Study of the convergence behavior of the complex kernel least mean square algorithm.

Science.gov (United States)

Paul, Thomas K; Ogunfunmi, Tokunbo

2013-09-01

The complex kernel least mean square (CKLMS) algorithm is recently derived and allows for online kernel adaptive learning for complex data. Kernel adaptive methods can be used in finding solutions for neural network and machine learning applications. The derivation of CKLMS involved the development of a modified Wirtinger calculus for Hilbert spaces to obtain the cost function gradient. We analyze the convergence of the CKLMS with different kernel forms for complex data. The expressions obtained enable us to generate theory-predicted mean-square error curves considering the circularity of the complex input signals and their effect on nonlinear learning. Simulations are used for verifying the analysis results.

Combination of Biorthogonal Wavelet Hybrid Kernel OCSVM with Feature Weighted Approach Based on EVA and GRA in Financial Distress Prediction

Directory of Open Access Journals (Sweden)

Chao Huang

2014-01-01

Full Text Available Financial distress prediction plays an important role in the survival of companies. In this paper, a novel biorthogonal wavelet hybrid kernel function is constructed by combining linear kernel function with biorthogonal wavelet kernel function. Besides, a new feature weighted approach is presented based on economic value added (EVA and grey relational analysis (GRA. Considering the imbalance between financially distressed companies and normal ones, the feature weighted one-class support vector machine based on biorthogonal wavelet hybrid kernel (BWH-FWOCSVM is further put forward for financial distress prediction. The empirical study with real data from the listed companies on Growth Enterprise Market (GEM in China shows that the proposed approach has good performance.

Hybrid Modified K-Means with C4.5 for Intrusion Detection Systems in Multiagent Systems.

Science.gov (United States)

Laftah Al-Yaseen, Wathiq; Ali Othman, Zulaiha; Ahmad Nazri, Mohd Zakree

2015-01-01

Presently, the processing time and performance of intrusion detection systems are of great importance due to the increased speed of traffic data networks and a growing number of attacks on networks and computers. Several approaches have been proposed to address this issue, including hybridizing with several algorithms. However, this paper aims at proposing a hybrid of modified K-means with C4.5 intrusion detection system in a multiagent system (MAS-IDS). The MAS-IDS consists of three agents, namely, coordinator, analysis, and communication agent. The basic concept underpinning the utilized MAS is dividing the large captured network dataset into a number of subsets and distributing these to a number of agents depending on the data network size and core CPU availability. KDD Cup 1999 dataset is used for evaluation. The proposed hybrid modified K-means with C4.5 classification in MAS is developed in JADE platform. The results show that compared to the current methods, the MAS-IDS reduces the IDS processing time by up to 70%, while improving the detection accuracy.

Stability-integrated Fuzzy C means segmentation for spatial ...

Indian Academy of Sciences (India)

V ROYNA DAISY

2018-03-16

Mar 16, 2018 ... clusters and including spatial information to basic Fuzzy C Means clustering are done in .... modify the objective function with Kernel distance function .... spatial information, thus making it sensitive to noise and outliers.

A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

Directory of Open Access Journals (Sweden)

Xianfeng Yuan

2015-01-01

presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel support vector machine (SVM and Dempster-Shafer (D-S fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

Protein fold recognition using geometric kernel data fusion.

Science.gov (United States)

Zakeri, Pooya; Jeuris, Ben; Vandebril, Raf; Moreau, Yves

2014-07-01

Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼ 86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/∼raf.vandebril/homepage/software/geomean.php?menu=5/. © The Author 2014. Published by Oxford University Press.

Composite population kernels in ytterbium-buffer collisions studied by means of laser-saturated absorption

International Nuclear Information System (INIS)

Zhu, X.

1986-01-01

We present a systematic study of composite population kernels for 174 Yb collisions with He, Ar, and Xe buffer gases, using laser-saturation spectroscopy. 174 Yb is chosen as the active species because of the simple structure of its 1 S 0 - 3 P 1 resonance transition (lambda = 556 nm). Elastic collisions are modeled by means of a composite collision kernel, an expression of which is explicitly derived based on arguments of a hard-sphere potential and two-category collisions. The corresponding coupled population-rate equations are solved by iteration to obtain an expression for the saturated-absorption line shape. This expression is fit to the data to obtain information about the composite kernel, along with reasonable values for other parameters. The results confirm that a composite kernel is more general and realistic than a single-component kernel, and the generality in principle and the practical necessity of the former are discussed

Continuous spin mean-field models : Limiting kernels and Gibbs properties of local transforms

NARCIS (Netherlands)

Kulske, Christof; Opoku, Alex A.

2008-01-01

We extend the notion of Gibbsianness for mean-field systems to the setup of general (possibly continuous) local state spaces. We investigate the Gibbs properties of systems arising from an initial mean-field Gibbs measure by application of given local transition kernels. This generalizes previous

Normed kernel function-based fuzzy possibilistic C-means (NKFPCM) algorithm for high-dimensional breast cancer database classification with feature selection is based on Laplacian Score

Science.gov (United States)

Lestari, A. W.; Rustam, Z.

2017-07-01

In the last decade, breast cancer has become the focus of world attention as this disease is one of the primary leading cause of death for women. Therefore, it is necessary to have the correct precautions and treatment. In previous studies, Fuzzy Kennel K-Medoid algorithm has been used for multi-class data. This paper proposes an algorithm to classify the high dimensional data of breast cancer using Fuzzy Possibilistic C-means (FPCM) and a new method based on clustering analysis using Normed Kernel Function-Based Fuzzy Possibilistic C-Means (NKFPCM). The objective of this paper is to obtain the best accuracy in classification of breast cancer data. In order to improve the accuracy of the two methods, the features candidates are evaluated using feature selection, where Laplacian Score is used. The results show the comparison accuracy and running time of FPCM and NKFPCM with and without feature selection.

Identification of flooded area from satellite images using Hybrid Kohonen Fuzzy C-Means sigma classifier

Directory of Open Access Journals (Sweden)

Krishna Kant Singh

2017-06-01

Full Text Available A novel neuro fuzzy classifier Hybrid Kohonen Fuzzy C-Means-σ (HKFCM-σ is proposed in this paper. The proposed classifier is a hybridization of Kohonen Clustering Network (KCN with FCM-σ clustering algorithm. The network architecture of HKFCM-σ is similar to simple KCN network having only two layers, i.e., input and output layer. However, the selection of winner neuron is done based on FCM-σ algorithm. Thus, embedding the features of both, a neural network and a fuzzy clustering algorithm in the classifier. This hybridization results in a more efficient, less complex and faster classifier for classifying satellite images. HKFCM-σ is used to identify the flooding that occurred in Kashmir area in September 2014. The HKFCM-σ classifier is applied on pre and post flooding Landsat 8 OLI images of Kashmir to detect the areas that were flooded due to the heavy rainfalls of September, 2014. The classifier is trained using the mean values of the various spectral indices like NDVI, NDWI, NDBI and first component of Principal Component Analysis. The error matrix was computed to test the performance of the method. The method yields high producer’s accuracy, consumer’s accuracy and kappa coefficient value indicating that the proposed classifier is highly effective and efficient.

The influence of maize kernel moisture on the sterilizing effect of gamma rays

International Nuclear Information System (INIS)

Khanymova, T.; Poloni, E.

1980-01-01

The influence of 4 levels of maize kernel moisture (16, 20, 25 and 30%) on gamma-ray sterilizing effect was studied and the after-effect of radiation on the microorganisms at short term storage was followed up. Maize kernels of the hybrid Knezha-36 produced in 1975 were used. Gamma-ray treatment of the kernels was effected by GUBEh-4000 irradiator at doses of 0.2 and 0.3 Mrad and after that they were stored for a month at 12 deg and 25 deg C and controlled moisture conditions. Surface and subepidermal infection of the kernels was determined immediately post irradiation and at the end of the experiment. Non-irradiated kernels were used as controls. Results indicated that the initial kernel moisture has a considerable influence on the sterilizing effect of gamma-rays at the rates used in the experiment and affects to a considerable extent the post-irradiation recovery of organisms. The speed of recovery was highest in the treatment with 30% moisture and lowest in the treatment with 16% kernel moisture. Irradiation of the kernels causes pronounced changes on the surface and subepidermal infection. This was due to the unequal radio resistance to the microbial components and to the modifying effect of the moisture holding capacity. The useful effect of maize kernel irradiation was more prolonged at 12 deg C than at 25 deg C

Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

Directory of Open Access Journals (Sweden)

Jinlu Sheng

2016-07-01

Full Text Available To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time–frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.

Interspecific hybridization between Crassostrea angulata and C. ariakensis

Science.gov (United States)

Yao, Tuo; Zhang, Yuehuan; Yan, Xiwu; Wang, Zhaoping; Li, Dongchun; Su, Jiaqi; Yu, Ruihai

2015-08-01

Interspecific hybridization can generate heterosis, which is proven to be a useful tool in selective breeding programs for oyster culture. Crassostrea angulata and C. ariakensis are two important economic shellfish species in China. We conducted 2 × 2 reciprocal crosses to determine whether these two species can cross-fertilize and their hybrids can hatch, survive and perform heterosis. Fertilization was found symmetrical without delay. The rate of fertilization success of C. angulata ♀ × C. ariakensis ♂ was lower than that of C. ariakensis ♀ × C. angulata ♂, and the success rate of both hybridizations was lower than that of two intraspecific crosses each. During the planktonic period, survival rate of the progeny was lower in the hybrid crosses than in the intraspecific crosses. On day 360, mean shell height of the progeny of C. angulata ♀ × C. angulata ♂ was highest, which was followed by that of C. angulata ♀ × C. ariakensis ♂, C. ariakensis ♀ × C. ariakensis ♂ and C. ariakensis ♀ × C. angulata ♂ in a descending order. Morphology of adults produced by the hybrid crosses was similar to that of C. angulata. Both hybrids underwent normal gonad development and produced mature gametes in the mating season. This study provided new insights into the quantitative traits in interspecific crosses of Crassostrea species, thus being of guidance value for selective breeding of oyster.

Semi-Supervised Kernel PCA

DEFF Research Database (Denmark)

Walder, Christian; Henao, Ricardo; Mørup, Morten

We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least...... squares regression and kernel PCA. The final LR-KPCA is an iteratively reweighted version of the previous which achieves a sigmoid loss function on the labeled points. We provide a theoretical risk bound as well as illustrative experiments on real and toy data sets....

Relationship between processing score and kernel-fraction particle size in whole-plant corn silage.

Science.gov (United States)

Dias Junior, G S; Ferraretto, L F; Salvati, G G S; de Resende, L C; Hoffman, P C; Pereira, M N; Shaver, R D

2016-04-01

Kernel processing increases starch digestibility in whole-plant corn silage (WPCS). Corn silage processing score (CSPS), the percentage of starch passing through a 4.75-mm sieve, is widely used to assess degree of kernel breakage in WPCS. However, the geometric mean particle size (GMPS) of the kernel-fraction that passes through the 4.75-mm sieve has not been well described. Therefore, the objectives of this study were (1) to evaluate particle size distribution and digestibility of kernels cut in varied particle sizes; (2) to propose a method to measure GMPS in WPCS kernels; and (3) to evaluate the relationship between CSPS and GMPS of the kernel fraction in WPCS. Composite samples of unfermented, dried kernels from 110 corn hybrids commonly used for silage production were kept whole (WH) or manually cut in 2, 4, 8, 16, 32 or 64 pieces (2P, 4P, 8P, 16P, 32P, and 64P, respectively). Dry sieving to determine GMPS, surface area, and particle size distribution using 9 sieves with nominal square apertures of 9.50, 6.70, 4.75, 3.35, 2.36, 1.70, 1.18, and 0.59 mm and pan, as well as ruminal in situ dry matter (DM) digestibilities were performed for each kernel particle number treatment. Incubation times were 0, 3, 6, 12, and 24 h. The ruminal in situ DM disappearance of unfermented kernels increased with the reduction in particle size of corn kernels. Kernels kept whole had the lowest ruminal DM disappearance for all time points with maximum DM disappearance of 6.9% at 24 h and the greatest disappearance was observed for 64P, followed by 32P and 16P. Samples of WPCS (n=80) from 3 studies representing varied theoretical length of cut settings and processor types and settings were also evaluated. Each WPCS sample was divided in 2 and then dried at 60 °C for 48 h. The CSPS was determined in duplicate on 1 of the split samples, whereas on the other split sample the kernel and stover fractions were separated using a hydrodynamic separation procedure. After separation, the

7 CFR 981.7 - Edible kernel.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Edible kernel. 981.7 Section 981.7 Agriculture... Regulating Handling Definitions § 981.7 Edible kernel. Edible kernel means a kernel, piece, or particle of almond kernel that is not inedible. [41 FR 26852, June 30, 1976] ...

Improved coronary in-stent visualization using a combined high-resolution kernel and a hybrid iterative reconstruction technique at 256-slice cardiac CT—Pilot study

International Nuclear Information System (INIS)

Oda, Seitaro; Utsunomiya, Daisuke; Funama, Yoshinori; Takaoka, Hiroko; Katahira, Kazuhiro; Honda, Keiichi; Noda, Katsuo; Oshima, Shuichi; Yamashita, Yasuyuki

2013-01-01

Objectives: To investigate the diagnostic performance of 256-slice cardiac CT for the evaluation of the in-stent lumen by using a hybrid iterative reconstruction (HIR) algorithm combined with a high-resolution kernel. Methods: This study included 28 patients with 28 stents who underwent cardiac CT. Three different reconstruction images were obtained with: (1) a standard filtered back projection (FBP) algorithm with a standard cardiac kernel (CB), (2) an FBP algorithm with a high-resolution cardiac kernel (CD), and (3) an HIR algorithm with the CD kernel. We measured image noise and kurtosis and used receiver operating characteristics analysis to evaluate observer performance in the detection of in-stent stenosis. Results: Image noise with FBP plus the CD kernel (80.2 ± 15.5 HU) was significantly higher than with FBP plus the CB kernel (28.8 ± 4.6 HU) and HIR plus the CD kernel (36.1 ± 6.4 HU). There was no significant difference in the image noise between FBP plus the CB kernel and HIR plus the CD kernel. Kurtosis was significantly better with the CD- than the CB kernel. The kurtosis values obtained with the CD kernel were not significantly different between the FBP- and HIR reconstruction algorithms. The areas under the receiver operating characteristics curves with HIR plus the CD kernel were significantly higher than with FBP plus the CB- or the CD kernel. The difference between FBP plus the CB- or the CD kernel was not significant. The average sensitivity, specificity, and positive and negative predictive value for the detection of in-stent stenosis were 83.3, 50.0, 33.3, and 91.6% for FBP plus the CB kernel, 100, 29.6, 40.0, and 100% for FBP plus the CD kernel, and 100, 54.5, 40.0, and 100% for HIR plus the CD kernel. Conclusions: The HIR algorithm combined with the high-resolution kernel significantly improved diagnostic performance in the detection of in-stent stenosis

7 CFR 981.8 - Inedible kernel.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.8 Section 981.8 Agriculture... Regulating Handling Definitions § 981.8 Inedible kernel. Inedible kernel means a kernel, piece, or particle of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or...

7 CFR 981.408 - Inedible kernel.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.408 Section 981.408 Agriculture... Administrative Rules and Regulations § 981.408 Inedible kernel. Pursuant to § 981.8, the definition of inedible kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as...

Evaluation of sintering effects on SiC-incorporated UO2 kernels under Ar and Ar–4%H2 environments

International Nuclear Information System (INIS)

Silva, Chinthaka M.; Lindemer, Terrence B.; Hunt, Rodney D.; Collins, Jack L.; Terrani, Kurt A.; Snead, Lance L.

2013-01-01

Silicon carbide (SiC) is suggested as an oxygen getter in UO 2 kernels used for tristructural isotropic (TRISO) particle fuels and to prevent kernel migration during irradiation. Scanning electron microscopy and X-ray diffractometry analyses performed on sintered kernels verified that an internal gelation process can be used to incorporate SiC in UO 2 fuel kernels. Even though the presence of UC in either argon (Ar) or Ar–4%H 2 sintered samples suggested a lowering of the SiC up to 3.5–1.4 mol%, respectively, the presence of other silicon-related chemical phases indicates the preservation of silicon in the kernels during sintering process. UC formation was presumed to occur by two reactions. The first was by the reaction of SiC with its protective SiO 2 oxide layer on SiC grains to produce volatile SiO and free carbon that subsequently reacted with UO 2 to form UC. The second process was direct UO 2 reaction with SiC grains to form SiO, CO, and UC. A slightly higher density and UC content were observed in the sample sintered in Ar–4%H 2 , but both atmospheres produced kernels with ∼95% of theoretical density. It is suggested that incorporating CO in the sintering gas could prevent UC formation and preserve the initial SiC content

Segmentation of 3D microPET images of the rat brain via the hybrid gaussian mixture method with kernel density estimation.

Science.gov (United States)

Chen, Tai-Been; Chen, Jyh-Cheng; Lu, Henry Horng-Shing

2012-01-01

Segmentation of positron emission tomography (PET) is typically achieved using the K-Means method or other approaches. In preclinical and clinical applications, the K-Means method needs a prior estimation of parameters such as the number of clusters and appropriate initialized values. This work segments microPET images using a hybrid method combining the Gaussian mixture model (GMM) with kernel density estimation. Segmentation is crucial to registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation locations with functional diagnosis and to estimate standardized uptake values (SUVs) of region of interests (ROIs) in PET images. Therefore, simulation studies are conducted to apply spherical targets to evaluate segmentation accuracy based on Tanimoto's definition of similarity. The proposed method generates a higher degree of similarity than the K-Means method. The PET images of a rat brain are used to compare the segmented shape and area of the cerebral cortex by the K-Means method and the proposed method by volume rendering. The proposed method provides clearer and more detailed activity structures of an FDG accumulation location in the cerebral cortex than those by the K-Means method.

7 CFR 981.9 - Kernel weight.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Kernel weight. 981.9 Section 981.9 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Regulating Handling Definitions § 981.9 Kernel weight. Kernel weight means the weight of kernels, including...

Classification With Truncated Distance Kernel.

Science.gov (United States)

Huang, Xiaolin; Suykens, Johan A K; Wang, Shuning; Hornegger, Joachim; Maier, Andreas

2018-05-01

This brief proposes a truncated distance (TL1) kernel, which results in a classifier that is nonlinear in the global region but is linear in each subregion. With this kernel, the subregion structure can be trained using all the training data and local linear classifiers can be established simultaneously. The TL1 kernel has good adaptiveness to nonlinearity and is suitable for problems which require different nonlinearities in different areas. Though the TL1 kernel is not positive semidefinite, some classical kernel learning methods are still applicable which means that the TL1 kernel can be directly used in standard toolboxes by replacing the kernel evaluation. In numerical experiments, the TL1 kernel with a pregiven parameter achieves similar or better performance than the radial basis function kernel with the parameter tuned by cross validation, implying the TL1 kernel a promising nonlinear kernel for classification tasks.

7 CFR 51.2295 - Half kernel.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half kernel. 51.2295 Section 51.2295 Agriculture... Standards for Shelled English Walnuts (Juglans Regia) Definitions § 51.2295 Half kernel. Half kernel means the separated half of a kernel with not more than one-eighth broken off. ...

Genetic relationship between plant growth, shoot and kernel sizes in ...

African Journals Online (AJOL)

Maize (Zea mays L.) ear vascular tissue transports nutrients that contribute to grain yield. To assess kernel heritabilities that govern ear development and plant growth, field studies were conducted to determine the combining abilities of parents that differed for kernel-size, grain-filling rates and shoot-size. Thirty two hybrids ...

The Hybrid Museum: Hybrid Economies of Meaning

DEFF Research Database (Denmark)

Vestergaard, Vitus

2013-01-01

Social media has created new ways of communicating and has brought about a new distinctive ethos. New literacies are not simply about new technology but also about this new ethos. Many museums are embracing this ethos by what is often called participatory practices. From a sociocultural perspective...... this article shows that there are two different museum mindsets where the second mindset leans towards participatory practices. It is shown how a museum can support a hybrid economy of meaning that builds on both a user generated economy of meaning and an institutional economy of meaning and adds value to both...

7 CFR 51.1441 - Half-kernel.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half-kernel. 51.1441 Section 51.1441 Agriculture... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume missing...

A quadratic kernel for computing the hybridization number of multiple trees

NARCIS (Netherlands)

L.J.J. van Iersel (Leo); S. Linz

2012-01-01

htmlabstractIt has recently been shown that the NP-hard problem of calculating the minimum number of hybridization events that is needed to explain a set of rooted binary phylogenetic trees by means of a hybridization network is fixed-parameter tractable if an instance of the problem consists of

Viscosity kernel of molecular fluids

DEFF Research Database (Denmark)

Puscasu, Ruslan; Todd, Billy; Daivis, Peter

2010-01-01

, temperature, and chain length dependencies of the reciprocal and real-space viscosity kernels are presented. We find that the density has a major effect on the shape of the kernel. The temperature range and chain lengths considered here have by contrast less impact on the overall normalized shape. Functional...... forms that fit the wave-vector-dependent kernel data over a large density and wave-vector range have also been tested. Finally, a structural normalization of the kernels in physical space is considered. Overall, the real-space viscosity kernel has a width of roughly 3–6 atomic diameters, which means...

Combined multi-kernel head computed tomography images optimized for depicting both brain parenchyma and bone.

Science.gov (United States)

Takagi, Satoshi; Nagase, Hiroyuki; Hayashi, Tatsuya; Kita, Tamotsu; Hayashi, Katsumi; Sanada, Shigeru; Koike, Masayuki

2014-01-01

The hybrid convolution kernel technique for computed tomography (CT) is known to enable the depiction of an image set using different window settings. Our purpose was to decrease the number of artifacts in the hybrid convolution kernel technique for head CT and to determine whether our improved combined multi-kernel head CT images enabled diagnosis as a substitute for both brain (low-pass kernel-reconstructed) and bone (high-pass kernel-reconstructed) images. Forty-four patients with nondisplaced skull fractures were included. Our improved multi-kernel images were generated so that pixels of >100 Hounsfield unit in both brain and bone images were composed of CT values of bone images and other pixels were composed of CT values of brain images. Three radiologists compared the improved multi-kernel images with bone images. The improved multi-kernel images and brain images were identically displayed on the brain window settings. All three radiologists agreed that the improved multi-kernel images on the bone window settings were sufficient for diagnosing skull fractures in all patients. This improved multi-kernel technique has a simple algorithm and is practical for clinical use. Thus, simplified head CT examinations and fewer images that need to be stored can be expected.

Penetuan Bilangan Iodin pada Hydrogenated Palm Kernel Oil (HPKO) dan Refined Bleached Deodorized Palm Kernel Oil (RBDPKO)

OpenAIRE

Sitompul, Monica Angelina

2015-01-01

Have been conducted Determination of Iodin Value by method titration to some Hydrogenated Palm Kernel Oil (HPKO) and Refined Bleached Deodorized Palm Kernel Oil (RBDPKO). The result of analysis obtained the Iodin Value in Hydrogenated Palm Kernel Oil (A) = 0,16 gr I2/100gr, Hydrogenated Palm Kernel Oil (B) = 0,20 gr I2/100gr, Hydrogenated Palm Kernel Oil (C) = 0,24 gr I2/100gr. And in Refined Bleached Deodorized Palm Kernel Oil (A) = 17,51 gr I2/100gr, Refined Bleached Deodorized Palm Kernel ...

Kernel versions of some orthogonal transformations

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

Kernel versions of orthogonal transformations such as principal components are based on a dual formulation also termed Q-mode analysis in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products of the original data are replaced...... by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution also known as the kernel trick these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of this kernel...... function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component analysis (PCA) and kernel minimum noise fraction (MNF) analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function...

Evaluation of sintering effects on SiC-incorporated UO{sub 2} kernels under Ar and Ar–4%H{sub 2} environments

Energy Technology Data Exchange (ETDEWEB)

Silva, Chinthaka M., E-mail: silvagw@ornl.gov [Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee TN 37831-6223 (United States); Materials Science and Engineering, The University of Tennessee Knoxville, TN 37996-2100, United States. (United States); Lindemer, Terrence B.; Hunt, Rodney D.; Collins, Jack L.; Terrani, Kurt A.; Snead, Lance L. [Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee TN 37831-6223 (United States)

2013-11-15

Silicon carbide (SiC) is suggested as an oxygen getter in UO{sub 2} kernels used for tristructural isotropic (TRISO) particle fuels and to prevent kernel migration during irradiation. Scanning electron microscopy and X-ray diffractometry analyses performed on sintered kernels verified that an internal gelation process can be used to incorporate SiC in UO{sub 2} fuel kernels. Even though the presence of UC in either argon (Ar) or Ar–4%H{sub 2} sintered samples suggested a lowering of the SiC up to 3.5–1.4 mol%, respectively, the presence of other silicon-related chemical phases indicates the preservation of silicon in the kernels during sintering process. UC formation was presumed to occur by two reactions. The first was by the reaction of SiC with its protective SiO{sub 2} oxide layer on SiC grains to produce volatile SiO and free carbon that subsequently reacted with UO{sub 2} to form UC. The second process was direct UO{sub 2} reaction with SiC grains to form SiO, CO, and UC. A slightly higher density and UC content were observed in the sample sintered in Ar–4%H{sub 2}, but both atmospheres produced kernels with ∼95% of theoretical density. It is suggested that incorporating CO in the sintering gas could prevent UC formation and preserve the initial SiC content.

Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods

OpenAIRE

Alam, Md. Ashad; Fukumizu, Kenji; Wang, Yu-Ping

2016-01-01

To the best of our knowledge, there are no general well-founded robust methods for statistical unsupervised learning. Most of the unsupervised methods explicitly or implicitly depend on the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). They are sensitive to contaminated data, even when using bounded positive definite kernels. First, we propose robust kernel covariance operator (robust kernel CO) and robust kernel crosscovariance operator (robust kern...

7 CFR 51.2296 - Three-fourths half kernel.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Three-fourths half kernel. 51.2296 Section 51.2296 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards...-fourths half kernel. Three-fourths half kernel means a portion of a half of a kernel which has more than...

Aida-CMK multi-algorithm optimization kernel applied to analog IC sizing

CERN Document Server

Lourenço, Ricardo; Horta, Nuno

2015-01-01

This work addresses the research and development of an innovative optimization kernel applied to analog integrated circuit (IC) design. Particularly, this works describes the modifications inside the AIDA Framework, an electronic design automation framework fully developed by at the Integrated Circuits Group-LX of the Instituto de Telecomunicações, Lisbon. It focusses on AIDA-CMK, by enhancing AIDA-C, which is the circuit optimizer component of AIDA, with a new multi-objective multi-constraint optimization module that constructs a base for multiple algorithm implementations. The proposed solution implements three approaches to multi-objective multi-constraint optimization, namely, an evolutionary approach with NSGAII, a swarm intelligence approach with MOPSO and stochastic hill climbing approach with MOSA. Moreover, the implemented structure allows the easy hybridization between kernels transforming the previous simple NSGAII optimization module into a more evolved and versatile module supporting multiple s...

Effects of muffin processing on fumonisins from 14C-labeled toxins produced in cultured corn kernels.

Science.gov (United States)

Avantaggiato, Giuseppina; De La Campa, Regina; Miller, J David; Visconti, Angelo

2003-10-01

The persistence of fumonisins during cooking is known to be affected by several factors, including thermal degradation and the presence of various ingredients in corn-based food recipes that can react with the toxin. A method for the production of corn kernels containing 14C-fumonisins was developed. The corn kernels were colonized by Fusarium verticillioides MRC 826 and supplemented with 1,2-14C-sodium acetate. The specific activity of 14C-FB1 produced made the study of its fate in cornmeal muffins possible. The double-extraction acetonitrile-water-methanol/immunoaffinity column/o-phthaldialdehyde high-performance liquid chromatography (HPLC) method was used to determine FB1 levels in cornmeal muffins. Reductions in FB1 levels in muffins spiked with 14C-labeled and unlabeled FB1 (43 and 48%, respectively) were similar, indicating that the extraction method was efficient and consistent with previous reports. However, with the labeled corn kernel material, recovery levels based on the 14C counts for the eluate from an immunoaffinity column were much higher (90%). This finding indicates that some fumonisin-related compounds other than FB1 that were present in the cornmeal were recognized by the antibodies but not by the HPLC method.

Optimized Kernel Entropy Components.

Science.gov (United States)

Izquierdo-Verdiguier, Emma; Laparra, Valero; Jenssen, Robert; Gomez-Chova, Luis; Camps-Valls, Gustau

2017-06-01

This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

Science.gov (United States)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

Stability Performance of Inductively Coupled Plasma Mass Spectrometry-Phenotyped Kernel Minerals Concentration and Grain Yield in Maize in Different Agro-Climatic Zones.

Science.gov (United States)

Mallikarjuna, Mallana Gowdra; Thirunavukkarasu, Nepolean; Hossain, Firoz; Bhat, Jayant S; Jha, Shailendra K; Rathore, Abhishek; Agrawal, Pawan Kumar; Pattanayak, Arunava; Reddy, Sokka S; Gularia, Satish Kumar; Singh, Anju Mahendru; Manjaiah, Kanchikeri Math; Gupta, Hari Shanker

2015-01-01

Deficiency of iron and zinc causes micronutrient malnutrition or hidden hunger, which severely affects ~25% of global population. Genetic biofortification of maize has emerged as cost effective and sustainable approach in addressing malnourishment of iron and zinc deficiency. Therefore, understanding the genetic variation and stability of kernel micronutrients and grain yield of the maize inbreds is a prerequisite in breeding micronutrient-rich high yielding hybrids to alleviate micronutrient malnutrition. We report here, the genetic variability and stability of the kernel micronutrients concentration and grain yield in a set of 50 maize inbred panel selected from the national and the international centres that were raised at six different maize growing regions of India. Phenotyping of kernels using inductively coupled plasma mass spectrometry (ICP-MS) revealed considerable variability for kernel minerals concentration (iron: 18.88 to 47.65 mg kg(-1); zinc: 5.41 to 30.85 mg kg(-1); manganese: 3.30 to 17.73 mg kg(-1); copper: 0.53 to 5.48 mg kg(-1)) and grain yield (826.6 to 5413 kg ha(-1)). Significant positive correlation was observed between kernel iron and zinc within (r = 0.37 to r = 0.52, p kernel minerals concentration and grain yield. Most of the variation was contributed by genotype main effect for kernel iron (39.6%), manganese (41.34%) and copper (41.12%), and environment main effects for both kernel zinc (40.5%) and grain yield (37.0%). Genotype main effect plus genotype-by-environment interaction (GGE) biplot identified several mega environments for kernel minerals and grain yield. Comparison of stability parameters revealed AMMI stability value (ASV) as the better representative of the AMMI stability parameters. Dynamic stability parameter GGE distance (GGED) showed strong and positive correlation with both mean kernel concentrations and grain yield. Inbreds (CM-501, SKV-775, HUZM-185) identified from the present investigation will be useful in

An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space

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Dongdong Yang

2015-02-01

Full Text Available Synthetic aperture radar (SAR image segmentation usually involves two crucial issues: suitable speckle noise removing technique and effective image segmentation methodology. Here, an efficient SAR image segmentation method considering both of the two aspects is presented. As for the first issue, the famous nonlocal mean (NLM filter is introduced in this study to suppress the multiplicative speckle noise in SAR image. Furthermore, to achieve a higher denoising accuracy, the local neighboring pixels in the searching window are projected into a lower dimensional subspace by principal component analysis (PCA. Thus, the nonlocal mean filter is implemented in the subspace. Afterwards, a multi-objective clustering algorithm is proposed using the principals of artificial immune system (AIS and kernel-induced distance measures. The multi-objective clustering has been shown to discover the data distribution with different characteristics and the kernel methods can improve its robustness to noise and outliers. Experiments demonstrate that the proposed method is able to partition the SAR image robustly and accurately than the conventional approaches.

Credit scoring analysis using kernel discriminant

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Widiharih, T.; Mukid, M. A.; Mustafid

2018-05-01

Credit scoring model is an important tool for reducing the risk of wrong decisions when granting credit facilities to applicants. This paper investigate the performance of kernel discriminant model in assessing customer credit risk. Kernel discriminant analysis is a non- parametric method which means that it does not require any assumptions about the probability distribution of the input. The main ingredient is a kernel that allows an efficient computation of Fisher discriminant. We use several kernel such as normal, epanechnikov, biweight, and triweight. The models accuracy was compared each other using data from a financial institution in Indonesia. The results show that kernel discriminant can be an alternative method that can be used to determine who is eligible for a credit loan. In the data we use, it shows that a normal kernel is relevant to be selected for credit scoring using kernel discriminant model. Sensitivity and specificity reach to 0.5556 and 0.5488 respectively.

Integral equations with contrasting kernels

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Theodore Burton

2008-01-01

Full Text Available In this paper we study integral equations of the form $x(t=a(t-\\int^t_0 C(t,sx(sds$ with sharply contrasting kernels typified by $C^*(t,s=\\ln (e+(t-s$ and $D^*(t,s=[1+(t-s]^{-1}$. The kernel assigns a weight to $x(s$ and these kernels have exactly opposite effects of weighting. Each type is well represented in the literature. Our first project is to show that for $a\\in L^2[0,\\infty$, then solutions are largely indistinguishable regardless of which kernel is used. This is a surprise and it leads us to study the essential differences. In fact, those differences become large as the magnitude of $a(t$ increases. The form of the kernel alone projects necessary conditions concerning the magnitude of $a(t$ which could result in bounded solutions. Thus, the next project is to determine how close we can come to proving that the necessary conditions are also sufficient. The third project is to show that solutions will be bounded for given conditions on $C$ regardless of whether $a$ is chosen large or small; this is important in real-world problems since we would like to have $a(t$ as the sum of a bounded, but badly behaved function, and a large well behaved function.

