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Chen, Zhengpu, Carl Wassgren, and Kingsly Ambrose. "A Review of Grain Kernel Damage: Mechanisms, Modeling, and Testing Procedures." Transactions of the ASABE 63, no. 2 (2020): 455–75. http://dx.doi.org/10.13031/trans.13643.
HighlightsPublished literature on grain kernel damage during handling is reviewed.Types and sources of grain kernel damage are discussed.Factors affecting the level of grain kernel damage are outlined.Models to predict grain kernel damage and corresponding test devices are summarized.Abstract. Grain kernel damage during harvest and handling continues to be a challenge in grain postharvest operations. This damage causes physical and physiological changes to grain, which reduces the grain quality and leads to significant yield loss. During harvesting and handling, grain kernels are subject to complex loading conditions consisting of a combination of impact, shear, and compression forces. The main damage mechanisms include impact, which causes external and internal cracks or even fragmentation of the kernel; attrition, which generates fine material; jamming, which deforms and breaks kernels due to high compressive forces; and fatigue, which produces broken kernels and fine material via repeatedly applied loads. Grain kernel damage accumulates as the grain moves through harvesting and handling operations. Harvesting is the major cause of cracks and breakage, while conveying after drying produces fine material. This article provides a comprehensive review of the types of grain kernel damage, sources of grain kernel damage, factors affecting damage, predictive damage models, and the experimental methods used to assess the damage. This review shows that although there is considerable empirical data focused on kernel damage, there is a lack of generalizable mechanics-based predictive models. Mechanics-based models are desirable because they would be useful for providing guidance on designing and operating grain handling processes to minimize kernel damage and thus improve grain quality. In addition, several damage models developed for non-grain particulate materials based on fracture mechanics are reviewed. With some modifications and detailed property analysis, there is potential for adapting the models developed for inorganic materials to predict grain kernel damage. Keywords: Grain kernel damage, Grain harvesting and handling, Breakage susceptibility, Grain damage prediction.
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Wu, Michael C., Arnab Maity, Seunggeun Lee, Elizabeth M. Simmons, Quaker E. Harmon, Xinyi Lin, Stephanie M. Engel, Jeffrey J. Molldrem, and Paul M. Armistead. "Kernel Machine SNP-Set Testing Under Multiple Candidate Kernels." Genetic Epidemiology 37, no. 3 (March 7, 2013): 267–75. http://dx.doi.org/10.1002/gepi.21715.
Kiefer, Nicholas M., and Timothy J. Vogelsang. "HETEROSKEDASTICITY-AUTOCORRELATION ROBUST TESTING USING BANDWIDTH EQUAL TO SAMPLE SIZE." Econometric Theory 18, no. 6 (September 24, 2002): 1350–66. http://dx.doi.org/10.1017/s026646660218604x.
Asymptotic theory for heteroskedasticity autocorrelation consistent (HAC) covariance matrix estimators requires the truncation lag, or bandwidth, to increase more slowly than the sample size. This paper considers an alternative approach covering the case with the asymptotic covariance matrix estimated by kernel methods with truncation lag equal to sample size. Although such estimators are inconsistent, valid tests (asymptotically pivotal) for regression parameters can be constructed. The limiting distributions explicitly capture the truncation lag and choice of kernel. A local asymptotic power analysis shows that the Bartlett kernel delivers the highest power within a group of popular kernels. Finite sample simulations suggest that, regardless of the kernel chosen, the null asymptotic approximation of the new tests is often more accurate than that for conventional HAC estimators and asymptotics. Finite sample results on power show that the new approach is competitive.
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Ahmad, Ibrahim, and A. R. Mugdadi. "Testing normality using kernel methods." Journal of Nonparametric Statistics 15, no. 3 (June 2003): 273–88. http://dx.doi.org/10.1080/1048525021000049649.
Martinez, Kara, Arnab Maity, Robert H. Yolken, Patrick F. Sullivan, and Jung‐Ying Tzeng. "Robust kernel association testing (RobKAT)." Genetic Epidemiology 44, no. 3 (January 14, 2020): 272–82. http://dx.doi.org/10.1002/gepi.22280.
