Academic literature on the topic 'Ridge bias'
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Journal articles on the topic "Ridge bias"
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DEVITA, HANY, I. KOMANG GDE SUKARSA, and I. PUTU EKA N. KENCANA. "KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS." E-Jurnal Matematika 3, no. 4 (2014): 146. http://dx.doi.org/10.24843/mtk.2014.v03.i04.p077.
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Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached. Multicollinearity is a linear correlation between independent variabels in model. Jackknife Ridge Regression(JRR) as an extension of Generalized Ridge Regression (GRR) for solving multicollinearity. Generalized Ridge Regression is used to overcome the bias of estimators caused of presents multicollinearity by adding different bias parameter for each independent variabel in least square equation after transforming the data into an orthoghonal form. Beside that, JRR can reduce the bias of the ridge estimator. The result showed that JRR model out performs GRR model.
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Tambunan, Ridho Febriansyah, and Suliadi. "Pemodelan New Ridge Regression Estimator pada Tingkat Kemiskinan di Kabupaten/Kota Provinsi Jawa Barat Tahun 2020." Bandung Conference Series: Statistics 2, no. 2 (2022): 317–23. http://dx.doi.org/10.29313/bcss.v2i2.4244.
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Abstract. Linear regression is a statistical method used to predict value dependent variable or response with one or more independent variables. If there is more than one predictor variable, multiple linear regression analysis is used. Ridge regression estimator has been introduced as an alternative to the ordinary least squares estimator (OLS) in the presence of multicollinearity. Ridge regression minimizes the mean square residual by introducing a bias constant and produced biased but stable coefficients estimate. The aim of this research is to apply a method introducing by Al-hassan (2010) to obtaine the bias constant in ridge regression that produces smaller bias than method given by Hoerl & Kennad. We apply this method to model the poverty rate in districts/cities in West Java in 2020. The dependent variable (Y) is the proverty rate and the independet variables are (average length of school), (unemployment rate), (gross domestic regional product), (human development index), (number of labor force participation rate). The value of the ridge constant using the Al-hassan (2010) method is 1.377633. The ridge regression model for the standardized variables is with , & that significanly affect the reponse. The regression model based on the original variable is
 Abstrak. Analisis regresi linier adalah metode statistika yang digunakan untuk membentuk model hubungan antara variabel terikat (dependent atau respon ) dengan satu atau lebih variabel bebas (independent atau prediktor). Apabila variabel prediktor lebih dari satu maka digunakan analisis regresi linier berganda. Ada beberapa asumsi yang harus terpenuhi dalam regresi linier berganda diantaranya asumsi multikolinearitas. Salah satu metode untuk mengatasi masalah multikolinieritas adalah menggunakan metode regresi ridge. Regresi ridge meminimumkan residual dengan menambahkan tetapan bias (k). Namun metode ini masih memiliki kelemahan yaitu masih terdapat bias. Untuk memperbaiki kelemahan tersebut Al-hassan mengajukan metode baru. Metode ini bertujuan untuk memperkecil nilai bias dari suatu penduga dengan cara memodifikasi nilai k. Dalam skripsi ini kami menerapkan metode tersebut untuk memodelkan tingkat kemiskinan di Kabupaten/Kota di Jawa Barat Tahun 2020. Variabel responnya adalah Y (tingkat kemiskinan) dan variabel bebasnya (lama rata-rata sekolah), (tingkat pengangguran terbuka), (produk domestik regional bruto), (indeks pembangunan manusia), (jumlah angkatan kerja). Nilai konstanta ridge menggunakan metode Al-hassan (2010) sebesar Sehingga didapatkan model persamaan ridge yaitu : Dengan variabel baku , dan varibel baku yang signifikan terhadap variabel . Dan model berdasarkan variabel aslinya adalah
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Xiao, Penghao, Juliana Duncan, Liang Zhang, and Graeme Henkelman. "Ridge-based bias potentials to accelerate molecular dynamics." Journal of Chemical Physics 143, no. 24 (2015): 244104. http://dx.doi.org/10.1063/1.4937393.
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Harini, Sri. "Pendeteksian Outlier dengan Metode Regresi Ridge." CAUCHY 1, no. 1 (2009): 7. http://dx.doi.org/10.18860/ca.v1i1.1699.
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Dalam analisis regresi linier berganda adanya satu atau lebih pengamatan pencilan (outlier) akan menimbulkan dilema bagi para peneliti. Keputusan untuk menghilangkan pencilan tersebut harus dilandasi alasan yang kuat, karena kadang-kadang pencilan dapat memberikan informasi penting yang diperlukan. Masalah outlier ini dapat diatasi dengan berbagai metode, diantaranya metode regresi ridge (ridge regression). Untuk mengetahui kekekaran regresi ridge perlu melihat nilai-nilai R2, PRESS, serta leverage (hii), untuk metode regresi ridge dengan berbagai nilai tetapan bias k yang dipilih.