Growth, 14C-sucrose uptake, and metabolites of starch synthesis in apical and basal kernels of corn (Zea mays L.)

International Nuclear Information System (INIS)

Greenberg, J.M.

1985-01-01

Developing field-grown kernels of corn (Zea mays L. cv. Cornell 175) from the base and apex of the ear were sampled from seven to 70 days after pollination (DAP) an compared with respect to dry weight, ability to take up 14 C-sucrose from solution in vitro, and content of sucrose, glucose, starch, glucose-1-P (G1P), glucose-6-P (G6P), fructose-6-P (F6P), ADP-glucose (ADPG), and UDP-glucose (UDPG). ADPG and UDPG were analyzed by HPLC. All other metabolites were analyzed enzymatically. Simultaneous hand-pollination of all ovaries in an ear did not reduce the difference between apical and basal kernels in dry weight, indicating that the latter fertilization of apical kernels was not responsible for their lesser mature dry weight. Detached kernels took up 14 C-sucrose (0.3-400 mM) and glucose (5-100 mM) at rates linearly proportional to the sugar concentration. Glucose, fructose, and sorbitol did not inhibit uptake of 14 C-sucrose. Uptake was not stimulated by 5 mM CaCl 2 or the addition of buffers (pH 4.5-6.7) to the medium. Sulfhydryl reagents (PCMBS, NEM) and metabolic inhibitors (TNBS, DNP, NaF) did not reduce uptake. These observations suggest that sucrose is taken up by a non-saturable, non-energy-requiring mechanism. Sucrose uptake increased throughout development, especially at the stage when basal kernels began to accumulate more dry weight than apical kernels (10-20 DAP in freely pollinated ears; 25 DAP in synchronously pollinated ears). Hydrolysis of incorporated sucrose increased from 87% at 14 DAP to 99% by 57 DAP

Evaluation of the OpenCL AES Kernel using the Intel FPGA SDK for OpenCL

Energy Technology Data Exchange (ETDEWEB)

Jin, Zheming [Argonne National Lab. (ANL), Argonne, IL (United States); Yoshii, Kazutomo [Argonne National Lab. (ANL), Argonne, IL (United States); Finkel, Hal [Argonne National Lab. (ANL), Argonne, IL (United States); Cappello, Franck [Argonne National Lab. (ANL), Argonne, IL (United States)

2017-04-20

The OpenCL standard is an open programming model for accelerating algorithms on heterogeneous computing system. OpenCL extends the C-based programming language for developing portable codes on different platforms such as CPU, Graphics processing units (GPUs), Digital Signal Processors (DSPs) and Field Programmable Gate Arrays (FPGAs). The Intel FPGA SDK for OpenCL is a suite of tools that allows developers to abstract away the complex FPGA-based development flow for a high-level software development flow. Users can focus on the design of hardware-accelerated kernel functions in OpenCL and then direct the tools to generate the low-level FPGA implementations. The approach makes the FPGA-based development more accessible to software users as the needs for hybrid computing using CPUs and FPGAs are increasing. It can also significantly reduce the hardware development time as users can evaluate different ideas with high-level language without deep FPGA domain knowledge. In this report, we evaluate the performance of the kernel using the Intel FPGA SDK for OpenCL and Nallatech 385A FPGA board. Compared to the M506 module, the board provides more hardware resources for a larger design exploration space. The kernel performance is measured with the compute kernel throughput, an upper bound to the FPGA throughput. The report presents the experimental results in details. The Appendix lists the kernel source code.

Biochemical and physical kernel properties of a standard maize hybrid in different TopCross™ Blends

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Jelena Vancetovic

Full Text Available ABSTRACT A pilot experiment was undertaken in order to examine high oil populations of maize (Zea mays L. to be used as pollinators in TopCross blends with commercial ZP341 standard hybrid. Five high oil populations (HOPs from the Maize Research Institute (MRI gene bank were chosen for this research, according to their high grain oil content, synchrony between silking of ZP341 and anthesis of the populations and good agronomic performances in 2012. Selfing of ZP341 and HOPs, as well as crosses of ZP341 cmsS sterile × HOPs were carried out in 2013. Oil content, fatty acid composition, protein and tryptophan content, and physical characteristics of the obtained kernels were measured. Four HOPs showed significant positive influence on the oil content in the TopCrosses (TC, 16.85 g kg−1 on average. Oleic acid, which is the principal monounsaturated fatty acid, was significantly lower in all HOPs and all TCs, while selfed ZP341 had almost twice the average value typical for standard maize. However, this decrease in TCs was in a narrow range from 1 % (in TC-3 to 5 % (in TC-4 and the oleic content of TCs was on average higher by 60 % compared to the typical standard maize. Different favorable and unfavorable significant changes were detected in fatty acid compositions, protein and tryptophan contents and physical kernel properties for each potential TC combination. Results indicate differences in gene effects present in different TC combinations and underscore the need to examine each potential TC blend by conducting similar simple experiments.

Stability Performance of Inductively Coupled Plasma Mass Spectrometry-Phenotyped Kernel Minerals Concentration and Grain Yield in Maize in Different Agro-Climatic Zones.

Directory of Open Access Journals (Sweden)

Mallana Gowdra Mallikarjuna

parameter GGE distance (GGED showed strong and positive correlation with both mean kernel concentrations and grain yield. Inbreds (CM-501, SKV-775, HUZM-185 identified from the present investigation will be useful in developing micronutrient-rich as well as stable maize hybrids without compromising grain yield.

LZW-Kernel: fast kernel utilizing variable length code blocks from LZW compressors for protein sequence classification.

Science.gov (United States)

Filatov, Gleb; Bauwens, Bruno; Kertész-Farkas, Attila

2018-05-07

Bioinformatics studies often rely on similarity measures between sequence pairs, which often pose a bottleneck in large-scale sequence analysis. Here, we present a new convolutional kernel function for protein sequences called the LZW-Kernel. It is based on code words identified with the Lempel-Ziv-Welch (LZW) universal text compressor. The LZW-Kernel is an alignment-free method, it is always symmetric, is positive, always provides 1.0 for self-similarity and it can directly be used with Support Vector Machines (SVMs) in classification problems, contrary to normalized compression distance (NCD), which often violates the distance metric properties in practice and requires further techniques to be used with SVMs. The LZW-Kernel is a one-pass algorithm, which makes it particularly plausible for big data applications. Our experimental studies on remote protein homology detection and protein classification tasks reveal that the LZW-Kernel closely approaches the performance of the Local Alignment Kernel (LAK) and the SVM-pairwise method combined with Smith-Waterman (SW) scoring at a fraction of the time. Moreover, the LZW-Kernel outperforms the SVM-pairwise method when combined with BLAST scores, which indicates that the LZW code words might be a better basis for similarity measures than local alignment approximations found with BLAST. In addition, the LZW-Kernel outperforms n-gram based mismatch kernels, hidden Markov model based SAM and Fisher kernel, and protein family based PSI-BLAST, among others. Further advantages include the LZW-Kernel's reliance on a simple idea, its ease of implementation, and its high speed, three times faster than BLAST and several magnitudes faster than SW or LAK in our tests. LZW-Kernel is implemented as a standalone C code and is a free open-source program distributed under GPLv3 license and can be downloaded from https://github.com/kfattila/LZW-Kernel. akerteszfarkas@hse.ru. Supplementary data are available at Bioinformatics Online.

Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory

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Qiang Shang

2016-08-01

Full Text Available Short-term traffic flow prediction is an important part of intelligent transportation systems research and applications. For further improving the accuracy of short-time traffic flow prediction, a novel hybrid prediction model (multivariate phase space reconstruction–combined kernel function-least squares support vector machine based on multivariate phase space reconstruction and combined kernel function-least squares support vector machine is proposed. The C-C method is used to determine the optimal time delay and the optimal embedding dimension of traffic variables’ (flow, speed, and occupancy time series for phase space reconstruction. The G-P method is selected to calculate the correlation dimension of attractor which is an important index for judging chaotic characteristics of the traffic variables’ series. The optimal input form of combined kernel function-least squares support vector machine model is determined by multivariate phase space reconstruction, and the model’s parameters are optimized by particle swarm optimization algorithm. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. The experimental results suggest that the new proposed model yields better predictions compared with similar models (combined kernel function-least squares support vector machine, multivariate phase space reconstruction–generalized kernel function-least squares support vector machine, and phase space reconstruction–combined kernel function-least squares support vector machine, which indicates that the new proposed model exhibits stronger prediction ability and robustness.

The heating of UO_2 kernels in argon gas medium on the physical properties of sintered UO_2 kernels

International Nuclear Information System (INIS)

Damunir; Sri Rinanti Susilowati; Ariyani Kusuma Dewi

2015-01-01

The heating of UO_2 kernels in argon gas medium on the physical properties of sinter UO_2 kernels was conducted. The heated of the UO_2 kernels was conducted in a sinter reactor of a bed type. The sample used was the UO_2 kernels resulted from the reduction results at 800 °C temperature for 3 hours that had the density of 8.13 g/cm"3; porosity of 0.26; O/U ratio of 2.05; diameter of 1146 μm and sphericity of 1.05. The sample was put into a sinter reactor, then it was vacuumed by flowing the argon gas at 180 mmHg pressure to drain the air from the reactor. After that, the cooling water and argon gas were continuously flowed with the pressure of 5 mPa with 1.5 liter/minutes velocity. The reactor temperature was increased and variated at 1200-1500 °C temperature and for 1-4 hours. The sinters UO_2 kernels resulted from the study were analyzed in term of their physical properties including the density, porosity, diameter, sphericity, and specific surface area. The density was analyzed using pycnometer with CCl_4 solution. The porosity was determined using Haynes equation. The diameters and sphericity were showed using the Dino-lite microscope. The specific surface area was determined using surface area meter Nova-1000. The obtained products showed the the heating of UO_2 kernel in argon gas medium were influenced on the physical properties of sinters UO_2 kernel. The condition of best relatively at 1400 °C temperature and 2 hours time. The product resulted from the study was relatively at its best when heating was conducted at 1400 °C temperature and 2 hours time, produced sinters UO_2 kernel with density of 10.14 gr/ml; porosity of 7 %; diameters of 893 μm; sphericity of 1.07 and specific surface area of 4.68 m"2/g with solidify shrinkage of 22 %. (author)

Coupling individual kernel-filling processes with source-sink interactions into GREENLAB-Maize.

Science.gov (United States)

Ma, Yuntao; Chen, Youjia; Zhu, Jinyu; Meng, Lei; Guo, Yan; Li, Baoguo; Hoogenboom, Gerrit

2018-02-13

Failure to account for the variation of kernel growth in a cereal crop simulation model may cause serious deviations in the estimates of crop yield. The goal of this research was to revise the GREENLAB-Maize model to incorporate source- and sink-limited allocation approaches to simulate the dry matter accumulation of individual kernels of an ear (GREENLAB-Maize-Kernel). The model used potential individual kernel growth rates to characterize the individual potential sink demand. The remobilization of non-structural carbohydrates from reserve organs to kernels was also incorporated. Two years of field experiments were conducted to determine the model parameter values and to evaluate the model using two maize hybrids with different plant densities and pollination treatments. Detailed observations were made on the dimensions and dry weights of individual kernels and other above-ground plant organs throughout the seasons. Three basic traits characterizing an individual kernel were compared on simulated and measured individual kernels: (1) final kernel size; (2) kernel growth rate; and (3) duration of kernel filling. Simulations of individual kernel growth closely corresponded to experimental data. The model was able to reproduce the observed dry weight of plant organs well. Then, the source-sink dynamics and the remobilization of carbohydrates for kernel growth were quantified to show that remobilization processes accompanied source-sink dynamics during the kernel-filling process. We conclude that the model may be used to explore options for optimizing plant kernel yield by matching maize management to the environment, taking into account responses at the level of individual kernels. © The Author(s) 2018. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

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Mailys Lopes

2017-07-01

Full Text Available This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object level by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the α -Gaussian mean kernel. It allows one to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in support vector machines for the supervised classification of grasslands from southwest France. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for the classification of grasslands’ management practices, while an inter-annual NDVI time series of Formosat-2 is used for old and young grasslands’ discrimination. Results are compared to other existing pixel- and object-based approaches in terms of classification accuracy and processing time. The proposed method is shown to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints, and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands.

Ford C-Max plug-in hybrid; Ford C-Max mit Plug-in-Hybridtechnik

Energy Technology Data Exchange (ETDEWEB)

Schamel, Andreas; D' Annunzio, Julie; Iorio, Rob [Ford Motor Company, Dearborn, MI (United States); Schmitz, Peter [Ford-Forschungszentrum Aachen GmbH, Aachen (Germany)

2013-03-01

Ford provides consumers a broad choice of electrified vehicles globally, including full hybrids, plug-in hybrids and all-electric vehicles. The all-new 2013 model year C-Max Energi Plug-in Hybrid utilises the third generation of Ford hybrid technology. This article discusses the hybrid powersplit architecture and components, as well as the charging capability and human-machine interfaces, used in the C-Max Energi Plug-In Hybrid. (orig.)

Influent of Carbonization of Sol Solution at the External Gelation Process on the Quality of Uranium Oxide Kernel

International Nuclear Information System (INIS)

Damunir; Sukarsono

2007-01-01

The influent of carbonization of sol solution at the external gelation process on the quality of uranium oxide kernel was done. Variables observed are the influent of carbon, temperature and time of reduction process of U 3 O 8 kernel resulted from carbonization of sol solution. First of all, uranyl nitrate was reacted with 1 M NH 4 OH solution, producing the colloid of UO 3 . Then by mixing and heating up to the temperature of 60-80 °C, the colloid solution was reacted with PVA, mono sorbitol oleate and paraffin producing of uranium-PVA sol. Then sol solution was carbonized with carbon black of mol ratio of carbon to uranium =2.32-6.62, produce of carbide gel. Gel then washed, dried and calcined at 800 °C for 4 hours to produce of U 3 O 8 kernel containing carbon. Then the kernel was reduced by H 2 gas in the medium of N 2 gas at 500-800 °C, 50 mmHg pressure for 3 hours. The process was repeated at 700 °C, 50 mmHg pressure for 1-4 hours. The characterization of chemical properties of the gel grains and uranium oxide kernel using FTIR covering the analysis of absorption band of infra red spectrum of UO 3 , C-OH, NH 3 , C-C, C-H and OH functional group. The physical properties of uranium oxide covering specific surface area, void volume, mean diameter using surface area meter Nova-1000 and as N 2 gas an absorbent. And O/U ratio of uranium dioxide kernel by gravimetry method. The result of experiment showed that carbonization of sol solution at the external gelation process give influencing the quality of uranium oxide kernel. (author)

Kernel based orthogonalization for change detection in hyperspectral images

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel PCA and MNF analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via...... analysis all 126 spectral bands of the HyMap are included. Changes on the ground are most likely due to harvest having taken place between the two acquisitions and solar effects (both solar elevation and azimuth have changed). Both types of kernel analysis emphasize change and unlike kernel PCA, kernel MNF...

A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzy c-Means Clustering

Directory of Open Access Journals (Sweden)

Li Ma

2015-01-01

Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA. The proposed algorithm combines artificial fish swarm algorithm (AFSA with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM.

Kernel methods in orthogonalization of multi- and hypervariate data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2009-01-01

A kernel version of maximum autocorrelation factor (MAF) analysis is described very briefly and applied to change detection in remotely sensed hyperspectral image (HyMap) data. The kernel version is based on a dual formulation also termed Q-mode analysis in which the data enter into the analysis...... via inner products in the Gram matrix only. In the kernel version the inner products are replaced by inner products between nonlinear mappings into higher dimensional feature space of the original data. Via kernel substitution also known as the kernel trick these inner products between the mappings...... are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel PCA and MAF analysis handle nonlinearities by implicitly transforming data into high (even infinite...

Putting Priors in Mixture Density Mercer Kernels

Science.gov (United States)

Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

2004-01-01

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

Locally linear approximation for Kernel methods : the Railway Kernel

OpenAIRE

Muñoz, Alberto; González, Javier

2008-01-01

In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel. In this way, we avoid potential problems due to the use of a general purpose kernel, like the RBF kernel, as the high dimension of the induced feature space. As a consequence, following our methodology the number of support vectors is much lower and, therefore, the generalization capab...

An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel

Directory of Open Access Journals (Sweden)

Senyue Zhang

2016-01-01

Full Text Available According to the characteristics that the kernel function of extreme learning machine (ELM and its performance have a strong correlation, a novel extreme learning machine based on a generalized triangle Hermitian kernel function was proposed in this paper. First, the generalized triangle Hermitian kernel function was constructed by using the product of triangular kernel and generalized Hermite Dirichlet kernel, and the proposed kernel function was proved as a valid kernel function of extreme learning machine. Then, the learning methodology of the extreme learning machine based on the proposed kernel function was presented. The biggest advantage of the proposed kernel is its kernel parameter values only chosen in the natural numbers, which thus can greatly shorten the computational time of parameter optimization and retain more of its sample data structure information. Experiments were performed on a number of binary classification, multiclassification, and regression datasets from the UCI benchmark repository. The experiment results demonstrated that the robustness and generalization performance of the proposed method are outperformed compared to other extreme learning machines with different kernels. Furthermore, the learning speed of proposed method is faster than support vector machine (SVM methods.

QTL Mapping of Kernel Number-Related Traits and Validation of One Major QTL for Ear Length in Maize.

Science.gov (United States)

Huo, Dongao; Ning, Qiang; Shen, Xiaomeng; Liu, Lei; Zhang, Zuxin

2016-01-01

The kernel number is a grain yield component and an important maize breeding goal. Ear length, kernel number per row and ear row number are highly correlated with the kernel number per ear, which eventually determines the ear weight and grain yield. In this study, two sets of F2:3 families developed from two bi-parental crosses sharing one inbred line were used to identify quantitative trait loci (QTL) for four kernel number-related traits: ear length, kernel number per row, ear row number and ear weight. A total of 39 QTLs for the four traits were identified in the two populations. The phenotypic variance explained by a single QTL ranged from 0.4% to 29.5%. Additionally, 14 overlapping QTLs formed 5 QTL clusters on chromosomes 1, 4, 5, 7, and 10. Intriguingly, six QTLs for ear length and kernel number per row overlapped in a region on chromosome 1. This region was designated qEL1.10 and was validated as being simultaneously responsible for ear length, kernel number per row and ear weight in a near isogenic line-derived population, suggesting that qEL1.10 was a pleiotropic QTL with large effects. Furthermore, the performance of hybrids generated by crossing 6 elite inbred lines with two near isogenic lines at qEL1.10 showed the breeding value of qEL1.10 for the improvement of the kernel number and grain yield of maize hybrids. This study provides a basis for further fine mapping, molecular marker-aided breeding and functional studies of kernel number-related traits in maize.

Wheat kernel dimensions: how do they contribute to kernel weight at ...

Indian Academy of Sciences (India)

2011-12-02

Dec 2, 2011 ... yield components, is greatly influenced by kernel dimensions. (KD), such as ..... six linkage gaps, and it covered 3010.70 cM of the whole genome with an ...... Ersoz E. et al. 2009 The Genetic architecture of maize flowering.

Uptake and metabolism of [14C]-aspartate by developing kernels of maize (Zea mays L.)

International Nuclear Information System (INIS)

Muhitch, M.J.

1990-01-01

Pulse-chase experiments were performed to determine the metabolic fate of [14C]-aspartate in the pedicel region and subsequent uptake into the endosperm. Kernels were removed from the cob, leaving the pedicel attached but removing glumes, palea, and lemma. The basal tips were incubated in [14C]-aspartate for 0.5 h, followed by a 2 h chase period with unlabeled aspartate. In contrast to a previous study in which 70% of the 14C from aspartate was recovered in the organic acid fraction (Lyznik, et al., Phytochemistry 24: 425, 1985), only 20 to 25% of the radioactivity found in the 2 h chase period. While a small amount of the 14C transiently appeared in alanine at the beginning of the chase period, the most heavily labeled non-fed amino acid was glutamine, which accounted for 21% of the radioactivity within the pedicel amino acid fraction by 0.5 h into the chase period. There was no evidence for asparagine synthesis within the pedicel region of the kernel. 14C recovered from the endosperm in the form of amino acids were aspartate (60%), glutamine (20%), glutamate (15%), and alanine (5%). These results suggest that some of the maternally supplied amino acids undergo metabolic conversion to other amino acids before being taken up by the endosperm

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

Science.gov (United States)

Wu, Michael C.; Maity, Arnab; Lee, Seunggeun; Simmons, Elizabeth M.; Harmon, Quaker E.; Lin, Xinyi; Engel, Stephanie M.; Molldrem, Jeffrey J.; Armistead, Paul M.

2013-01-01

Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel. PMID:23471868

Spent Fuel Pool Dose Rate Calculations Using Point Kernel and Hybrid Deterministic-Stochastic Shielding Methods

International Nuclear Information System (INIS)

Matijevic, M.; Grgic, D.; Jecmenica, R.

2016-01-01

This paper presents comparison of the Krsko Power Plant simplified Spent Fuel Pool (SFP) dose rates using different computational shielding methodologies. The analysis was performed to estimate limiting gamma dose rates on wall mounted level instrumentation in case of significant loss of cooling water. The SFP was represented with simple homogenized cylinders (point kernel and Monte Carlo (MC)) or cuboids (MC) using uranium, iron, water, and dry-air as bulk region materials. The pool is divided on the old and new section where the old one has three additional subsections representing fuel assemblies (FAs) with different burnup/cooling time (60 days, 1 year and 5 years). The new section represents the FAs with the cooling time of 10 years. The time dependent fuel assembly isotopic composition was calculated using ORIGEN2 code applied to the depletion of one of the fuel assemblies present in the pool (AC-29). The source used in Microshield calculation is based on imported isotopic activities. The time dependent photon spectra with total source intensity from Microshield multigroup point kernel calculations was then prepared for two hybrid deterministic-stochastic sequences. One is based on SCALE/MAVRIC (Monaco and Denovo) methodology and another uses Monte Carlo code MCNP6.1.1b and ADVANTG3.0.1. code. Even though this model is a fairly simple one, the layers of shielding materials are thick enough to pose a significant shielding problem for MC method without the use of effective variance reduction (VR) technique. For that purpose the ADVANTG code was used to generate VR parameters (SB cards in SDEF and WWINP file) for MCNP fixed-source calculation using continuous energy transport. ADVATNG employs a deterministic forward-adjoint transport solver Denovo which implements CADIS/FW-CADIS methodology. Denovo implements a structured, Cartesian-grid SN solver based on the Koch-Baker-Alcouffe parallel transport sweep algorithm across x-y domain blocks. This was first

Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

Science.gov (United States)

Wu, Qi

2010-03-01

Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

Linear and kernel methods for multi- and hypervariate change detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Canty, Morton J.

2010-01-01

. Principal component analysis (PCA) as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (which are nonlinear), may further enhance change signals relative to no-change background. The kernel versions are based on a dual...... formulation, also termed Q-mode analysis, in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products of the original data are replaced by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution......, also known as the kernel trick, these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of the kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component...

Irradiation performance of coated fuel particles with fission product retaining kernel additives

International Nuclear Information System (INIS)

Foerthmann, R.

1979-10-01

The four irradiation experiments FRJ2-P17, FRJ2-P18, FRJ2-P19, and FRJ2-P20 for testing the efficiency of fission product-retaining kernel additives in coated fuel particles are described. The evaluation of the obtained experimental data led to the following results: - zirconia and alumina kernel additives are not suitable for an effective fission product retention in oxide fuel kernels, - alumina-silica kernel additives reduce the in-pile release of Sr 90 and Ba 140 from BISO-coated particles at temperatures of about 1200 0 C by two orders of magnitude, and the Cs release from kernels by one order of magnitude, - effective transport coefficients including all parameters which contribute to kernel release are given for (Th,U)O 2 mixed oxide kernels and low enriched UO 2 kernels containing 5 wt.% alumina-silica additives: 10g sub(K)/cm 2 s -1 = - 36 028/T + 6,261 (Sr 90), 10g Dsub(K)/cm 2 c -2 = - 29 646/T + 5,826 (Cs 134/137), alumina-silica kernel additives are ineffective for retaining Ag 110 m in coated particles. However, also an intact SiC-interlayer was found not to be effective at temperatures above 1200 0 C, - the penetration of the buffer layer by fission product containing eutectic additive melt during irradiation can be avoided by using additives which consist of alumina and mullite without an excess of silica, - annealing of LASER-failed irradiated particles and the irradiation test FRJ12-P20 indicate that the efficiency of alumina-silica kernel additives is not altered if the coating becomes defect. (orig.) [de

Embedded real-time operating system micro kernel design

Science.gov (United States)

Cheng, Xiao-hui; Li, Ming-qiang; Wang, Xin-zheng

2005-12-01

Embedded systems usually require a real-time character. Base on an 8051 microcontroller, an embedded real-time operating system micro kernel is proposed consisting of six parts, including a critical section process, task scheduling, interruption handle, semaphore and message mailbox communication, clock managent and memory managent. Distributed CPU and other resources are among tasks rationally according to the importance and urgency. The design proposed here provides the position, definition, function and principle of micro kernel. The kernel runs on the platform of an ATMEL AT89C51 microcontroller. Simulation results prove that the designed micro kernel is stable and reliable and has quick response while operating in an application system.

Data-variant kernel analysis

CERN Document Server

Motai, Yuichi

2015-01-01

Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include

A kernel adaptive algorithm for quaternion-valued inputs.

Science.gov (United States)

Paul, Thomas K; Ogunfunmi, Tokunbo

2015-10-01

The use of quaternion data can provide benefit in applications like robotics and image recognition, and particularly for performing transforms in 3-D space. Here, we describe a kernel adaptive algorithm for quaternions. A least mean square (LMS)-based method was used, resulting in the derivation of the quaternion kernel LMS (Quat-KLMS) algorithm. Deriving this algorithm required describing the idea of a quaternion reproducing kernel Hilbert space (RKHS), as well as kernel functions suitable with quaternions. A modified HR calculus for Hilbert spaces was used to find the gradient of cost functions defined on a quaternion RKHS. In addition, the use of widely linear (or augmented) filtering is proposed to improve performance. The benefit of the Quat-KLMS and widely linear forms in learning nonlinear transformations of quaternion data are illustrated with simulations.

Direct Kernel Perceptron (DKP): ultra-fast kernel ELM-based classification with non-iterative closed-form weight calculation.

Science.gov (United States)

Fernández-Delgado, Manuel; Cernadas, Eva; Barro, Senén; Ribeiro, Jorge; Neves, José

2014-02-01

The Direct Kernel Perceptron (DKP) (Fernández-Delgado et al., 2010) is a very simple and fast kernel-based classifier, related to the Support Vector Machine (SVM) and to the Extreme Learning Machine (ELM) (Huang, Wang, & Lan, 2011), whose α-coefficients are calculated directly, without any iterative training, using an analytical closed-form expression which involves only the training patterns. The DKP, which is inspired by the Direct Parallel Perceptron, (Auer et al., 2008), uses a Gaussian kernel and a linear classifier (perceptron). The weight vector of this classifier in the feature space minimizes an error measure which combines the training error and the hyperplane margin, without any tunable regularization parameter. This weight vector can be translated, using a variable change, to the α-coefficients, and both are determined without iterative calculations. We calculate solutions using several error functions, achieving the best trade-off between accuracy and efficiency with the linear function. These solutions for the α coefficients can be considered alternatives to the ELM with a new physical meaning in terms of error and margin: in fact, the linear and quadratic DKP are special cases of the two-class ELM when the regularization parameter C takes the values C=0 and C=∞. The linear DKP is extremely efficient and much faster (over a vast collection of 42 benchmark and real-life data sets) than 12 very popular and accurate classifiers including SVM, Multi-Layer Perceptron, Adaboost, Random Forest and Bagging of RPART decision trees, Linear Discriminant Analysis, K-Nearest Neighbors, ELM, Probabilistic Neural Networks, Radial Basis Function neural networks and Generalized ART. Besides, despite its simplicity and extreme efficiency, DKP achieves higher accuracies than 7 out of 12 classifiers, exhibiting small differences with respect to the best ones (SVM, ELM, Adaboost and Random Forest), which are much slower. Thus, the DKP provides an easy and fast way

Nonparametric evaluation of dynamic disease risk: a spatio-temporal kernel approach.

Directory of Open Access Journals (Sweden)

Zhijie Zhang

Full Text Available Quantifying the distributions of disease risk in space and time jointly is a key element for understanding spatio-temporal phenomena while also having the potential to enhance our understanding of epidemiologic trajectories. However, most studies to date have neglected time dimension and focus instead on the "average" spatial pattern of disease risk, thereby masking time trajectories of disease risk. In this study we propose a new idea titled "spatio-temporal kernel density estimation (stKDE" that employs hybrid kernel (i.e., weight functions to evaluate the spatio-temporal disease risks. This approach not only can make full use of sample data but also "borrows" information in a particular manner from neighboring points both in space and time via appropriate choice of kernel functions. Monte Carlo simulations show that the proposed method performs substantially better than the traditional (i.e., frequency-based kernel density estimation (trKDE which has been used in applied settings while two illustrative examples demonstrate that the proposed approach can yield superior results compared to the popular trKDE approach. In addition, there exist various possibilities for improving and extending this method.

Molecular Evidence for Natural Hybridization between Cotoneaster dielsianus and C. glaucophyllus

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Mingwan Li

2017-05-01

Full Text Available Hybridization accompanied by polyploidization and apomixis has been demonstrated as a driving force in the evolution and speciation of many plants. A good example to study the evolutionary process of hybridization associated with polyploidy and apomixis is the genus Cotoneaster (Rosaceae, which includes approximately 150 species, most of which are polyploid apomicts. In this study, we investigated all Cotoneaster taxa distributed in a small region of Malipo, Yunnan, China. Based on the morphological characteristics, four Cotoneaster taxa were identified and sampled: C. dielsianus, C. glaucophyllus, C. franchetii, and a putative hybrid. Flow cytometry analyses showed that C. glaucophyllus was diploid, while the other three taxa were tetraploid. A total of five low-copy nuclear genes and six chloroplast regions were sequenced to validate the status of the putative hybrid. Sequence analyses showed that C. dielsianus and C. glaucophyllus are distantly related and they could be well separated using totally 50 fixed nucleotide substitutions and four fixed indels at the 11 investigated genes. All individuals of the putative hybrid harbored identical sequences: they showed chromatogram additivity for all fixed differences between C. dielsianus and C. glaucophyllus at the five nuclear genes, and were identical with C. glaucophyllus at the six chloroplast regions. Haplotype analysis revealed that C. dielsianus possessed nine haplotypes for the 11 genes, while C. glaucophyllus had ten, and there were no shared haplotypes between the two species. The putative hybrid harbored two haplotypes for each nuclear gene: one shared with C. dielsianus and the other with C. glaucophyllus. They possessed the same chloroplast haplotype with C. glaucophyllus. Our study provided convincing evidence for natural hybridization between C. dielsianus and C. glaucophyllus, and revealed that all hybrid individuals were derivatives of one initial F1 via apomixes. C. glaucophyllus

Study on the scattering law and scattering kernel of hydrogen in zirconium hydride

International Nuclear Information System (INIS)

Jiang Xinbiao; Chen Wei; Chen Da; Yin Banghua; Xie Zhongsheng

1999-01-01

The nuclear analytical model of calculating scattering law and scattering kernel for the uranium zirconium hybrid reactor is described. In the light of the acoustic and optic model of zirconium hydride, its frequency distribution function f(ω) is given and the scattering law of hydrogen in zirconium hydride is obtained by GASKET. The scattering kernel σ l (E 0 →E) of hydrogen bound in zirconium hydride is provided by the SMP code in the standard WIMS cross section library. Along with this library, WIMS is used to calculate the thermal neutron energy spectrum of fuel cell. The results are satisfied

Yields of ZP sweet maize hybrids in dependence on sowing densities

Directory of Open Access Journals (Sweden)

Srdić Jelena

2008-01-01

Full Text Available Sweet maize differs from maize of standard grain quality by many important traits that affect the ear appearance, and especially by traits controlling taste. The ear appearance trait encompasses the kernel row number, configuration, row pattern (direction and arrangement, seed set, kernel width and depth, ear shape and size. The quality of immature kernels is controlled by genes by which sweet maize differs from common maize. In order to obtain high-ranking and high-quality yields, it is necessary to provide the most suitable cropping practices for sweet maize hybrids developed at the Maize Research Institute, Zemun Polje. The adequate sowing density is one of more important elements of correct cropping practices. The objective of the present study was to determine the effect of four sowing densities in four ZP sweet maize hybrids of different FAO maturity groups on ear qualitative traits and yields obtained on chernozem type of soil in Zemun Polje. The observed traits of sweet maize (ear length, kernel row number, number of kernels per row, yield and shelling percentage significantly varied over years. The higher sowing density was the higher yield of sweet maize was, hence the highest ear yield of 9.67 t ha-1 , on the average for all four hybrids, was recorded at the highest sowing density of 70,000 plants ha-1. The highest yield was detected in the hybrid ZP 424su. The highest shelling percentage (67.81% was found in the hybrid ZP 521su at the sowing density of 60,000 plants ha-1. Generally, it can be stated that sweet maize hybrids of a shorter growing season (FAO 400 could be cultivated up to 70,000 plants ha-1, while those of a longer growing season (FAO 500 could be grown up to 60,000 plants ha-1. In such a way, the most favorable parameters of yields and the highest yields can be obtained.

Approximate kernel competitive learning.

Science.gov (United States)

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

Kernel Methods for Mining Instance Data in Ontologies

Science.gov (United States)

Bloehdorn, Stephan; Sure, York

The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

Moisture Adsorption Isotherm and Storability of Hazelnut Inshells and Kernels Produced in Oregon, USA.