Corn kernels detection can be implemented in industry area. This can be implemented in the selection and packaging the corn kernels before it is distributed. This technique can be implemented in the selection and packaging machine to detect corn kernels accurately. Corn kernel images was used before it is implemented in real-time. The objective of this research was corn kernel detection using Convolutional Neural Network (CNN) deep learning. This technique consists of 3 main stages, the first preprocessing or normalizing the input of corn kernels image data by wrapping and cropping, both modeling and training the system, and testing. The experiment used CNN method to classify images of dry corn kernels and to determine the accuracy value. This research used 20 dry corn kernels images as testing from 80 dry corn kernels images which used in training dataset. The accuracy of detection was dependent from the size of image and position when the image was taken. The accuracy is around 80% - 100% by using 7 convolutional layers and the average of accuracy for testing data was 0,90296. The convolutional layer which implemented in CNN has the strength to detect features in the input image.
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Bruggink, H., H. L. Kraak, M. H. G. E. Dijkema, and J. Bekendam. "Some factors influencing electrolyte leakage from maize (Zea mays L.) kernels." Seed Science Research 1, no. 1 (March 1991): 15–20. http://dx.doi.org/10.1017/s0960258500000581.
AbstractEven though the embryo of a maize (Zea mays L.) kernel contributes relatively little to total kernel weight, it is a main source of electrolytes which leach from the kernel during imbibition. Ageing of maize kernels for 18 days at 40°C and a moisture content of about 15% results in an increase of electrolyte leakage which almost exclusively originates from the embryo. The effect of ageing is most apparent after prolonged periods of imbibition. Mechanical damage increases leakage early during imbibition, the effect of damage being considerably larger for aged than for unaged kernels. The large amount of electrolytes measured after the first hour of imbibition of undamaged kernels comes mainly from the pericarp. The electrolyte content of the pericarp is variety dependent and may interfere with quality testing by conductivity measurements.
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Hidayatullah, Martin Sulung, Tamrin Tamrin, Oktafri Oktafri, and Warji Warji. "Rancang Bangun dan Uji Kinerja Alat Pemisah Kernel Sawit dari Cangkangnya dengan Menggunakan Larutan Garam." Jurnal Agricultural Biosystem Engineering 2, no. 2 (June 22, 2023): 281. http://dx.doi.org/10.23960/jabe.v2i2.7482.
. Palm oil has an important role to play in improving the country's foreign exchange. The largest selling point in palm oil is palm kernel oil (PKO), this palm kernel oil processing process involves a mixture of kernels and shells that will later be separated. This research aims to design the build and produce a prototype kernel separator from its shell by using a saline solution to minimize excess costs and able to separate the kernel and shell >80%. Methods carried out in this study include designing, manufacturing and testing. After that, prototype kernel separator with palm shell using salt solution with tool dimensions on container length 53 cm, width 40 cm, height 42 cm, water receiver body length 22 cm, width 16 cm, height 28. The kernel separator with palm shell using this saline solution is able to separate the kernel and shell mixture by 82%. Keywords: Design, Kernel,Palm Oil, Shell, Separation.
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Pan, Shuang, Jianguo Wei, and Hao Pan. "Study on Evaluation Model of Chinese P2P Online Lending Platform Based on Hybrid Kernel Support Vector Machine." Scientific Programming 2020 (May 8, 2020): 1–7. http://dx.doi.org/10.1155/2020/4561834.
Accurate evaluation of the risk level and operation performances of P2P online lending platforms is not only conducive to better functioning of information intermediaries but also effective protection of investors’ interests. This paper proposes a genetic algorithm (GA) improved hybrid kernel support vector machine (SVM) with an index system to construct such an evaluation model. A hybrid kernel consisting of polynomial function and radial basis function is improved, specifically kernel parameters and the weight of two kernels, by GA method with excellent global optimization and rapid convergence. Empirical testing based on cross-sectional data from Chinese P2P lending market demonstrates the superiority of the improved hybrid kernel SVM model. The classification accuracy of credit risk level and operation quality is higher than the single kernel SVM model as well as the hybrid kernel model with empirical parameter values.
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Gao, Jiti, and Irène Gijbels. "Bandwidth Selection in Nonparametric Kernel Testing." Journal of the American Statistical Association 103, no. 484 (December 2008): 1584–94. http://dx.doi.org/10.1198/016214508000000968.