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Yanagihara, Hirokazu, Isamu Nagai, and Kenichi Satoh. "A Bias-Corrected Cp Criterion for Optimizing Ridge Parameters in Multivariate Generalized Ridge Regression." Japanese Journal of Applied Statistics 38, no. 3 (2009): 151–72. http://dx.doi.org/10.5023/jappstat.38.151.
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Kelly, R. J. "GDOP, Ridge Regression and the Kalman Filter." Journal of Navigation 43, no. 03 (1990): 409–27. http://dx.doi.org/10.1017/s0373463300014041.
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Multicollinearity and its effect on parameter estimators such as the Kalman filter is analysed using the navigation application as a special example. All position-fix navigation systems suffer loss of accuracy when their navigation landmarks are nearly collinear. Nearly collinear measurement geometry is termed the geometric dilution of position (GDOP). Its presence causes the errors of the position estimates to be highly inflated. In 1970 Hoerl and Kennard developed ridge regression to combat near collinearity when it arises in the predictor matrix of a linear regression model. Since GDOP is mathematically equivalent to a nearly collinear predictor matrix, Kelly suggested using ridge regression techniques in navigation signal processors to reduce the effects of GDOP. The original programme intended to use ridge regression not only to reduce variance inflation but also to reduce bias inflation. Reducing bias inflation is an extension of Hoerl's ridge concept by Kelly. Preliminary results show that ridge regression will reduce the effects of variance inflation caused by GDOP. However, recent results (Kelly) conclude it will not reduce bias inflation as it arises in the navigation problem, GDOP is not a mismatched estimator/model problem. Even with an estimator matched to the model, GDOP may inflate the MSE of the ordinary Kalman filter while the ridge recursive filter chooses a suitable biased estimator that will reduce the MSE. The main goal is obtaining a smaller MSE for the estimator, rather than minimizing the residual sum of squares. This is a different operation than tuning the Kalman filter's dynamic process noise covariance Q, in order to compensate for unmodelled errors. Although ridge regression has not yielded a satisfactory solution to the general GDOP problem, it has provided insight into exactly what causes multicollinearity in navigation signal processors such as the Kalman filter and under what conditions an estimator's performance can be improved.
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Tian, Wei, Gaoming Huang, Huafu Peng, Xuebao Wang, and Xiaohong Lin. "Sensor Bias Estimation Based on Ridge Least Trimmed Squares." IEEE Transactions on Aerospace and Electronic Systems 56, no. 2 (2020): 1645–51. http://dx.doi.org/10.1109/taes.2019.2929973.
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Friendly, Michael. "The Generalized Ridge Trace Plot: Visualizing Bias and Precision." Journal of Computational and Graphical Statistics 22, no. 1 (2012): 50–68. http://dx.doi.org/10.1080/10618600.2012.681237.
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Arashi, M., M. Roozbeh, N. A. Hamzah, and M. Gasparini. "Ridge regression and its applications in genetic studies." PLOS ONE 16, no. 4 (2021): e0245376. http://dx.doi.org/10.1371/journal.pone.0245376.
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With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling. When multicollinearity exists in the data set with outliers, we consider a robust ridge estimator, namely the rank ridge regression estimator, for parameter estimation and prediction. On the other hand, the efficiency of the rank ridge regression estimator is highly dependent on the ridge parameter. In general, it is difficult to provide a satisfactory answer about the selection for the ridge parameter. Because of the good properties of generalized cross validation (GCV) and its simplicity, we use it to choose the optimum value of the ridge parameter. The GCV function creates a balance between the precision of the estimators and the bias caused by the ridge estimation. It behaves like an improved estimator of risk and can be used when the number of explanatory variables is larger than the sample size in high-dimensional problems. Finally, some numerical illustrations are given to support our findings.
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Xu, Jianwen, and Hu Yang. "Preliminary test almost unbiased ridge estimator in a linear regression model with multivariate Student-t errors." Acta et Commentationes Universitatis Tartuensis de Mathematica 15, no. 1 (2020): 27–43. http://dx.doi.org/10.12697/acutm.2011.15.03.
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In this paper, the preliminary test almost unbiased ridge estimators of the regression coefficients based on the conflicting Wald (W), Likelihood ratio (LR) and Lagrangian multiplier (LM) tests in a multiple regression model with multivariate Student-t errors are introduced when it is suspected that the regression coefficients may be restricted to a subspace. The bias and quadratic risks of the proposed estimators are derived and compared. Sufficient conditions on the departure parameter ∆ and the ridge parameter k are derived for the proposed estimators to be superior to the almost unbiased ridge estimator, restricted almost unbiased ridge estimator and preliminary test estimator. Furthermore, some graphical results are provided to illustrate theoretical results.