Science.gov (United States)

Jung, Jooyeoun; Wang, Wenjie; McGorrin, Robert J; Zhao, Yanyun

2018-02-01

Moisture adsorption isotherms and storability of dried hazelnut inshells and kernels produced in Oregon were evaluated and compared among cultivars, including Barcelona, Yamhill, and Jefferson. Experimental moisture adsorption data fitted to Guggenheim-Anderson-de Boer (GAB) model, showing less hygroscopic properties in Yamhill than other cultivars of inshells and kernels due to lower content of carbohydrate and protein, but higher content of fat. The safe levels of moisture content (MC, dry basis) of dried inshells and kernels for reaching kernel water activity (a w ) ≤0.65 were estimated using the GAB model as 11.3% and 5.0% for Barcelona, 9.4% and 4.2% for Yamhill, and 10.7% and 4.9% for Jefferson, respectively. Storage conditions (2 °C at 85% to 95% relative humidity [RH], 10 °C at 65% to 75% RH, and 27 °C at 35% to 45% RH), times (0, 4, 8, or 12 mo), and packaging methods (atmosphere vs. vacuum) affected MC, a w , bioactive compounds, lipid oxidation, and enzyme activity of dried hazelnut inshells or kernels. For inshells packaged at woven polypropylene bag, MC and a w of inshells and kernels (inside shells) increased at 2 and 10 °C, but decreased at 27 °C during storage. For kernels, lipid oxidation and polyphenol oxidase activity also increased with extended storage time (P adsorption and physicochemical and enzymatic stability during storage. Moisture adsorption isotherm of hazelnut inshells and kernels is useful for predicting the storability of nuts. This study found that water adsorption and storability varied among the different cultivars of nuts, in which Yamhill was less hygroscopic than Barcelona and Jefferson, thus more stable during storage. For ensuring food safety and quality of nuts during storage, each cultivar of kernels should be dried to a certain level of MC. Lipid oxidation and enzyme activity of kernel could be increased with extended storage time. Vacuum packaging was recommended to kernels for reducing moisture adsorption

Participation of cob tissue in the transport of medium components into maize kernels cultured in vitro

International Nuclear Information System (INIS)

Felker, F.C.

1990-01-01

Maize (Zea mays L.) kernels cultured in vitro while still attached to cob pieces have been used as a model system to study the physiology of kernel development. In this study, the role of the cob tissue in uptake of medium components into kernels was examined. Cob tissue was essential for in vitro kernel growth, and better growth occurred with larger cob/kernel ratios. A symplastically transported fluorescent dye readily permeated the endosperm when supplied in the medium, while an apoplastic dye did not. Slicing the cob tissue to disrupt vascular connections, but not apoplastic continuity, greatly reduced [ 14 C]sucrose uptake into kernels. [ 14 C]Sucrose uptake by cob and kernel tissue was reduced 31% and 68%, respectively, by 5 mM PCMBS. L-[ 14 C]glucose was absorbed much more slowly than D-[ 14 C]glucose. These and other results indicate that phloem loading of sugars occurs in the cob tissue. Passage of medium components through the symplast cob tissue may be a prerequisite for uptake into the kernel. Simple diffusion from the medium to the kernels is unlikely. Therefore, the ability of substances to be transported into cob tissue cells should be considered in formulating culture medium

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

Science.gov (United States)

Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

2018-02-19

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

An Ensemble Approach to Building Mercer Kernels with Prior Information

Science.gov (United States)

Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

2005-01-01

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Characterization of Brazilian mango kernel fat before and after gamma irradiation

International Nuclear Information System (INIS)

Aquino, Fabiana da Silva; Ramos, Clecio Souza; Aquino, Katia Aparecida da Silva

2013-01-01

Mangifera indica Linn (family of Anacardiaceae) is a tree indigenous to India, whose both unripe and ripe fruits (mangoes) are widely used by the local population. After consumption or industrial processing of the fruits, considerable amounts of mango seeds are discarded as waste. The kernel inside the seed represents from 45% to 75% of the seed and about 20% of the whole fruit and lipid composition of mango seed kernels has attracted the attention of researches because of their unique physical and chemical characteristics. Our study showed that fat of the mango kernel obtained by Soxhlet extraction with hexane had a solid consistency at environmental temperature (27 deg C) because it is rich in saturated acid. The fat contents of the seed of Mangifera indica was calculated to 10% and are comparable to the ones for commercial vegetable oils like soybean (11-25%). One problem found in the storage of fast and oils is the attack by microorganisms and the sterilization process becomes necessary. Samples of kernel fat were irradiated with gamma radiation ( 60 Co) at room temperature and air atmosphere at 5 and 10 kGy (sterilization doses). The data of GC-MS analysis revealed the presence of four major fatty acids in the sample of mango kernel examined and that the chemical profile of the sample not altered after being irradiated. Moreover, analysis of Proton Nuclear Magnetic Resonance (NMR H 1 ) was used to obtain the mango kernel fat parameters before and after gamma irradiation. The data interpretation of RMN H 1 indicated that there are significant differences in the acidity and saponification indexes of fat. However, it was found an increase of 14% in iodine index of fat after irradiation. This result means that some double bonds were formed on the irradiation process of the fat. (author)

Characterization of Brazilian mango kernel fat before and after gamma irradiation

Energy Technology Data Exchange (ETDEWEB)

Aquino, Fabiana da Silva; Ramos, Clecio Souza, E-mail: fasiaquino@yahoo.com.br, E-mail: clecio@dcm.ufrpe.br [Universidade Federal Rural de Pernambuco (UFRPE), Recife, PE (Brazil); Aquino, Katia Aparecida da Silva, E-mail: aquino@ufpe.br [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil)

2013-07-01

Mangifera indica Linn (family of Anacardiaceae) is a tree indigenous to India, whose both unripe and ripe fruits (mangoes) are widely used by the local population. After consumption or industrial processing of the fruits, considerable amounts of mango seeds are discarded as waste. The kernel inside the seed represents from 45% to 75% of the seed and about 20% of the whole fruit and lipid composition of mango seed kernels has attracted the attention of researches because of their unique physical and chemical characteristics. Our study showed that fat of the mango kernel obtained by Soxhlet extraction with hexane had a solid consistency at environmental temperature (27 deg C) because it is rich in saturated acid. The fat contents of the seed of Mangifera indica was calculated to 10% and are comparable to the ones for commercial vegetable oils like soybean (11-25%). One problem found in the storage of fast and oils is the attack by microorganisms and the sterilization process becomes necessary. Samples of kernel fat were irradiated with gamma radiation ({sup 60}Co) at room temperature and air atmosphere at 5 and 10 kGy (sterilization doses). The data of GC-MS analysis revealed the presence of four major fatty acids in the sample of mango kernel examined and that the chemical profile of the sample not altered after being irradiated. Moreover, analysis of Proton Nuclear Magnetic Resonance (NMR H{sup 1}) was used to obtain the mango kernel fat parameters before and after gamma irradiation. The data interpretation of RMN H{sup 1} indicated that there are significant differences in the acidity and saponification indexes of fat. However, it was found an increase of 14% in iodine index of fat after irradiation. This result means that some double bonds were formed on the irradiation process of the fat. (author)

Exact Heat Kernel on a Hypersphere and Its Applications in Kernel SVM

Directory of Open Access Journals (Sweden)

Chenchao Zhao

2018-01-01

Full Text Available Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to other competing similarity measures. Specifically, the idea of using heat diffusion on a hypersphere to measure similarity has been previously proposed and tested by Lafferty and Lebanon [1], demonstrating promising results based on a heuristic heat kernel obtained from the zeroth order parametrix expansion; however, how well this heuristic kernel agrees with the exact hyperspherical heat kernel remains unknown. This paper presents a higher order parametrix expansion of the heat kernel on a unit hypersphere and discusses several problems associated with this expansion method. We then compare the heuristic kernel with an exact form of the heat kernel expressed in terms of a uniformly and absolutely convergent series in high-dimensional angular momentum eigenmodes. Being a natural measure of similarity between sample points dwelling on a hypersphere, the exact kernel often shows superior performance in kernel SVM classifications applied to text mining, tumor somatic mutation imputation, and stock market analysis.

Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

DEFF Research Database (Denmark)

Arenas-Garcia, J.; Petersen, K.; Camps-Valls, G.

2013-01-01

correlation analysis (CCA), and orthonormalized PLS (OPLS), as well as their nonlinear extensions derived by means of the theory of reproducing kernel Hilbert spaces (RKHSs). We also review their connections to other methods for classification and statistical dependence estimation and introduce some recent...

Borneo: The new NS sunflower confectionary type hybrid

Directory of Open Access Journals (Sweden)

Hladni Nada

2009-01-01

Full Text Available At the Institute of Field and Vegetable Crops in Novi Sad, beside the basic direction in sunflower breeding, the creation of standard hybrids with high oil content with high seed and oil yield, hybrids are being created for special confectionery purposes directed towards the final kernel products. In this program, apart from breeding in order to improve main seed yield components, special attention is given to the increase of protein content and the quality with the decrease of seed oil content, to the increase of the weight of 1000 seeds and to the decrease of content of the shell. Two-line SC hybrid was created by crossing the cytoplasmatic male sterile line of the mother with a father line with fertility restoration genes. Borneo is a high protein confectionary type hybrid that has been registered in Slovakia (EU in 2009. It contains successfully combined genes responsible for high genetic potential for yield and valuable technical and technological seed traits. The hybrid is adequate for nutrition, dehuling and kernel production. Borneo is a medium early hybrid with the genetic potential for yield of over 4,5t/ha and its seed oil content is under 43%. .

Deep-Burn MHR Neutronic Analysis with a SiC-Gettered TRU Kernel

International Nuclear Information System (INIS)

Jo, Chang Keun; Noh, Jae Man; Kim, Yong Hee; Venneric, F.

2010-01-01

This paper is focused on the nuclear core design of a DB-MHR (Deep Burn-Modular Helium Reactor) core loaded with a SiC-gettered TRU fuel. The SiC oxygen getter is added to reduce the CO pressure in the buffer zone of TRISO. In the paper, the cycle length, reactivity swing, discharged burnup, and the burning rate of plutonium were calculated for the DB-MHR. Also, impacts of uranium addition to the TRU kernel were investigated. Recently, the decay heat of TRU fueled DB core was found to be highly dependent on the TRU loading: the higher the loading, the higher the decay heat. The high decay heat of TRU fuel may lead to unacceptably high peak fuel temperature during an LPCC (Low Pressure Conduction Cooling) accident. Thus, we tried to minimize the decay heat of the core for a minimal peak fuel temperature during LPCC

A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan.

Science.gov (United States)

Lee, Yii-Ching; Huang, Shian-Chang; Huang, Chih-Hsuan; Wu, Hsin-Hung

2016-01-01

This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a regional teaching hospital in Taiwan. The number of effective questionnaires including the entire staffs such as physicians, nurses, technicians, pharmacists, medical administrators, and respiratory therapists is 680. The results show that 8 clusters are generated by kernel k-means method. Employees in clusters 1, 4, and 5 are relatively in good conditions, whereas employees in clusters 2, 3, 6, 7, and 8 need to be closely monitored from time to time because they have relatively higher degree of burnout. When employees with higher degree of burnout are identified, the hospital management can take actions to improve the resilience, reduce the potential medical errors, and, eventually, enhance the patient safety. This study also suggests that the hospital management needs to keep track of medical staffs' fatigue conditions and provide timely assistance for burnout recovery through employee assistance programs, mindfulness-based stress reduction programs, positivity currency buildup, and forming appreciative inquiry groups. © The Author(s) 2016.

A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan

Directory of Open Access Journals (Sweden)

Yii-Ching Lee PhD

2016-11-01

Full Text Available This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a regional teaching hospital in Taiwan. The number of effective questionnaires including the entire staffs such as physicians, nurses, technicians, pharmacists, medical administrators, and respiratory therapists is 680. The results show that 8 clusters are generated by kernel k-means method. Employees in clusters 1, 4, and 5 are relatively in good conditions, whereas employees in clusters 2, 3, 6, 7, and 8 need to be closely monitored from time to time because they have relatively higher degree of burnout. When employees with higher degree of burnout are identified, the hospital management can take actions to improve the resilience, reduce the potential medical errors, and, eventually, enhance the patient safety. This study also suggests that the hospital management needs to keep track of medical staffs’ fatigue conditions and provide timely assistance for burnout recovery through employee assistance programs, mindfulness-based stress reduction programs, positivity currency buildup, and forming appreciative inquiry groups.

Subsampling Realised Kernels

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger

2011-01-01

In a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our...... that subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance. We also study the performance of subsampled...

Ensemble-based forecasting at Horns Rev: Ensemble conversion and kernel dressing

DEFF Research Database (Denmark)

Pinson, Pierre; Madsen, Henrik

. The obtained ensemble forecasts of wind power are then converted into predictive distributions with an original adaptive kernel dressing method. The shape of the kernels is driven by a mean-variance model, the parameters of which are recursively estimated in order to maximize the overall skill of obtained...

Surface EMG decomposition based on K-means clustering and convolution kernel compensation.

Science.gov (United States)

Ning, Yong; Zhu, Xiangjun; Zhu, Shanan; Zhang, Yingchun

2015-03-01

A new approach has been developed by combining the K-mean clustering (KMC) method and a modified convolution kernel compensation (CKC) method for multichannel surface electromyogram (EMG) decomposition. The KMC method was first utilized to cluster vectors of observations at different time instants and then estimate the initial innervation pulse train (IPT). The CKC method, modified with a novel multistep iterative process, was conducted to update the estimated IPT. The performance of the proposed K-means clustering-Modified CKC (KmCKC) approach was evaluated by reconstructing IPTs from both simulated and experimental surface EMG signals. The KmCKC approach successfully reconstructed all 10 IPTs from the simulated surface EMG signals with true positive rates (TPR) of over 90% with a low signal-to-noise ratio (SNR) of -10 dB. More than 10 motor units were also successfully extracted from the 64-channel experimental surface EMG signals of the first dorsal interosseous (FDI) muscles when a contraction force was held at 8 N by using the KmCKC approach. A "two-source" test was further conducted with 64-channel surface EMG signals. The high percentage of common MUs and common pulses (over 92% at all force levels) between the IPTs reconstructed from the two independent groups of surface EMG signals demonstrates the reliability and capability of the proposed KmCKC approach in multichannel surface EMG decomposition. Results from both simulated and experimental data are consistent and confirm that the proposed KmCKC approach can successfully reconstruct IPTs with high accuracy at different levels of contraction.

Reduced multiple empirical kernel learning machine.

Science.gov (United States)

Wang, Zhe; Lu, MingZhe; Gao, Daqi

2015-02-01

Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3

Mechanical properties of hybrid SiC/CNT filled toughened epoxy nanocomposite

Science.gov (United States)

Ratim, S.; Ahmad, S.; Bonnia, N. N.; Yahaya, Sabrina M.

2018-01-01

Mechanical properties of epoxy nanocomposites filled single filler have been extensively studied by various researchers. However, there are not much discovery on the behavior of hybrid nanocomposite. In this study, single and hybrid nanocomposites of toughened epoxy filled CNT/SiC nanoparticles were investigated. The hybrid nanocomposites samples were prepared by combining CNT and SiC nanoparticles in toughened epoxy matrix via mechanical stirring method assisted with ultrasonic cavitations. Epoxy resin and liquid epoxidized natural rubber (LENR) mixture were first blend prior to the addition of nanofillers. Then, the curing process of the nanocomposite samples were conducted by compression molding technique at 130°C for 2 hours. The purpose of this study is to investigate the hybridization effect of CNT and SiC nanoparticles on mechanical properties toughened epoxy matrix. The total loading of single and hybrid nanofillers were fixed to 4% volume are 0, 4C, 4S, 3S1C, 2S2C, and 1S3C. Mechanical properties of hybrid composites show that the highest value of tensile strength achieved by 3S1C sample at about 7% increment and falls between their single composite values. Meanwhile, the stiffness of the same sample is significantly increased at about 31% of the matrix. On the other hand, a highest flexural property is obtained by 1S3C sample at about 20% increment dominated by CNT content. However, the impact strength shows reduction trend with the addition of SiC and CNT into the matrix. The hybridization of SiC and CNT show highest value in sample 1S3C at about 3.37 kJ/m2 of impact energy absorbed. FESEM micrograph have confirmed that better distributions and interaction observed between SiC nanoparticles and matrix compared to CNT, which contributed to higher tensile strength and modulus.

A hybrid sequential approach for data clustering using K-Means and ...

African Journals Online (AJOL)

Experiments on four kinds of data sets have been conducted. The obtained results are compared with K-Means, PSO, Hybrid, K-Means+Genetic Algorithm and it has been found that the proposed algorithm generates more accurate, robust and better clustering results. International Journal of Engineering, Science and ...

Significant characteristics of the new maize hybrid Rubin-7

Directory of Open Access Journals (Sweden)

Jeličić Zora

2003-01-01

Full Text Available The Rubin-7 maize hybrid belongs to the FAO 700 maturity group. It is characterized by high yield potential for kernels, which was proven during investigations by the Committee for Species. During the three year monitoring period, from 1999 to 2001, the average yield of kernel was 9.412 t/ha which is 5% above the ZP 704 standard, and was highly statistically significant. Resistance to disease was high for Ustilago maydis 0.49, Fusarium spp. 0.13, and Exerohilum turcicum 1.25. Tolerance against Ostrinia nubilalis is 3-33. All of the above parameters and the agreeable phenotype of this hybrid indicate the value of Rubin-7. .

Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection

Science.gov (United States)

Wang, Jinjin; Ma, Yi; Zhang, Jingyu

2018-03-01

Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out. The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2% (1.9%) 3.4% (1.8%), and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.

Preparation and characterization of B-C-N hybrid thin films

International Nuclear Information System (INIS)

Uddin, Md. Nizam; Shimoyama, Iwao; Sekiguchi, Tetsuhiro; Baba, Yuji; Nath, Krishna G.; Nagano, Masamitsu

2006-06-01

Two dimensional thin films composed of boron, carbon and nitrogen (B-C-N hybrid) were synthesized by ion beam deposition, and their electronic and geometrical structures were characterized by core-level spectroscopy using synchrotron radiation. B-C-N hybrid thin films were grown from ion beam plasma of borazine on highly oriented pyrolitic graphite (HOPG) at various temperatures. The films were characterized in-situ by X-ray photoelectron spectroscopy (XPS) and near edge X-ray absorption fine structure (NEXAFS). XPS study suggested that B, N and C atoms in the deposited films were in a wide variety of chemical bonds e.g., B-C, B-N, N-C, and B-C-N. It was found that B-C-N hybrid formation was enhanced at high temperature, and that the B-C-N component was dominantly synthesized at low boron content. In the NEXAFS spectra, the resonance peaks from B 1s to unoccupied π * -like orbitals were clearly observed. The polarization dependence of the B 1s → π * resonance peaks confirmed that the highly oriented graphite-like B-C-N hybrids surely exist at low boron content. (author)

Multiple insect resistance in 59 commmerical corn hybrids - 2017

Science.gov (United States)

Commercial corn hybrids were screened for ear- and kernel-feeding insect resistance under field conditions at Tifton, GA. Nine hybrids were rated Very Good (VG), the highest rating for multiple insect resistance in 2017 (see following table). Thirteen were Good (G), 19 were Fair (F), and 13 were Poo...

Identification of QTL for maize grain yield and kernel-related traits

Indian Academy of Sciences (India)

[Yang C., Zhang L., Jia A. and Rong T. 2016 Identification of QTL for maize grain yield and kernel-related traits. ... 2010; Zhang et al. ...... in the structure and evolution of genetic systems. ... 2013 Fine mapping a major QTL for kernel number per.

Genetic dissection of the maize kernel development process via conditional QTL mapping for three developing kernel-related traits in an immortalized F2 population.

Science.gov (United States)

Zhang, Zhanhui; Wu, Xiangyuan; Shi, Chaonan; Wang, Rongna; Li, Shengfei; Wang, Zhaohui; Liu, Zonghua; Xue, Yadong; Tang, Guiliang; Tang, Jihua

2016-02-01

Kernel development is an important dynamic trait that determines the final grain yield in maize. To dissect the genetic basis of maize kernel development process, a conditional quantitative trait locus (QTL) analysis was conducted using an immortalized F2 (IF2) population comprising 243 single crosses at two locations over 2 years. Volume (KV) and density (KD) of dried developing kernels, together with kernel weight (KW) at different developmental stages, were used to describe dynamic changes during kernel development. Phenotypic analysis revealed that final KW and KD were determined at DAP22 and KV at DAP29. Unconditional QTL mapping for KW, KV and KD uncovered 97 QTLs at different kernel development stages, of which qKW6b, qKW7a, qKW7b, qKW10b, qKW10c, qKV10a, qKV10b and qKV7 were identified under multiple kernel developmental stages and environments. Among the 26 QTLs detected by conditional QTL mapping, conqKW7a, conqKV7a, conqKV10a, conqKD2, conqKD7 and conqKD8a were conserved between the two mapping methodologies. Furthermore, most of these QTLs were consistent with QTLs and genes for kernel development/grain filling reported in previous studies. These QTLs probably contain major genes associated with the kernel development process, and can be used to improve grain yield and quality through marker-assisted selection.

Do Hybrid Trees Inherit Invasive Characteristics? Fruits of Corymbia torelliana X C. citriodora Hybrids and Potential for Seed Dispersal by Bees.

Science.gov (United States)

Wallace, Helen Margaret; Leonhardt, Sara Diana

2015-01-01

Tree invasions have substantial impacts on biodiversity and ecosystem functioning, and trees that are dispersed by animals are more likely to become invasive. In addition, hybridisation between plants is well documented as a source of new weeds, as hybrids gain new characteristics that allow them to become invasive. Corymbia torelliana is an invasive tree with an unusual animal dispersal mechanism: seed dispersal by stingless bees, that hybridizes readily with other species. We examined hybrids between C. torelliana and C. citriodora subsp. citriodora to determine whether hybrids have inherited the seed dispersal characteristics of C. torelliana that allow bee dispersal. Some hybrid fruits displayed the characteristic hollowness, resin production and resin chemistry associated with seed dispersal by bees. However, we did not observe bees foraging on any hybrid fruits until they had been damaged. We conclude that C. torelliana and C. citriodora subsp. citriodora hybrids can inherit some fruit characters that are associated with dispersal by bees, but we did not find a hybrid with the complete set of characters that would enable bee dispersal. However, around 20,000 hybrids have been planted in Australia, and ongoing monitoring is necessary to identify any hybrids that may become invasive.

Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting

DEFF Research Database (Denmark)

Quinonero, Joaquin; Girard, Agathe; Larsen, Jan

2003-01-01

The object of Bayesian modelling is predictive distribution, which, in a forecasting scenario, enables evaluation of forecasted values and their uncertainties. We focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models such as the Gaus......The object of Bayesian modelling is predictive distribution, which, in a forecasting scenario, enables evaluation of forecasted values and their uncertainties. We focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models...... such as the Gaussian process and the relevance vector machine. We derive novel analytic expressions for the predictive mean and variance for Gaussian kernel shapes under the assumption of a Gaussian input distribution in the static case, and of a recursive Gaussian predictive density in iterative forecasting...

A novel adaptive kernel method with kernel centers determined by a support vector regression approach

NARCIS (Netherlands)

Sun, L.G.; De Visser, C.C.; Chu, Q.P.; Mulder, J.A.

2012-01-01

The optimality of the kernel number and kernel centers plays a significant role in determining the approximation power of nearly all kernel methods. However, the process of choosing optimal kernels is always formulated as a global optimization task, which is hard to accomplish. Recently, an

On Improving Convergence Rates for Nonnegative Kernel Density Estimators

OpenAIRE

Terrell, George R.; Scott, David W.

1980-01-01

To improve the rate of decrease of integrated mean square error for nonparametric kernel density estimators beyond $0(n^{-\\frac{4}{5}}),$ we must relax the constraint that the density estimate be a bonafide density function, that is, be nonnegative and integrate to one. All current methods for kernel (and orthogonal series) estimators relax the nonnegativity constraint. In this paper we show how to achieve similar improvement by relaxing the integral constraint only. This is important in appl...

Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection.

Science.gov (United States)

Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu

2017-12-15

Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.

Reaction kinetics aspect of U3O8 kernel with gas H2 on the characteristics of activation energy, reaction rate constant and O/U ratio of UO2 kernel

International Nuclear Information System (INIS)

Damunir

2007-01-01

The reaction kinetics aspect of U 3 O 8 kernel with gas H 2 on the characteristics of activation energy, reaction rate constant and O/U ratio of UO 2 kernel had been studied. U 3 O 8 kernel was reacted with gas H 2 in a reduction furnace at varied reaction time and temperature. The reaction temperature was varied at 600, 700, 750 and 850 °C with a pressure of 50 mmHg for 3 hours in gas N 2 atmosphere. The reation time was varied at 1, 2, 3 and 4 hours at a temperature of 750 °C using similar conditions. The reaction product was UO 2 kernel. The reaction kinetic aspect between U 3 O 8 and gas H 2 comprised the minimum activation energy (ΔE), the reaction rate constant and the O/U ratio of UO 2 kernel. The minimum activation energy was determined from a straight line slope of equation ln [{D b . R o {(1 - (1 - X b ) ⅓ } / (b.t.Cg)] = -3.9406 x 10 3 / T + 4.044. By multiplying with the straight line slope -3.9406 x 10 3 , the ideal gas constant (R) 1.985 cal/mol and the molarity difference of reaction coefficient 2, a minimum activation energy of 15.644 kcal/mol was obtained. The reaction rate constant was determined from first-order chemical reaction control and Arrhenius equation. The O/U ratio of UO 2 kernel was obtained using gravimetric method. The analysis result of reaction rate constant with chemical reaction control equation yielded reaction rate constants of 0.745 - 1.671 s -1 and the Arrhenius equation at temperatures of 650 - 850 °C yielded reaction rate constants of 0.637 - 2.914 s -1 . The O/U ratios of UO 2 kernel at the respective reaction rate constants were 2.013 - 2.014 and the O/U ratios at reaction time 1 - 4 hours were 2.04 - 2.011. The experiment results indicated that the minimum activation energy influenced the rate constant of first-order reaction and the O/U ratio of UO 2 kernel. The optimum condition was obtained at reaction rate constant of 1.43 s -1 , O/U ratio of UO 2 kernel of 2.01 at temperature of 750 °C and reaction time of 3

Consistent Valuation across Curves Using Pricing Kernels

Directory of Open Access Journals (Sweden)

Andrea Macrina

2018-03-01

Full Text Available The general problem of asset pricing when the discount rate differs from the rate at which an asset’s cash flows accrue is considered. A pricing kernel framework is used to model an economy that is segmented into distinct markets, each identified by a yield curve having its own market, credit and liquidity risk characteristics. The proposed framework precludes arbitrage within each market, while the definition of a curve-conversion factor process links all markets in a consistent arbitrage-free manner. A pricing formula is then derived, referred to as the across-curve pricing formula, which enables consistent valuation and hedging of financial instruments across curves (and markets. As a natural application, a consistent multi-curve framework is formulated for emerging and developed inter-bank swap markets, which highlights an important dual feature of the curve-conversion factor process. Given this multi-curve framework, existing multi-curve approaches based on HJM and rational pricing kernel models are recovered, reviewed and generalised and single-curve models extended. In another application, inflation-linked, currency-based and fixed-income hybrid securities are shown to be consistently valued using the across-curve valuation method.

Studies on the use of nuclear fuel kernels in cladding tubes

International Nuclear Information System (INIS)

Thomas, G.

1981-12-01

Two approaches for using UO 2 -kernels in cladding tubes have been investigated, viz. the preparation of dense sphere-pacs and direct pelletizing (spherical). A theoretical study on the packing of spheres of different sizes showed that practical experiments were required. Model tests were, therefore, carried out, mostly with glass spheres. The most important results obtained are: A packing density of 80% can be exceeded if spheres of two sizes are used; quick and simple packing can be achieved with the mixing chute presented here; spheres pacs with a density of 90% for LWR cannot be prepared with kernels of practicable sizes; packing results can be translated to other tube diameters and to spheres and tubes made of other materials. The only suitable way to prepare dense pellets from kernels is pressing with a floating matrix at about 10 kbar, followed by removal under residual load. The kernels used should be produced without PVA and be reduced between 500 0 C and 800 0 C. Sintering is best accomplished in a limited oxidizing atmosphere at 1100 0 C with subsequent reduction. Stable pellets with up to 96% of their theoretical density could be produced this way. (orig.) [de

Model Selection in Kernel Ridge Regression

DEFF Research Database (Denmark)

Exterkate, Peter

Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...

Quantitative study of fruit flavonoids in citrus hybrids of King (C. nobilis) and Mukaku Kishu (C. kinokuni).

Science.gov (United States)

Kawaii, S; Tomono, Y; Katase, E; Ogawa, K; Nonomura-Nakano, M; Nesumi, H; Yoshida, T; Sugiura, M; Yano, M

2001-08-01

Twenty-four Citrus hybrids of King (C. nobilis) and Mukaku Kishu (C. kinokuni) were examined for their flavonoid profiles of the edible part by reversed-phase HPLC analysis. Two hybrids (G-155 and G-156) contained higher amounts of natsudaidain than their parents, whereas the remainder of the hybrids had a character intermediate between those of King and Mukaku Kishu on the basis of polymethoxylated flavone composition. Principal component analysis revealed the distribution of the hybrids by quantifying 23 flavonoid contents.

Hybrid K-means Dan Particle Swarm Optimization Untuk Clustering Nasabah Kredit

Directory of Open Access Journals (Sweden)

Yusuf Priyo Anggodo

2017-05-01

Credit is the biggest revenue for the bank. However, banks have to be selective in deciding which clients can receive the credit. This issue is becoming increasingly complex because when the bank was wrong to give credit to customers can do harm, apart of that a large number of deciding parameter in determining customer credit. Clustering is one way to be able to resolve this issue. K-means is a simple and popular method for solving clustering. However, K-means pure can’t provide optimum solutions so that needs to be done to get the optimum solution to improve. One method of optimization that can solve the problems of optimization with particle swarm optimization is good (PSO. PSO is very helpful in the process of clustering to perform optimization on the central point of each cluster. To improve better results on PSO there are some that do improve. The first use of time-variant inertia to make the dynamic value of inertial w each iteration. Both control the speed of the particle velocity or clamping to get the best position. Besides to overcome premature convergence do hybrid PSO with random injection. The results of this research provide the optimum results for solving clustering of customer credits. The test results showed the hybrid PSO K-means provide the greatest results than K-means and PSO K-means, where the silhouette of the K-means, PSO K-means, and hybrid PSO K-means respectively 0.57343, 0.792045, 1. Keywords: Credit, Clustering, PSO, K-means, Random Injection

A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

Directory of Open Access Journals (Sweden)

Fang Su

2013-01-01

Full Text Available Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

Model selection in kernel ridge regression

DEFF Research Database (Denmark)

Exterkate, Peter

2013-01-01

Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...

SU-E-T-154: Calculation of Tissue Dose Point Kernels Using GATE Monte Carlo Simulation Toolkit to Compare with Water Dose Point Kernel

Energy Technology Data Exchange (ETDEWEB)

Khazaee, M [shahid beheshti university, Tehran, Tehran (Iran, Islamic Republic of); Asl, A Kamali [Shahid Beheshti University, Tehran, Iran., Tehran, Tehran (Iran, Islamic Republic of); Geramifar, P [Shariati Hospital, Tehran, Iran., Tehran, Tehran (Iran, Islamic Republic of)

2015-06-15

Purpose: the objective of this study was to assess utilizing water dose point kernel (DPK)instead of tissue dose point kernels in convolution algorithms.to the best of our knowledge, in providing 3D distribution of absorbed dose from a 3D distribution of the activity, the human body is considered equivalent to water. as a Result tissue variations are not considered in patient specific dosimetry. Methods: In this study Gate v7.0 was used to calculate tissue dose point kernel. the beta emitter radionuclides which have taken into consideration in this simulation include Y-90, Lu-177 and P-32 which are commonly used in nuclear medicine. the comparison has been performed for dose point kernels of adipose, bone, breast, heart, intestine, kidney, liver, lung and spleen versus water dose point kernel. Results: In order to validate the simulation the Result of 90Y DPK in water were compared with published results of Papadimitroulas et al (Med. Phys., 2012). The results represented that the mean differences between water DPK and other soft tissues DPKs range between 0.6 % and 1.96% for 90Y, except for lung and bone, where the observed discrepancies are 6.3% and 12.19% respectively. The range of DPK difference for 32P is between 1.74% for breast and 18.85% for bone. For 177Lu, the highest difference belongs to bone which is equal to 16.91%. For other soft tissues the least discrepancy is observed in kidney with 1.68%. Conclusion: In all tissues except for lung and bone, the results of GATE for dose point kernel were comparable to water dose point kernel which demonstrates the appropriateness of applying water dose point kernel instead of soft tissues in the field of nuclear medicine.

