME 2012 10th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/fedsm2012-72477.

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Ocean engineers are routinely faced with design problems for coastal and deepwater structures that must survive a wide range of environmental conditions. One of the most challenging problems in the field of ocean engineering is the accurate characterization and modeling of the interaction of ocean waves with these offshore structures. The random characteristic of ocean environment requires engineers to consider the effects of random variability of the pertinent variables in their predictive models and design processes. Thus, for ocean engineering purposes, one needs to have accurate estimates of the probability distribution of the key random variables that will be used in sensitivity studies, reliability analysis, and risk assessment in the design process. In this study, a family of semi-empirical probability distribution is developed based on the quadratic transformation of linear random variable assuming that the linear random variable follows a Rayleigh distribution law. The estimates of model parameters are obtained from two moment based parameter estimation methods, i.e. method of moments and method of linear moments. The studied semi-empirical distribution can be applied to estimate the probability distribution of a wide range of non-linear random variables in the fields of ocean wave mechanics and wave-structure interaction. As examples, the application of the semi-empirical model in estimation of probability distribution of: a) ocean wave power, b) ocean wave crests interacting with an offshore structure is illustrated. For this purpose, numerically generated timeseries and experimentally measured data sets are utilized.

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Aristodemo, Francesco, Giuseppe R. Tomasicchio, and Paolo Veltri. "Modelling of Periodic and Random Wave Forces on Submarine Pipelines." In 25th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/omae2006-92353.

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A numerical model for the prediction of the time variation of the flow field and the hydrodynamic forces on bottom submarine pipelines is proposed. The model is an extension for periodic and random waves of the Wake II hydrodynamic forces model (Soedigdo et al., 1999), originally proposed for sinusoidal waves. An extensive laboratory investigation has been carried out in order to calibrate the model. The numerical model is based on an analysis of the time history of the velocity field at each wave semi-cycle. A modified relationship of the wake velocity is introduced and the time history of the drag and lift hydrodynamic coefficients are obtained using a Gauss integration of the start-up function. The laboratory investigation was performed at the large wave flume of the Centro Sperimentale per Modelli Idraulici at Voltabarozzo (Padua, Italy). The tests were carried out by measuring the pressure values at 8 transducers mounted on a cylinder subjected to different periodic and random waves. The experiments refer to the range 4 ÷ 12 of the Keulegan-Carpenter number for periodic waves and to the range 4 ÷ 9 for random waves. The empirical parameters involved in the extended Wake II and in the classical Morison models were calibrated using the results of the sampled velocities and force time histories under different wave conditions. The comparisons between the experimental and numerical results indicate that the extended Wake II model allows an accurate evaluation of the peaks and of the phase shifts of the horizontal and vertical forces and is more accurate than the Morison model.

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Ellermann, Katrin. "The Motion of Floating Systems: Nonlinear Dynamics in Periodic and Random Waves." In 25th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/omae2006-92037.

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Floating systems, such as ships, barges or semi-submersibles, show a dynamical behavior which is determined by their internal structure and the operating conditions caused by external forces e.g. due to waves and wind. Due to the complexity of the system which commonly includes coupling of multiple components or nonlinear restoring forces, the response can exhibit inherently nonlinear characteristics. In this paper different models of floating systems are considered. For the idealized case of purely harmonic forcing they all show nonlinear behavior such as subharmonic motion or different steady state responses at constant operating conditions. The introduction of random disturbances leads to deviations from the idealized case which may change the overall response significantly. Advantages and limitations of the different mathematical models and the applied numerical techniques are discussed.

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Jiang, Zhenyu, Moustafa El-Gindy, and Donald Streit. "Ride Comfort of Five-Axle Tractor/Semi-Trailer." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-1202.