Lee, Kevin Sung-ho. "Kernel-adaptor interface testing of Project Timeliner." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/49939.
Ozier-Lafontaine, Anthony. "Kernel-based testing and their application to single-cell data." Electronic Thesis or Diss., Ecole centrale de Nantes, 2023. http://www.theses.fr/2023ECDN0025.
Les technologies de sequençage en cellule unique mesurent des informations à l’échelle de chaque cellule d’une population. Les données issues de ces technologies présentent de nombreux défis : beaucoup d’observations en grande dimension et souvent parcimonieuses. De nombreuses expériences de biologie consistent à comparer des conditions.L’objet de la thèse est de développer un ensemble d’outils qui compare des échantillons de données issues des technologies de séquençage en cellule unique afin de détecter et décrire les différences qui existent. Pour cela, nous proposons d’appliquer les tests de comparaison de deux échantillons basés sur les méthodes à noyaux existants. Nous proposons de généraliser ces tests à noyaux pour les designs expérimentaux quelconques, ce test s’inspire du test de la trace de Hotelling- Lawley. Nous implémentons pour la première fois ces tests à noyaux dans un packageR et Python nommé ktest, et nos applications sur données simulées et issues d’expériences démontrent leurs performances. L’application de ces méthodes à des données expérimentales permet d’identifier les observations qui expliquent les différences détectées. Enfin, nous proposons une implémentation efficace de ces tests basée sur des factorisations matricielles de type Nyström, ainsi qu’un ensemble d’outils de diagnostic et d’interprétation des résultats pour rendre ces méthodes accessibles et compréhensibles par des nonspécialistes Single-cell technologies generate data at the single-cell level. They are coumposed of hundreds to thousands of observations (i.e. cells) and tens of thousands of variables (i.e. genes). New methodological challenges arose to fully exploit the potentialities of these complex data. A major statistical challenge is to distinguish biological informationfrom technical noise in order to compare conditions or tissues. This thesis explores the application of kernel testing on single-cell datasets in order to detect and describe the potential differences between compared conditions.To overcome the limitations of existing kernel two-sample tests, we propose a kernel test inspired from the Hotelling-Lawley test that can apply to any experimental design. We implemented these tests in a R and Python package called ktest that is their first useroriented implementation. We demonstrate the performances of kernel testing on simulateddatasets and on various experimental singlecell datasets. The geometrical interpretations of these methods allows to identify the observations leading a detected difference. Finally, we propose a Nyström-based efficient implementationof these kernel tests as well as a range of diagnostic and interpretation tools
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Kotlyarova, Yulia. "Kernel estimators : testing and bandwidth selection in models of unknown smoothness." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85179.
Semiparametric and nonparametric estimators are becoming indispensable tools in applied econometrics. Many of these estimators depend on the choice of smoothing bandwidth and kernel function. Optimality of such parameters is determined by unobservable smoothness of the model, that is, by differentiability of the distribution functions of random variables in the model. In this thesis we consider two estimators of this class: the smoothed maximum score estimator for binary choice models and the kernel density estimator. We present theoretical results on the asymptotic distribution of the estimators under various smoothness assumptions and derive the limiting joint distributions for estimators with different combinations of bandwidths and kernel functions. Using these nontrivial joint distributions, we suggest a new way of improving accuracy and robustness of the estimators by considering a linear combination of estimators with different smoothing parameters. The weights in the combination minimize an estimate of the mean squared error. Monte Carlo simulations confirm suitability of this method for both smooth and non-smooth models. For the original and smoothed maximum score estimators, a formal procedure is introduced to test for equivalence of the maximum likelihood estimators and these semiparametric estimators, which converge to the true value at slower rates. The test allows one to identify heteroskedastic misspecifications in the logit/probit models. The method has been applied to analyze the decision of married women to join the labour force.
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Liero, Hannelore. "Testing the Hazard Rate, Part I." Universität Potsdam, 2003. http://opus.kobv.de/ubp/volltexte/2011/5151/.
We consider a nonparametric survival model with random censoring. To test whether the hazard rate has a parametric form the unknown hazard rate is estimated by a kernel estimator. Based on a limit theorem stating the asymptotic normality of the quadratic distance of this estimator from the smoothed hypothesis an asymptotic ®-test is proposed. Since the test statistic depends on the maximum likelihood estimator for the unknown parameter in the hypothetical model properties of this parameter estimator are investigated. Power considerations complete the approach.