Dissertations / Theses on the topic "Ridge bias"
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Ayme, Alexis. "Supervised learning with missing data : a non-asymptotic point of view." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS252.
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Les valeurs manquantes sont courantes dans la plupart des ensembles de données du monde réel, en raison de la combinaison de sources multiples et d'informations intrinsèquement manquantes, telles que des défaillances de capteurs ou des questions d'enquête sans réponse. La présence de valeurs manquantes empêche souvent l'application d'algorithmes d'apprentissage standard. Cette thèse examinevaleurs manquantes dans un contexte de prédiction, visant à obtenir des prédictions précises malgré l'occurrence de données manquantes dans les données d'apprentissage et de test. L'objectif de cette thèse est d'analyser théoriquement des algorithmes spécifiques pour obtenir des garanties d'échantillons finis. Nous dérivons des bornes inférieures minimax sur le risque des prédictions linéaires en présence de valeurs manquantes. Ces bornes inférieures dépendent de la distribution du motif de valeurs manquantes et peuvent croître de manière exponentielle avec la dimension. Nous proposons une méthode très simple consistant à appliquer la procédure des moindres carrés uniquement aux motifs de valeurs manquantes les plus fréquents. Une telle méthode simple se révèle être une procédure presque minimax-optimale, qui s'écarte de l'algorithme des moindres carrés appliqué à tous les motifs de valeurs manquantes. Par la suite, nous explorons la méthode de l'imputation puis régression, où l'imputation est effectuée en utilisant l'imputation naïve par zéro, et l'étape de régression est réalisée via des modèles linéaires, dont les paramètres sont appris via la descente de gradient stochastique. Nous démontrons que cette méthode très simple offre de fortes garanties pour des échantillons finis dans des contextes de grande dimension. Plus précisément, nous montrons que le biais de cette méthode est inférieur au biais de la régression ridge. Étant donné que la régression ridge est souvent utilisée en haute dimension, cela prouve que le biais des données manquantes (via l'imputation par zéro) est négligeable dans certains contextes de grande dimension. Enfin, nous étudions différents algorithmes pour gérer la classification linéaire en présence de données manquantes (régression logistique, perceptron, LDA). Nous prouvons que la LDA est le seul modèle qui peut être valide pour des données complètes et manquantes dans certains contextes génériques<br>Missing values are common in most real-world data sets due to the combination of multiple sources andinherently missing information, such as sensor failures or unanswered survey questions. The presenceof missing values often prevents the application of standard learning algorithms. This thesis examinesmissing values in a prediction context, aiming to achieve accurate predictions despite the occurrence ofmissing data in both training and test datasets. The focus of this thesis is to theoretically analyze specific algorithms to obtain finite-sample guarantees. We derive minimax lower bounds on the excess risk of linear predictions in presence of missing values.Such lower bounds depend on the distribution of the missing pattern, and can grow exponentially withthe dimension. We propose a very simple method consisting in applying Least-Square procedure onthe most frequent missing patterns only. Such a simple method turns out to be near minimax-optimalprocedure, which departs from the Least-Square algorithm applied to all missing patterns. Followingthis, we explore the impute-then-regress method, where imputation is performed using the naive zeroimputation, and the regression step is carried out via linear models, whose parameters are learned viastochastic gradient descent. We demonstrate that this very simple method offers strong finite-sampleguarantees in high-dimensional settings. Specifically, we show that the bias of this method is lowerthan the bias of ridge regression. As ridge regression is often used in high dimensions, this proves thatthe bias of missing data (via zero imputation) is negligible in some high-dimensional settings. Thesefindings are illustrated using random features models, which help us to precisely understand the role ofdimensionality. Finally, we study different algorithm to handle linear classification in presence of missingdata (logistic regression, perceptron, LDA). We prove that LDA is the only model that can be valid forboth complete and missing data for some generic settings
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Armbruster, Florian. "Darstellung bor- und phosphorhaltiger Ringe durch Reaktionen von Halogenboranen und -phosphanen mit Bis(tert.-butyl-methyl)ketazin." Doctoral thesis, [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973984112.
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Riege, Iris [Verfasser]. "Ambulante Interventionen der DDR-Jugendhilfe in die Familien in den 1960er bis 1980er Jahren. : Rechtliche Normierung sowie tatsächliche Anlässe. / Iris Riege." Berlin : Duncker & Humblot, 2020. http://d-nb.info/123844377X/34.
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Ringe, Arne [Verfasser]. "Synthese und Charakterisierung asymmetrischer Bis(Thiophosphoryl)amine zur Darstellung von Münzmetallclustern / vorgelegt von Arne Ringe." 2008. http://d-nb.info/993186866/34.
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Armbruster, Florian [Verfasser]. "Darstellung bor- und phosphorhaltiger Ringe durch Reaktionen von Halogenboranen und -phosphanen mit Bis(tert.-butyl-methyl)ketazin / vorgelegt von Florian Armbruster." 2004. http://d-nb.info/973984112/34.