SU-E-T-154: Calculation of Tissue Dose Point Kernels Using GATE Monte Carlo Simulation Toolkit to Compare with Water Dose Point Kernel

International Nuclear Information System (INIS)

Khazaee, M; Asl, A Kamali; Geramifar, P

2015-01-01

Purpose: the objective of this study was to assess utilizing water dose point kernel (DPK)instead of tissue dose point kernels in convolution algorithms.to the best of our knowledge, in providing 3D distribution of absorbed dose from a 3D distribution of the activity, the human body is considered equivalent to water. as a Result tissue variations are not considered in patient specific dosimetry. Methods: In this study Gate v7.0 was used to calculate tissue dose point kernel. the beta emitter radionuclides which have taken into consideration in this simulation include Y-90, Lu-177 and P-32 which are commonly used in nuclear medicine. the comparison has been performed for dose point kernels of adipose, bone, breast, heart, intestine, kidney, liver, lung and spleen versus water dose point kernel. Results: In order to validate the simulation the Result of 90Y DPK in water were compared with published results of Papadimitroulas et al (Med. Phys., 2012). The results represented that the mean differences between water DPK and other soft tissues DPKs range between 0.6 % and 1.96% for 90Y, except for lung and bone, where the observed discrepancies are 6.3% and 12.19% respectively. The range of DPK difference for 32P is between 1.74% for breast and 18.85% for bone. For 177Lu, the highest difference belongs to bone which is equal to 16.91%. For other soft tissues the least discrepancy is observed in kidney with 1.68%. Conclusion: In all tissues except for lung and bone, the results of GATE for dose point kernel were comparable to water dose point kernel which demonstrates the appropriateness of applying water dose point kernel instead of soft tissues in the field of nuclear medicine

Kernel learning algorithms for face recognition

CERN Document Server

Li, Jun-Bao; Pan, Jeng-Shyang

2013-01-01

Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its new

Primary and scattering contributions to beta scaled dose point kernels by means of Monte Carlo simulations

International Nuclear Information System (INIS)

Valente, Mauro; Botta, Francesca; Pedroli, Guido

2012-01-01

Beta-emitters have proved to be appropriate for radioimmunotherapy. The dosimetric characterization of each radionuclide has to be carefully investigated. One usual and practical dosimetric approach is the calculation of dose distribution from a unit point source emitting particles according to any radionuclide of interest, which is known as dose point kernel. Absorbed dose distributions are due to primary and radiation scattering contributions. This work presented a method capable of performing dose distributions for nuclear medicine dosimetry by means of Monte Carlo methods. Dedicated subroutines have been developed in order to separately compute primary and scattering contributions to the total absorbed dose, performing particle transport up to 1 keV or least. Preliminarily, the suitability of the calculation method has been satisfactory, being tested for monoenergetic sources, and it was further applied to the characterization of different beta-minus radionuclides of nuclear medicine interests for radioimmunotherapy. (author)

Partial Deconvolution with Inaccurate Blur Kernel.

Science.gov (United States)

Ren, Dongwei; Zuo, Wangmeng; Zhang, David; Xu, Jun; Zhang, Lei

2017-10-17

Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning

Kernel methods for deep learning

OpenAIRE

Cho, Youngmin

2012-01-01

We introduce a new family of positive-definite kernels that mimic the computation in large neural networks. We derive the different members of this family by considering neural networks with different activation functions. Using these kernels as building blocks, we also show how to construct other positive-definite kernels by operations such as composition, multiplication, and averaging. We explore the use of these kernels in standard models of supervised learning, such as support vector mach...

Analisis Perbandingan Algoritma Fuzzy C-Means dan K-Means

OpenAIRE

Yohannes, Yohannes

2016-01-01

Klasterisasi merupakan teknik pengelompokkan data berdasarkan kemiripan data. Teknik klasterisasi ini banyak digunakan pada bidang ilmu komputer khususnya pengolahan citra, pengenalan pola, dan data mining. Banyak sekali algoritma yang digunakan untuk klasterisasi data. Algoritma yang sering digunakan untuk klasterisasi data pada umumnya adalah Fuzzy C-Means dan K-Means. Algoritma Fuzzy C-Means merupakan algoritma klasterisasi dimana data dikelompokkan ke dalam suatu pusat cluster data denga...

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

Science.gov (United States)

Tamiminia, Haifa; Homayouni, Saeid; McNairn, Heather; Safari, Abdoreza

2017-06-01

Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

Directory of Open Access Journals (Sweden)

Qiang Shang

Full Text Available Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS. Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM is proposed based on singular spectrum analysis (SSA and kernel extreme learning machine (KELM. SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

Assessing pigmented pericarp of maize kernels as possible source of resistance to fusarium ear rot, Fusarium spp. infection and fumonisin accumulation.

Science.gov (United States)

Venturini, Giovanni; Babazadeh, Laleh; Casati, Paola; Pilu, Roberto; Salomoni, Daiana; Toffolatti, Silvia L

2016-06-16

One of the purposes of maize genetic improvement is the research of genotypes resistant to fusarium ear rot (FER) and fumonisin accumulation. Flavonoids in the pericarp of the kernels are considered particularly able to reduce the fumonisin accumulation (FUM). The aim of this field study was to assess the effect of flavonoids, associated with anti-insect protection and Fusarium verticillioides inoculation, on FER symptoms and fumonisin contamination in maize kernels. Two isogenic hybrids, one having pigmentation in the pericarp (P1-rr) and the other without it (P1-wr), were compared. P1-rr showed lower values of FER symptoms and FUM contamination than P1-wr only if the anti-insect protection and the F. verticillioides inoculations were applied in combination. Fusarium spp. kernel infection was not influenced by the presence of flavonoids in the pericarp. Artificial F. verticillioides inoculation was more effective than anti-insect protection in enhancing the inhibition activity of flavonoids toward FUM contamination. The interactions between FUM contamination levels and FER ratings were better modeled in the pigmented hybrid than in the unpigmented one. The variable role that the pigment played in kernel defense against FER and FUM indicates that flavonoids alone may not be completely effective in the resistance of fumonisin contamination in maize. Copyright © 2016 Elsevier B.V. All rights reserved.

Life history attributes of Indian meal moth (Lepidoptera: Pyralidae) and Angoumois grain moth (Lepidoptera: Gelechiidae) reared on transgenic corn kernels.

Science.gov (United States)

Sedlacek, J D; Komaravalli, S R; Hanley, A M; Price, B D; Davis, P M

2001-04-01

The Indian meal moth, Plodia interpunctella (Hübner), and Angoumois grain moth, Sitotroga cerealella (Olivier), are two globally distributed stored-grain pests. Laboratory experiments were conducted to examine the impact that corn (Zea mays L.) kernels (i.e., grain) of some Bacillus thuringiensis Berliner (Bt) corn hybrids containing CrylAb Bt delta-endotoxin have on life history attributes of Indian meal moth and Angoumois grain moth. Stored grain is at risk to damage from Indian meal moth and Angoumois grain moth; therefore, Bt corn may provide a means of protecting this commodity from damage. Thus, the objective of this research was to quantify the effects of transgenic corn seed containing CrylAb delta-endotoxin on Indian meal moth and Angoumois grain moth survival, fecundity, and duration of development. Experiments with Bt grain, non-Bt isolines, and non-Bt grain were conducted in environmental chambers at 27 +/- 1 degrees C and > or = 60% RH in continuous dark. Fifty eggs were placed in ventilated pint jars containing 170 g of cracked or whole corn for the Indian meal moth and Angoumois grain moth, respectively. Emergence and fecundity were observed for 5 wk. Emergence and fecundity of Indian meal moth and emergence of Angoumois grain moth were significantly lower for individuals reared on P33V08 and N6800Bt, MON 810 and Bt-11 transformed hybrids, respectively, than on their non-Bt transformed isolines. Longer developmental times were observed for Indian meal moth reared on P33V08 and N6800Bt than their non-Bt-transformed isolines. These results indicate that MON 810 and Bt-11 CrylAb delta-endotoxin-containing kernels reduce laboratory populations of Indian meal moth and Angoumois grain moth. Thus, storing Bt-transformed grain is a management tactic that warrants bin scale testing and may effectively reduce Indian meal moth and Angoumois grain moth populations in grain without application of synthetic chemicals or pesticides.

A Realization of Temperature Monitoring System Based on Real-Time Kernel μC/OS and 1-wire Bus

Directory of Open Access Journals (Sweden)

Yanmei Qi

2013-06-01

Full Text Available The traditional temperature monitoring system generally adopt some analog sensors for collecting data and a microcontroller for processing data for the purpose of temperature monitoring. However, this back-fore ground system has the disadvantages that the system has poor real-time property and single function, the amount of sensors is not easy to expand, and the software system has a difficulty in upgrading. Aiming at these disadvantages, the system designed in this paper adopts brand-new hardware and software structures: a digitaltemperature sensor array is connected to 1-wire bus and communicated with a control core through 1-wire bus protocol, thus a great convenience is provided for the expansion of the sensor; a real-time operating system is introduced into the software, an application program capable of realizing various functions runs on the real-time kernel μC/OS-II platform. The application of the real-time kernel also provides a good lower layer interface for the late-stage software upgrading.

Separation of oil palm shell and kernel by using kaolinite media

Directory of Open Access Journals (Sweden)

Sukpong Sirinupong

2003-05-01

Full Text Available The objective of this research is to investigate the possibility of using kaolinite from Ranong province as media in the oil palm shell and kernel separation process by means of heavy media separation. The effect of specific gravity of the slurry, type and amount of dispersant and type of clays on suspension of media and efficiency of separation were studied. It was found that the specific gravity of oil palm shell and kernel are 1.40 and 1.20 respectively. While the average specific gravity of kaolinite grade MRD-B85, RANONG-325 and commercial clay from Univanich Group. PCL., are 2.54, 2.65 and 2.46 respectively. It was apparent that the viscosity of clay slurry increased with the specific gravity of the slurry. For MRD-B85 and RANONG- 325 clays which have the average particle sizes of 10 and 12 microns, the pH of their slurries of about 5.84 and 6.33 respectively were obtained and at these conditions stability of the slurry rarely occurred and they could not be used for separation. However, these clays can also be utilized as media when dispersant such asCalgon or sodium silicate is applied to their slurries. It was found that the efficiency of separation depends on specific gravity and viscosity of the slurry, type and particle size of kaolinite and dosage of dispersant. The optimum separating conditions for MRD-B85 clay were the dosage of Calgon of 0.15% (or 1.5 kg/t of clay at the specific gravity of the slurry of 1.20-1.24 (27-32% Solids in which a pH of 6.14 and viscosity of 104 cP to very low value (could not be measured were obtained. Thus, kernel yielded 97.57-100% and shell contamination of 1.48-6.32% was achieved. While sodium silicate was applied to the slurry about 0.15% at the specific gravity of 1.22, pH of 6.74 and viscosity of 238 cP were obtained and kernel could be recovered 100% with shell contamination of 8.36%. When 0.15% Calgon or 0.25% sodium silicate was introduced to the RANONG-325 clay slurry at the specific gravity

Veto-Consensus Multiple Kernel Learning

NARCIS (Netherlands)

Zhou, Y.; Hu, N.; Spanos, C.J.

2016-01-01

We propose Veto-Consensus Multiple Kernel Learning (VCMKL), a novel way of combining multiple kernels such that one class of samples is described by the logical intersection (consensus) of base kernelized decision rules, whereas the other classes by the union (veto) of their complements. The

Survival rate of Plodia interpunctella (Lepidoptera: Pyralidae: On different states of wheat and rye kernels previously infested by beetle pests

Directory of Open Access Journals (Sweden)

Vukajlović Filip N.

2017-01-01

Full Text Available The present study was undertaken to determine survival rate of Plodia interpunctella (Hübner, 1813, reared on different mechanical states of Vizija winter wheat cultivar and Raša winter rye cultivar, previously infested with different beetle pests. Wheat was previously infested with Rhyzopertha dominica, Sitophilus granarius, Oryzaephilus surinamensis and Cryptolestes ferrugineus, while rye was infested only with O. surinamensis. Kernels were tested in three different mechanical states: (A whole undamaged kernels; (B kernels already damaged by pests and (C original storage kernels (mixture of B and C type. No P. interpunctella adult emerged on wheat kernels, while 36 adults developed on rye kernels. The highest abundance reached beetle species who fed with a mixture of kernels damaged by pests and whole undamaged kernels. Development and survival rate of five different storage insect pests depends on type of kernels and there exist significant survivorship correlations among them.

Bit Error-Rate Minimizing Detector for Amplify-and-Forward Relaying Systems Using Generalized Gaussian Kernel

KAUST Repository

Ahmed, Qasim Zeeshan

2013-01-01

In this letter, a new detector is proposed for amplifyand- forward (AF) relaying system when communicating with the assistance of relays. The major goal of this detector is to improve the bit error rate (BER) performance of the receiver. The probability density function is estimated with the help of kernel density technique. A generalized Gaussian kernel is proposed. This new kernel provides more flexibility and encompasses Gaussian and uniform kernels as special cases. The optimal window width of the kernel is calculated. Simulations results show that a gain of more than 1 dB can be achieved in terms of BER performance as compared to the minimum mean square error (MMSE) receiver when communicating over Rayleigh fading channels.

Characterization of the Bulgarian sunflower hybrid Valin

Directory of Open Access Journals (Sweden)

G. Georgiev

2016-09-01

Full Text Available Abstract. A male sterile two-linear sunflower hybrid was developed at Dobrudzha Agricultural Institute – General Toshevo using the method of inter-linear hybridization. The mother component is line 10517 which possesses cytoplasmic male sterility, and the father component is line 10595R, a fertility restorer of branched type. Both parental forms have very good general and specific combining ability. Hybrid Valin is medium early, with vegetation period of 110 – 115 days, plant height 165 – 175 cm and head diameter 17 – 18 cm. The percent of kernel in seed is within the range of 65 – 73%. Seeds have absolute weight 50 – 55 g, and oil content in seed is 46 – 50%. The hybrid is resistant to lodging and moderately resistant to phoma and phomopsis. The resistance to downy mildew is as high as 95 % up to race 700, and the resistance to the parasite Orobanche is 100% (races A-F. In the breeding fields of DAI the hybrid went through threeyear testing according to a scheme and growing technology approved for this crop. During the first two years of the control testing it exceeded the mean standard by seed yield with 11.0% and 14.4%, respectively. During the third year, hybrid Valin was above the mean standard with 10.9% by seed yield and with 6.6% by oil yield in a unified competitive varietal testing. The maximum yield obtained at DAI was 4483 kg/ha, and the maximum oil content was 50.1%. In 2007 and 2008 hybrid Valin went through official testing within the structure of the Executive Agency of Variety Testing, Field Inspection and Seed Control of Bulgaria. By the index seed yield, the exceeding of the mean standard was 23.6 % in the first year, and in the second the yield was with 1.4 % below the standard. Oil content was a little below the standard in both years. The aim of this investigation was to make as full as possible a characterization of the new registered sunflower hybrid Valin on the base of biological, phytopatological and

Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations.

Science.gov (United States)

Schaid, Daniel J

2010-01-01

Measures of genomic similarity are the basis of many statistical analytic methods. We review the mathematical and statistical basis of similarity methods, particularly based on kernel methods. A kernel function converts information for a pair of subjects to a quantitative value representing either similarity (larger values meaning more similar) or distance (smaller values meaning more similar), with the requirement that it must create a positive semidefinite matrix when applied to all pairs of subjects. This review emphasizes the wide range of statistical methods and software that can be used when similarity is based on kernel methods, such as nonparametric regression, linear mixed models and generalized linear mixed models, hierarchical models, score statistics, and support vector machines. The mathematical rigor for these methods is summarized, as is the mathematical framework for making kernels. This review provides a framework to move from intuitive and heuristic approaches to define genomic similarities to more rigorous methods that can take advantage of powerful statistical modeling and existing software. A companion paper reviews novel approaches to creating kernels that might be useful for genomic analyses, providing insights with examples [1]. Copyright © 2010 S. Karger AG, Basel.

Influence of THFA on the surface area and porosity of U3O8 kernel of produced by external gelation process

International Nuclear Information System (INIS)

Susilowati, Sri Rinanti; Triyono; Nawangsih, Endang; Widiyati, Sri

2013-01-01

The research of the influence of THFA on the surface area and porosity of U 3 O 8 by kernel external gelation results has done. U 3 0 8 kernel made by external gelation process using uranyl nitrate solution with various concentration of THFA in sol solution. UN solution preparation by dissolving UO 3 powder into 7N HNO 3 at a temperature of 60 °C with a certain amount and volume, resulting in a solution of uranyl nitrate with high uranium concentrations but low acidity. Preparation of sol begins by dissolving 1.8 9 of polyvinyl alcohol in demineralized water at 70 °C, the volume varied tetrahydrofurfural alcohol, then added a solution of UN stirring constantly until homogeneous. Sol solution was then dripped in 7N ammonia in the medium gelation column. Gel ADU diaging in ammonia solution 7N for 30 minutes, then washed with ammonia 2.5% and n propanol. ADU gel diameter was measured using calipers. ADU silenced gel at room temperature 48 hours and then dried in an oven 100 °C at a rate of temperature rising 5°C/minutes. After drying, the gel ADU calcined at 600°C with an increase rate of 5°C/minutes for 2 hours. Kernel density U 3 O 8 then analyzed, surface area, mean pore radius and total pore volume. U 3 O 8 best kernel at 2.5% THFA concentration conditions, because it has a high density is 7.877 g/mL and extensive porosity and lower surface area is 5,180 % and 4,898 m 2 /g. (author)

An Approximate Approach to Automatic Kernel Selection.

Science.gov (United States)

Ding, Lizhong; Liao, Shizhong

2016-02-02

Kernel selection is a fundamental problem of kernel-based learning algorithms. In this paper, we propose an approximate approach to automatic kernel selection for regression from the perspective of kernel matrix approximation. We first introduce multilevel circulant matrices into automatic kernel selection, and develop two approximate kernel selection algorithms by exploiting the computational virtues of multilevel circulant matrices. The complexity of the proposed algorithms is quasi-linear in the number of data points. Then, we prove an approximation error bound to measure the effect of the approximation in kernel matrices by multilevel circulant matrices on the hypothesis and further show that the approximate hypothesis produced with multilevel circulant matrices converges to the accurate hypothesis produced with kernel matrices. Experimental evaluations on benchmark datasets demonstrate the effectiveness of approximate kernel selection.

Iterative software kernels

Energy Technology Data Exchange (ETDEWEB)

Duff, I.

1994-12-31

This workshop focuses on kernels for iterative software packages. Specifically, the three speakers discuss various aspects of sparse BLAS kernels. Their topics are: `Current status of user lever sparse BLAS`; Current status of the sparse BLAS toolkit`; and `Adding matrix-matrix and matrix-matrix-matrix multiply to the sparse BLAS toolkit`.

Viscozyme L pretreatment on palm kernels improved the aroma of palm kernel oil after kernel roasting.

Science.gov (United States)

Zhang, Wencan; Leong, Siew Mun; Zhao, Feifei; Zhao, Fangju; Yang, Tiankui; Liu, Shaoquan

2018-05-01

With an interest to enhance the aroma of palm kernel oil (PKO), Viscozyme L, an enzyme complex containing a wide range of carbohydrases, was applied to alter the carbohydrates in palm kernels (PK) to modulate the formation of volatiles upon kernel roasting. After Viscozyme treatment, the content of simple sugars and free amino acids in PK increased by 4.4-fold and 4.5-fold, respectively. After kernel roasting and oil extraction, significantly more 2,5-dimethylfuran, 2-[(methylthio)methyl]-furan, 1-(2-furanyl)-ethanone, 1-(2-furyl)-2-propanone, 5-methyl-2-furancarboxaldehyde and 2-acetyl-5-methylfuran but less 2-furanmethanol and 2-furanmethanol acetate were found in treated PKO; the correlation between their formation and simple sugar profile was estimated by using partial least square regression (PLS1). Obvious differences in pyrroles and Strecker aldehydes were also found between the control and treated PKOs. Principal component analysis (PCA) clearly discriminated the treated PKOs from that of control PKOs on the basis of all volatile compounds. Such changes in volatiles translated into distinct sensory attributes, whereby treated PKO was more caramelic and burnt after aqueous extraction and more nutty, roasty, caramelic and smoky after solvent extraction. Copyright © 2018 Elsevier Ltd. All rights reserved.

Caractérisation Physico-Chimique de l'Amande d'Hybride de ...

African Journals Online (AJOL)

utilisée en biscuiterie, pâtisserie et savonnerie comme le PB 121 ordinaire. Mots clés: Cocotier, hybride, vitroculture, amande, caractéristique. Abstract. Chemical and physical characterization of kernel from coconut hybrid PB 121 (Cocos nucifera L.) regenerated by in vitro culture at different storage duration and mature ...

A kernel version of spatial factor analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2009-01-01

. Schölkopf et al. introduce kernel PCA. Shawe-Taylor and Cristianini is an excellent reference for kernel methods in general. Bishop and Press et al. describe kernel methods among many other subjects. Nielsen and Canty use kernel PCA to detect change in univariate airborne digital camera images. The kernel...... version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply kernel versions of PCA, maximum autocorrelation factor (MAF) analysis...

Systematic approach in optimizing numerical memory-bound kernels on GPU

KAUST Repository

Abdelfattah, Ahmad

2013-01-01

The use of GPUs has been very beneficial in accelerating dense linear algebra computational kernels (DLA). Many high performance numerical libraries like CUBLAS, MAGMA, and CULA provide BLAS and LAPACK implementations on GPUs as well as hybrid computations involving both, CPUs and GPUs. GPUs usually score better performance than CPUs for compute-bound operations, especially those characterized by a regular data access pattern. This paper highlights a systematic approach for efficiently implementing memory-bound DLA kernels on GPUs, by taking advantage of the underlying device\\'s architecture (e.g., high throughput). This methodology proved to outperform existing state-of-the-art GPU implementations for the symmetric matrix-vector multiplication (SYMV), characterized by an irregular data access pattern, in a recent work (Abdelfattah et. al, VECPAR 2012). We propose to extend this methodology to the general matrix-vector multiplication (GEMV) kernel. The performance results show that our GEMV implementation achieves better performance for relatively small to medium matrix sizes, making it very influential in calculating the Hessenberg and bidiagonal reductions of general matrices (radar applications), which are the first step toward computing eigenvalues and singular values, respectively. Considering small and medium size matrices (≤4500), our GEMV kernel achieves an average 60% improvement in single precision (SP) and an average 25% in double precision (DP) over existing open-source and commercial software solutions. These results improve reduction algorithms for both small and large matrices. The improved GEMV performances engender an averge 30% (SP) and 15% (DP) in Hessenberg reduction and up to 25% (SP) and 14% (DP) improvement for the bidiagonal reduction over the implementation provided by CUBLAS 5.0. © 2013 Springer-Verlag.

Flame kernel generation and propagation in turbulent partially premixed hydrocarbon jet

KAUST Repository

Mansour, Mohy S.

2014-04-23

Flame development, propagation, stability, combustion efficiency, pollution formation, and overall system efficiency are affected by the early stage of flame generation defined as flame kernel. Studying the effects of turbulence and chemistry on the flame kernel propagation is the main aim of this work for natural gas (NG) and liquid petroleum gas (LPG). In addition the minimum ignition laser energy (MILE) has been investigated for both fuels. Moreover, the flame stability maps for both fuels are also investigated and analyzed. The flame kernels are generated using Nd:YAG pulsed laser and propagated in a partially premixed turbulent jet. The flow field is measured using 2-D PIV technique. Five cases have been selected for each fuel covering different values of Reynolds number within a range of 6100-14400, at a mean equivalence ratio of 2 and a certain level of partial premixing. The MILE increases by increasing the equivalence ratio. Near stoichiometric the energy density is independent on the jet velocity while in rich conditions it increases by increasing the jet velocity. The stability curves show four distinct regions as lifted, attached, blowout, and a fourth region either an attached flame if ignition occurs near the nozzle or lifted if ignition occurs downstream. LPG flames are more stable than NG flames. This is consistent with the higher values of the laminar flame speed of LPG. The flame kernel propagation speed is affected by both turbulence and chemistry. However, at low turbulence level chemistry effects are more pronounced while at high turbulence level the turbulence becomes dominant. LPG flame kernels propagate faster than those for NG flame. In addition, flame kernel extinguished faster in LPG fuel as compared to NG fuel. The propagation speed is likely to be consistent with the local mean equivalence ratio and its corresponding laminar flame speed. Copyright © Taylor & Francis Group, LLC.

An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine

Directory of Open Access Journals (Sweden)

Yudong Zhang

2013-01-01

Full Text Available Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wavelet transform to extract features then used principal component analysis (PCA to reduce the feature space. Afterwards, we constructed a kernel support vector machine (KSVM with RBF kernel, using particle swarm optimization (PSO to optimize the parameters C and σ. Fivefold cross-validation was utilized to avoid overfitting. In the experimental procedure, we created a 90 images dataset brain downloaded from Harvard Medical School website. The abnormal brain MR images consist of the following diseases: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, meningioma, sarcoma, Alzheimer, Huntington, motor neuron disease, cerebral calcinosis, Pick’s disease, Alzheimer plus visual agnosia, multiple sclerosis, AIDS dementia, Lyme encephalopathy, herpes encephalitis, Creutzfeld-Jakob disease, and cerebral toxoplasmosis. The 5-folded cross-validation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BP-NN and 91.33% by RBF-NN. For the parameter selection, we compared PSO with those of random selection method. The results showed that the PSO is more effective to build optimal KSVM.

Assortative mating and differential male mating success in an ash hybrid zone population

Directory of Open Access Journals (Sweden)

Frascaria-Lacoste Nathalie

2006-11-01

Full Text Available Abstract Background The structure and evolution of hybrid zones depend mainly on the relative importance of dispersal and local adaptation, and on the strength of assortative mating. Here, we study the influence of dispersal, temporal isolation, variability in phenotypic traits and parasite attacks on the male mating success of two parental species and hybrids by real-time pollen flow analysis. We focus on a hybrid zone population between the two closely related ash species Fraxinus excelsior L. (common ash and F. angustifolia Vahl (narrow-leaved ash, which is composed of individuals of the two species and several hybrid types. This population is structured by flowering time: the F. excelsior individuals flower later than the F. angustifolia individuals, and the hybrid types flower in-between. Hybrids are scattered throughout the population, suggesting favorable conditions for their local adaptation. We estimate jointly the best-fitting dispersal kernel, the differences in male fecundity due to variation in phenotypic traits and level of parasite attack, and the strength of assortative mating due to differences in flowering phenology. In addition, we assess the effect of accounting for genotyping error on these estimations. Results We detected a very high pollen immigration rate and a fat-tailed dispersal kernel, counter-balanced by slight phenological assortative mating and short-distance pollen dispersal. Early intermediate flowering hybrids, which had the highest male mating success, showed optimal sex allocation and increased selfing rates. We detected asymmetry of gene flow, with early flowering trees participating more as pollen donors than late flowering trees. Conclusion This study provides striking evidence that long-distance gene flow alone is not sufficient to counter-act the effects of assortative mating and selfing. Phenological assortative mating and short-distance dispersal can create temporal and spatial structuring that appears

Dynamic PET Image reconstruction for parametric imaging using the HYPR kernel method

Science.gov (United States)

Spencer, Benjamin; Qi, Jinyi; Badawi, Ramsey D.; Wang, Guobao

2017-03-01

Dynamic PET image reconstruction is a challenging problem because of the ill-conditioned nature of PET and the lowcounting statistics resulted from short time-frames in dynamic imaging. The kernel method for image reconstruction has been developed to improve image reconstruction of low-count PET data by incorporating prior information derived from high-count composite data. In contrast to most of the existing regularization-based methods, the kernel method embeds image prior information in the forward projection model and does not require an explicit regularization term in the reconstruction formula. Inspired by the existing highly constrained back-projection (HYPR) algorithm for dynamic PET image denoising, we propose in this work a new type of kernel that is simpler to implement and further improves the kernel-based dynamic PET image reconstruction. Our evaluation study using a physical phantom scan with synthetic FDG tracer kinetics has demonstrated that the new HYPR kernel-based reconstruction can achieve a better region-of-interest (ROI) bias versus standard deviation trade-off for dynamic PET parametric imaging than the post-reconstruction HYPR denoising method and the previously used nonlocal-means kernel.

Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm.

Science.gov (United States)

Gaber, Tarek; Ismail, Gehad; Anter, Ahmed; Soliman, Mona; Ali, Mona; Semary, Noura; Hassanien, Aboul Ella; Snasel, Vaclav

2015-08-01

The early detection of breast cancer makes many women survive. In this paper, a CAD system classifying breast cancer thermograms to normal and abnormal is proposed. This approach consists of two main phases: automatic segmentation and classification. For the former phase, an improved segmentation approach based on both Neutrosophic sets (NS) and optimized Fast Fuzzy c-mean (F-FCM) algorithm was proposed. Also, post-segmentation process was suggested to segment breast parenchyma (i.e. ROI) from thermogram images. For the classification, different kernel functions of the Support Vector Machine (SVM) were used to classify breast parenchyma into normal or abnormal cases. Using benchmark database, the proposed CAD system was evaluated based on precision, recall, and accuracy as well as a comparison with related work. The experimental results showed that our system would be a very promising step toward automatic diagnosis of breast cancer using thermograms as the accuracy reached 100%.

Considerations on absorbed dose estimates based on different β-dose point kernels in internal dosimetry

International Nuclear Information System (INIS)

Uchida, Isao; Yamada, Yasuhiko; Yamashita, Takashi; Okigaki, Shigeyasu; Oyamada, Hiyoshimaru; Ito, Akira.

1995-01-01

In radiotherapy with radiopharmaceuticals, more accurate estimates of the three-dimensional (3-D) distribution of absorbed dose is important in specifying the activity to be administered to patients to deliver a prescribed absorbed dose to target volumes without exceeding the toxicity limit of normal tissues in the body. A calculation algorithm for the purpose has already been developed by the authors. An accurate 3-D distribution of absorbed dose based on the algorithm is given by convolution of the 3-D dose matrix for a unit cubic voxel containing unit cumulated activity, which is obtained by transforming a dose point kernel into a 3-D cubic dose matrix, with the 3-D cumulated activity distribution given by the same voxel size. However, beta-dose point kernels affecting accurate estimates of the 3-D absorbed dose distribution have been different among the investigators. The purpose of this study is to elucidate how different beta-dose point kernels in water influence on the estimates of the absorbed dose distribution due to the dose point kernel convolution method by the authors. Computer simulations were performed using the MIRD thyroid and lung phantoms under assumption of uniform activity distribution of 32 P. Using beta-dose point kernels derived from Monte Carlo simulations (EGS-4 or ACCEPT computer code), the differences among their point kernels gave little differences for the mean and maximum absorbed dose estimates for the MIRD phantoms used. In the estimates of mean and maximum absorbed doses calculated using different cubic voxel sizes (4x4x4 mm and 8x8x8 mm) for the MIRD thyroid phantom, the maximum absorbed doses for the 4x4x4 mm-voxel were estimated approximately 7% greater than the cases of the 8x8x8 mm-voxel. They were found in every beta-dose point kernel used in this study. On the other hand, the percentage difference of the mean absorbed doses in the both voxel sizes for each beta-dose point kernel was less than approximately 0.6%. (author)

Local Observed-Score Kernel Equating

Science.gov (United States)

Wiberg, Marie; van der Linden, Wim J.; von Davier, Alina A.

2014-01-01

Three local observed-score kernel equating methods that integrate methods from the local equating and kernel equating frameworks are proposed. The new methods were compared with their earlier counterparts with respect to such measures as bias--as defined by Lord's criterion of equity--and percent relative error. The local kernel item response…

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.

Science.gov (United States)

Suraj; Tiwari, Purnendu; Ghosh, Subhojit; Sinha, Rakesh Kumar

2015-01-01

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

CLASS-PAIR-GUIDED MULTIPLE KERNEL LEARNING OF INTEGRATING HETEROGENEOUS FEATURES FOR CLASSIFICATION

Directory of Open Access Journals (Sweden)

Q. Wang

2017-10-01

Full Text Available In recent years, many studies on remote sensing image classification have shown that using multiple features from different data sources can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL can conveniently be embedded in a variety of characteristics. The conventional combined kernel learned by MKL can be regarded as the compromise of all basic kernels for all classes in classification. It is the best of the whole, but not optimal for each specific class. For this problem, this paper proposes a class-pair-guided MKL method to integrate the heterogeneous features (HFs from multispectral image (MSI and light detection and ranging (LiDAR data. In particular, the one-against-one strategy is adopted, which converts multiclass classification problem to a plurality of two-class classification problem. Then, we select the best kernel from pre-constructed basic kernels set for each class-pair by kernel alignment (KA in the process of classification. The advantage of the proposed method is that only the best kernel for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on two real data sets, and the experimental results show that the proposed method achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms.

Stability Parameters for Grain Yield and its Component Traits in Maize Hybrids of Different FAO Maturity Groups

Directory of Open Access Journals (Sweden)

Dragan Djurovic

2014-12-01

Full Text Available An objective evaluation of maize hybrids in intensive cropping systems requires identification not only of yield components and other agronomically important traits but also of stability parameters. Grain yield and its components were assessed in 11 maize hybrids with different lengths of growing season (FAO 300-700 maturity groups using analysis of variance and regression analysis at three different locations in Western Serbia. The test hybrids and locations showed significant differences in grain yield, grain moisture content at maturity, 1,000-kernel weight and ear length. A significant interaction was observed between all traits and the environment. The hybrids with higher mean values of the traits, regardless of maturity group, generally exhibited sensitivity i.e. adaptation to more favourable environmental conditions as compared to those having lower mean values. Regression coefficient (bi values for grain yield mostly suggested no significant differences relative to the mean. The medium-season hybrid gave high yields and less favourable values of stability parameters at most locations and in most years, as compared to mediumlate hybrids. As compared to medium-early hybrids, medium-late hybrids (FAO 600 and 700 mostly exhibited unfavourable values of stability parameters i.e. a specific response and better adaptation to favourable environmental conditions, and gave higher average yields. Apart from producing lower average yields, FAO 300 and 400 hybrids showed higher yield stability as compared to the other hybrids tested. Medium-late hybrids had higher yields and showed a better response to favourable environmental conditions compared to early-maturing hybrids. Therefore, they can be recommended for intensive cultural practices and low-stress environments. Due to their more favourable stability parameter values, medium-early hybrids can be recommended for low-intensity cultural practices and stressful environments.

Kernel parameter dependence in spatial factor analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2010-01-01

kernel PCA. Shawe-Taylor and Cristianini [4] is an excellent reference for kernel methods in general. Bishop [5] and Press et al. [6] describe kernel methods among many other subjects. The kernel version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional...... feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply a kernel version of maximum autocorrelation factor (MAF) [7, 8] analysis to irregularly sampled stream sediment geochemistry data from South Greenland and illustrate the dependence...... of the kernel width. The 2,097 samples each covering on average 5 km2 are analyzed chemically for the content of 41 elements....