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Friedrichs, Stefanie Verfasser], Heike [Akademischer Betreuer] Bickeböller, Thomas [Gutachter] [Kneib, and Tim [Gutachter] Beißbarth. "Kernel-Based Pathway Approaches for Testing and Selection / Stefanie Friedrichs ; Gutachter: Thomas Kneib, Tim Beißbarth ; Betreuer: Heike Bickeböller." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2017. http://d-nb.info/114137952X/34.
Copy-number variation (CNV) has been implicated in many complex diseases. It is of great interest to detect and locate such regions through genetic association testings. However, the association testings are complicated by the fact that CNVs usually span multiple markers and thus such markers are correlated to each other. To overcome the difficulty, it is desirable to pool information across the markers. In this thesis, we propose a kernel-based method for aggregation of marker-level tests, in which first we obtain a bunch of p-values through association tests for every marker and then the association test involving CNV is based on the statistic of p-values combinations. In addition, we explore several aspects of its implementation.
Since p-values among markers are correlated, it is complicated to obtain the null distribution of test statistics for kernel-base aggregation of marker-level tests. To solve the problem, we develop two proper methods that are both demonstrated to preserve the family-wise error rate of the test procedure. They are permutation based and correlation base approaches. Many implementation aspects of kernel-based method are compared through the empirical power studies in a number of simulations constructed from real data involving a pharmacogenomic study of gemcitabine. In addition, more performance comparisons are shown between permutation-based and correlation-based approach. We also apply those two approaches to the real data.
The main contribution of the dissertation is the development of marker-level association testing, a comparable and powerful approach to detect phenotype-associated CNVs. Furthermore, the approach is extended to high dimension setting with high efficiency.
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Akcin, Haci Mustafa. "NONPARAMETRIC INFERENCES FOR THE HAZARD FUNCTION WITH RIGHT TRUNCATION." Digital Archive @ GSU, 2013. http://digitalarchive.gsu.edu/math_diss/12.
Incompleteness is a major feature of time-to-event data. As one type of incompleteness, truncation refers to the unobservability of the time-to-event variable because it is smaller (or greater) than the truncation variable. A truncated sample always involves left and right truncation.
Left truncation has been studied extensively while right truncation has not received the same level of attention. In one of the earliest studies on right truncation, Lagakos et al. (1988) proposed to transform a right truncated variable to a left truncated variable and then apply existing methods to the transformed variable. The reverse-time hazard function is introduced through transformation. However, this quantity does not have a natural interpretation. There exist gaps in the inferences for the regular forward-time hazard function with right truncated data. This dissertation discusses variance estimation of the cumulative hazard estimator, one-sample log-rank test, and comparison of hazard rate functions among finite independent samples under the context of right truncation.
First, the relation between the reverse- and forward-time cumulative hazard functions is clarified. This relation leads to the nonparametric inference for the cumulative hazard function. Jiang (2010) recently conducted a research on this direction and proposed two variance estimators of the cumulative hazard estimator. Some revision to the variance estimators is suggested in this dissertation and evaluated in a Monte-Carlo study.
Second, this dissertation studies the hypothesis testing for right truncated data. A series of tests is developed with the hazard rate function as the target quantity. A one-sample log-rank test is first discussed, followed by a family of weighted tests for comparison between finite $K$-samples. Particular weight functions lead to log-rank, Gehan, Tarone-Ware tests and these three tests are evaluated in a Monte-Carlo study.
Finally, this dissertation studies the nonparametric inference for the hazard rate function for the right truncated data. The kernel smoothing technique is utilized in estimating the hazard rate function. A Monte-Carlo study investigates the uniform kernel smoothed estimator and its variance estimator. The uniform, Epanechnikov and biweight kernel estimators are implemented in the example of blood transfusion infected AIDS data.
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Li, Na. "MMD and Ward criterion in a RKHS : application to Kernel based hierarchical agglomerative clustering." Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0033/document.