Full textBooks on the topic "Ridge bias"
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Patricia, Anderson. Mini bus ride: A journey through the informal sector of Kingston's mass transportation system. Institute of Social and Economic Research, University of the West Indies, Jamaica, 1987.
Find full textBook chapters on the topic "Ridge bias"
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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Bias." In Regularized System Identification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_1.
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AbstractAdopting a quadratic loss, the performance of an estimator can be measured in terms of its mean squared error which decomposes into a variance and a bias component. This introductory chapter contains two linear regression examples which describe the importance of designing estimators able to well balance these two components. The first example will deal with estimation of the means of independent Gaussians. We will review the classical least squares approach which, at first sight, could appear the most appropriate solution to the problem. Remarkably, we will instead see that this unbiased approach can be dominated by a particular biased estimator, the so-called James–Stein estimator. Within this book, this represents the first example of regularized least squares, an estimator which will play a key role in subsequent chapters. The second example will deal with a classical system identification problem: impulse response estimation. A simple numerical experiment will show how the variance of least squares can be too large, hence leading to unacceptable system reconstructions. The use of an approach, known as ridge regression, will give first simple intuitions on the usefulness of regularization in the system identification scenario.
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Loewenstein, George, Ted O’Donoghue,, and Matthew Rabin. "Projection Bias in Predicting Future Utility." In Exotic Preferences. Oxford University PressOxford, 2007. http://dx.doi.org/10.1093/oso/9780199257072.003.0013.
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Abstract On the second day of an overnight hike with my son Max on the Laurel Ridge trail in Pennsylvania, I asked him whether, when we got back to our bicycles (which were waiting at the end of the hike so we could bike back to the car), he wanted to take the route we had been planning to take or a longer, more scenic route. Max (who was 10 at the time) knew that I wanted to take the longer, scenic route and informed me that I had made a strategic error: “Daddy: you should have asked me when we were resting back on that rock, not while we are hiking. Of course I won’t want to take the long route if you ask me when I’m all tired out”. Max’s response reflected a deep appreciation for projection bias (though I had never mentioned it to him).
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Craig, Paul. "Natural Justice." In English Administrative Law from 1550. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198908326.003.0009.
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Abstract This chapter deals with the historical development of natural justice from the early 17th century onwards. The story begins with the foundational case law concerning the right to a hearing, which is followed by discussion of its applicability and content. The focus then shifts to analysis of bias, with discussion of the doctrinal foundations, followed by consideration of the breadth of its application. The analysis thereafter is on the 20th century case law prior to the 1960s, which exhibited elements of continuity with the earlier case law, but also change, in the sense of limitations engrafted on natural justice that were not present in the earlier case law. The final section of the chapter examines the seminal decision in Ridge v Baldwin, which reconnected with the case law from the 17th to 19th centuries and struck down a number of the limitations imposed in the earlier part of the 20th century.
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Seastedt, Timothy R. "Soils." In Structure and Function of an Alpine Ecosystem. Oxford University Press, 2001. http://dx.doi.org/10.1093/oso/9780195117288.003.0014.
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This chapter examines alpine soils from a traditional soil science and ecological perspective, with a bias toward the latter. Soil physical and chemical properties are presented, but the soils as a resource for the biota as well as the feedbacks between abiotic and biotic processes are emphasized. Over half a century ago, Hans Jenny (1941) developed a conceptual model of the factors responsible for soil development. Jenny recognized that parent material, climate, topography, and geological and ecological disturbance factors could be viewed as independent phenomena that interact to produce soils. Jenny (1980) subsequently expanded this model to one that was also useful to describe entire ecosystems. To date, I've found no better framework with which to explain soils as true ecosystem characteristics—an entity generated by the interaction of biota with the abiotic environment. Accordingly, the roles that parent materials, topography, climate, biota, and disturbance frequencies have in controlling the structural and functional aspects of alpine soils are discussed. Because each of these five factors of soil formation has the potential to interact with various combinations of the other four factors, the number of possible combinations—and soil types—is surprisingly large, especially when one or more of the five factors exhibits tremendous within-site variability. Certainly the alpine must rank “most heterogeneous” among terrestrial ecosystem types in terms of topography, making this variable particularly important in any discussion of soil characteristics. As will be demonstrated, however, the other four factors also exhibit significant variation that contributes to the complexity of the alpine soil landscape. Soil characteristics emphasized here include those variables that affect and are affected by biotic processes over time scales ranging from a single growing season to decades to centuries. Hence, cation exchange capacity (CEC), soil acidity (pH), soil water content, nutrient content and flux, and carbon storage and flux, are of primary concern. Detailed information about the soils of this region comes primarily from two sources, Scott Burns’s 1980 dissertation on soil distribution and development in the Niwot Ridge-Green Lakes region, and an extensive series of publications by M. I. Litaor. Burns provided classical soil descriptions based on the analysis of 97 extensive soil pit excavations.