Optimizing Thermal-Elastic Properties of C/C–SiC Composites Using a Hybrid Approach and PSO Algorithm

Science.gov (United States)

Xu, Yingjie; Gao, Tian

2016-01-01

Carbon fiber-reinforced multi-layered pyrocarbon–silicon carbide matrix (C/C–SiC) composites are widely used in aerospace structures. The complicated spatial architecture and material heterogeneity of C/C–SiC composites constitute the challenge for tailoring their properties. Thus, discovering the intrinsic relations between the properties and the microstructures and sequentially optimizing the microstructures to obtain composites with the best performances becomes the key for practical applications. The objective of this work is to optimize the thermal-elastic properties of unidirectional C/C–SiC composites by controlling the multi-layered matrix thicknesses. A hybrid approach based on micromechanical modeling and back propagation (BP) neural network is proposed to predict the thermal-elastic properties of composites. Then, a particle swarm optimization (PSO) algorithm is interfaced with this hybrid model to achieve the optimal design for minimizing the coefficient of thermal expansion (CTE) of composites with the constraint of elastic modulus. Numerical examples demonstrate the effectiveness of the proposed hybrid model and optimization method. PMID:28773343

Fuzzy C-means method for clustering microarray data.

Science.gov (United States)

Dembélé, Doulaye; Kastner, Philippe

2003-05-22

Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes. A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster. Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/

Multiple Kernel Learning with Data Augmentation

Science.gov (United States)

2016-11-22

JMLR: Workshop and Conference Proceedings 63:49–64, 2016 ACML 2016 Multiple Kernel Learning with Data Augmentation Khanh Nguyen nkhanh@deakin.edu.au...University, Australia Editors: Robert J. Durrant and Kee-Eung Kim Abstract The motivations of multiple kernel learning (MKL) approach are to increase... kernel expres- siveness capacity and to avoid the expensive grid search over a wide spectrum of kernels . A large amount of work has been proposed to

IDENTIFICATION AND MAPPING OF A GENE FOR RICE SLENDER KERNEL USING Oryza glumaepatula INTROGRESSION LINES

Directory of Open Access Journals (Sweden)

Sobrizal Sobrizal

2016-10-01

Full Text Available World demand for superior rice grain quality tends to increase. One of the  criteria of appearance quality of rice grain is grain shape. Rice consumers  exhibit wide preferences for grain shape, but most Indonesian rice consumers prefer long and slender grain. The objectives of this study were to identify and map a gene for rice slender kernel trait using Oryza  glumaepatula introgression lines with O. sativa cv. Taichung 65 genetic background. A segregation analysis of BC4F2 population derived from backcrosses of a donor parent O. glumaepatula into a recurrent parent Taichung 65 showed that the slender kernel was controlled by a single recessive gene. This new identified gene was designated as sk1 (slender kernel 1. Moreover, based on the RFLP analyses using 14 RFLP markers located on chromosomes 2, 8, 9, and 10 in which the O. glumaepatula chromosomal segments were retained in BC4F2 population, the sk1 was located between RFLP markers C679 and C560 on the long arm of chromosome 2, with map distances of 2.8 and 1.5 cM, respectively. The wild rice O. glumaepatula carried a recessive allele for slender kernel. This allele may be useful in breeding of rice with slender kernel types. In addition, the development of plant materials and RFLP map associated with slender kernel in this study is the preliminary works in the effort to isolate this important grain shape gene.

OS X and iOS Kernel Programming

CERN Document Server

Halvorsen, Ole Henry

2011-01-01

OS X and iOS Kernel Programming combines essential operating system and kernel architecture knowledge with a highly practical approach that will help you write effective kernel-level code. You'll learn fundamental concepts such as memory management and thread synchronization, as well as the I/O Kit framework. You'll also learn how to write your own kernel-level extensions, such as device drivers for USB and Thunderbolt devices, including networking, storage and audio drivers. OS X and iOS Kernel Programming provides an incisive and complete introduction to the XNU kernel, which runs iPhones, i

Model selection for Gaussian kernel PCA denoising

DEFF Research Database (Denmark)

Jørgensen, Kasper Winther; Hansen, Lars Kai

2012-01-01

We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...

Performance Evaluation at the Hardware Architecture Level and the Operating System Kernel Design Level.

Science.gov (United States)

1977-12-01

program utilizing kernel semaphores for synchronization . The Hydra kernel instructions were sampled at random using the hardware monitor. The changes in...thatf r~i~h olvrAt- 1,o;lil armcrl han itf,. own sell of primitive func ions; and c onparinoms acrosns dif fc’rnt opt ratieg ; .emsf is riot possiblc...kcrnel dcsign level is complicated by the fact that each operating system kernel ha. its own set of primitive functions and compari!ons across

Production and detailed characterization of bio-oil from fast pyrolysis of palm kernel shell

International Nuclear Information System (INIS)

Asadullah, Mohammad; Ab Rasid, Nurul Suhada; Kadir, Sharifah Aishah Syed A.; Azdarpour, Amin

2013-01-01

Bio-oil has been produced from palm kernel shell in a fluidized bed reactor. The process conditions were optimized and the detailed characteristics of bio-oil were carried out. The higher feeding rate and higher gas flow rate attributed to higher bio-oil yield. The maximum mass fraction of biomass (57%) converted to bio-oil at 550 °C when 2 L min −1 of gas and 10 g min −1 of biomass were fed. The bio-oil produced up to 500 °C existed in two distinct phases, while it formed one homogeneous phase when it was produced above 500 °C. The higher heating value of bio-oil produced at 550 °C was found to be 23.48 MJ kg −1 . As GC–MS data shows, the area ratio of phenol is the maximum among the area ratio of identified compounds in 550 °C bio-oil. The UV–Fluorescence absorption, which is the indication of aromatic content, is also the highest in 550 °C bio-oil. -- Highlights: • Maximum 56 wt% yield of bio-oil was obtained at 550 °C from palm kernel shell. • Two layer of bio-oil was observed up to 500 °C, while it was one layer above 500 °C. • Bio-oil from palm kernel shell provides more than 40% area ratio of phenol in GC–MS analysis. • The calorific value of palm kernel shell bio-oil is higher than other bio-oil

The development of the production process for the thorium/uranium dicarbide fuel kernels for the first charge of the Dragon Reactor

International Nuclear Information System (INIS)

Burnett, R.C.; Hankart, L.J.; Horsley, G.W.

1965-05-01

The development of methods of producing spheroidal sintered porous kernels of hyperstoichiometric thorium/uranium dicarbide solid solution from thorium/uranium monocarbide/carbon and thoria/urania/carbon powder mixes is described. The work has involved study of (i) Methods of preparing green kernels from UC/Th/C powder mixes using the rotary sieve technique. (ii) Methods of producing green kernels from UO2/Th02/C powder mixes using the planetary mill technique. (iii) The conversion by appropriate heat treatment of green kernels produced by both routes to sintered porous kernels of thorium/uranium carbide. (iv) The efficiency of the processes. (author)

Impact of deep learning on the normalization of reconstruction kernel effects in imaging biomarker quantification: a pilot study in CT emphysema

Science.gov (United States)

Jin, Hyeongmin; Heo, Changyong; Kim, Jong Hyo

2018-02-01

Differing reconstruction kernels are known to strongly affect the variability of imaging biomarkers and thus remain as a barrier in translating the computer aided quantification techniques into clinical practice. This study presents a deep learning application to CT kernel conversion which converts a CT image of sharp kernel to that of standard kernel and evaluates its impact on variability reduction of a pulmonary imaging biomarker, the emphysema index (EI). Forty cases of low-dose chest CT exams obtained with 120kVp, 40mAs, 1mm thickness, of 2 reconstruction kernels (B30f, B50f) were selected from the low dose lung cancer screening database of our institution. A Fully convolutional network was implemented with Keras deep learning library. The model consisted of symmetric layers to capture the context and fine structure characteristics of CT images from the standard and sharp reconstruction kernels. Pairs of the full-resolution CT data set were fed to input and output nodes to train the convolutional network to learn the appropriate filter kernels for converting the CT images of sharp kernel to standard kernel with a criterion of measuring the mean squared error between the input and target images. EIs (RA950 and Perc15) were measured with a software package (ImagePrism Pulmo, Seoul, South Korea) and compared for the data sets of B50f, B30f, and the converted B50f. The effect of kernel conversion was evaluated with the mean and standard deviation of pair-wise differences in EI. The population mean of RA950 was 27.65 +/- 7.28% for B50f data set, 10.82 +/- 6.71% for the B30f data set, and 8.87 +/- 6.20% for the converted B50f data set. The mean of pair-wise absolute differences in RA950 between B30f and B50f is reduced from 16.83% to 1.95% using kernel conversion. Our study demonstrates the feasibility of applying the deep learning technique for CT kernel conversion and reducing the kernel-induced variability of EI quantification. The deep learning model has a

Paramecium: An Extensible Object-Based Kernel

NARCIS (Netherlands)

van Doorn, L.; Homburg, P.; Tanenbaum, A.S.

1995-01-01

In this paper we describe the design of an extensible kernel, called Paramecium. This kernel uses an object-based software architecture which together with instance naming, late binding and explicit overrides enables easy reconfiguration. Determining which components reside in the kernel protection

Theory of reproducing kernels and applications

CERN Document Server

Saitoh, Saburou

2016-01-01

This book provides a large extension of the general theory of reproducing kernels published by N. Aronszajn in 1950, with many concrete applications. In Chapter 1, many concrete reproducing kernels are first introduced with detailed information. Chapter 2 presents a general and global theory of reproducing kernels with basic applications in a self-contained way. Many fundamental operations among reproducing kernel Hilbert spaces are dealt with. Chapter 2 is the heart of this book. Chapter 3 is devoted to the Tikhonov regularization using the theory of reproducing kernels with applications to numerical and practical solutions of bounded linear operator equations. In Chapter 4, the numerical real inversion formulas of the Laplace transform are presented by applying the Tikhonov regularization, where the reproducing kernels play a key role in the results. Chapter 5 deals with ordinary differential equations; Chapter 6 includes many concrete results for various fundamental partial differential equations. In Chapt...

Carbothermic Synthesis of ~820- m UN Kernels. Investigation of Process Variables

Energy Technology Data Exchange (ETDEWEB)

Lindemer, Terrence [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Silva, Chinthaka M [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Henry, Jr, John James [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); McMurray, Jake W [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Jolly, Brian C [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Hunt, Rodney Dale [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Terrani, Kurt A [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2015-06-01

This report details the continued investigation of process variables involved in converting sol-gel-derived, urainia-carbon microspheres to ~820-μm-dia. UN fuel kernels in flow-through, vertical refractory-metal crucibles at temperatures up to 2123 K. Experiments included calcining of air-dried UO₃-H₂O-C microspheres in Ar and H₂-containing gases, conversion of the resulting UO₂-C kernels to dense UO₂:2UC in the same gases and vacuum, and its conversion in N₂ to in UC_1-xN_x. The thermodynamics of the relevant reactions were applied extensively to interpret and control the process variables. Producing the precursor UO₂:2UC kernel of ~96% theoretical density was required, but its subsequent conversion to UC_1-xN_x at 2123 K was not accompanied by sintering and resulted in ~83-86% of theoretical density. Decreasing the UC_1-xN_x kernel carbide component via HCN evolution was shown to be quantitatively consistent with present and past experiments and the only useful application of H2 in the entire process.

Kernels for structured data

CERN Document Server

Gärtner, Thomas

2009-01-01

This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by

Accurate convolution/superposition for multi-resolution dose calculation using cumulative tabulated kernels

International Nuclear Information System (INIS)

Lu Weiguo; Olivera, Gustavo H; Chen Mingli; Reckwerdt, Paul J; Mackie, Thomas R

2005-01-01

Convolution/superposition (C/S) is regarded as the standard dose calculation method in most modern radiotherapy treatment planning systems. Different implementations of C/S could result in significantly different dose distributions. This paper addresses two major implementation issues associated with collapsed cone C/S: one is how to utilize the tabulated kernels instead of analytical parametrizations and the other is how to deal with voxel size effects. Three methods that utilize the tabulated kernels are presented in this paper. These methods differ in the effective kernels used: the differential kernel (DK), the cumulative kernel (CK) or the cumulative-cumulative kernel (CCK). They result in slightly different computation times but significantly different voxel size effects. Both simulated and real multi-resolution dose calculations are presented. For simulation tests, we use arbitrary kernels and various voxel sizes with a homogeneous phantom, and assume forward energy transportation only. Simulations with voxel size up to 1 cm show that the CCK algorithm has errors within 0.1% of the maximum gold standard dose. Real dose calculations use a heterogeneous slab phantom, both the 'broad' (5 x 5 cm 2 ) and the 'narrow' (1.2 x 1.2 cm 2 ) tomotherapy beams. Various voxel sizes (0.5 mm, 1 mm, 2 mm, 4 mm and 8 mm) are used for dose calculations. The results show that all three algorithms have negligible difference (0.1%) for the dose calculation in the fine resolution (0.5 mm voxels). But differences become significant when the voxel size increases. As for the DK or CK algorithm in the broad (narrow) beam dose calculation, the dose differences between the 0.5 mm voxels and the voxels up to 8 mm (4 mm) are around 10% (7%) of the maximum dose. As for the broad (narrow) beam dose calculation using the CCK algorithm, the dose differences between the 0.5 mm voxels and the voxels up to 8 mm (4 mm) are around 1% of the maximum dose. Among all three methods, the CCK algorithm

7 CFR 981.401 - Adjusted kernel weight.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel weight... kernels in excess of five percent; less shells, if applicable; less processing loss of one percent for...

Migration of ThO2 kernels under the influence of a temperature gradient

International Nuclear Information System (INIS)

Smith, C.L.

1976-11-01

BISO coated ThO 2 fertile fuel kernels will migrate up the thermal gradients imposed across coated particles during HTGR operation. Thorium dioxide kernel migration has been studied as a function of temperature (1300 to 1700 0 C) and ThO 2 kernel burnup (0.9 to 5.8 percent FIMA) in out-of-pile, postirradiation thermal gradient heating experiments. The studies were conducted to obtain descriptions of migration rates that will be used in core design studies to evaluate the impact of ThO 2 migration on fertile fuel performance in an operating HTGR and to define characteristics needed by any comprehensive model describing ThO 2 kernel migration. The kinetics data generated in these postirradiation studies are consistent with in-pile data collected by investigators at Oak Ridge National Laboratory, which supports use of the more precise postirradiation heating results in HTGR core design studies. Observations of intergranular carbon deposits on the cool side of migrating kernels support the assumption that the kinetics of kernel migration are controlled by solid state diffusion within irradiated ThO 2 kernels. The migration is characterized by a period of no migration (incubation period) followed by migration at the equilibrium rate for ThO 2 . The incubation period decreases with increasing temperature and kernel burnup. The improved understanding of the kinetics of ThO 2 kernel migration provided by this work will contribute to an optimization of HTGR core design and an increased confidence in fuel performance predictions

Adsorption studies on bleaching of palm-kernel oil with activated ...

African Journals Online (AJOL)

Adsorption studies were performed on bleaching of palm-kernel oil (PKO) with different weight ratios of activated bentonite clay/PKO, ranging from 0.25% to 3% and at different temperatures of 70ºC, 80ºC, 90ºC and 100ºC. The equilibrium data generated were subsequently fitted with Langmuir and Freundlich adsorption ...

Testing Infrastructure for Operating System Kernel Development

DEFF Research Database (Denmark)

Walter, Maxwell; Karlsson, Sven

2014-01-01

Testing is an important part of system development, and to test effectively we require knowledge of the internal state of the system under test. Testing an operating system kernel is a challenge as it is the operating system that typically provides access to this internal state information. Multi......-core kernels pose an even greater challenge due to concurrency and their shared kernel state. In this paper, we present a testing framework that addresses these challenges by running the operating system in a virtual machine, and using virtual machine introspection to both communicate with the kernel...... and obtain information about the system. We have also developed an in-kernel testing API that we can use to develop a suite of unit tests in the kernel. We are using our framework for for the development of our own multi-core research kernel....

7 CFR 51.1403 - Kernel color classification.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Kernel color classification. 51.1403 Section 51.1403... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Kernel Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color...

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.

Science.gov (United States)

Bandeira E Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose

2017-06-07

Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. Copyright © 2017 Bandeira e Sousa et al.

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

Directory of Open Access Journals (Sweden)

Massaine Bandeira e Sousa

2017-06-01

Full Text Available Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1 single-environment, main genotypic effect model (SM; (2 multi-environment, main genotypic effects model (MM; (3 multi-environment, single variance G×E deviation model (MDs; and (4 multi-environment, environment-specific variance G×E deviation model (MDe. Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB, and a nonlinear kernel Gaussian kernel (GK. The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets, having different numbers of maize hybrids evaluated in different environments for grain yield (GY, plant height (PH, and ear height (EH. Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.

Theoretical developments for interpreting kernel spectral clustering from alternative viewpoints

Directory of Open Access Journals (Sweden)

Diego Peluffo-Ordóñez

2017-08-01

Full Text Available To perform an exploration process over complex structured data within unsupervised settings, the so-called kernel spectral clustering (KSC is one of the most recommended and appealing approaches, given its versatility and elegant formulation. In this work, we explore the relationship between (KSC and other well-known approaches, namely normalized cut clustering and kernel k-means. To do so, we first deduce a generic KSC model from a primal-dual formulation based on least-squares support-vector machines (LS-SVM. For experiments, KSC as well as other consider methods are assessed on image segmentation tasks to prove their usability.

Mean propagation kernels for transport in correlated stochastic media at unresolved scales, illustration with a problem in atmospheric radiation

International Nuclear Information System (INIS)

Davis, A. B.

2007-01-01

A simple and effective framework is presented for modeling transport processes unfolding at computationally and/or observationally unresolved scales in scattering, absorbing and emitting media. The new approach acts directly on the spatial (i.e., propagation) part of the kernel in the integral formulation of the generic linear transport equation framed for stochastic media with a wide variety of spatial correlations, going far beyond the Markov-Poisson class used in the classic Pomraning-Levermore model. This statistical look at the extinction of un-collided particle beams takes us away from the standard exponential law of transmission. New transmission laws arise that are generally not exponential, often not even for asymptotically large jumps. This means that, from this perspective on random spatial variability, there is no 'effective medium' per se nor homogenization technique that can be used to describe the effects of unresolved fluctuations of the collision coefficient. However, one can still rewrite the transport equation, at least in its integral form, in a manner that looks like its counterpart for uniform media, but with a modified propagation kernel. Implementation in a Monte Carlo scheme is trivially simple and numerical results are presented that illustrate the bulk effect of the new parameterization for plane-parallel geometry. We survey time-domain diagnostics of solar radiative transfer in the Earth's cloudy atmosphere obtained recently from high-resolution ground-based spectroscopy, and it is shown that they are explained comprehensively by the new model. Finally, we discuss possible applications of this modeling framework in nuclear engineering. (authors)

A new license plate extraction framework based on fast mean shift

Science.gov (United States)

Pan, Luning; Li, Shuguang

2010-08-01

License plate extraction is considered to be the most crucial step of Automatic license plate recognition (ALPR) system. In this paper, a region-based license plate hybrid detection method is proposed to solve practical problems under complex background in which existing large quantity of disturbing information. In this method, coarse license plate location is carried out firstly to get the head part of a vehicle. Then a new Fast Mean Shift method based on random sampling of Kernel Density Estimate (KDE) is adopted to segment the color vehicle images, in order to get candidate license plate regions. The remarkable speed-up it brings makes Mean Shift segmentation more suitable for this application. Feature extraction and classification is used to accurately separate license plate from other candidate regions. At last, tilted license plate regulation is used for future recognition steps.

Dielectric properties of almond kernels associated with radio frequency and microwave pasteurization

Science.gov (United States)

Li, Rui; Zhang, Shuang; Kou, Xiaoxi; Ling, Bo; Wang, Shaojin

2017-02-01

To develop advanced pasteurization treatments based on radio frequency (RF) or microwave (MW) energy, dielectric properties of almond kernels were measured by using an open-ended coaxial-line probe and impedance analyzer at frequencies between 10 and 3000 MHz, moisture contents between 4.2% to 19.6% w.b. and temperatures between 20 and 90 °C. The results showed that both dielectric constant and loss factor of the almond kernels decreased sharply with increasing frequency over the RF range (10-300 MHz), but gradually over the measured MW range (300-3000 MHz). Both dielectric constant and loss factor of almond kernels increased with increasing temperature and moisture content, and largely enhanced at higher temperature and moisture levels. Quadratic polynomial equations were developed to best fit the relationship between dielectric constant or loss factor at 27, 40, 915 or 2450 MHz and sample temperature/moisture content with R2 greater than 0.967. Penetration depth of electromagnetic wave into samples decreased with increasing frequency (27-2450 MHz), moisture content (4.2-19.6% w.b.) and temperature (20-90 °C). The temperature profiles of RF heated almond kernels under three moisture levels were made using experiment and computer simulation based on measured dielectric properties. Based on the result of this study, RF treatment has potential to be practically used for pasteurization of almond kernels with acceptable heating uniformity.

A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries

Directory of Open Access Journals (Sweden)

Wen-An Yang

2016-01-01

Full Text Available Lithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Remaining useful life (RUL prediction of lithium-ion batteries before the future failure event is extremely crucial for proactive maintenance/safety actions. This study proposes a hybrid prognostic approach that can predict the RUL of degraded lithium-ion batteries using physical laws and data-driven modeling simultaneously. In this hybrid prognostic approach, the relevant vectors obtained with the selective kernel ensemble-based relevance vector machine (RVM learning algorithm are fitted to the physical degradation model, which is then extrapolated to failure threshold for estimating the RUL of the lithium-ion battery of interest. The experimental results indicated that the proposed hybrid prognostic approach can accurately predict the RUL of degraded lithium-ion batteries. Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs like the conventional backpropagation (BP algorithm and support vector machines (SVMs. In addition, an investigation is also conducted to identify the effects of RVM learning algorithm on the proposed hybrid prognostic approach.

The definition of kernel Oz

OpenAIRE

Smolka, Gert

1994-01-01

Oz is a concurrent language providing for functional, object-oriented, and constraint programming. This paper defines Kernel Oz, a semantically complete sublanguage of Oz. It was an important design requirement that Oz be definable by reduction to a lean kernel language. The definition of Kernel Oz introduces three essential abstractions: the Oz universe, the Oz calculus, and the actor model. The Oz universe is a first-order structure defining the values and constraints Oz computes with. The ...

Insecticidal effect and impact of fitness of three diatomaceous earths on different maize hybrids for the eco-friendly control of the invasive stored-product pest Prostephanus truncatus (Horn).

Science.gov (United States)

Kavallieratos, Nickolas G; Athanassiou, Christos G; Peteinatos, Gerassimos G; Boukouvala, Maria C; Benelli, Giovanni

2018-04-01

Diatomaceous earths (DEs) are able to successfully protect grain commodities from noxious stored-product insect and mite infestations; however, their effectiveness may be moderated by the grain hybrid or variety they are applied to. There is a gap of information on the comparison of the efficacy of different DEs when are applied on different maize hybrids against Prostephanus truncatus (Horn). Therefore, here we tested three commercially available DEs (DEA-P at 75 and 150 ppm, Protect-It at 500 ppm, and PyriSec at 500 ppm) on five different maize hybrids (Calaria, Doxa, Rio Grande, Sisco, and Studio) for the control of P. truncatus adults in terms of mortality (at 7 and 14 days), progeny production, properties of the infested maize hybrids (number and weight of kernels with or without holes, number of holes per kernel) and the adherence level of the tested DEs to the kernels. DEA-P was very effective at 75 ppm while a considerable proportion of the exposed P. truncatus adults was still alive after 14 days of exposure on all maize hybrids treated with 500 ppm of Protect-It or PyriSec, even though it was 3.3 times higher than the maximal application tested dose of DEA-P. Apart from parental mortality, DEA-P was able to reduce P. truncatus progeny production in all hybrids contrary to Protect-It or PyriSec. The adherence ratios were always higher for DEA-P than Protect-It or PyriSec to all maize hybrids. The highest numbers of kernels (or weight of kernels) without holes were noticed after their treatment with DEA-P. Doxa and Sisco performed better than Calaria, Rio Grande, or Studio based on the differences found concerning the numbers of kernels without holes at treatments with DEA-P and Protect-It. Overall, the findings of our study indicate the high potentiality of DEA-P as protectant of different maize hybrids to P. truncatus infestations at low doses, a fact that could help the eco-friendly management of this noxious species in the stored

Fabrication of Uranium Oxycarbide Kernels for HTR Fuel

International Nuclear Information System (INIS)

Barnes, Charles; Richardson, Clay; Nagley, Scott; Hunn, John; Shaber, Eric

2010-01-01

Babcock and Wilcox (B and W) has been producing high quality uranium oxycarbide (UCO) kernels for Advanced Gas Reactor (AGR) fuel tests at the Idaho National Laboratory. In 2005, 350-(micro)m, 19.7% 235U-enriched UCO kernels were produced for the AGR-1 test fuel. Following coating of these kernels and forming the coated-particles into compacts, this fuel was irradiated in the Advanced Test Reactor (ATR) from December 2006 until November 2009. B and W produced 425-(micro)m, 14% enriched UCO kernels in 2008, and these kernels were used to produce fuel for the AGR-2 experiment that was inserted in ATR in 2010. B and W also produced 500-(micro)m, 9.6% enriched UO2 kernels for the AGR-2 experiments. Kernels of the same size and enrichment as AGR-1 were also produced for the AGR-3/4 experiment. In addition to fabricating enriched UCO and UO2 kernels, B and W has produced more than 100 kg of natural uranium UCO kernels which are being used in coating development tests. Successive lots of kernels have demonstrated consistent high quality and also allowed for fabrication process improvements. Improvements in kernel forming were made subsequent to AGR-1 kernel production. Following fabrication of AGR-2 kernels, incremental increases in sintering furnace charge size have been demonstrated. Recently small scale sintering tests using a small development furnace equipped with a residual gas analyzer (RGA) has increased understanding of how kernel sintering parameters affect sintered kernel properties. The steps taken to increase throughput and process knowledge have reduced kernel production costs. Studies have been performed of additional modifications toward the goal of increasing capacity of the current fabrication line to use for production of first core fuel for the Next Generation Nuclear Plant (NGNP) and providing a basis for the design of a full scale fuel fabrication facility.

Preparation and characterization of active carbon using palm kernel ...

African Journals Online (AJOL)

Activated carbons were prepared from Palm kernel shells. Carbonization temperature was 6000C, at a residence time of 5 min for each process. Chemical activation was done by heating a mixture of carbonized material and the activating agents at a temperature of 700C to form a paste, followed by subsequent cooling and ...

Anisotropic hydrodynamics with a scalar collisional kernel

Science.gov (United States)

Almaalol, Dekrayat; Strickland, Michael

2018-04-01

Prior studies of nonequilibrium dynamics using anisotropic hydrodynamics have used the relativistic Anderson-Witting scattering kernel or some variant thereof. In this paper, we make the first study of the impact of using a more realistic scattering kernel. For this purpose, we consider a conformal system undergoing transversally homogenous and boost-invariant Bjorken expansion and take the collisional kernel to be given by the leading order 2 ↔2 scattering kernel in scalar λ ϕ4 . We consider both classical and quantum statistics to assess the impact of Bose enhancement on the dynamics. We also determine the anisotropic nonequilibrium attractor of a system subject to this collisional kernel. We find that, when the near-equilibrium relaxation-times in the Anderson-Witting and scalar collisional kernels are matched, the scalar kernel results in a higher degree of momentum-space anisotropy during the system's evolution, given the same initial conditions. Additionally, we find that taking into account Bose enhancement further increases the dynamically generated momentum-space anisotropy.

Object classification and detection with context kernel descriptors

DEFF Research Database (Denmark)

Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

2014-01-01

Context information is important in object representation. By embedding context cue of image attributes into kernel descriptors, we propose a set of novel kernel descriptors called Context Kernel Descriptors (CKD) for object classification and detection. The motivation of CKD is to use spatial...... consistency of image attributes or features defined within a neighboring region to improve the robustness of descriptor matching in kernel space. For feature selection, Kernel Entropy Component Analysis (KECA) is exploited to learn a subset of discriminative CKD. Different from Kernel Principal Component...

Ranking Support Vector Machine with Kernel Approximation.

Science.gov (United States)

Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

Ranking Support Vector Machine with Kernel Approximation

Directory of Open Access Journals (Sweden)

Kai Chen

2017-01-01

Full Text Available Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels can give higher accuracy than linear RankSVM (RankSVM with a linear kernel for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

Dose point kernels for beta-emitting radioisotopes

International Nuclear Information System (INIS)

Prestwich, W.V.; Chan, L.B.; Kwok, C.S.; Wilson, B.

1986-01-01

Knowledge of the dose point kernel corresponding to a specific radionuclide is required to calculate the spatial dose distribution produced in a homogeneous medium by a distributed source. Dose point kernels for commonly used radionuclides have been calculated previously using as a basis monoenergetic dose point kernels derived by numerical integration of a model transport equation. The treatment neglects fluctuations in energy deposition, an effect which has been later incorporated in dose point kernels calculated using Monte Carlo methods. This work describes new calculations of dose point kernels using the Monte Carlo results as a basis. An analytic representation of the monoenergetic dose point kernels has been developed. This provides a convenient method both for calculating the dose point kernel associated with a given beta spectrum and for incorporating the effect of internal conversion. An algebraic expression for allowed beta spectra has been accomplished through an extension of the Bethe-Bacher approximation, and tested against the exact expression. Simplified expression for first-forbidden shape factors have also been developed. A comparison of the calculated dose point kernel for 32 P with experimental data indicates good agreement with a significant improvement over the earlier results in this respect. An analytic representation of the dose point kernel associated with the spectrum of a single beta group has been formulated. 9 references, 16 figures, 3 tables

Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT).

Science.gov (United States)

Urrutia, Eugene; Lee, Seunggeun; Maity, Arnab; Zhao, Ni; Shen, Judong; Li, Yun; Wu, Michael C

Analysis of rare genetic variants has focused on region-based analysis wherein a subset of the variants within a genomic region is tested for association with a complex trait. Two important practical challenges have emerged. First, it is difficult to choose which test to use. Second, it is unclear which group of variants within a region should be tested. Both depend on the unknown true state of nature. Therefore, we develop the Multi-Kernel SKAT (MK-SKAT) which tests across a range of rare variant tests and groupings. Specifically, we demonstrate that several popular rare variant tests are special cases of the sequence kernel association test which compares pair-wise similarity in trait value to similarity in the rare variant genotypes between subjects as measured through a kernel function. Choosing a particular test is equivalent to choosing a kernel. Similarly, choosing which group of variants to test also reduces to choosing a kernel. Thus, MK-SKAT uses perturbation to test across a range of kernels. Simulations and real data analyses show that our framework controls type I error while maintaining high power across settings: MK-SKAT loses power when compared to the kernel for a particular scenario but has much greater power than poor choices.

Modelling microwave heating of discrete samples of oil palm kernels

International Nuclear Information System (INIS)

Law, M.C.; Liew, E.L.; Chang, S.L.; Chan, Y.S.; Leo, C.P.

2016-01-01

Highlights: • Microwave (MW) drying of oil palm kernels is experimentally determined and modelled. • MW heating of discrete samples of oil palm kernels (OPKs) is simulated. • OPK heating is due to contact effect, MW interference and heat transfer mechanisms. • Electric field vectors circulate within OPKs sample. • Loosely-packed arrangement improves temperature uniformity of OPKs. - Abstract: Recently, microwave (MW) pre-treatment of fresh palm fruits has showed to be environmentally friendly compared to the existing oil palm milling process as it eliminates the condensate production of palm oil mill effluent (POME) in the sterilization process. Moreover, MW-treated oil palm fruits (OPF) also possess better oil quality. In this work, the MW drying kinetic of the oil palm kernels (OPK) was determined experimentally. Microwave heating/drying of oil palm kernels was modelled and validated. The simulation results show that temperature of an OPK is not the same over the entire surface due to constructive and destructive interferences of MW irradiance. The volume-averaged temperature of an OPK is higher than its surface temperature by 3–7 °C, depending on the MW input power. This implies that point measurement of temperature reading is inadequate to determine the temperature history of the OPK during the microwave heating process. The simulation results also show that arrangement of OPKs in a MW cavity affects the kernel temperature profile. The heating of OPKs were identified to be affected by factors such as local electric field intensity due to MW absorption, refraction, interference, the contact effect between kernels and also heat transfer mechanisms. The thermal gradient patterns of OPKs change as the heating continues. The cracking of OPKs is expected to occur first in the core of the kernel and then it propagates to the kernel surface. The model indicates that drying of OPKs is a much slower process compared to its MW heating. The model is useful

A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval.

Science.gov (United States)

Cai, Jia; Tang, Yi

2018-02-01

Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method. The main contributions of the paper are: (1) A new kernel CCA algorithm is developed, (2) theoretical convergence of the proposed algorithm is addressed by means of scaled condition number, (3) a lower bound which addresses the minimum number of iterations is presented. We test on both synthetic dataset and several real-world datasets in cross-language document retrieval and content-based image retrieval to demonstrate the effectiveness of the proposed algorithm. Numerical results imply the performance and efficiency of the new algorithm, which is competitive with several state-of-the-art kernel CCA methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Wigner functions defined with Laplace transform kernels.

Science.gov (United States)

Oh, Se Baek; Petruccelli, Jonathan C; Tian, Lei; Barbastathis, George

2011-10-24

We propose a new Wigner-type phase-space function using Laplace transform kernels--Laplace kernel Wigner function. Whereas momentum variables are real in the traditional Wigner function, the Laplace kernel Wigner function may have complex momentum variables. Due to the property of the Laplace transform, a broader range of signals can be represented in complex phase-space. We show that the Laplace kernel Wigner function exhibits similar properties in the marginals as the traditional Wigner function. As an example, we use the Laplace kernel Wigner function to analyze evanescent waves supported by surface plasmon polariton. © 2011 Optical Society of America

Metabolic network prediction through pairwise rational kernels.