La classification non supervisée consiste à regrouper des objets afin de former des groupes homogènes au sens d’une mesure de similitude. C’est un outil utile pour explorer la structure d’un ensemble de données non étiquetées. Par ailleurs, les méthodes à noyau, introduites initialement dans le cadre supervisé, ont démontré leur intérêt par leur capacité à réaliser des traitements non linéaires des données en limitant la complexité algorithmique. En effet, elles permettent de transformer un problème non linéaire en un problème linéaire dans un espace de plus grande dimension. Dans ce travail, nous proposons un algorithme de classification hiérarchique ascendante utilisant le formalisme des méthodes à noyau. Nous avons tout d’abord recherché des mesures de similitude entre des distributions de probabilité aisément calculables à l’aide de noyaux. Parmi celles-ci, la maximum mean discrepancy a retenu notre attention. Afin de pallier les limites inhérentes à son usage, nous avons proposé une modification qui conduit au critère de Ward, bien connu en classification hiérarchique. Nous avons enfin proposé un algorithme itératif de clustering reposant sur la classification hiérarchique à noyau et permettant d’optimiser le noyau et de déterminer le nombre de classes en présence Clustering, as a useful tool for unsupervised classification, is the task of grouping objects according to some measured or perceived characteristics of them and it has owned great success in exploring the hidden structure of unlabeled data sets. Kernel-based clustering algorithms have shown great prominence. They provide competitive performance compared with conventional methods owing to their ability of transforming nonlinear problem into linear ones in a higher dimensional feature space. In this work, we propose a Kernel-based Hierarchical Agglomerative Clustering algorithms (KHAC) using Ward’s criterion. Our method is induced by a recently arisen criterion called Maximum Mean Discrepancy (MMD). This criterion has firstly been proposed to measure difference between different distributions and can easily be embedded into a RKHS. Close relationships have been proved between MMD and Ward's criterion. In our KHAC method, selection of the kernel parameter and determination of the number of clusters have been studied, which provide satisfactory performance. Finally an iterative KHAC algorithm is proposed which aims at determining the optimal kernel parameter, giving a meaningful number of clusters and partitioning the data set automatically
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Bissyande, Tegawende. "Contributions for improving debugging of kernel-level services in a monolithic operating system." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00821893.
Alors que la recherche sur la qualité du code des systèmes a connu un formidable engouement, les systèmes d'exploitation sont encore aux prises avec des problèmes de fiabilité notamment dus aux bogues de programmation au niveau des services noyaux tels que les pilotes de périphériques et l'implémentation des systèmes de fichiers. Des études ont en effet montré que chaque version du noyau Linux contient entre 600 et 700 fautes, et que la propension des pilotes de périphériques à contenir des erreurs est jusqu'à sept fois plus élevée que toute autre partie du noyau. Ces chiffres suggèrent que le code des services noyau n'est pas suffisamment testé et que de nombreux défauts passent inaperçus ou sont difficiles à réparer par des programmeurs non-experts, ces derniers formant pourtant la majorité des développeurs de services. Cette thèse propose une nouvelle approche pour le débogage et le test des services noyau. Notre approche est focalisée sur l'interaction entre les services noyau et le noyau central en abordant la question des "trous de sûreté" dans le code de définition des fonctions de l'API du noyau. Dans le contexte du noyau Linux, nous avons mis en place une approche automatique, dénommée Diagnosys, qui repose sur l'analyse statique du code du noyau afin d'identifier, classer et exposer les différents trous de sûreté de l'API qui pourraient donner lieu à des fautes d'exécution lorsque les fonctions sont utilisées dans du code de service écrit par des développeurs ayant une connaissance limitée des subtilités du noyau. Pour illustrer notre approche, nous avons implémenté Diagnosys pour la version 2.6.32 du noyau Linux. Nous avons montré ses avantages à soutenir les développeurs dans leurs activités de tests et de débogage.
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Singh, Yuvraj. "Regression Models to Predict Coastdown Road Load for Various Vehicle Types." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595265184541326.
Cai, Zongwu. Functional Coefficient Models for Economic and Financial Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.6.