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Bukovsky, Ivo, Peter M. Benes, and Martin Vesely. "Introduction and Application Aspects of Machine Learning for Model Reference Adaptive Control With Polynomial Neurons." In Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0301-0.ch004.
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This chapter recalls the nonlinear polynomial neurons and their incremental and batch learning algorithms for both plant identification and neuro-controller adaptation. Authors explain and demonstrate the use of feed-forward as well as recurrent polynomial neurons for system approximation and control via fundamental, though for practice efficient machine learning algorithms such as Ridge Regression, Levenberg-Marquardt, and Conjugate Gradients, authors also discuss the use of novel optimizers such as ADAM and BFGS. Incremental gradient descent and RLS algorithms for plant identification and control are explained and demonstrated. Also, novel BIBS stability for recurrent HONUs and for closed control loops with linear plant and nonlinear (HONU) controller is discussed and demonstrated.
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Lorbiecki, Marybeth. "A Cowboy in Love: 1909– 1912." In A Fierce Green Fire. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780199965038.003.0010.
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Twenty-two-year-old Aldo Leopold arrived in Albuquerque, New Mexico Territory, in July 1909, burning with the “fervor of a sawdust evangelist.” The Forest Service had sent him to his first choice—District 3, encompassing the twenty-one forests of the South and Southwest. His duties were outlined in his manual: preserve a perpetual supply of timber for home industries, prevent destruction of forest cover (which regulates the flow of streams), and protect local industries from unfair competition in the use of forest and range. The district chief was Arthur Ringland, a stocky, energetic Yale graduate only a few years older than Leopold. Ringland sensed the new graduate’s enthusiasm and assigned him to the wildest lands in the district—the Apache National Forest in Arizona Territory. The land had originally belonged to the Apache Nation, but in 1886, the US Army forced most of the members onto a nearby reservation. This left but a few ranchers, farmers, and miners in the region. The forest headquarters rested in Springerville, Arizona, a two-day stagecoach ride from the last railroad stop. No automobiles carved tire treads over these plateaus and canyons. Travel was by foot, horse, or mule. Forest Assistant Leopold, the newest greenhorn among many, wasted no time in purchasing a feisty gray stallion called Jiminy Hicks, a saddle, a rope, and a few good roping lessons. Within the month, he also acquired pistols and a “rubber butt plate” for those long days in the saddle. The rubber plate came in handy since Aldo put in a good deal of time astride Jiminy Hicks. Throughout July and the beginning of August, Leopold inspected trees, marked them for cutting, planted seed plots, fixed fences, and met the other rangers. Working under Supervisor John D. Guthrie, Aldo contributed his two bits on policy decisions about grazing permits, water rights, and timber sales. Guthrie’s long hours and dedicated stance inspired the young ranger. The simplicity of life on the range, where one had to live out of a pack, made Leopold feel tough and free. On his own time, he hunted, mapped out the forest for himself, climbed mountains, and tested trout streams.
Conference papers on the topic "Ridge bias"
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Solo, Victor. "Asymptotic Bias and Variance of Kernel Ridge Regression." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10096774.
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Omer, Pareekhan. "Improving Prediction Accuracy of Lasso and Ridge Regression as an Alternative to LS Regression to Identify Variable Selection Problems." In 3rd International Conference of Mathematics and its Applications. Salahaddin University-Erbil, 2020. http://dx.doi.org/10.31972/ticma22.05.
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This paper introduces the Lasso and Ridge Regression methods, which are two popular regularization approaches. The method they give a penalty to the coefficients differs in both of them. L1 Regularization refers to Lasso linear regression, while L2 Regularization refers to Ridge regression. As we all know, regression models serve two main purposes: explanation and prediction of scientific phenomena. Where prediction accuracy will be optimized by balancing each of the bias and variance of predictions, while explanation will be gained by constructing interpretable regression models by variable selection. The penalized regression method, also known as Lasso regression, adds bias to the model's estimates and reduces variance to enhance prediction. Ridge regression, on the other hand, introduces a minor amount of bias in the data to get long-term predictions. In the presence of multicollinearity, both regression methods have been offered as an alternative to the least square approach (LS). Because they deal with multicollinearity, they have the appropriate properties to reduce numerical instability caused by overfitting. As a result, prediction accuracy can be improved. For this study, the Corona virus disease (Covid-19) dataset was used, which has had a significant impact on global life. Particularly in our region (Kurdistan), where life has altered dramatically and many people have succumbed to this deadly sickness. Our data is utilized to analyze the benefits of each of the two regression methods. The results show that the Lasso approach produces more accurate and dependable or reliable results in the presence of multicollinearity than Ridge and LS methods when compared in terms of accuracy of predictions by using NCSS10, EViews 12 and SPSS 25.