Science.gov (United States)

Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

2014-09-26

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy

Integrating K-means Clustering with Kernel Density Estimation for the Development of a Conditional Weather Generation Downscaling Model

Science.gov (United States)

Chen, Y.; Ho, C.; Chang, L.

2011-12-01

In previous decades, the climate change caused by global warming increases the occurrence frequency of extreme hydrological events. Water supply shortages caused by extreme events create great challenges for water resource management. To evaluate future climate variations, general circulation models (GCMs) are the most wildly known tools which shows possible weather conditions under pre-defined CO2 emission scenarios announced by IPCC. Because the study area of GCMs is the entire earth, the grid sizes of GCMs are much larger than the basin scale. To overcome the gap, a statistic downscaling technique can transform the regional scale weather factors into basin scale precipitations. The statistic downscaling technique can be divided into three categories include transfer function, weather generator and weather type. The first two categories describe the relationships between the weather factors and precipitations respectively based on deterministic algorithms, such as linear or nonlinear regression and ANN, and stochastic approaches, such as Markov chain theory and statistical distributions. In the weather type, the method has ability to cluster weather factors, which are high dimensional and continuous variables, into weather types, which are limited number of discrete states. In this study, the proposed downscaling model integrates the weather type, using the K-means clustering algorithm, and the weather generator, using the kernel density estimation. The study area is Shihmen basin in northern of Taiwan. In this study, the research process contains two steps, a calibration step and a synthesis step. Three sub-steps were used in the calibration step. First, weather factors, such as pressures, humidities and wind speeds, obtained from NCEP and the precipitations observed from rainfall stations were collected for downscaling. Second, the K-means clustering grouped the weather factors into four weather types. Third, the Markov chain transition matrixes and the

Selection and properties of alternative forming fluids for TRISO fuel kernel production

Energy Technology Data Exchange (ETDEWEB)

Baker, M.P. [Colorado School of Mines, 1500 Illinois St., Golden, CO 80401 (United States); King, J.C., E-mail: kingjc@mines.edu [Colorado School of Mines, 1500 Illinois St., Golden, CO 80401 (United States); Gorman, B.P. [Colorado School of Mines, 1500 Illinois St., Golden, CO 80401 (United States); Marshall, D.W. [Idaho National Laboratory, 2525 N. Fremont Avenue, P.O. Box 1625, Idaho Falls, ID 83415 (United States)

2013-01-15

Highlights: Black-Right-Pointing-Pointer Forming fluid selection criteria developed for TRISO kernel production. Black-Right-Pointing-Pointer Ten candidates selected for further study. Black-Right-Pointing-Pointer Density, viscosity, and surface tension measured for first time. Black-Right-Pointing-Pointer Settling velocity and heat transfer rates calculated. Black-Right-Pointing-Pointer Three fluids recommended for kernel production testing. - Abstract: Current Very High Temperature Reactor (VHTR) designs incorporate TRi-structural ISOtropic (TRISO) fuel, which consists of a spherical fissile fuel kernel surrounded by layers of pyrolytic carbon and silicon carbide. An internal sol-gel process forms the fuel kernel using wet chemistry to produce uranium oxyhydroxide gel spheres by dropping a cold precursor solution into a hot column of trichloroethylene (TCE). Over time, gelation byproducts inhibit complete gelation, and the TCE must be purified or discarded. The resulting TCE waste stream contains both radioactive and hazardous materials and is thus considered a mixed hazardous waste. Changing the forming fluid to a non-hazardous alternative could greatly improve the economics of TRISO fuel kernel production. Selection criteria for a replacement forming fluid narrowed a list of {approx}10,800 chemicals to yield ten potential replacement forming fluids: 1-bromododecane, 1-bromotetradecane, 1-bromoundecane, 1-chlorooctadecane, 1-chlorotetradecane, 1-iododecane, 1-iodododecane, 1-iodohexadecane, 1-iodooctadecane, and squalane. The density, viscosity, and surface tension for each potential replacement forming fluid were measured as a function of temperature between 25 Degree-Sign C and 80 Degree-Sign C. Calculated settling velocities and heat transfer rates give an overall column height approximation. 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane show the greatest promise as replacements, and future tests will verify their ability to form satisfactory

Influence Function and Robust Variant of Kernel Canonical Correlation Analysis

OpenAIRE

Alam, Md. Ashad; Fukumizu, Kenji; Wang, Yu-Ping

2017-01-01

Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). Both kernel CO and kernel CCO are sensitive to contaminated data, even when bounded positive definite kernels are used. To the best of our knowledge, there are few well-founded robust kernel methods for statistical unsupervised learning. In addition, while the influence function (IF) of an estimator can characterize its robustness, asymptotic ...

Spectral Kernel Approach to Study Radiative Response of Climate Variables and Interannual Variability of Reflected Solar Spectrum

Science.gov (United States)

Jin, Zhonghai; Wielicki, Bruce A.; Loukachine, Constantin; Charlock, Thomas P.; Young, David; Noeel, Stefan

2011-01-01

The radiative kernel approach provides a simple way to separate the radiative response to different climate parameters and to decompose the feedback into radiative and climate response components. Using CERES/MODIS/Geostationary data, we calculated and analyzed the solar spectral reflectance kernels for various climate parameters on zonal, regional, and global spatial scales. The kernel linearity is tested. Errors in the kernel due to nonlinearity can vary strongly depending on climate parameter, wavelength, surface, and solar elevation; they are large in some absorption bands for some parameters but are negligible in most conditions. The spectral kernels are used to calculate the radiative responses to different climate parameter changes in different latitudes. The results show that the radiative response in high latitudes is sensitive to the coverage of snow and sea ice. The radiative response in low latitudes is contributed mainly by cloud property changes, especially cloud fraction and optical depth. The large cloud height effect is confined to absorption bands, while the cloud particle size effect is found mainly in the near infrared. The kernel approach, which is based on calculations using CERES retrievals, is then tested by direct comparison with spectral measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) (a different instrument on a different spacecraft). The monthly mean interannual variability of spectral reflectance based on the kernel technique is consistent with satellite observations over the ocean, but not over land, where both model and data have large uncertainty. RMS errors in kernel ]derived monthly global mean reflectance over the ocean compared to observations are about 0.001, and the sampling error is likely a major component.

Thermochemical equilibrium in a kernel of a UN TRISO coated fuel particle

International Nuclear Information System (INIS)

Kim, Young Min; Jo, C. K.; Lim, H. S.; Cho, M. S.; Lee, W. J.

2012-01-01

A coated fuel particle (CFP) with a uranium mononitride (UN) kernel has been recently considered as an advanced fuel option, such as in fully ceramic micro encapsulated (FCM) replacement fuel for light water reactors (LWRs). In FCM fuel, a large number of tri isotropic coated fuel particles (TRISOs) are embedded in a silicon carbide (SiC) matrix. Thermochemical equilibrium calculations can predict the chemical behaviors of a kernel in a TRISO of FCM fuel during irradiation. They give information on the kind and quantity of gases generated in a kernel during irradiation. This study treats the quantitative analysis of thermochemical equilibrium in a UN TRISO of FCM LWR fuel using HSC software

Nitrogen deficiency in maize. I. Effects on crop growth, development, dry matter partitioning, and kernel set

International Nuclear Information System (INIS)

Uhart, S.A.; Andrade, F.H.

1995-01-01

Variations in N availability affect growth and development of maize (Zea mays L.) and may lead to changes in crop physiological conditions at flowering and in kernel set. The objectives of this study were (i) to establish the effect of N availability on crop development, crop radiation interception, radiation use efficiency, and dry matter partitioning; and (ii) to study the relationship between kernel number and crop growth at flowering and between kernel number and crop N accumulation at flowering. Three experiments with a commercial hybrid (DK636) were carried out under field conditions at the INTA Balcarce Experimental Station, Argentina, without water limitations. The treatments consisted of different radiation levels, obtained by shading, combined with different levels of N availability obtained by the addition of N fertilizer or organic matter to immobilize N. Nitrogen deficiencies delayed both vegetative and reproductive phenological development, slightly reduced leaf emergence rate, and strongly diminished leaf expansion rate and leaf area duration. Nitrogen deficiencies reduced radiation interception as much as radiation use efficiency and their effects on the ear dry mater/total dry matter ratio at harvest were associated with crop growth rate reductions at flowering. Dry matter partitioning to reproductive sinks at flowering and the ear dry matter/total dry matter ratio at harvest were reduced by N shortages. Significant relationships between kernel number and N accumulation rate or crop growth rate at flowering were fitted by linear + plateau functions with thresholds above which kernel number and grain yield did not increase

The Linux kernel as flexible product-line architecture

NARCIS (Netherlands)

M. de Jonge (Merijn)

2002-01-01

textabstractThe Linux kernel source tree is huge ($>$ 125 MB) and inflexible (because it is difficult to add new kernel components). We propose to make this architecture more flexible by assembling kernel source trees dynamically from individual kernel components. Users then, can select what

A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the Support Vector Machine

International Nuclear Information System (INIS)

Chia, Yen Yee; Lee, Lam Hong; Shafiabady, Niusha; Isa, Dino

2015-01-01

energy management system with conventional sequential programming control (if-else condition). An optimized load prediction classification model is investigated and implemented. This C-Support Vector Classification yields classification accuracy of 100% using 17 support vectors in 0.004866 s of training time. The Polynomial kernel is the optimum kernel in our experiments where the C and g values are 2 and 0.25 respectively. However, for the load profile regression model which was implemented in the K-step ahead of load prediction, the radial basis function (RBF) kernel was chosen due to the highest squared correlation coefficient and the lowest mean squared error. Results obtained shows that the proposed SVM load predictive energy management system accurately identifies and predicts the load demand. This has been justified by the supercapacitor charging and leading the peak current demand by 200 ms for different load profiles with different optimized regression models. This methodology optimizes the cost of the system by reducing the amount of power electronics within the hybrid energy storage system, and also prolongs the batteries’ lifespan as previously mentioned

Interspecific somatic hybrids between Cyclamen persicum and C. coum, two sexually incompatible species.

Science.gov (United States)

Prange, Anika Nadja Sabine; Bartsch, Melanie; Meiners, Julia; Serek, Margrethe; Winkelmann, Traud

2012-04-01

By applying polyethylene glycol (PEG)-mediated protoplast fusion, the first somatic hybrids were obtained between Cyclamen persicum (2n = 2x = 48) and C. coum (2n = 2x = 30)-two species that cannot be combined by cross breeding. Heterofusion was detected by double fluorescent staining with fluorescein diacetate and scopoletin. The highest heterofusion frequencies (of about 5%) resulted from a protocol using a protoplast density of 1 × 10(6)/mL and 40% PEG. The DNA content of C. coum was estimated for the first time by propidium iodide staining to be 14.7 pg/2C and was 4.6 times higher than that of C. persicum. Among 200 in vitro plantlets regenerated from fusion experiments, most resembled the C. coum parent, whereas only 5 plants showed typical C. persicum phenotypes and 46 had a deviating morphology. By flow cytometry, six putative somatic hybrids were identified. A species-specific DNA marker was developed based on the sequence of the 5.8S gene in the ribosomal nuclear DNA and its flanking internal transcribed spacers ITS1 and ITS2. The hybrid status of only one plant could be verified by the species-specific DNA marker as well as sequencing of the amplification product. RAPD markers turned out to be less informative and applicable for hybrid identification, as no clear additivity of the parental marker bands was observed. Chromosome counting in root tips of four hybrids revealed the presence of the 30 C. coum chromosomes and 2-41 additional ones indicating elimination of C. persicum chromosomes. © Springer-Verlag 2011

Exploiting graph kernels for high performance biomedical relation extraction.

Science.gov (United States)

Panyam, Nagesh C; Verspoor, Karin; Cohn, Trevor; Ramamohanarao, Kotagiri

2018-01-30

Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences. Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM

The Influence of Oxidation on the Quality of U3O8 Kernels

International Nuclear Information System (INIS)

Damunir; Sukarsono; Indra Suryawan

2002-01-01

The influence of oxidation on quality of U 3 O 8 kernels have been studied. The investigated the influence was changed of time and temperature oxidation of Uranyl-4(ammonia)-2(polyvinyl alcohol) gel on surface area, pore radius, pore volume, porosity and diameter size of U 3 O 8 kernel. The spherical of uranyl-4(ammonia)-2(polyvinyl alcohol) containing 150g U/l were oxidized at 200-800 o C temperature for 2-24 hours, formed U 3 O 8 kernel. After that, the quality of U 3 O 8 kernel were measured by their physical properties i.e. the surface area and pore radius using Surface areameter with N 2 gas as absorbent. The pore volume and porosity using pycnometer with aquabidest of water as a solvent, diameter size using a optical microscope. The experiment results, showed that the time and temperature oxidation of uranyl-4(ammonia)-2(polyvinyl alcohol) grain the influence to quality of U 3 O 8 in formed the surface area of specific, pore radius, pore volume of specific, porosity, and diameter size of U 3 O 8 kernel. The best accurred at 600-800 o C oxidation temperature and oxidation time was 2-5 hours. The resulted quality of U 3 O 8 kernel i.e surface area of specific was 10.84 - 5.99 m 2 /g, pore volume of specific was 10.35x10 -2 - 3.23x10 -2 cc/g, pore radius was 21.05 - 24.62 Angstrom, diameter size was 1264 - 1456 μm and porosity was 49.49 - 21.36 % Vol with Cumulative analysis error was 8.55 % Vol. (author)

Adiabatic-connection fluctuation-dissipation DFT for the structural properties of solids—The renormalized ALDA and electron gas kernels

Energy Technology Data Exchange (ETDEWEB)

Patrick, Christopher E., E-mail: chripa@fysik.dtu.dk; Thygesen, Kristian S., E-mail: thygesen@fysik.dtu.dk [Center for Atomic-Scale Materials Design (CAMD), Department of Physics, Technical University of Denmark, DK—2800 Kongens Lyngby (Denmark)

2015-09-14

We present calculations of the correlation energies of crystalline solids and isolated systems within the adiabatic-connection fluctuation-dissipation formulation of density-functional theory. We perform a quantitative comparison of a set of model exchange-correlation kernels originally derived for the homogeneous electron gas (HEG), including the recently introduced renormalized adiabatic local-density approximation (rALDA) and also kernels which (a) satisfy known exact limits of the HEG, (b) carry a frequency dependence, or (c) display a 1/k{sup 2} divergence for small wavevectors. After generalizing the kernels to inhomogeneous systems through a reciprocal-space averaging procedure, we calculate the lattice constants and bulk moduli of a test set of 10 solids consisting of tetrahedrally bonded semiconductors (C, Si, SiC), ionic compounds (MgO, LiCl, LiF), and metals (Al, Na, Cu, Pd). We also consider the atomization energy of the H{sub 2} molecule. We compare the results calculated with different kernels to those obtained from the random-phase approximation (RPA) and to experimental measurements. We demonstrate that the model kernels correct the RPA’s tendency to overestimate the magnitude of the correlation energy whilst maintaining a high-accuracy description of structural properties.

GRIM : Leveraging GPUs for Kernel integrity monitoring

NARCIS (Netherlands)

Koromilas, Lazaros; Vasiliadis, Giorgos; Athanasopoulos, Ilias; Ioannidis, Sotiris

2016-01-01

Kernel rootkits can exploit an operating system and enable future accessibility and control, despite all recent advances in software protection. A promising defense mechanism against rootkits is Kernel Integrity Monitor (KIM) systems, which inspect the kernel text and data to discover any malicious

Blow-up in multidimensional aggregation equations with mildly singular interaction kernels

International Nuclear Information System (INIS)

Bertozzi, Andrea L; Laurent, Thomas; Carrillo, José A

2009-01-01

We consider the multidimensional aggregation equation u t − ∇· (u∇K * u) = 0 in which the radially symmetric attractive interaction kernel has a mild singularity at the origin (Lipschitz or better). In the case of bounded initial data, finite time singularity has been proved for kernels with a Lipschitz point at the origin (Bertozzi and Laurent 2007 Commun. Math. Sci. 274 717–35), whereas for C 2 kernels there is no finite-time blow-up. We prove, under mild monotonicity assumptions on the kernel K, that the Osgood condition for well-posedness of the ODE characteristics determines global in time well-posedness of the PDE with compactly supported bounded nonnegative initial data. When the Osgood condition is violated, we present a new proof of finite time blow-up that extends previous results, requiring radially symmetric data, to general bounded, compactly supported nonnegative initial data without symmetry. We also present a new analysis of radially symmetric solutions under less strict monotonicity conditions. Finally, we conclude with a discussion of similarity solutions for the case K(x) = |x| and some open problems

Review of Palm Kernel Oil Processing And Storage Techniques In South East Nigeria

Directory of Open Access Journals (Sweden)

Okeke CG

2017-06-01

Full Text Available An assessment of palm kernel processing and storage in South-Eastern Nigeria was carried out by investigative survey approach. The survey basically ascertained the extent of mechanization applicable in the area to enable the palm kernel processors and agricultural policy makers, device the modalities for improving palm kernel processing in the area. According to the results obtained from the study, in Abia state, 85% of the respondents use mechanical method while 15% use manual method in cracking their kernels. In Imo state, 83% of the processors use mechanical method while 17% use manual method. In Enugu and Ebonyi state, 70% and 50% of the processors respectively use mechanical method. It is only in Anambra state that greater number of the processors (50% use manual method while 45% use mechanical means. It is observable from the results that palm kernel oil extraction has not received much attention in mechanization. The ANOVA of the palm kernel oil extraction technique in South- East Nigeria showed significant difference in both the study area and oil extraction techniques at 5% level of probability. Results further revealed that in Abia State, 70% of the processors use complete fractional process in refining the palm kernel oil; 25% and 5% respectively use incomplete fractional process and zero refining process. In Anambra, 60% of the processors use complete fractional process and 40% use incomplete fractional process. Zero refining method is not applicable in Anambra state. In Enugu sate, 53% use complete fractional process while 25% and 22% respectively use zero refining and incomplete fractional process in refining the palm kernel oil. Imo state, mostly use complete fractional process (85% in refining palm kernel oil. About 10% use zero refining method while 5% of the processors use incomplete fractional process. Plastic containers and metal drums are dominantly used in most areas in south-east Nigeria for the storage of palm kernel oil.

Examining Potential Boundary Bias Effects in Kernel Smoothing on Equating: An Introduction for the Adaptive and Epanechnikov Kernels.

Science.gov (United States)

Cid, Jaime A; von Davier, Alina A

2015-05-01

Test equating is a method of making the test scores from different test forms of the same assessment comparable. In the equating process, an important step involves continuizing the discrete score distributions. In traditional observed-score equating, this step is achieved using linear interpolation (or an unscaled uniform kernel). In the kernel equating (KE) process, this continuization process involves Gaussian kernel smoothing. It has been suggested that the choice of bandwidth in kernel smoothing controls the trade-off between variance and bias. In the literature on estimating density functions using kernels, it has also been suggested that the weight of the kernel depends on the sample size, and therefore, the resulting continuous distribution exhibits bias at the endpoints, where the samples are usually smaller. The purpose of this article is (a) to explore the potential effects of atypical scores (spikes) at the extreme ends (high and low) on the KE method in distributions with different degrees of asymmetry using the randomly equivalent groups equating design (Study I), and (b) to introduce the Epanechnikov and adaptive kernels as potential alternative approaches to reducing boundary bias in smoothing (Study II). The beta-binomial model is used to simulate observed scores reflecting a range of different skewed shapes.

Adaptive Kernel in Meshsize Boosting Algorithm in KDE ...

African Journals Online (AJOL)

This paper proposes the use of adaptive kernel in a meshsize boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...

A kernel version of multivariate alteration detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

2013-01-01

Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.......Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations....

Moisture Dependence of physical Properties and Specific Heat Capacity of Neem (Azadirachta Indica A. Juss Kernels

Directory of Open Access Journals (Sweden)

A. Dauda

2017-02-01

Full Text Available This study investigated the effect of moisture content on the physical properties and specific heat capacity of Neem (Azadirachta Indica A. Juss nut kernels. The major, intermediate and minor axial dimensions of the kernels increased from 1.04 to 1.23cm, 0.42 to 0.6cm, and 0.32 to 0.45cm respectively, as the moisture content increased from 5.2 to 44.9 % (db. The arithmetic and geometric mean diameters determined at the same moisture level were significantly different from each other, with the arithmetic mean diameter being higher. In the above moisture range, one thousand kernel weight, true density, porosity, sphericity, roundness and surface area all increased linearly from 0.0987 to 0.1755kg, 632 to 733kgm-3, 6.42 to 32.14%, 41.3 to 47.5%, 22 to 36% and 13 to 24cm2 respectively, while bulk density decreased from 591.4 to 497.4kgm-3 with increase in moisture content. Angle of repose increased from 21.22 to 29.8o with increase in moisture content. The Static coefficient of friction on ply wood with grains parallel to the direction of movement ranged from 0.41 to 0.61, it ranged from 0.19 to 0.24 on on fiber glass, 0.28 to .038 on hessian bag material and 0.25 to 0.33 on galvanized steel sheet. The specific heat of the seed varied from 2738.1- 4345.4J/kg/oC in the above moisture range.

Migration of the ThO2 kernels under the influence of a temperature gradient

International Nuclear Information System (INIS)

Smith, C.L.

1977-01-01

Biso-coated ThO 2 fertile fuel kernels will migrate up the thermal gradients imposed across coated particles during high-temperature gas-cooled reactor (HTGR) operation. Thorium dioxide kernel migration has been studied as a function of temperature (1290 to 1705 0 C) (1563 to 1978 K) and ThO 2 kernel burnup (0.9 to 5.8 percent FIMA) in out-of-pile postirradiation thermal gradient heating experiments. The studies were conducted to obtain descriptions of migration rates that will be used in core design studies to evaluate the impact of ThO 2 migration on fertile fuel performance in an operating HTGR and to define characteristics needed by any comprehensive model describing ThO 2 kernel migration. The kinetics data generated in these postirradiation studies are consistent with in-pile data collected by investigators at Oak Ridge National Laboratory, which supports use of the more precise postirradiation heating results in HTGR core design studies. Observations of intergranular carbon deposits on the cool side of migrating kernels support the assumption that the kinetics of kernel migration are controlled by solid-state diffusion within irradiated ThO 2 kernels. The migration is characterized by a period of no migration (incubation period), followed by migration at the equilibrium rate for ThO 2 . The incubation period decreases with increasing temperature and kernel burnup. The improved understanding of the kinetics of ThO 2 kernel migration provided by this work will contribute to an optimization of HTGR core design and an increased confidence in fuel performance predictions

Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.

Science.gov (United States)

Kwak, Nojun

2016-05-20

Recently, the nonlinear projection trick (NPT) was introduced enabling direct computation of coordinates of samples in a reproducing kernel Hilbert space. With NPT, any machine learning algorithm can be extended to a kernel version without relying on the so called kernel trick. However, NPT is inherently difficult to be implemented incrementally because an ever increasing kernel matrix should be treated as additional training samples are introduced. In this paper, an incremental version of the NPT (INPT) is proposed based on the observation that the centerization step in NPT is unnecessary. Because the proposed INPT does not change the coordinates of the old data, the coordinates obtained by INPT can directly be used in any incremental methods to implement a kernel version of the incremental methods. The effectiveness of the INPT is shown by applying it to implement incremental versions of kernel methods such as, kernel singular value decomposition, kernel principal component analysis, and kernel discriminant analysis which are utilized for problems of kernel matrix reconstruction, letter classification, and face image retrieval, respectively.

Hybrid Origins of Carex rostrata var. borealis and C. stenolepis, Two Problematic Taxa in Carex Section Vesicariae (Cyperaceae).

Science.gov (United States)

M Pedersen, A Tiril; Nowak, Michael D; Brysting, Anne K; Elven, Reidar; Bjorå, Charlotte S

2016-01-01

Hybridization is frequent in the large and ecologically significant genus Carex (Cyperaceae). In four important sections of the northern regions (Ceratocystis, Glareosae, Phacocystis and Vesicariae), the frequent occurrence of hybrids often renders the identification of "pure" species and hybrids difficult. In this study we address the origins and taxonomic rank of two taxa of section Vesicariae: Carex rostrata var. borealis and C. stenolepis. The origin and taxonomic status of C. stenolepis has been the subject of substantial debate over the years, whereas C. rostrata var. borealis has received very little attention in the years since its first description in the 19th century. By performing an extensive sampling of relevant taxa from a broad distribution range, and analyzing data from fifteen microsatellite loci developed specifically for our study together with pollen stainability measures, we resolve the hybrid origins of C. rostrata var. borealis and C. stenolepis and provide new insights into this taxonomically challenging group of sedges. Our results are in accordance with previous findings suggesting that C. stenolepis is a hybrid between C. vesicaria and C. saxatilis. They are also in accordance with a previous proposition that C. rostrata var. borealis is a hybrid between C. rostrata and C. rotundata, and furthermore suggest that both hybrids are the result of multiple, recent (i.e., postglacial) hybridization events. We found little evidence for successful sexual reproduction within C. rostrata var. borealis and C. stenolepis, but conclude that the common and recurrent, largely predictable occurrence of these taxa justifies accepting both hybrids as hybrid species with binomial names. There are, however, complications as to types and priority names, and we therefore choose to address these problems in a separate paper.

KARAKTERISTIK MINYAK CAMPURAN RED PALM OIL DENGAN PALM KERNEL OLEIN (Characteristics of Oil Blends from Red Palm Oil and Palm Kernel Olein

Directory of Open Access Journals (Sweden)

Maria Ulfah

2016-10-01

Full Text Available Characteristics of oil blends has been produced from red palm oil (RPO and palm kernel olein (PKOo with seven ratios with a total of 100, namely A (0:100, B (25:75, C (40:60, D (50:50, E (60:40, F (75:25 and G (100:0 v/v investigated with randomized complete block design. The result showed that different of ratio levels RPO and PKOo have some effects on peroxide value, saponification value, melting point, cloud point and β-carotene content from RPO-PKOo oil blends, but has not effect on free fatty acid content. Higher level of PKOo content on formulas oil blends were decreased of saponification value and melting point, but was increased of cloud point. The best of RPOPKOo oil blends has been obtained at ratio 50:50 (v/v, with 459.52 ppm β-carotene, 1.35 meq/kg peroxide value, 0.09 % free fatty acid, 202.60 saponification value, 24.15 oC melting point and 7.15 oC cloud point. Fatty acids composition were 1.24 % capric acid, 29.00 % lauric acid, 10.09 % miristic acid, 23.10 % palmitic acid, 5.84 linoleic acid, 27.30 % oleic acid and 3.43 % stearic acid. Keywords: Red palm oil, palm kernel olein, oil blends, chemical and physical properties ABSTRAK Sifat-sifat minyak campuran yang dihasilkan dari red palm oil (RPO dan palm kernel olein (PKOo dengan tujuh tingkat rasio yang totalnya 100, yaitu A (0:100, B (25:75, C (40:60, D (50:50, E (60:40, F (75:25 dan G (100:0 (v/v dikaji menggunakan rancangan acak lengkap kelompok. Hasil penelitian menunjukkan bahwa rasio RPO:PKOo mempengaruhi angka peroksida, angka penyabunan, melting point, cloud point dan kadar β-karoten dari minyak campuran RPO-PKOo yang dihasilkan, namun tidak mempengaruhi kadar asam lemak bebas. Peningkatan jumlah PKOo yang ditambahkan dalam minyak campuran RPO-PKOo, akan menurunkan angka penyabunan dan melting point, namun akan menaikkan cloud point. Produk minyak campuran RPO-PKOo terbaik diperoleh pada rasio 50:50 (v/v, dengan kadar β-karoten 459,52 ppm, angka peroksida 1,35 meq

Uranium kernel formation via internal gelation

International Nuclear Information System (INIS)

Hunt, R.D.; Collins, J.L.

2004-01-01

In the 1970s and 1980s, U.S. Department of Energy (DOE) conducted numerous studies on the fabrication of nuclear fuel particles using the internal gelation process. These amorphous kernels were prone to flaking or breaking when gases tried to escape from the kernels during calcination and sintering. These earlier kernels would not meet today's proposed specifications for reactor fuel. In the interim, the internal gelation process has been used to create hydrous metal oxide microspheres for the treatment of nuclear waste. With the renewed interest in advanced nuclear fuel by the DOE, the lessons learned from the nuclear waste studies were recently applied to the fabrication of uranium kernels, which will become tri-isotropic (TRISO) fuel particles. These process improvements included equipment modifications, small changes to the feed formulations, and a new temperature profile for the calcination and sintering. The modifications to the laboratory-scale equipment and its operation as well as small changes to the feed composition increased the product yield from 60% to 80%-99%. The new kernels were substantially less glassy, and no evidence of flaking was found. Finally, key process parameters were identified, and their effects on the uranium microspheres and kernels are discussed. (orig.)

Kernel learning at the first level of inference.

Science.gov (United States)

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

Global Polynomial Kernel Hazard Estimation

DEFF Research Database (Denmark)

Hiabu, Munir; Miranda, Maria Dolores Martínez; Nielsen, Jens Perch

2015-01-01

This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it asymptotically redu...

[Super sweet corn hybrids adaptability for industrial processing. I freezing].

Science.gov (United States)

Alfonzo, Braunnier; Camacho, Candelario; Ortiz de Bertorelli, Ligia; De Venanzi, Frank

2002-09-01

With the purpose of evaluating adaptability to the freezing process of super sweet corn sh2 hybrids Krispy King, Victor and 324, 100 cobs of each type were frozen at -18 degrees C. After 120 days of storage, their chemical, microbiological and sensorial characteristics were compared with a sweet corn su. Industrial quality of the process of freezing and length and number of rows in cobs were also determined. Results revealed yields above 60% in frozen corns. Length and number of rows in cobs were acceptable. Most of the chemical characteristics of super sweet hybrids were not different from the sweet corn assayed at the 5% significance level. Moisture content and soluble solids of hybrid Victor, as well as total sugars of hybrid 324 were statistically different. All sh2 corns had higher pH values. During freezing, soluble solids concentration, sugars and acids decreased whereas pH increased. Frozen cobs exhibited acceptable microbiological rank, with low activities of mesophiles and total coliforms, absence of psychrophiles and fecal coliforms, and an appreciable amount of molds. In conclusion, sh2 hybrids adapted with no problems to the freezing process, they had lower contents of soluble solids and higher contents of total sugars, which almost doubled the amount of su corn; flavor, texture, sweetness and appearance of kernels were also better. Hybrid Victor was preferred by the evaluating panel and had an outstanding performance due to its yield and sensorial characteristics.

Quantum tomography, phase-space observables and generalized Markov kernels

International Nuclear Information System (INIS)

Pellonpaeae, Juha-Pekka

2009-01-01

We construct a generalized Markov kernel which transforms the observable associated with the homodyne tomography into a covariant phase-space observable with a regular kernel state. Illustrative examples are given in the cases of a 'Schroedinger cat' kernel state and the Cahill-Glauber s-parametrized distributions. Also we consider an example of a kernel state when the generalized Markov kernel cannot be constructed.

Microstructural evolution and mechanical properties of Mg composites containing nano-B4C hybridized micro-Ti particulates

International Nuclear Information System (INIS)

Sankaranarayanan, S.; Sabat, R.K.; Jayalakshmi, S.; Suwas, S.; Gupta, M.

2014-01-01

In this work, the microstructural evolution and mechanical properties of extruded Mg composites containing micro-Ti particulates hybridized with varying contents of nano-B 4 C are investigated, and compared with Mg-5.6Ti. Microstructural characterization showed the presence of uniformly distributed micro-Ti particles embedded with nano-B 4 C particulates that resulted in significant grain refinement. Electron back scattered diffraction (EBSD) analyses of Mg-(5.6Ti + x-B 4 C) BM hybrid composites showed that the addition of hybridized particle resulted in relatively more recrystallized grains, realignment of basal planes and extension of weak basal fibre texture when compared to Mg-5.6Ti. The evaluation of mechanical properties indicated improved strength with ductility retention in Mg-(5.6Ti + x-B 4 C) BM hybrid composites. When compared to Mg-5.6Ti, the superior strength properties of the Mg-(5.6Ti + x-B 4 C) BM hybrid composites are attributed to the presence of nano-reinforcements, the uniform distribution of the hybridized particles, better interfacial bonding between the matrix and the reinforcement particles and the matrix grain refinement achieved by nano-B 4 C addition. The ductility enhancement obtained in hybrid composites can be attributed to the fibre texture spread and favourable basal plane orientation achieved due to nano B 4 C addition. - Highlights: • Micro-Ti particulates are hybridized with varying weight fractions of nano-B 4 C. • The hybrid mixture was used as hybrid reinforcements in magnesium. • Microstructure and mechanical properties of Mg-(5.6Ti + x-B 4 C) BM are compared with Mg-5.6Ti. • Electron back scattered diffraction (EBSD) analysis conducted to study the microtexture evolution

Optimal Bandwidth Selection for Kernel Density Functionals Estimation

Directory of Open Access Journals (Sweden)

Su Chen

2015-01-01

Full Text Available The choice of bandwidth is crucial to the kernel density estimation (KDE and kernel based regression. Various bandwidth selection methods for KDE and local least square regression have been developed in the past decade. It has been known that scale and location parameters are proportional to density functionals ∫γ(xf2(xdx with appropriate choice of γ(x and furthermore equality of scale and location tests can be transformed to comparisons of the density functionals among populations. ∫γ(xf2(xdx can be estimated nonparametrically via kernel density functionals estimation (KDFE. However, the optimal bandwidth selection for KDFE of ∫γ(xf2(xdx has not been examined. We propose a method to select the optimal bandwidth for the KDFE. The idea underlying this method is to search for the optimal bandwidth by minimizing the mean square error (MSE of the KDFE. Two main practical bandwidth selection techniques for the KDFE of ∫γ(xf2(xdx are provided: Normal scale bandwidth selection (namely, “Rule of Thumb” and direct plug-in bandwidth selection. Simulation studies display that our proposed bandwidth selection methods are superior to existing density estimation bandwidth selection methods in estimating density functionals.