This article discusses the use of functional coefficient models for economic and financial data analysis. It first provides an overview of recent developments in the nonparametric estimation and testing of functional coefficient models, with particular emphasis on the kernel local polynomial smoothing method, before considering misspecification testing as an important econometric question when fitting a functional (varying) coefficient model or a trending time-varying coefficient model. It then describes two major real-life applications of functional coefficient models in economics and finance: the first deals with the use of functional coefficient instrumental-variable models to investigate the empirical relation between wages and education in a random sample of young Australian female workers from the 1985 wave of the Australian Longitudinal Survey, and the second is concerned with the use of functional coefficient beta models to analyze the common stock price of Microsoft stock (MSFT) during the year 2000 using the daily closing prices.
Garn, Bernhard, Fabian Würfl, and Dimitris E. Simos. "KERIS: A CT Tool of the Linux Kernel with Dynamic Memory Analysis Capabilities." In Hardware and Software: Verification and Testing, 225–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70389-3_17.
Gheorghe, Marian, Rodica Ceterchi, Florentin Ipate, and Savas Konur. "Kernel P Systems Modelling, Testing and Verification - Sorting Case Study." In Membrane Computing, 233–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54072-6_15.
Gheorghe, Marian, Florentin Ipate, Raluca Lefticaru, and Ana Turlea. "Testing Identifiable Kernel P Systems Using an X-Machine Approach." In Membrane Computing, 142–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12797-8_11.
Rijmen, Frank, Yanxuan Qu, and Alina A. Von Davier. "Hypothesis Testing of Equating Differences in the Kernel Equating Framework." In Statistical Models for Test Equating, Scaling, and Linking, 317–26. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-98138-3_19.
Drebes, Roberto Jung, Gabriela Jacques-Silva, Joana Matos Fonseca da Trindade, and Taisy Silva Weber. "A Kernel-Based Communication Fault Injector for Dependability Testing of Distributed Systems." In Lecture Notes in Computer Science, 177–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11678779_13.
Akcam, Halil, and Volker Lohweg. "Pollen Classification Based on Binary 2D Projections of Pollen Grains." In Technologien für die intelligente Automation, 273–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-64283-2_20.
AbstractPollen is one of the main causes of allergic diseases in humans. Therefore, it is indispensable to develop and conduct effective treatment and prevention measures. For this purpose, detailed and differentiated information about the respective local exposure profiles for the individual patients is required. The present paper serves the purpose of testing a new approach which aims at detecting and classifying individual pollen grains by using binary 2D projection. This paper explores the question of whether and to what extent a classification of individual pollen grains is possible using this new imaging technology. To this end, using artificial pollen grains, binary 2D projections with different levels of resolution are simulated. To extract the respective features, both shape-based Fourier descriptors and topological features are used. Apart from that, Zernike moments for different orders are measured to extract the respective characteristics of the pollen grains. While the feature selection is conducted by means of a feature forward selection method, a kernel machine (Support Vector Machine) with a Gaussian kernel is used for the classification. First results of the simulation show that with a resolution of 0.1 μm, 100% of the allergologically relevant artificial pollen are classified correctly. Conversely, a lower resolution corresponds with a higher error rate in the classification.
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Kamil, Firmanilah, and Nely Kurnila. "Preliminary Testing of Coarse Aggregate, Fine Aggregate, and Palm Kernel Shell Waste Characteristics in Sustainable Construction." In Proceedings of the International Conference on Applied Science and Technology on Social Science 2023 (iCAST-SS 2023), 614–20. Paris: Atlantis Press SARL, 2023. http://dx.doi.org/10.2991/978-2-38476-202-6_88.
Dubat, A. "Whole-Kernel Mixolab Testing for Different Cereals." In Mixolab, 85–88. Elsevier, 2013. http://dx.doi.org/10.1016/b978-1-891127-77-9.50016-9.
Schmitt, Marcelo, and Paulo Meirelles. "Trusting Critical Open Source Components." In Business Models and Strategies for Open Source Projects, 175–99. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-4785-7.ch006.
Device drivers are an elementary part of the Linux kernel and comprise roughly 2/3 of the project's lines of code. Even though the fraction of device driver code in a conventional operating system (OS) can vary, some of these components are essential for system functioning. In addition, the Linux kernel is used in a wide range of applications, from cloud service providers to embedded systems and supercomputers. If GNU/Linux systems should be trustworthy to justify running them in those environments, then testing the kernel is fundamental. However, since device drivers are designed to interface with hardware, conventional test approaches may not suit the occasions when devices are unavailable at test time. This raises the question: How are device drivers tested?