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Sokolov, Mikhail A., and Randy K. Nanstad. "On Bias in To Values Derived With Compact and PCVN Specimens." In ASME/JSME 2004 Pressure Vessels and Piping Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/pvp2004-2306.
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The Heavy-Section Steel Irradiation (HSSI) Program at Oak Ridge National Laboratory (ORNL) includes a task to investigate the bias in the reference fracture toughness transition temperature values, To, derived with the pre-cracked Charpy (PCVN) and compact specimens. The PCVN specimen, as well as any other fracture toughness specimen that can be made out of the broken Charpy specimens, may have exceptional utility for the evaluation of RPV steels. The Charpy V-notch specimen is the most commonly used specimen geometry in surveillance programs. Precracking and testing of Charpy surveillance specimens would allow one to determine and monitor directly actual fracture toughness instead of requiring indirect predictions using correlations established with impact data. However, there are a growing number of indications that there might be a bias in To values derived from PCVN and compact specimens. The present paper summarizes data from the series of experiments that use subsize specimens for evaluation of the transition fracture toughness of reactor pressure vessel (RPV) steels conducted within the HSSI Program. Two types of compact specimens and three types of three-point bend specimens from five RPV materials were used in these subsize experiments. The current results showed that To determined from PCVN specimens with width (W) to thickness (B) ratio W/B=1, on average, are lower than To determined from compact specimens with W/B=2. At the same time, three-point bend specimens with W/B=2 exhibited To values that were very similar to To values derived from compact specimens.
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Vawter, G. A., V. M. Hietala, S. H. Kravitz, R. F. Carson, M. G. Armendariz, and G. R. Hadley. "A photonic integrated circuit technology for coherent optical processing." In OSA Annual Meeting. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.thh4.
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A complete photonic integrated circuit (PIC) technology for coherent homodyne and heterodyne optical signal processing is presented. The GaAs/AlGaAs PIC is based exclusively on integration of waveguide phase modulators with interconnecting waveguides, directional couplers, turning mirrors, and gratings. Emphasis is on simplicity of fabrication and high yield of complex PIC designs achieved through the use of only one or two etch steps to define the PIC followed by conventional isolation and metalization steps. Current progress on critical device and fabrication technologies will be discussed. These include high resolution ridge-waveguide etching for definition of directional coupler waveguide power splitters, fabrication of ridge-waveguide phase modulators, without significant bias-dependent absorption modulation, as well as alignment and etch issues for turning-mirrors and gratings. Finally, all of these elements are designed for integration into a single PIC without epitaxial regrowth. Fully integrated Mach-Zehnder amplitude modulators are demonstrated. Systems applications of this PIC technology, including optical frequency modulation of an input single-frequency source, tunable bandpass optical filters, and complete control of frequency and far field of a phased-array radar antenna, will be presented.
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Lall, Pradeep, Dinesh Arunachalam, and Jeff Suhling. "Ridge Regression Based Development of Norris-Landzberg Acceleration Factors and Goldmann Constants for Leadfree Electronics." In ASME 2011 Pacific Rim Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Systems. ASMEDC, 2011. http://dx.doi.org/10.1115/ipack2011-52195.
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Goldmann Constants and Norris-Landzberg acceleration factors for lead-free solders have been developed based on ridge regression models (RR) for reliability prediction and part selection of area-array packaging architectures under thermo-mechanical loads. Ridge regression adds a small positive bias to the diagonal of the covariance matrix to prevent high sensitivity to variables that are correlated. The proposed procedure proves to be a better tool for prediction than multiple-linear regression models. Models have been developed in conjunction with Stepwise Regression Methods for identification of the main effects. Package architectures studied include, BGA packages mounted on copper-core and no-core printed circuit assemblies in harsh environments. The models have been developed based on thermo-mechanical reliability data acquired on copper-core and no-core assemblies in four different thermal cycling conditions. Packages with Sn3Ag0.5Cu solder alloy interconnects have been examined. The models have been developed based on perturbation of accelerated test thermo-mechanical failure data. Data has been gathered on nine different thermal cycle conditions with SAC305 alloys. The thermal cycle conditions differ in temperature range, dwell times, maximum temperature and minimum temperature to enable development of constants needed for the life prediction and assessment of acceleration factors. Norris-Landzberg acceleration factors have been benchmarked against previously published values. In addition, model predictions have been validated against validation datasets which have not been used for model development. Convergence of statistical models with experimental data has been demonstrated using a single factor design of experiment study for individual factors including temperature cycle magnitude, relative coefficient of thermal expansion, and diagonal length of the chip. The predicted and measured acceleration factors have also been computed and correlated. Good correlations have been achieved for parameters examined.
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Langlois, Patrick, and Michel Piché. "Self-mode-locked semiconductor laser in a ring cavity." In The European Conference on Lasers and Electro-Optics. Optica Publishing Group, 1998. http://dx.doi.org/10.1364/cleo_europe.1998.cpd1.3.