Mapping and validation of major quantitative trait loci for kernel length in wild barley (Hordeum vulgare ssp. spontaneum).

Science.gov (United States)

Zhou, Hong; Liu, Shihang; Liu, Yujiao; Liu, Yaxi; You, Jing; Deng, Mei; Ma, Jian; Chen, Guangdeng; Wei, Yuming; Liu, Chunji; Zheng, Youliang

2016-09-13

Kernel length is an important target trait in barley (Hordeum vulgare L.) breeding programs. However, the number of known quantitative trait loci (QTLs) controlling kernel length is limited. In the present study, we aimed to identify major QTLs for kernel length, as well as putative candidate genes that might influence kernel length in wild barley. A recombinant inbred line (RIL) population derived from the barley cultivar Baudin (H. vulgare ssp. vulgare) and the long-kernel wild barley genotype Awcs276 (H.vulgare ssp. spontaneum) was evaluated at one location over three years. A high-density genetic linkage map was constructed using 1,832 genome-wide diversity array technology (DArT) markers, spanning a total of 927.07 cM with an average interval of approximately 0.49 cM. Two major QTLs for kernel length, LEN-3H and LEN-4H, were detected across environments and further validated in a second RIL population derived from Fleet (H. vulgare ssp. vulgare) and Awcs276. In addition, a systematic search of public databases identified four candidate genes and four categories of proteins related to LEN-3H and LEN-4H. This study establishes a fundamental research platform for genomic studies and marker-assisted selection, since LEN-3H and LEN-4H could be used for accelerating progress in barley breeding programs that aim to improve kernel length.

Scalable and Power Efficient Data Analytics for Hybrid Exascale Systems

Energy Technology Data Exchange (ETDEWEB)

Choudhary, Alok [Northwestern Univ., Evanston, IL (United States); Samatova, Nagiza [North Carolina State Univ., Raleigh, NC (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Wu, Kesheng [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Liao, Wei-keng [Northwestern Univ., Evanston, IL (United States)

2015-03-19

This project developed a generic and optimized set of core data analytics functions. These functions organically consolidate a broad constellation of high performance analytical pipelines. As the architectures of emerging HPC systems become inherently heterogeneous, there is a need to design algorithms for data analysis kernels accelerated on hybrid multi-node, multi-core HPC architectures comprised of a mix of CPUs, GPUs, and SSDs. Furthermore, the power-aware trend drives the advances in our performance-energy tradeoff analysis framework which enables our data analysis kernels algorithms and software to be parameterized so that users can choose the right power-performance optimizations.

Hybrid parallel computing architecture for multiview phase shifting

Science.gov (United States)

Zhong, Kai; Li, Zhongwei; Zhou, Xiaohui; Shi, Yusheng; Wang, Congjun

2014-11-01

The multiview phase-shifting method shows its powerful capability in achieving high resolution three-dimensional (3-D) shape measurement. Unfortunately, this ability results in very high computation costs and 3-D computations have to be processed offline. To realize real-time 3-D shape measurement, a hybrid parallel computing architecture is proposed for multiview phase shifting. In this architecture, the central processing unit can co-operate with the graphic processing unit (GPU) to achieve hybrid parallel computing. The high computation cost procedures, including lens distortion rectification, phase computation, correspondence, and 3-D reconstruction, are implemented in GPU, and a three-layer kernel function model is designed to simultaneously realize coarse-grained and fine-grained paralleling computing. Experimental results verify that the developed system can perform 50 fps (frame per second) real-time 3-D measurement with 260 K 3-D points per frame. A speedup of up to 180 times is obtained for the performance of the proposed technique using a NVIDIA GT560Ti graphics card rather than a sequential C in a 3.4 GHZ Inter Core i7 3770.

How yield relates to ash content, Delta 13C and Delta 18O in maize grown under different water regimes.

Science.gov (United States)

Cabrera-Bosquet, Llorenç; Sánchez, Ciro; Araus, José Luis

2009-11-01

Stable isotopes have proved a valuable phenotyping tool when breeding for yield potential and drought adaptation; however, the cost and technical skills involved in isotope analysis limit its large-scale application in breeding programmes. This is particularly so for Delta(18)O despite the potential relevance of this trait in C(4) crops. The accumulation of minerals (measured as ash content) has been proposed as an inexpensive way to evaluate drought adaptation and yield in C(3) cereals, but little is known of the usefulness of this measure in C(4) cereals such as maize (Zea mays). The present study investigates how yield relates to ash content, Delta(13)C and Delta(18)O, and evaluates the use of ash content as an alternative or complementary criterion to stable isotopes in assessing yield potential and drought resistance in maize. A set of tropical maize hybrids developed by CIMMYT were subjected to different water availabilities, in order to induce water stress during the reproductive stages under field conditions. Ash content and Delta(13)C were determined in leaves and kernels. In addition, Delta(18)O was measured in kernels. Water regime significantly affected yield, ash content and stable isotopes. The results revealed a close relationship between ash content in leaves and the traits informing about plant water status. Ash content in kernels appeared to reflect differences in sink-source balance. Genotypic variation in grain yield was mainly explained by the combination of ash content and Delta(18)O, whilst Delta(13)C did not explain a significant percentage of such variation. Ash content in leaves and kernels proved a useful alternative or complementary criterion to Delta(18)O in kernels for assessing yield performance in maize grown under drought conditions.

Relationship between attenuation coefficients and dose-spread kernels

International Nuclear Information System (INIS)

Boyer, A.L.

1988-01-01

Dose-spread kernels can be used to calculate the dose distribution in a photon beam by convolving the kernel with the primary fluence distribution. The theoretical relationships between various types and components of dose-spread kernels relative to photon attenuation coefficients are explored. These relations can be valuable as checks on the conservation of energy by dose-spread kernels calculated by analytic or Monte Carlo methods

Enhancement of sp3 hybridized C in amorphous carbon films by Ar ion bombardment and Si incorporation

International Nuclear Information System (INIS)

Jung, Hae-Suk; Park, Hyung-Ho; Mendieta, I.R.; Smith, D.A.

2003-01-01

We report an effective method of increasing the sp 3 hybridization fraction in sputtered amorphous carbon (a-C) film by the combination of Ar ion bombardment and Si incorporation. In the deposition of an a-C film, Ar ion bombardment by controlling the applied bias voltage plays a role in creating high stress in film and causes the local bonding configuration to change to a sp 3 hybridized bond. Simultaneously, the incorporated Si in an a-C network breaks the sp 2 hybridized bonded ring and promotes the formation of a sp 3 hybridized bond. This enhancement of the sp 3 hybridized bonding characteristic is maximized for an a-C film with 23 at. % of Si and 100-150 V of applied bias voltage. In this region, the increase of resistivity, optical band gap, and mechanical hardness of a-C is attributed to the reduction of the sp 2 hybridized bonded ring and increased fraction of the sp 3 hybridized bond. However, at a higher bias voltage above 150 V, the enhancement effect is reduced due to the resputtering and thermally activated reconversion of a sp 3 to a sp 2 hybridized bond

Mixture Density Mercer Kernels: A Method to Learn Kernels

Data.gov (United States)

National Aeronautics and Space Administration — This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian...

An obstructive sleep apnea detection approach using kernel density classification based on single-lead electrocardiogram.

Science.gov (United States)

Chen, Lili; Zhang, Xi; Wang, Hui

2015-05-01

Obstructive sleep apnea (OSA) is a common sleep disorder that often remains undiagnosed, leading to an increased risk of developing cardiovascular diseases. Polysomnogram (PSG) is currently used as a golden standard for screening OSA. However, because it is time consuming, expensive and causes discomfort, alternative techniques based on a reduced set of physiological signals are proposed to solve this problem. This study proposes a convenient non-parametric kernel density-based approach for detection of OSA using single-lead electrocardiogram (ECG) recordings. Selected physiologically interpretable features are extracted from segmented RR intervals, which are obtained from ECG signals. These features are fed into the kernel density classifier to detect apnea event and bandwidths for density of each class (normal or apnea) are automatically chosen through an iterative bandwidth selection algorithm. To validate the proposed approach, RR intervals are extracted from ECG signals of 35 subjects obtained from a sleep apnea database ( http://physionet.org/cgi-bin/atm/ATM ). The results indicate that the kernel density classifier, with two features for apnea event detection, achieves a mean accuracy of 82.07 %, with mean sensitivity of 83.23 % and mean specificity of 80.24 %. Compared with other existing methods, the proposed kernel density approach achieves a comparably good performance but by using fewer features without significantly losing discriminant power, which indicates that it could be widely used for home-based screening or diagnosis of OSA.

Non-separable pairing interaction kernels applied to superconducting cuprates

International Nuclear Information System (INIS)

Haley, Stephen B.; Fink, Herman J.

2014-01-01

Highlights: • Non-separable interaction kernels with weak interactions produces HTS. • A probabilistic approach is used in filling the electronic states in the unit cell. • A set of coupled equations is derived which describes the energy gap. • SC properties of separable with non-separable interactions are compared. • There is agreement with measured properties of the SC and normal states. - Abstract: A pairing Hamiltonian H(Γ) with a non-separable interaction kernel Γ produces HTS for relatively weak interactions. The doping and temperature dependence of Γ(x,T) and the chemical potential μ(x) is determined by a probabilistic filling of the electronic states in the cuprate unit cell. A diverse set of HTS and normal state properties is examined, including the SC phase transition boundary T C (x), SC gap Δ(x,T), entropy S(x,T), specific heat C(x,T), and spin susceptibility χ s (x,T). Detailed x,T agreement with cuprate experiment is obtained for all properties

Phenolic compounds and antioxidant activity of kernels and shells of Mexican pecan (Carya illinoinensis).

Science.gov (United States)

de la Rosa, Laura A; Alvarez-Parrilla, Emilio; Shahidi, Fereidoon

2011-01-12

The phenolic composition and antioxidant activity of pecan kernels and shells cultivated in three regions of the state of Chihuahua, Mexico, were analyzed. High concentrations of total extractable phenolics, flavonoids, and proanthocyanidins were found in kernels, and 5-20-fold higher concentrations were found in shells. Their concentrations were significantly affected by the growing region. Antioxidant activity was evaluated by ORAC, DPPH•, HO•, and ABTS•-- scavenging (TAC) methods. Antioxidant activity was strongly correlated with the concentrations of phenolic compounds. A strong correlation existed among the results obtained using these four methods. Five individual phenolic compounds were positively identified and quantified in kernels: ellagic, gallic, protocatechuic, and p-hydroxybenzoic acids and catechin. Only ellagic and gallic acids could be identified in shells. Seven phenolic compounds were tentatively identified in kernels by means of MS and UV spectral comparison, namely, protocatechuic aldehyde, (epi)gallocatechin, one gallic acid-glucose conjugate, three ellagic acid derivatives, and valoneic acid dilactone.

Expression of the C3-C 4 intermediate character in somatic hybrids between Brassica napus and the C3-C 4 species Moricandia arvensis.

Science.gov (United States)

O'Neill, C M; Murata, T; Morgan, C L; Mathias, R J

1996-12-01

The wild crucifer Moricandia arvensis is a potential source of alien genes for the genetic improvement of related Brassica crops. In particular M. arvensis has a C3-C4 intermediate photosynthetic mechanism which results in enhanced recapture of photorespired CO2 and may increase plant water-use efficiency. In order to transfer this trait into Brassica napus, somatic hybridisations were made between leaf mesophyll protoplasts from cultured M. arvensis shoot tips and hypocotyl protoplasts from three Brassica napus cultivars, 'Ariana', 'Cobra' and 'Westar'. A total of 23 plants were recovered from fusion experiments and established in the greenhouse. A wide range of chromosome numbers were observed among the regenerated plants, including some apparent mixoploids. Thirteen of the regenerated plants were identified as nuclear hybrids between B. napus and M. arvensis on the basis of isozyme analysis. The phenotypes of these hybrids were typically rather B. napus-like, but much variability was observed, including variation in flower colour, leaf shape and colour, leaf waxiness, fertility and plant vigour. CO2 compensation point measurements on the regenerated plants demonstrated that 3 of the hybrids express the M. arvensis C3-C4 intermediate character at the physiological level. Semi-thin sections through leaf tissues of these 3 plants revealed the presence of a Kranz-like leaf anatomy characteristic of M. arvensis but not found in B. napus. This is the first report of the expression of this potentially important agronomic trait, transferred from Moricandia, in M. arvensis x B. napus hybrids.

Composition of open pollinated varieties and newly developed hybrids for yield and contributing trials in maize

International Nuclear Information System (INIS)

Malik, H.N.; Ara, I.; Naeem, M.; Hussain, M.; Hanif, M.; Yousaf, M.M.

2010-01-01

Eighteen hybrids and 13 open pollinated varieties of maize were evaluated at the National Agricultural Research Centre, Islamabad during kharif 2007. Significant differences were observed for days to 50% tasseling and silking, plant height, ear height, number of kernel rows per ear, number of grain per row, 100 grain weight. grain moisture and grain yield. The hybrids NT-6622 and NT-6651 ranked top and second in grain yield by producing 7842 and 7759 kg ha/sup -1/, respectively. Generally the hybrids produced more grain yield than the open pollinated varieties. Days to 50% tasseling ranged from 47.33 (EV-1098) to 64 (NT- 6632) while for silking varied from 47.67 (EV-1098) to 63.33 (30-K-95). The variety Soan-3 (149 cm) was the shortest and hybrid 30-K-95 (202.3 cm) was the tallest amongst all the varieties and hybrids. Ear height ranged from 70.33 (Soan-3) to 107 cm (NT-6651) while number of kernel rows per ear varied from 12 (NT-6622, 30- K95, 2512 and 2514) to 18 (R-2207). The hybrid P-30-25 produced the maximum number of grains (51) Grast-8288 produced the lowest (29). One hundred grain weight ranged from 23g (EV-6098) to 39g (2512). (author)

Kernel based subspace projection of near infrared hyperspectral images of maize kernels

DEFF Research Database (Denmark)

Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben

2009-01-01

In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods ......- tor transform outperform the linear methods as well as kernel principal components in producing interesting projections of the data.......In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...... including principal component analysis and maximum autocorrelation factor analysis. The latter utilizes the fact that interesting phenomena in images exhibit spatial autocorrelation. However, linear projections often fail to grasp the underlying variability on the data. Therefore we propose to use so...

Fabrication and Characterization of Surrogate TRISO Particles Using 800μm ZrO₂ Kernels

Energy Technology Data Exchange (ETDEWEB)

Jolly, Brian C. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Helmreich, Grant [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Cooley, Kevin M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Dyer, John [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Terrani, Kurt [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2016-07-01

In support of fully ceramic microencapsulated (FCM) fuel development, coating development work is ongoing at Oak Ridge National Laboratory (ORNL) to produce tri-structural isotropic (TRISO) coated fuel particles with both UN kernels and surrogate (uranium-free) kernels. The nitride kernels are used to increase fissile density in these SiC-matrix fuel pellets with details described elsewhere. The surrogate TRISO particles are necessary for separate effects testing and for utilization in the consolidation process development. This report focuses on the fabrication and characterization of surrogate TRISO particles which use 800μm in diameter ZrO₂ microspheres as the kernel.

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

Science.gov (United States)

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model, is able to model the rate of occurrence of... kernel methods made use of: (i) the Bayesian property of improving predictive accuracy as data are dynamically obtained, and (ii) the kernel function

Evaluating the Application of Tissue-Specific Dose Kernels Instead of Water Dose Kernels in Internal Dosimetry : A Monte Carlo Study

NARCIS (Netherlands)

Moghadam, Maryam Khazaee; Asl, Alireza Kamali; Geramifar, Parham; Zaidi, Habib

2016-01-01

Purpose: The aim of this work is to evaluate the application of tissue-specific dose kernels instead of water dose kernels to improve the accuracy of patient-specific dosimetry by taking tissue heterogeneities into consideration. Materials and Methods: Tissue-specific dose point kernels (DPKs) and

Parsimonious Wavelet Kernel Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Wang Qin

2015-11-01

Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

A new hybrid-FBP inversion algorithm with inverse distance backprojection weight for CT reconstruction

Energy Technology Data Exchange (ETDEWEB)

Narasimhadhan, A.V.; Rajgopal, Kasi

2011-07-01

This paper presents a new hybrid filtered backprojection (FBP) algorithm for fan-beam and cone-beam scan. The hybrid reconstruction kernel is the sum of the ramp and Hilbert filters. We modify the redundancy weighting function to reduce the inverse square distance weighting in the backprojection to inverse distance weight. The modified weight also eliminates the derivative associated with the Hilbert filter kernel. Thus, the proposed reconstruction algorithm has the advantages of the inverse distance weight in the backprojection. We evaluate the performance of the new algorithm in terms of the magnitude level and uniformity in noise for the fan-beam geometry. The computer simulations show that the spatial resolution is nearly identical to the standard fan-beam ramp filtered algorithm while the noise is spatially uniform and the noise variance is reduced. (orig.)

Difference between standard and quasi-conformal BFKL kernels

International Nuclear Information System (INIS)

Fadin, V.S.; Fiore, R.; Papa, A.

2012-01-01

As it was recently shown, the colour singlet BFKL kernel, taken in Möbius representation in the space of impact parameters, can be written in quasi-conformal shape, which is unbelievably simple compared with the conventional form of the BFKL kernel in momentum space. It was also proved that the total kernel is completely defined by its Möbius representation. In this paper we calculated the difference between standard and quasi-conformal BFKL kernels in momentum space and discovered that it is rather simple. Therefore we come to the conclusion that the simplicity of the quasi-conformal kernel is caused mainly by using the impact parameter space.

Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

Science.gov (United States)

Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen

2014-09-01

For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

A laser optical method for detecting corn kernel defects

Energy Technology Data Exchange (ETDEWEB)

Gunasekaran, S.; Paulsen, M. R.; Shove, G. C.

1984-01-01

An opto-electronic instrument was developed to examine individual corn kernels and detect various kernel defects according to reflectance differences. A low power helium-neon (He-Ne) laser (632.8 nm, red light) was used as the light source in the instrument. Reflectance from good and defective parts of corn kernel surfaces differed by approximately 40%. Broken, chipped, and starch-cracked kernels were detected with nearly 100% accuracy; while surface-split kernels were detected with about 80% accuracy. (author)

Kernel maximum autocorrelation factor and minimum noise fraction transformations

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2010-01-01

in hyperspectral HyMap scanner data covering a small agricultural area, and 3) maize kernel inspection. In the cases shown, the kernel MAF/MNF transformation performs better than its linear counterpart as well as linear and kernel PCA. The leading kernel MAF/MNF variates seem to possess the ability to adapt...

Identification of Fusarium damaged wheat kernels using image analysis

Directory of Open Access Journals (Sweden)

Ondřej Jirsa

2011-01-01

Full Text Available Visual evaluation of kernels damaged by Fusarium spp. pathogens is labour intensive and due to a subjective approach, it can lead to inconsistencies. Digital imaging technology combined with appropriate statistical methods can provide much faster and more accurate evaluation of the visually scabby kernels proportion. The aim of the present study was to develop a discrimination model to identify wheat kernels infected by Fusarium spp. using digital image analysis and statistical methods. Winter wheat kernels from field experiments were evaluated visually as healthy or damaged. Deoxynivalenol (DON content was determined in individual kernels using an ELISA method. Images of individual kernels were produced using a digital camera on dark background. Colour and shape descriptors were obtained by image analysis from the area representing the kernel. Healthy and damaged kernels differed significantly in DON content and kernel weight. Various combinations of individual shape and colour descriptors were examined during the development of the model using linear discriminant analysis. In addition to basic descriptors of the RGB colour model (red, green, blue, very good classification was also obtained using hue from the HSL colour model (hue, saturation, luminance. The accuracy of classification using the developed discrimination model based on RGBH descriptors was 85 %. The shape descriptors themselves were not specific enough to distinguish individual kernels.

Cis and trans monoenoic fatty acids of hydrogenated mango kernel fats

International Nuclear Information System (INIS)

Ali, Z.; Saleem, M.; Siddiqui, H.L.

2007-01-01

The fats produced as a result of hydrogenation of the oils of three varieties of mango kernel (Mangifera indica), were found to consist of trans fatty acids, C/sub 16:1/ (0.02-0.60 %), C/sub 18:1/ (2.69-4.48 %), C/sub 20:1/ (0.02-0.04 %), C/sub 22:1/ (traces-0.05 %), C/sub 24:1/ (traces-0.02 %) in addition to cis fatty acids. (author)

Optical properties of alkali halide crystals from all-electron hybrid TD-DFT calculations

Energy Technology Data Exchange (ETDEWEB)

Webster, R., E-mail: ross.webster07@imperial.ac.uk; Harrison, N. M. [Thomas Young Centre, Department of Chemistry, Imperial College London, South Kensington, London SW7 2AZ (United Kingdom); Bernasconi, L. [Rutherford Appleton Laboratory, STFC, Harwell Oxford, Didcot OX11 0QX (United Kingdom)

2015-06-07

We present a study of the electronic and optical properties of a series of alkali halide crystals AX, with A = Li, Na, K, Rb and X = F, Cl, Br based on a recent implementation of hybrid-exchange time-dependent density functional theory (TD-DFT) (TD-B3LYP) in the all-electron Gaussian basis set code CRYSTAL. We examine, in particular, the impact of basis set size and quality on the prediction of the optical gap and exciton binding energy. The formation of bound excitons by photoexcitation is observed in all the studied systems and this is shown to be correlated to specific features of the Hartree-Fock exchange component of the TD-DFT response kernel. All computed optical gaps and exciton binding energies are however markedly below estimated experimental and, where available, 2-particle Green’s function (GW-Bethe-Salpeter equation, GW-BSE) values. We attribute this reduced exciton binding to the incorrect asymptotics of the B3LYP exchange correlation ground state functional and of the TD-B3LYP response kernel, which lead to a large underestimation of the Coulomb interaction between the excited electron and hole wavefunctions. Considering LiF as an example, we correlate the asymptotic behaviour of the TD-B3LYP kernel to the fraction of Fock exchange admixed in the ground state functional c{sub HF} and show that there exists one value of c{sub HF} (∼0.32) that reproduces at least semi-quantitatively the optical gap of this material.

Hybrid density functional study on the mechanism for the enhanced photocatalytic properties of the ultrathin hybrid layered nanocomposite g-C3N4/BiOCl

Science.gov (United States)

Yao, Wenzhi; Zhang, Jihua; Wang, Yuanxu; Ren, Fengzhu

2018-03-01

To investigate the origin of the high photocatalytic performance of experimentally synthesized g-C3N4/ BiOCl, we studied its geometry structure, electronic structure, and photocatalytic properties by means of hybrid density-functional theory (DFT). The calculated band alignment of g-C3N4 and few-layer BiOCl sheets clearly shows that g-C3N4/ BiOCl is a standard type-II nanocomposite. The density of states, Bader charge, partial charge density, charge density difference, and the effective masses show that electron-hole pair can be effectively separated in the g-C3N4/BiOCl interface. The calculated absorption coefficients indicate an obvious redshift of the absorption edge. The band gap of g-C3N4/BiOCl can be modulated by external electric field, and a semiconductor-semimetal transition is observed. The type-II vdW heterostructure is still maintained during the changes of external electric field. Especially, when the electric field reaches to +0.7 V/Å, the impurity states have been eliminated with the band gap of 2.3 eV. An analysis of optical properties shows that the absorption coefficient in the visible-light region is enhanced considerably as the electric-field strength increases. Our calculation results suggest that the ultrathin hybrid layered g-C3N4/BiOCl nanocomposite may have significant advantages for visible-light photocatalysis.

Determining the minimum required uranium carbide content for HTGR UCO fuel kernels

International Nuclear Information System (INIS)

McMurray, Jacob W.; Lindemer, Terrence B.; Brown, Nicholas R.; Reif, Tyler J.; Morris, Robert N.; Hunn, John D.

2017-01-01

Highlights: • The minimum required uranium carbide content for HTGR UCO fuel kernels is calculated. • More nuclear and chemical factors have been included for more useful predictions. • The effect of transmutation products, like Pu and Np, on the oxygen distribution is included for the first time. - Abstract: Three important failure mechanisms that must be controlled in high-temperature gas-cooled reactor (HTGR) fuel for certain higher burnup applications are SiC layer rupture, SiC corrosion by CO, and coating compromise from kernel migration. All are related to high CO pressures stemming from O release when uranium present as UO 2 fissions and the O is not subsequently bound by other elements. In the HTGR kernel design, CO buildup from excess O is controlled by the inclusion of additional uranium apart from UO 2 in the form of a carbide, UC x and this fuel form is designated UCO. Here general oxygen balance formulas were developed for calculating the minimum UC x content to ensure negligible CO formation for 15.5% enriched UCO taken to 16.1% actinide burnup. Required input data were obtained from CALPHAD (CALculation of PHAse Diagrams) chemical thermodynamic models and the Serpent 2 reactor physics and depletion analysis tool. The results are intended to be more accurate than previous estimates by including more nuclear and chemical factors, in particular the effect of transmuted Pu and Np oxides on the oxygen distribution as the fuel kernel composition evolves with burnup.

Unified heat kernel regression for diffusion, kernel smoothing and wavelets on manifolds and its application to mandible growth modeling in CT images.

Science.gov (United States)

Chung, Moo K; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K

2015-05-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel method is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, the method is applied to characterize the localized growth pattern of mandible surfaces obtained in CT images between ages 0 and 20 by regressing the length of displacement vectors with respect to a surface template. Copyright © 2015 Elsevier B.V. All rights reserved.

Digital signal processing with kernel methods

CERN Document Server

Rojo-Alvarez, José Luis; Muñoz-Marí, Jordi; Camps-Valls, Gustavo

2018-01-01

A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. * Presents the necess...

Adaptive Kernel In The Bootstrap Boosting Algorithm In KDE ...

African Journals Online (AJOL)

This paper proposes the use of adaptive kernel in a bootstrap boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...

Windows Vista Kernel-Mode: Functions, Security Enhancements and Flaws

Directory of Open Access Journals (Sweden)

Mohammed D. ABDULMALIK

2008-06-01

Full Text Available Microsoft has made substantial enhancements to the kernel of the Microsoft Windows Vista operating system. Kernel improvements are significant because the kernel provides low-level operating system functions, including thread scheduling, interrupt and exception dispatching, multiprocessor synchronization, and a set of routines and basic objects.This paper describes some of the kernel security enhancements for 64-bit edition of Windows Vista. We also point out some weakness areas (flaws that can be attacked by malicious leading to compromising the kernel.

Design, development and evaluation of a divergent roller sizer for almond kernels

Directory of Open Access Journals (Sweden)

D Ghanbarian

2015-09-01

options. So, a divergent roller grader was developed and built in this research. The prototype consisted of two diverging rotating rollers which were made of mild steel with an outside diameter of 95 mm and a length of 700 mm. They were mounted to provide an adjustable slope towards the wide opening end. These roller beds were fixed on a box shaped frame of size 500×1000×1200 mm. The slope and counter-rotating action of the rollers encouraged the kernels to continue moving toward the end where the gap between the rollers was the widest. A tray was fixed at the feeding end of the machine for feeding the kernels. A provision was given to adjust the gap from a minimum to a maximum level. Based on the required activate torques of the rollers, an electric 74 W motor was selected to drive the machine. The power transmission mechanism consists of two pulleys 150 and 250 mm diameters and a V belt type A with a length of 1448 mm. The center distance of the pulleys is calculated to be 410 mm. Standard criterions of weighted sorting error index (C ̅_R, weighted sorting efficiencies index (EW and operation capacity (Q were used to evaluate the machine. Practical analysis showed that the machine’s performance is influenced mainly by the slope of the rollers, the rotation speed of the rollers, and the feeding rate of the product. In order to evaluate the performance of the prototype, a factorial experiment in a completely randomized design with three replications for each test was done. The performance evaluation was carried out for three levels of feeding rates (600, 1000, and 1400 kg 8h-1, three levels of slopes (2, 7, and 12 degree, and three rotation speeds of rollers (50, 80, and 110 rpm. Spreadsheet software MSTATC and SPSS were used to analyze the data and the Duncan's multiple range tests were used to compare the means. Results and discussion: Analysis of variance showed that all three studied factors have significant effects on evaluating standard criterions of C ̅_R, EW

Generalization Performance of Regularized Ranking With Multiscale Kernels.

Science.gov (United States)

Zhou, Yicong; Chen, Hong; Lan, Rushi; Pan, Zhibin

2016-05-01

The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.

Some fundamental and applicative properties of [polymer/nano-SiC] hybrid nanocomposites

International Nuclear Information System (INIS)

Kassiba, A; Boucle, J; Makowska-Janusik, M; Errien, N

2007-01-01

Hybrid nanocomposites which combine polymer as host matrix and nanocrystals as active elements are promising functional materials for electronics, optics or photonics. In these systems, the physical response is governed by the nanocrystal features (size, surface and defect states), the polymer properties and the polymer-nanocrystal interface. This work reviews some selective nanostructured architectures based on active elements such as silicon carbide (SiC) nanocrystals and polymer host matrices. Beyond an overview of some key properties of the nanocrystals, a main part will be devoted to the electro-optical (EO) properties of SiC based hybrid systems where SiC nanocrystals are embedded in polymer matrices of different chemical nature such as poly-(methylmethacrylate) (PMMA), poly-vinylcarbazole (PVK) or polycarbonate. Using this approach, the organic-inorganic interface effects are emphasised with regard to the dielectric or hole transporting behaviour of PMMA and PVK respectively. These effects are illustrated through different EO responses associated with hybrid composites based on PMMA or PVK

Some fundamental and applicative properties of [polymer/nano-SiC] hybrid nanocomposites

Science.gov (United States)

Kassiba, A.; Bouclé, J.; Makowska-Janusik, M.; Errien, N.

2007-08-01

Hybrid nanocomposites which combine polymer as host matrix and nanocrystals as active elements are promising functional materials for electronics, optics or photonics. In these systems, the physical response is governed by the nanocrystal features (size, surface and defect states), the polymer properties and the polymer-nanocrystal interface. This work reviews some selective nanostructured architectures based on active elements such as silicon carbide (SiC) nanocrystals and polymer host matrices. Beyond an overview of some key properties of the nanocrystals, a main part will be devoted to the electro-optical (EO) properties of SiC based hybrid systems where SiC nanocrystals are embedded in polymer matrices of different chemical nature such as poly-(methylmethacrylate) (PMMA), poly-vinylcarbazole (PVK) or polycarbonate. Using this approach, the organic-inorganic interface effects are emphasised with regard to the dielectric or hole transporting behaviour of PMMA and PVK respectively. These effects are illustrated through different EO responses associated with hybrid composites based on PMMA or PVK.

Effect of gamma-ray irradiation on hybridization between Chamaecyparis obtusa S. et Z. and C. pisifera S. et Z

International Nuclear Information System (INIS)

Maeta, Takehiko

1980-01-01

The effect of gamma irradiation on the interspecific hybridization between Chamaecyparis obtusa and C. pisifera was investigated, and F 1 seedlings were examined cytologically. Acute gamma irradiation and chronic gamma irradiation given throughout annual growing seasons on male flowers were effective in producing hybrids between these two species. The crossing of C. obtusa x C. pisifera was better than the reverse crossing because the pollen of C. obtusa rapidly loses its fertility, and C. pisifera blooms earlier than C. obtusa. Chronic gamma irradiation of male flowers throughout the annual growing season was slightly effective for producing the hybrids, but that of female flowers was not. Acute gamma irradiation of male flowers at the first to second telophase (300 R) was considered to be desirable for hybrid production. However, it was confirmed by cytological observation that all of the hybrids produced were triploid which had two identical genomes originated from either parent. Therefore, the unreduced male gametophytes or unreduced eggs seem to participate in the formation of the hybrids. It seems difficult to produce diploid hybrids between C. obtusa and C. pisifera. (Kaihara, S.)

Multineuron spike train analysis with R-convolution linear combination kernel.

Science.gov (United States)

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

ADSORPTION OF COPPER FROM AQUEOUS SOLUTION BY ELAIS GUINEENSIS KERNEL ACTIVATED CARBON

Directory of Open Access Journals (Sweden)

NAJUA DELAILA TUMIN

2008-08-01

Full Text Available In this study, a series of batch laboratory experiments were conducted in order to investigate the feasibility of Elais Guineensis kernel or known as palm kernel shell (PKS-based activated carbon for the removal of copper from aqueous solution by the adsorption process. Investigation was carried out by studying the influence of initial solution pH, adsorbent dosage and initial concentration of copper. The particle size of PKS used was categorized as PKS–M. All batch experiments were carried out at a constant temperature of 30°C (±2°C using mechanical shaker that operated at 100 rpm. The single component equilibrium data was analyzed using Langmuir, Freundlich, Redlich-Peterson, Temkin and Toth adsorption isotherms.

An analysis of 1-D smoothed particle hydrodynamics kernels

International Nuclear Information System (INIS)

Fulk, D.A.; Quinn, D.W.

1996-01-01

In this paper, the smoothed particle hydrodynamics (SPH) kernel is analyzed, resulting in measures of merit for one-dimensional SPH. Various methods of obtaining an objective measure of the quality and accuracy of the SPH kernel are addressed. Since the kernel is the key element in the SPH methodology, this should be of primary concern to any user of SPH. The results of this work are two measures of merit, one for smooth data and one near shocks. The measure of merit for smooth data is shown to be quite accurate and a useful delineator of better and poorer kernels. The measure of merit for non-smooth data is not quite as accurate, but results indicate the kernel is much less important for these types of problems. In addition to the theory, 20 kernels are analyzed using the measure of merit demonstrating the general usefulness of the measure of merit and the individual kernels. In general, it was decided that bell-shaped kernels perform better than other shapes. 12 refs., 16 figs., 7 tabs

A One-Sample Test for Normality with Kernel Methods

OpenAIRE

Kellner , Jérémie; Celisse , Alain

2015-01-01

We propose a new one-sample test for normality in a Reproducing Kernel Hilbert Space (RKHS). Namely, we test the null-hypothesis of belonging to a given family of Gaussian distributions. Hence our procedure may be applied either to test data for normality or to test parameters (mean and covariance) if data are assumed Gaussian. Our test is based on the same principle as the MMD (Maximum Mean Discrepancy) which is usually used for two-sample tests such as homogeneity or independence testing. O...