Тези доповідей конференцій з теми "Kernel testing":
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Kriege, Nils M., Christopher Morris, Anja Rey, and Christian Sohler. "A Property Testing Framework for the Theoretical Expressivity of Graph Kernels." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/325.
Graph kernels are applied heavily for the classification of structured data. However, their expressivity is assessed almost exclusively from experimental studies and there is no theoretical justification why one kernel is in general preferable over another. We introduce a theoretical framework for investigating the expressive power of graph kernels, which is inspired by concepts from the area of property testing. We introduce the notion of distinguishability of a graph property by a graph kernel. For several established graph kernels we show that they cannot distinguish essential graph properties. In order to overcome this, we consider a kernel based on k-disc frequencies. We show that this efficiently computable kernel can distinguish fundamental graph properties. Finally, we obtain learning guarantees for nearest neighbor classifiers in our framework.
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Patrick, Matthew, and Yue Jia. "Kernel Density Adaptive Random Testing." In 2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2015. http://dx.doi.org/10.1109/icstw.2015.7107451.
Sun, Zhongchang, and Shaofeng Zou. "Robust Hypothesis Testing with Kernel Uncertainty Sets." In 2022 IEEE International Symposium on Information Theory (ISIT). IEEE, 2022. http://dx.doi.org/10.1109/isit50566.2022.9834349.
Chen, Yu, Fengguang Wu, Kuanlong Yu, Lei Zhang, Yuheng Chen, Yang Yang, and Junjie Mao. "Instant Bug Testing Service for Linux Kernel." In 2013 IEEE International Conference on High Performance Computing and Communications (HPCC) & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2013. http://dx.doi.org/10.1109/hpcc.and.euc.2013.347.
Pambudi, Afief Dias, Michael Fauss, and Abdelhak M. Zoubir. "Kernel-based cooperative robust sequential hypothesis testing." In 2018 International Conference on Signals and Systems (ICSigSys). IEEE, 2018. http://dx.doi.org/10.1109/icsigsys.2018.8373565.
Nikeshin, Alexei Viacheslavovich, and Victor Zinovievich Shnitman. "Testing the OpenvSwitch module of the Linux kernel network subsystem." In 25th Scientific Conference “Scientific Services & Internet – 2023”. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/abrau-2023-4.
This paper presents the experience of researching the security of the OpenvSwitch module of the Linux kernel network subsystem. Today Linux is one of the most popular operating systems in the world. The quality of the code of this OS (and in particular its kernel) directly affects the reliability and security of the entire range of products based on it. The international community of Linux developers is making great efforts to improve its reliability, using all kinds of testing methods and error detections. However, the number of new errors detected annually and the number of old errors that have not been corrected shows that these efforts are clearly insufficient. The methods of architectural analysis and fuzzing testing of the OpenvSwitch module were applied in the work. The syzkaller tool was used for fuzzing testing. One error, accepted into the main branch of the kernel, was found. The work is part of the project of the Linux Verification Center (ISP RAS).
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Pawlak, Miroslaw. "Signal model specification testing via kernel reconstruction methods." In 2015 International Conference on Sampling Theory and Applications (SampTA). IEEE, 2015. http://dx.doi.org/10.1109/sampta.2015.7148939.
Ni, Tao, Zhongxu Yin, Qiang Wei, and Qingxian Wang. "High-Coverage Security Testing for Windows Kernel Drivers." In 2012 4th International Conference on Multimedia Information Networking and Security (MINES). IEEE, 2012. http://dx.doi.org/10.1109/mines.2012.117.
Grixti, S., N. Sammut, M. Hernek, E. Carrascosa, M. Masmano, and A. Crespo. "Separation Kernel Robustness Testing: The XtratuM Case Study." In 2016 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2016. http://dx.doi.org/10.1109/cluster.2016.91.
Wu, Jiagu, Huajun Feng, Zhihai Xu, Qi Li, and Zhongliang Fu. "Method to detect and calculate motion blur kernel." In 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies, edited by Yudong Zhang, José Sasián, Libin Xiang, and Sandy To. SPIE, 2010. http://dx.doi.org/10.1117/12.866645.