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We report on the generation of short (2 ps) pulses directly from a self-mode-locked semiconductor amplifier inserted in a ring cavity. The amplifier is in fact a superluminescent diode consisting of a 500 mm long double quantum-well InGaAlAs ridge waveguide with strained InGaAs active layers ; an angled stripe geometry provides a low coherence emission with a broad spectrum centered near 855 nm. The amplifier is placed in a ring cavity with three gold minors and a 83% transmission output coupler. Collimation of the laser output and careful alignment of the cavity mirrors allows laser emission with a threshold bias current Ith near 46 mA. Self-mode locking is achieved when the dc bias current is set at ~1.2 Ith, and when the cavity is slightly misaligned. Figures 1 and 2 show typical non-colinear autocorrelation traces and optical spectra of the pulses circulating in the clockwise (cw) and counterclockwise (ccw) directions. The counterpropagating pulses have similar temporal profiles with durations from 2 to 6 ps (after deconvolution) and characterized by a weak pedestal that falls to zero. Remarkably, the pulse respective optical spectra are different, as the spectrum of the ccw pulse has a 2.9 nm bandwidth peaked at 864 nm, whereas that of the cw pulse is peaked near 867 nm with a bandwidth of 0.9 nm. Both circulating pulses have a time-bandwidth product exceeding several times the Fourier-transformed limit, which suggests that they can be compressed to subpicosecond durations.
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Nanstad, Randy K., Donald E. McCabe, Mikhail A. Sokolov, and John G. Merkle. "Experimental Evaluation of Deformation and Constraint Characteristics in Precracked Charpy and Other Three-Point Bend Specimens." In ASME 2007 Pressure Vessels and Piping Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/pvp2007-26651.
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To enable determination of the fracture toughness reference temperature, T0, with reactor pressure vessel surveillance specimens, the precracked Charpy (PCVN) three-point bend, SE(B), specimen is of interest. Compared with the 25-mm (1 in.) thick compact, 1TC(T), specimen, tests with the PCVN specimen (10×10×55 mm) have resulted in T0 temperatures as much as 40°C lower (a so-called specimen bias effect). The Heavy-Section Steel Irradiation (HSSI) Program at Oak Ridge National Laboratory developed a two-part project to evaluate the C(T) versus PCVN differences, (1) calibration experiments concentrating on test practices, and (2) a matrix of transition range tests with various specimen geometries and sizes, including 1T SE(B) and 1TC(T). The test material selected was a plate of A533 grade B class 1 steel. The calibration experiments included assessment of the computational validity of J-integral determinations, while the constraint characteristics of various specimen types and sizes were evaluated using key curves and notch strength determinations. The results indicate that J-integral solutions for the small PCVN specimen are comparable in terms of J-integral validity with 1T bend specimens. Regarding constraint evaluations, Phase I deformation is defined where plastic deformation is confined to crack tip plastic zone development, whereas Phase II deformation is defined where plastic hinging deformation develops. In Phase II deformation, the 0.5T SE(B) BxB specimen (slightly larger than the PCVN specimen) consistently showed the highest constraint of all SE(B) specimens evaluated for constraint comparisons. The PCVN specimen begins the Phase II type of deformation at relatively low KR levels, with the result that KJc values above about 70 MPa√m from precracked Charpy specimens are under extensive plastic hinging deformation. For the second part, about twenty specimens of each type and size (65 PCVN) have been tested to enable statistically reliable comparisons of T0 for the various cases, with tests completed at temperatures from −28 to −37°C. The KJc test data obtained for HSST Plates 13B and 13A show PCVN bias values from −30 to −40°C relative to 1TC(T), but the standard 1TSE(B) specimen shows a bias of about −10°C. However, the SE(B) specimens exhibit a tendency for decreasing T0 with decreasing specimen size (B and/or b). The results are compared with those from other materials and observations are noted regarding potential effects of test temperature and metallurgical factors.
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Chen, Xiaoyuan, Xinlong Li, Wenhao He, et al. "Experimental Study on Rock-Breaking Characteristics of Conglomerate with Special-Shaped PDC Cutters Under High Confining Pressure." In 58th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2024. http://dx.doi.org/10.56952/arma-2024-0993.