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Abstract The issue of ride comfort for vehicle operations has recently generated considerable interest especially in heavy vehicle systems since long-distance drivers are more likely to experience high levels of vibration. This paper introduces the general concept of vibration-related health problems, discusses ride comfort assessment criteria and methods, and then focuses on the methodology of using computer simulation to analyze ride comfort. The computer-based ride comfort model can be divided into three sub-models: vehicle model, driver/seat model, and road profile input model. Several vehicle models and driver/seat models are reviewed and detailed modeling techniques are introduced. A five-axle tractor/semi-trailer/driver combination ride comfort simulation model is developed in this paper using the software DADS. Both four-spring tandem suspension and independent air spring suspension are studied. Road profiles are assumed as static zero mean Gaussian random process. Vertical acceleration at the interface between seat and driver body is obtained from simulation results. Power spectral density and root mean square (RMS) vertical acceleration are calculated based on simulation results. RMS acceleration at ISO classified good and average roads are compared with ISO 8-hour fatigue vibration limit. It is found that RMS acceleration of this particular vehicle simulated in this paper is below the ISO 8-hour fatigue limit for both good and average roads when traveling at the speed of fifty miles per hour. This implies a good ride comfort. Axle dynamic load coefficients (DLC) are calculated for four suspension configurations that are combinations of air springs and steel springs. Results show that large DLC doesn’t necessarily indicate bad ride quality.

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Clauss, Gu¨nther F., and Janou Hennig. "Computer Controlled Capsizing Tests Using Tailored Wave Sequences." In ASME 2002 21st International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/omae2002-28297.

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This paper presents a new kind of computer controlled sea-keeping test procedure for investigating large roll motions and capsizing behavior of ship models. In cooperation with the Hamburg Ship Model Basin (HSVA) our research group has carried out computer controlled capsizing tests in steep transient wave groups for two different types of ship models which show typically two different modes of rolling. The parameters of the model seas – transient wave sequences consisting of random seas or regular wave trains with an embedded deterministic high transient wave – are systematically varied to investigate the ship model response with regard to metacentric height, model velocity, and course angle for each of both ship types. The wave elevation at the position of the ship model at any position in time and space is calculated by a semi-empirical method (and controlled by registrations during model tests) in order to relate wave excitation to the resulting roll motion.

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Lambert, Gregory M., and Donald G. Baird. "Evaluating Rigid and Semi-Flexible Fiber Orientation Evolution Models in Simple Flows." In ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8678.

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As American vehicle fuel efficiency requirements have become more stringent due to the CAFE standards, the auto industry has turned to fiber reinforced polymer composites as replacements for metal parts to reduce weight while simultaneously maintaining established safety standards. Furthermore, these composites may be easily processed using established techniques such as injection molding and compression molding. The mechanical properties of these composites are dependent on, among other variables, the orientation of the fibers within the part. Several models have been proposed to correlate fiber orientation with the kinematics of the polymer matrix during processing, each using various strategies to account for fiber interactions and fiber flexing. However, these all require the use of empirical fitting parameters. Previous work has obtained these parameters by fitting to orientation data at a specific location in an injection-molded part. This ties the parameters to the specific mold design used. Obtaining empirical parameters is not a trivial undertaking and adds significant time to the entire mold design process. Considering that new parameters must be obtained any time some aspect of the part or mold is changed, an alternative technique that obtains model parameters independent of the mold design could be advantageous. This paper continues work looking to obtain empirical parameters from rheological tests. During processing, the fiberpolymer suspension is subjected to a complex flow with both shear and extensional behavior. Rather than use a complex flow, this study seeks to isolate and compare the effects of shear and extension on two orientation models. To this end, simple shear and planar extension are employed and the evolution of orientation from a planar random initial condition is tracked as a function of strain. Simple shear was imparted using a sliding plate rheometer designed and fabricated in-house. A novel rheometer tool was developed and fabricated in-house to impart planar extension using a lubricated squeeze flow technique, where a low viscosity Newtonian lubricant is applied to the solid boundaries to minimize the effect of shearing due to the no-slip boundary condition. The Folgar-Tucker model with a strain reduction factor is used as a rigid fiber model and compared against a Bead-Rod model (a semi-flexible model) proposed by Ortman. Both models are capable of predicting the data, with the Bead-Rod model performing slightly better. Orientation occurs at a much faster rate under startup of planar extension, and also attains a much higher degree of flow alignment when compared with startup of steady shear.

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