Sparks, Paul, Jesse Sherburn, William Heard, and Brett Williams. Penetration modeling of ultra‐high performance concrete using multiscale meshfree methods. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41963.
Terminal ballistics of concrete is of extreme importance to the military and civil communities. Over the past few decades, ultra‐high performance concrete (UHPC) has been developed for various applications in the design of protective structures because UHPC has an enhanced ballistic resistance over conventional strength concrete. Developing predictive numerical models of UHPC subjected to penetration is critical in understanding the material's enhanced performance. This study employs the advanced fundamental concrete (AFC) model, and it runs inside the reproducing kernel particle method (RKPM)‐based code known as the nonlinear meshfree analysis program (NMAP). NMAP is advantageous for modeling impact and penetration problems that exhibit extreme deformation and material fragmentation. A comprehensive experimental study was conducted to characterize the UHPC. The investigation consisted of fracture toughness testing, the utilization of nondestructive microcomputed tomography analysis, and projectile penetration shots on the UHPC targets. To improve the accuracy of the model, a new scaled damage evolution law (SDEL) is employed within the microcrack informed damage model. During the homogenized macroscopic calculation, the corresponding microscopic cell needs to be dimensionally equivalent to the mesh dimension when the partial differential equation becomes ill posed and strain softening ensues. Results of numerical investigations will be compared with results of penetration experiments.
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McMurray, J. W., C. M. Silva, G. W. Helmreich, T. J. Gerczak, J. A. Dyer, J. L. Collins, R. D. Hunt, T. B. Lindemer, and K. A. Terrani. Production of Low Enriched Uranium Nitride Kernels for TRISO Particle Irradiation Testing. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1376320.
Bhattacharya, Sumit, Rachel Seibert, Andrew Nelson, Heather Connaway, and Abdellatif Yacout. Preliminary results from Low Pressure Steam Oxidation Testing of ALD ZrN and ZrO2 Coating Deposited over UCN Fuel Kernels. Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1807683.
Lynk, John. PR-610-163756-WEB Material Strength Verification. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2019. http://dx.doi.org/10.55274/r0011573.
DATE: Tuesday, April 30, 2019 TIME: 11:00 a.m. ET CLICK THE DOWNLOAD/BUY BUTTON TO ACCESS THE WEBINAR REGISTRATION LINK Join the PRCI Integrity and Inspection technical committee for a pipeline operator driven discussion regarding PRCI research related to non-destructive technologies for the purpose of pipe material verification and how operators have applied this research in the field. This webinar will include; research project overview, operator case studies and analysis of current technology gaps. Panelists: Mark Piazza, Manager Pipeline Compliance and R and D, Colonial Pipeline Company Mike Kern, Director of Gas Transmission Engineering, National Grid Oliver Burkinshaw, Senior Materials Engineer, ROSEN Simon Bellemare, Founder and CEO of Massachusetts Materials Technologies John Lynk, Program Manager, Integrity and Inspection and Subsea Technical Committees, PRCI Expected Benefits/Learning Outcomes: - In-ditch non-destructive evaluation for material yield strength that has been utilized on in-service lines to confirm incomplete records of pipe grades and/or to evaluate acquired assets - How the data has been utilized to collect opportunistic data as part of external corrosion direct assessments to provide a basis for maximum allowable operating pressure, as well as prioritizing and setting criteria for further inspection and potential capital projects. - The ability to differentiate specific manufacturing processes, such as low frequency and high frequency electro-resistance welded longitudinal seams, have been successfully applied on a number of pipeline integrity projects - Enhancement of inline inspection technologies combined with verification digs have demonstrated the potential to apply pipe joint specific strength data in fitness-for-service, as opposed to lower minimum values set by pipe grade or by nominal conservative assumptions. Who should attend: - Pipeline integrity engineers, specialists and management - Pipe materials specialists Recommended pre-reading: PR-610-163756-R01 Hardness Stength and Ductility (HSD) Testing of Line Pipes Initial Validation Testing Phase I PR-335-173816-MV Validation of insitu Methods for Material Property Determination CLICK THE DOWNLOAD/BUY BUTTON TO ACCESS THE WEBINAR REGISTRATION LINK Not able to attend? Register anyway to automatically receive a link to the webinar recording to view on-demand at your convenience.