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ABSTRACT: The characteristics of conglomerate non-uniformity and complex pore structure lead to severe wear and low rock-breaking efficiency of PDC bits when drilling into conglomerate formations. In this paper, the characteristics of breaking conglomerate of three kinds of PDC cutters such as planar cutter, triple-ridged cutter and axe-shaped cutter are studied under high confining pressure. By designing and carrying out experiments of breaking conglomerate with the planar cutter, axe-shaped cutter and triple-ridged cutter under high confining pressure, the characteristics of cutting force, rock breaking volume, MSE of rock breaking and distribution of cuttings of PDC fractured conglomerate, and the surface topography of the cutting groove of PDC fractured conglomerate are compared. The experimental results show that the cutting force of the triple-ridged cutter is the most stable and the mechanical specific energy of the axe-shaped cutter is the least under the condition of high confining pressure. In addition, the grooving boundary of the triple-ridged cutter is more obvious, and the ability of triple-ridged cutter to peel the whole gravel is stronger under the condition of large cutting depth. In the design of PDC bits in conglomerate formations, the triple-ridged cutter can be selected as the front cutter of the nose and shoulder. The research provides theoretical support for the cutter selection design of hybrid-cutters PDC bit in conglomerate formation to improve rock breaking efficiency and lifespan of conglomerate formation drilling. 1. INTRODUCTION After years of oil exploration and exploitation, it is increasingly difficult to find new large oil and gas reservoirs in the middle and shallow strata, the oil and gas exploration has been extended to deep (4500∼6000 m) and ultra-deep (&gt;6000 m) around the world(Bian et al., 2022). Petroleum drilling technology is gradually entering the stage of "efficient drilling of deep and ultra-deep complex formations". PDC (polycrystalline diamond bit) bits are widely used in the drilling process in oil & gas industries. According to statistics, the drilling length of PDC bits has exceeded 90% of the total drilling length in the world, accounting for about 80% of the global market(Huang et al., 2017). However, when drilling into hard, highly abrasive, and heterogeneous formations, planar PDC drill bits suffer from problems such as low mechanical drilling speed, and the severe wear of PDC cutters, greatly limiting the service life of a single drill bit(Cheng, 2019). In recent years, special-shaped PDC cutters of different shapes, such as triple-ridged, axe-shaped, wedge (V) shape and chisel shape, have been continuously developed to transform PDC bits from the original shear rock-breaking mode to the mixed-rock breaking mode with multiple rock breaking functions, such as shear and plough, which greatly improves the drilling ability and rock breaking efficiency of PDC bits in hard, abrasive and heterogeneous layers.
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Khan, W. S., and F. A. Dar. "Ride Comfort Evaluation of Bias Ply and Radial Run - Flat Tire in High Mobility Vehicle." In Symposium on International Automotive Technology 2013. SAE International, 2013. http://dx.doi.org/10.4271/2013-26-0143.
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Yao, Jianlin, Bin Liu, and Haiyan Zhu. "Discrete Element Study on Rock Failure Mechanism by Shaped PDC Cutter Under Formation Conditions with In-Situ Stress and Temperature." In International Geomechanics Symposium. ARMA, 2022. http://dx.doi.org/10.56952/igs-2022-107.
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Abstract Polycrystalline diamond compact (PDC) bits have been widely used in drilling process due to their high efficiency and great reliability. To investigate rock failure mechanism and further improve the performance of PDC bits, a series of single cutter tests have been conducted in previous studies. However, most of them were performed under atmosphere conditions. That is, the effects of temperature and in-situ stress were ignored due to the limitation of experimental facility, which restricts the developing of novel bits. To solve the aforementioned problem and deeply study the rock failure mechanism of PDC bits, numerical simulation researches were conducted based on the discrete element method (DEM). Firstly, a numerical triaxial test model was established to calibrate microscopic parameters of rocks that collected from Sichuan basin Ziliujing formation. Then, boundary conditions such as bottom hole temperature, surrounding pressure and hydrostatic pressure were loaded to rocks, thus simulating the actual bottom conditions. Based on this model, rock failure characteristics under the impacts of cutters with different shapes (cylindrical cutter, triangular prismatic cutter, ridge cutter, V-cutter) and different cutting parameters (cutting depth, back rake angles, etc) were studied. The results indicate that the triangular prismatic cutter has the greatest rock-breaking efficiency in our studies, followed by V-cutter and cylinder cutter. The triangular prismatic cutter can obtain the highest rock cutting efficiency when the cutting depth, back rake angles and the diameter of cutter are equal to 2 mm, 20 degrees and 16 mm respectively. The present study provides an in-depth understanding on rock failure mechanism and a basis for the development of efficient PDC bits. Introduction The Sichuan Basin is rich in natural gas resources and is one of the major gas production regions in China. However, the corresponding geological conditions are complex. Under the impacts of high temperature and high pressure at the bottom hole, the formation rock becomes highly abrasive and poorly drillable, resulting in a significant decrease in the drilling speed. To improve the drilling efficiency and enhance the performance of PDC bits, it is necessary to carry out studies on cutting and investigate rock failure mechanisms under the actual bottom hole temperature and pressure.
Reports on the topic "Ridge bias"
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Dismantlement and removal of Old Hydrofracture Facility bulk storage bins and water tank, Oak Ridge National Laboratory, Oak Ridge, Tennessee. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/645461.
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