NLO corrections to the Kernel of the BKP-equations

Energy Technology Data Exchange (ETDEWEB)

Bartels, J. [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Fadin, V.S. [Budker Institute of Nuclear Physics, Novosibirsk (Russian Federation); Novosibirskij Gosudarstvennyj Univ., Novosibirsk (Russian Federation); Lipatov, L.N. [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Petersburg Nuclear Physics Institute, Gatchina, St. Petersburg (Russian Federation); Vacca, G.P. [INFN, Sezione di Bologna (Italy)

2012-10-02

We present results for the NLO kernel of the BKP equations for composite states of three reggeized gluons in the Odderon channel, both in QCD and in N=4 SYM. The NLO kernel consists of the NLO BFKL kernel in the color octet representation and the connected 3{yields}3 kernel, computed in the tree approximation.

A Fast and Simple Graph Kernel for RDF

NARCIS (Netherlands)

de Vries, G.K.D.; de Rooij, S.

2013-01-01

In this paper we study a graph kernel for RDF based on constructing a tree for each instance and counting the number of paths in that tree. In our experiments this kernel shows comparable classification performance to the previously introduced intersection subtree kernel, but is significantly faster

Kernel based eigenvalue-decomposition methods for analysing ham

DEFF Research Database (Denmark)

Christiansen, Asger Nyman; Nielsen, Allan Aasbjerg; Møller, Flemming

2010-01-01

methods, such as PCA, MAF or MNF. We therefore investigated the applicability of kernel based versions of these transformation. This meant implementing the kernel based methods and developing new theory, since kernel based MAF and MNF is not described in the literature yet. The traditional methods only...... have two factors that are useful for segmentation and none of them can be used to segment the two types of meat. The kernel based methods have a lot of useful factors and they are able to capture the subtle differences in the images. This is illustrated in Figure 1. You can see a comparison of the most...... useful factor of PCA and kernel based PCA respectively in Figure 2. The factor of the kernel based PCA turned out to be able to segment the two types of meat and in general that factor is much more distinct, compared to the traditional factor. After the orthogonal transformation a simple thresholding...

Kernel principal component analysis for change detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Morton, J.C.

2008-01-01

region acquired at two different time points. If change over time does not dominate the scene, the projection of the original two bands onto the second eigenvector will show change over time. In this paper a kernel version of PCA is used to carry out the analysis. Unlike ordinary PCA, kernel PCA...... with a Gaussian kernel successfully finds the change observations in a case where nonlinearities are introduced artificially....

Screening of the kernels of Pentadesma butyracea from various ...

African Journals Online (AJOL)

Gwla10

Joseph D. Hounhouigan. 2. 1Laboratoire de .... laboratory. Kernels were washed and dried at 45°C for 72 h before analysis. ... generated values allow calculating the various shape ... (LLYOD Instruments, USA) fit with a 0.42 cm thick blade with a triangular ... vacuum. Extraction was run in triplicate on germ, albumen and.

Enhanced gluten properties in soft kernel durum wheat

Science.gov (United States)

Soft kernel durum wheat is a relatively recent development (Morris et al. 2011 Crop Sci. 51:114). The soft kernel trait exerts profound effects on kernel texture, flour milling including break flour yield, milling energy, and starch damage, and dough water absorption (DWA). With the caveat of reduce...

7 CFR 981.61 - Redetermination of kernel weight.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Redetermination of kernel weight. 981.61 Section 981... GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.61 Redetermination of kernel weight. The Board, on the basis of reports by handlers, shall redetermine the kernel weight of almonds...

Adaptive metric kernel regression

DEFF Research Database (Denmark)

Goutte, Cyril; Larsen, Jan

2000-01-01

Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...

Consistent Estimation of Pricing Kernels from Noisy Price Data

OpenAIRE

Vladislav Kargin

2003-01-01

If pricing kernels are assumed non-negative then the inverse problem of finding the pricing kernel is well-posed. The constrained least squares method provides a consistent estimate of the pricing kernel. When the data are limited, a new method is suggested: relaxed maximization of the relative entropy. This estimator is also consistent. Keywords: $\\epsilon$-entropy, non-parametric estimation, pricing kernel, inverse problems.

The Use of C-MnO2 as Hybrid Precursor Support for a Pt/C-MnxO1+x Catalyst with Enhanced Activity for the Methanol Oxidation Reaction (MOR

Directory of Open Access Journals (Sweden)

Alessandro H.A. Monteverde Videla

2015-07-01

Full Text Available Platinum (Pt nanoparticles are deposited on a hybrid support (C-MnO2 according to a polyol method. The home-made catalyst, resulted as Pt/C-MnxO1+x, is compared with two different commercial platinum based materials (Pt/C and PtRu/C. The synthesized catalyst is characterized by means of FESEM, XRD, ICP-MS, XPS and μRS analyses. MnO2 is synthesized and deposited over a commercial grade of carbon (Vulcan XC72 by facile reduction of potassium permanganate in acidic solution. Pt nanoparticles are synthesized on the hybrid support by a polyol thermal assisted method (microwave irradiation, followed by an annealing at 600 °C. The obtained catalyst displays a support constituted by a mixture of manganese oxides (Mn2O3 and Mn3O4 with a Pt loading of 19 wt. %. The electro-catalytic activity towards MOR is assessed by RDE in acid conditions (0.5 M H2SO4, evaluating the ability to oxidize methanol in 1 M concentration. The synthesized Pt/C-MnxO1+x catalyst shows good activity as well as good stability compared to the commercial Pt/C based catalyst.

Stable Kernel Representations as Nonlinear Left Coprime Factorizations

NARCIS (Netherlands)

Paice, A.D.B.; Schaft, A.J. van der

1994-01-01

A representation of nonlinear systems based on the idea of representing the input-output pairs of the system as elements of the kernel of a stable operator has been recently introduced. This has been denoted the kernel representation of the system. In this paper it is demonstrated that the kernel

7 CFR 981.60 - Determination of kernel weight.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Determination of kernel weight. 981.60 Section 981.60... Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which settlement...

End-use quality of soft kernel durum wheat

Science.gov (United States)

Kernel texture is a major determinant of end-use quality of wheat. Durum wheat has very hard kernels. We developed soft kernel durum wheat via Ph1b-mediated homoeologous recombination. The Hardness locus was transferred from Chinese Spring to Svevo durum wheat via back-crossing. ‘Soft Svevo’ had SKC...

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Directory of Open Access Journals (Sweden)

Ling-Yu Duan

2010-01-01

Full Text Available Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Directory of Open Access Journals (Sweden)

Tian Yonghong

2010-01-01

Full Text Available Abstract Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Directory of Open Access Journals (Sweden)

Narinder Singh

2018-03-01

Full Text Available The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO and Whale Optimizer Algorithm (WOA. We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO algorithm: (i firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

Discrete non-parametric kernel estimation for global sensitivity analysis

International Nuclear Information System (INIS)

Senga Kiessé, Tristan; Ventura, Anne

2016-01-01

This work investigates the discrete kernel approach for evaluating the contribution of the variance of discrete input variables to the variance of model output, via analysis of variance (ANOVA) decomposition. Until recently only the continuous kernel approach has been applied as a metamodeling approach within sensitivity analysis framework, for both discrete and continuous input variables. Now the discrete kernel estimation is known to be suitable for smoothing discrete functions. We present a discrete non-parametric kernel estimator of ANOVA decomposition of a given model. An estimator of sensitivity indices is also presented with its asymtotic convergence rate. Some simulations on a test function analysis and a real case study from agricultural have shown that the discrete kernel approach outperforms the continuous kernel one for evaluating the contribution of moderate or most influential discrete parameters to the model output. - Highlights: • We study a discrete kernel estimation for sensitivity analysis of a model. • A discrete kernel estimator of ANOVA decomposition of the model is presented. • Sensitivity indices are calculated for discrete input parameters. • An estimator of sensitivity indices is also presented with its convergence rate. • An application is realized for improving the reliability of environmental models.

Individual and combined (Plus-hybrid effect of cytoplasmic male sterility and xenia on maize grain yield

Directory of Open Access Journals (Sweden)

Sofija Bozinovic

2015-06-01

Full Text Available Plus-hybrid effect refere to a combined effect of cytoplasmic male sterility (CMS and xenia in maize (Zea mays L. It could be used in commercial production by growing a mixture of 80% CMS hybrid and 20% of another fertile hybrid. The aim of this research was to examine individual and combined CMS and xenia effects on two hybrids widely grown in Serbia. Sterile and fertile versions of ZP 1 and ZP 2 hybrids (three-way; Iodent x Lancaster dents were used as females, while ZP 1, ZP 2, ZP 3, ZP 4, and ZP 5 (three-way or single cross; Iodent (BSSS x Lancaster dents were used as pollinators. All of them belong to medium maturity group. The trial was set up at one location in Serbia (Zemun Polje in 2009, 2010, and 2011. Molecular analysis of the five genotypes was done using simple sequence repeat (SSR primers. Plus-hybrid effect on grain yield ranged from -6.2% to 6.2%; on thousand kernel weight from -1.7% to 5.2%; on number of kernels per area from -1.0% to 8.0%. The poor response could be due to a use of three-way instead of single cross hybrids in S type of sterility. Modified Rogers' distance between hybrids was in the range 0.211 to 0.378 and was not relevant for the effect, which depended mostly on the sterile hybrid genotype and the fertile hybrid pollinator ability. This approach should be more suitable for female hybrids with slightly poorer performance, already being produced on a sterile base.

AMITIS: A 3D GPU-Based Hybrid-PIC Model for Space and Plasma Physics

Science.gov (United States)

Fatemi, Shahab; Poppe, Andrew R.; Delory, Gregory T.; Farrell, William M.

2017-05-01

We have developed, for the first time, an advanced modeling infrastructure in space simulations (AMITIS) with an embedded three-dimensional self-consistent grid-based hybrid model of plasma (kinetic ions and fluid electrons) that runs entirely on graphics processing units (GPUs). The model uses NVIDIA GPUs and their associated parallel computing platform, CUDA, developed for general purpose processing on GPUs. The model uses a single CPU-GPU pair, where the CPU transfers data between the system and GPU memory, executes CUDA kernels, and writes simulation outputs on the disk. All computations, including moving particles, calculating macroscopic properties of particles on a grid, and solving hybrid model equations are processed on a single GPU. We explain various computing kernels within AMITIS and compare their performance with an already existing well-tested hybrid model of plasma that runs in parallel using multi-CPU platforms. We show that AMITIS runs ∼10 times faster than the parallel CPU-based hybrid model. We also introduce an implicit solver for computation of Faraday’s Equation, resulting in an explicit-implicit scheme for the hybrid model equation. We show that the proposed scheme is stable and accurate. We examine the AMITIS energy conservation and show that the energy is conserved with an error < 0.2% after 500,000 timesteps, even when a very low number of particles per cell is used.

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

Science.gov (United States)

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Improved modeling of clinical data with kernel methods.

Science.gov (United States)

Daemen, Anneleen; Timmerman, Dirk; Van den Bosch, Thierry; Bottomley, Cecilia; Kirk, Emma; Van Holsbeke, Caroline; Valentin, Lil; Bourne, Tom; De Moor, Bart

2012-02-01

Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. For clinical data consisting of variables of different types, the proposed kernel function--which takes into account the type and range of each variable--has shown to be a better alternative for linear and non-linear classification problems

Mitigation of artifacts in rtm with migration kernel decomposition

KAUST Repository

Zhan, Ge; Schuster, Gerard T.

2012-01-01

The migration kernel for reverse-time migration (RTM) can be decomposed into four component kernels using Born scattering and migration theory. Each component kernel has a unique physical interpretation and can be interpreted differently

Adaptive Metric Kernel Regression

DEFF Research Database (Denmark)

Goutte, Cyril; Larsen, Jan

1998-01-01

Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...

Research on offense and defense technology for iOS kernel security mechanism

Science.gov (United States)

Chu, Sijun; Wu, Hao

2018-04-01

iOS is a strong and widely used mobile device system. It's annual profits make up about 90% of the total profits of all mobile phone brands. Though it is famous for its security, there have been many attacks on the iOS operating system, such as the Trident apt attack in 2016. So it is important to research the iOS security mechanism and understand its weaknesses and put forward targeted protection and security check framework. By studying these attacks and previous jailbreak tools, we can see that an attacker could only run a ROP code and gain kernel read and write permissions based on the ROP after exploiting kernel and user layer vulnerabilities. However, the iOS operating system is still protected by the code signing mechanism, the sandbox mechanism, and the not-writable mechanism of the system's disk area. This is far from the steady, long-lasting control that attackers expect. Before iOS 9, breaking these security mechanisms was usually done by modifying the kernel's important data structures and security mechanism code logic. However, after iOS 9, the kernel integrity protection mechanism was added to the 64-bit operating system and none of the previous methods were adapted to the new versions of iOS [1]. But this does not mean that attackers can not break through. Therefore, based on the analysis of the vulnerability of KPP security mechanism, this paper implements two possible breakthrough methods for kernel security mechanism for iOS9 and iOS10. Meanwhile, we propose a defense method based on kernel integrity detection and sensitive API call detection to defense breakthrough method mentioned above. And we make experiments to prove that this method can prevent and detect attack attempts or invaders effectively and timely.

21 CFR 176.350 - Tamarind seed kernel powder.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 3 2010-04-01 2009-04-01 true Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

C Sacerdoti Coen

Indian Academy of Sciences (India)

Home; Journals; Sadhana. C Sacerdoti Coen. Articles written in Sadhana. Volume 34 Issue 1 February 2009 pp 71-144. A compact kernel for the calculus of inductive constructions · A Asperti W Ricciotti C Sacerdoti Coen E Tassi · More Details Abstract Fulltext PDF. The paper describes the new kernel for the Calculus of ...

Hybrid DNA i-motif: Aminoethylprolyl-PNA (pC5) enhance the stability of DNA (dC5) i-motif structure.

Science.gov (United States)

Gade, Chandrasekhar Reddy; Sharma, Nagendra K

2017-12-15

This report describes the synthesis of C-rich sequence, cytosine pentamer, of aep-PNA and its biophysical studies for the formation of hybrid DNA:aep-PNAi-motif structure with DNA cytosine pentamer (dC 5 ) under acidic pH conditions. Herein, the CD/UV/NMR/ESI-Mass studies strongly support the formation of stable hybrid DNA i-motif structure with aep-PNA even near acidic conditions. Hence aep-PNA C-rich sequence cytosine could be considered as potential DNA i-motif stabilizing agents in vivo conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effect of Orbital Hybridization on Spin-Polarized Tunneling across Co/C60 Interfaces.

Science.gov (United States)

Wang, Kai; Strambini, Elia; Sanderink, Johnny G M; Bolhuis, Thijs; van der Wiel, Wilfred G; de Jong, Michel P

2016-10-26

The interaction between ferromagnetic surfaces and organic semiconductors leads to the formation of hybrid interfacial states. As a consequence, the local magnetic moment is altered, a hybrid interfacial density of states (DOS) is formed, and spin-dependent shifts of energy levels occur. Here, we show that this hybridization affects spin transport across the interface significantly. We report spin-dependent electronic transport measurements for tunnel junctions comprising C 60 molecular thin films grown on top of face-centered-cubic (fcc) epitaxial Co electrodes, an AlO x tunnel barrier, and an Al counter electrode. Since only one ferromagnetic electrode (Co) is present, spin-polarized transport is due to tunneling anisotropic magnetoresistance (TAMR). An in-plane TAMR ratio of approximately 0.7% has been measured at 5 K under application of a magnetic field of 800 mT. The magnetic switching behavior shows some remarkable features, which are attributed to the rotation of interfacial magnetic moments. This behavior can be ascribed to the magnetic coupling between the Co thin films and the newly formed Co/C 60 hybridized interfacial states. Using the Tedrow-Meservey technique, the tunnel spin polarization of the Co/C 60 interface was found to be 43%.

Microstructural characterization of hybrid CFRP/SiC composites

International Nuclear Information System (INIS)

Von Dollinger, C.F.A.; Pardini, L.C.; Alves, S.C.N.

2016-01-01

In present work a hybrid matrix C-C/SiC composites were produced. Carbon fiber fabric was impregnated with phenolic resin mixed with powder Si in proportions of 5%, 10%, 15% e 20%wt. Optical microscopy under reflected light and polarized light were used in order to characterize samples in the as molded condition and after carbonization at 1000°C, and heat treatment 1600°C in order to react carbon and liquid silicon in order to form in situ SiC . The pore volume fraction ranges from 33% to 41% for composites after heat treatment at 1600°C due to volatiles released specially during carbonization process. Complementary analyses were done by Scanning Electron microscopy (SEM) and X-Ray diffraction to confirm in situ conversion of SiC. The results showed that the impregnation of a carbon fabric with phenolic resin added with silicon proved to be an alternative route to produce CFRP/SiC composites. (author)

Dense Medium Machine Processing Method for Palm Kernel/ Shell ...

African Journals Online (AJOL)

ADOWIE PERE

Cracked palm kernel is a mixture of kernels, broken shells, dusts and other impurities. In ... machine processing method using dense medium, a separator, a shell collector and a kernel .... efficiency, ease of maintenance and uniformity of.

Multivariate and semiparametric kernel regression

OpenAIRE

Härdle, Wolfgang; Müller, Marlene

1997-01-01

The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...

Notes on the gamma kernel

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole E.

The density function of the gamma distribution is used as shift kernel in Brownian semistationary processes modelling the timewise behaviour of the velocity in turbulent regimes. This report presents exact and asymptotic properties of the second order structure function under such a model......, and relates these to results of von Karmann and Horwath. But first it is shown that the gamma kernel is interpretable as a Green’s function....

A New Hybrid Approach for Wind Speed Prediction Using Fast Block Least Mean Square Algorithm and Artificial Neural Network

Directory of Open Access Journals (Sweden)

Ummuhan Basaran Filik

2016-01-01

Full Text Available A new hybrid wind speed prediction approach, which uses fast block least mean square (FBLMS algorithm and artificial neural network (ANN method, is proposed. FBLMS is an adaptive algorithm which has reduced complexity with a very fast convergence rate. A hybrid approach is proposed which uses two powerful methods: FBLMS and ANN method. In order to show the efficiency and accuracy of the proposed approach, seven-year real hourly collected wind speed data sets belonging to Turkish State Meteorological Service of Bozcaada and Eskisehir regions are used. Two different ANN structures are used to compare with this approach. The first six-year data is handled as a train set; the remaining one-year hourly data is handled as test data. Mean absolute error (MAE and root mean square error (RMSE are used for performance evaluations. It is shown for various cases that the performance of the new hybrid approach gives better results than the different conventional ANN structure.

Reconfigurable c-Si/Au hybrid nanoantenna

Science.gov (United States)

Chebykin, A. V.; Zalogina, A. S.; Zuev, D. A.; Makarov, S. V.

2017-09-01

We have performed numerical optimization of hybrid c-Si/Au nanoantenna's geometry to improve efficiency of NV-centers radiation. We have shown that Purcell factor at the wavelength 635 nm can be as much as 4550 for point light emitter placed in the gap between gold spherical nanoparticle and truncated silicon nanocone. We have demonstrated that electric field enhancement can reach a value of 12.9 at the wavelength of NV-center pumping, 532 nm. Our results can be useful for the development of more efficient sources of single photons based on NV-centers in nanodiamonds.

Calculation of the thermal neutron scattering kernel using the synthetic model. Pt. 2. Zero-order energy transfer kernel

International Nuclear Information System (INIS)

Drozdowicz, K.

1995-01-01

A comprehensive unified description of the application of Granada's Synthetic Model to the slow-neutron scattering by the molecular systems is continued. Detailed formulae for the zero-order energy transfer kernel are presented basing on the general formalism of the model. An explicit analytical formula for the total scattering cross section as a function of the incident neutron energy is also obtained. Expressions of the free gas model for the zero-order scattering kernel and for total scattering kernel are considered as a sub-case of the Synthetic Model. (author). 10 refs

Effect of different ripening stages on walnut kernel quality: antioxidant activities, lipid characterization and antibacterial properties.

Science.gov (United States)

Amin, Furheen; Masoodi, F A; Baba, Waqas N; Khan, Asma Ashraf; Ganie, Bashir Ahmad

2017-11-01

Packing tissue between and around the kernel halves just turning brown (PTB) is a phenological indicator of kernel ripening at harvest in walnuts. The effect of three ripening stages (Pre-PTB, PTB and Post-PTB) on kernel quality characteristics, mineral composition, lipid characterization, sensory analysis, antioxidant and antibacterial activity were investigated in fresh kernels of indigenous numbered walnut selection of Kashmir valley "SKAU-02". Proximate composition, physical properties and sensory analysis of walnut kernels showed better results for Pre-PTB and PTB while higher mineral content was seen for kernels at Post-PTB stage in comparison to other stages of ripening. Kernels showed significantly higher levels of Omega-3 PUFA (C18:3 n3 ) and low n6/n3 ratio when harvested at Pre-PTB and PTB stages. The highest phenolic content and antioxidant activity was observed at the first stage of ripening and a steady decrease was observed at later stages. TBARS values increased as ripening advanced but did not show any significant difference in malonaldehyde formation during early ripening stages whereas it showed marked increase in walnut kernels at post-PTB stage. Walnut extracts inhibited growth of Gram-positive bacteria ( B. cereus, B. subtilis, and S. aureus ) with respective MICs of 1, 1 and 5 mg/mL and gram negative bacteria ( E. coli, P. and K. pneumonia ) with MIC of 100 mg/mL. Zone of inhibition obtained against all the bacterial strains from walnut kernel extracts increased with increase in the stage of ripening. It is concluded that Pre-PTB harvest stage with higher antioxidant activities, better fatty acid profile and consumer acceptability could be preferred harvesting stage for obtaining functionally superior walnut kernels.

Convergence of barycentric coordinates to barycentric kernels

KAUST Repository

Kosinka, Jiří

2016-02-12

We investigate the close correspondence between barycentric coordinates and barycentric kernels from the point of view of the limit process when finer and finer polygons converge to a smooth convex domain. We show that any barycentric kernel is the limit of a set of barycentric coordinates and prove that the convergence rate is quadratic. Our convergence analysis extends naturally to barycentric interpolants and mappings induced by barycentric coordinates and kernels. We verify our theoretical convergence results numerically on several examples.

Convergence of barycentric coordinates to barycentric kernels

KAUST Repository

Kosinka, Jiří ; Barton, Michael

2016-01-01

We investigate the close correspondence between barycentric coordinates and barycentric kernels from the point of view of the limit process when finer and finer polygons converge to a smooth convex domain. We show that any barycentric kernel is the limit of a set of barycentric coordinates and prove that the convergence rate is quadratic. Our convergence analysis extends naturally to barycentric interpolants and mappings induced by barycentric coordinates and kernels. We verify our theoretical convergence results numerically on several examples.

Hadamard Kernel SVM with applications for breast cancer outcome predictions.

Science.gov (United States)

Jiang, Hao; Ching, Wai-Ki; Cheung, Wai-Shun; Hou, Wenpin; Yin, Hong

2017-12-21

Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation. Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods. Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.

Aflatoxin contamination of developing corn kernels.

Science.gov (United States)

Amer, M A

2005-01-01

Preharvest of corn and its contamination with aflatoxin is a serious problem. Some environmental and cultural factors responsible for infection and subsequent aflatoxin production were investigated in this study. Stage of growth and location of kernels on corn ears were found to be one of the important factors in the process of kernel infection with A. flavus & A. parasiticus. The results showed positive correlation between the stage of growth and kernel infection. Treatment of corn with aflatoxin reduced germination, protein and total nitrogen contents. Total and reducing soluble sugar was increase in corn kernels as response to infection. Sucrose and protein content were reduced in case of both pathogens. Shoot system length, seeding fresh weigh and seedling dry weigh was also affected. Both pathogens induced reduction of starch content. Healthy corn seedlings treated with aflatoxin solution were badly affected. Their leaves became yellow then, turned brown with further incubation. Moreover, their total chlorophyll and protein contents showed pronounced decrease. On the other hand, total phenolic compounds were increased. Histopathological studies indicated that A. flavus & A. parasiticus could colonize corn silks and invade developing kernels. Germination of A. flavus spores was occurred and hyphae spread rapidly across the silk, producing extensive growth and lateral branching. Conidiophores and conidia had formed in and on the corn silk. Temperature and relative humidity greatly influenced the growth of A. flavus & A. parasiticus and aflatoxin production.

Kernel Korner : The Linux keyboard driver

NARCIS (Netherlands)

Brouwer, A.E.

1995-01-01

Our Kernel Korner series continues with an article describing the Linux keyboard driver. This article is not for "Kernel Hackers" only--in fact, it will be most useful to those who wish to use their own keyboard to its fullest potential, and those who want to write programs to take advantage of the

Hollow microspheres with a tungsten carbide kernel for PEMFC application.

Science.gov (United States)

d'Arbigny, Julien Bernard; Taillades, Gilles; Marrony, Mathieu; Jones, Deborah J; Rozière, Jacques

2011-07-28

Tungsten carbide microspheres comprising an outer shell and a compact kernel prepared by a simple hydrothermal method exhibit very high surface area promoting a high dispersion of platinum nanoparticles, and an exceptionally high electrochemically active surface area (EAS) stability compared to the usual Pt/C electrocatalysts used for PEMFC application.

Mitigation of artifacts in rtm with migration kernel decomposition

KAUST Repository

Zhan, Ge

2012-01-01

The migration kernel for reverse-time migration (RTM) can be decomposed into four component kernels using Born scattering and migration theory. Each component kernel has a unique physical interpretation and can be interpreted differently. In this paper, we present a generalized diffraction-stack migration approach for reducing RTM artifacts via decomposition of migration kernel. The decomposition leads to an improved understanding of migration artifacts and, therefore, presents us with opportunities for improving the quality of RTM images.

Realized kernels in practice

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Hansen, P. Reinhard; Lunde, Asger

2009-01-01

and find a remarkable level of agreement. We identify some features of the high-frequency data, which are challenging for realized kernels. They are when there are local trends in the data, over periods of around 10 minutes, where the prices and quotes are driven up or down. These can be associated......Realized kernels use high-frequency data to estimate daily volatility of individual stock prices. They can be applied to either trade or quote data. Here we provide the details of how we suggest implementing them in practice. We compare the estimates based on trade and quote data for the same stock...

Antinutritional factors and hypocholesterolemic effect of wild apricot kernel (Prunus armeniaca L.) as affected by detoxification.

Science.gov (United States)

Tanwar, Beenu; Modgil, Rajni; Goyal, Ankit

2018-04-25

The present investigation was aimed to study the effect of detoxification on the nutrients and antinutrients of wild apricot kernel followed by its hypocholesterolemic effect in male Wistar albino rats. The results revealed a non-significant (p > 0.05) effect of detoxification on the proximate composition except total carbohydrates and protein content. However, detoxification led to a significant (p acid (76.82%), β-carotene (25.90%), dietary fiber constituents (10.51-28.92%), minerals (4.76-31.08%) and antinutritional factors (23.92-77.05%) (phenolics, tannins, trypsin inhibitor activity, saponins, phytic acid, alkaloids, flavonoids, oxalates) along with the complete removal (100%) of bitter and potentially toxic hydrocyanic acid (HCN). The quality parameters of kernel oil indicated no adverse effects of detoxification on free fatty acids, lipase activity, acid value and peroxide value, which remained well below the maximum permissible limit. Blood lipid profile demonstrated that the detoxified apricot kernel group exhibited significantly (p < 0.05) increased levels of HDL-cholesterol (48.79%) and triglycerides (15.09%), and decreased levels of total blood cholesterol (6.99%), LDL-C (22.95%) and VLDL-C (7.90%) compared to that of the raw (untreated) kernel group. Overall, it can be concluded that wild apricot kernel flour could be detoxified efficiently by employing a simple, safe, domestic and cost-effective method, which further has the potential for formulating protein supplements and value-added food products.

Anatomically-aided PET reconstruction using the kernel method.

Science.gov (United States)

Hutchcroft, Will; Wang, Guobao; Chen, Kevin T; Catana, Ciprian; Qi, Jinyi

2016-09-21

This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.

Kernel Temporal Differences for Neural Decoding

Science.gov (United States)

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

Primary and scattering contributions to beta scaled dose point kernels by means of Monte Carlo simulations; Contribuicoes primaria e espalhada para dosimetria beta calculadas pelo dose point kernels empregando simulacoes pelo Metodo Monte Carlo

Energy Technology Data Exchange (ETDEWEB)

Valente, Mauro [CONICET - Consejo Nacional de Investigaciones Cientificas y Tecnicas de La Republica Argentina (Conicet), Buenos Aires, AR (Brazil); Botta, Francesca; Pedroli, Guido [European Institute of Oncology, Milan (Italy). Medical Physics Department; Perez, Pedro, E-mail: valente@famaf.unc.edu.ar [Universidad Nacional de Cordoba, Cordoba (Argentina). Fac. de Matematica, Astronomia y Fisica (FaMAF)

2012-07-01

Beta-emitters have proved to be appropriate for radioimmunotherapy. The dosimetric characterization of each radionuclide has to be carefully investigated. One usual and practical dosimetric approach is the calculation of dose distribution from a unit point source emitting particles according to any radionuclide of interest, which is known as dose point kernel. Absorbed dose distributions are due to primary and radiation scattering contributions. This work presented a method capable of performing dose distributions for nuclear medicine dosimetry by means of Monte Carlo methods. Dedicated subroutines have been developed in order to separately compute primary and scattering contributions to the total absorbed dose, performing particle transport up to 1 keV or least. Preliminarily, the suitability of the calculation method has been satisfactory, being tested for monoenergetic sources, and it was further applied to the characterization of different beta-minus radionuclides of nuclear medicine interests for radioimmunotherapy. (author)

Collision kernels in the eikonal approximation for Lennard-Jones interaction potential

International Nuclear Information System (INIS)

Zielinska, S.

1985-03-01

The velocity changing collisions are conveniently described by collisional kernels. These kernels depend on an interaction potential and there is a necessity for evaluating them for realistic interatomic potentials. Using the collision kernels, we are able to investigate the redistribution of atomic population's caused by the laser light and velocity changing collisions. In this paper we present the method of evaluating the collision kernels in the eikonal approximation. We discuss the influence of the potential parameters Rsub(o)sup(i), epsilonsub(o)sup(i) on kernel width for a given atomic state. It turns out that unlike the collision kernel for the hard sphere model of scattering the Lennard-Jones kernel is not so sensitive to changes of Rsub(o)sup(i) as the previous one. Contrary to the general tendency of approximating collisional kernels by the Gaussian curve, kernels for the Lennard-Jones potential do not exhibit such a behaviour. (author)

The construction of a two-dimensional reproducing kernel function and its application in a biomedical model.

Science.gov (United States)

Guo, Qi; Shen, Shu-Ting

2016-04-29

There are two major classes of cardiac tissue models: the ionic model and the FitzHugh-Nagumo model. During computer simulation, each model entails solving a system of complex ordinary differential equations and a partial differential equation with non-flux boundary conditions. The reproducing kernel method possesses significant applications in solving partial differential equations. The derivative of the reproducing kernel function is a wavelet function, which has local properties and sensitivities to singularity. Therefore, study on the application of reproducing kernel would be advantageous. Applying new mathematical theory to the numerical solution of the ventricular muscle model so as to improve its precision in comparison with other methods at present. A two-dimensional reproducing kernel function inspace is constructed and applied in computing the solution of two-dimensional cardiac tissue model by means of the difference method through time and the reproducing kernel method through space. Compared with other methods, this method holds several advantages such as high accuracy in computing solutions, insensitivity to different time steps and a slow propagation speed of error. It is suitable for disorderly scattered node systems without meshing, and can arbitrarily change the location and density of the solution on different time layers. The reproducing kernel method has higher solution accuracy and stability in the solutions of the two-dimensional cardiac tissue model.

Classification of maize kernels using NIR hyperspectral imaging

DEFF Research Database (Denmark)

Williams, Paul; Kucheryavskiy, Sergey V.

2016-01-01

NIR hyperspectral imaging was evaluated to classify maize kernels of three hardness categories: hard, medium and soft. Two approaches, pixel-wise and object-wise, were investigated to group kernels according to hardness. The pixel-wise classification assigned a class to every pixel from individual...... and specificity of 0.95 and 0.93). Both feature extraction methods can be recommended for classification of maize kernels on production scale....

Application of Reproducing Kernel Method for Solving Nonlinear Fredholm-Volterra Integrodifferential Equations

Directory of Open Access Journals (Sweden)

Omar Abu Arqub

2012-01-01

Full Text Available This paper investigates the numerical solution of nonlinear Fredholm-Volterra integro-differential equations using reproducing kernel Hilbert space method. The solution ( is represented in the form of series in the reproducing kernel space. In the mean time, the n-term approximate solution ( is obtained and it is proved to converge to the exact solution (. Furthermore, the proposed method has an advantage that it is possible to pick any point in the interval of integration and as well the approximate solution and its derivative will be applicable. Numerical examples are included to demonstrate the accuracy and applicability of the presented technique. The results reveal that the method is very effective and simple.

Influence of wheat kernel physical properties on the pulverizing process.

Science.gov (United States)

Dziki, Dariusz; Cacak-Pietrzak, Grażyna; Miś, Antoni; Jończyk, Krzysztof; Gawlik-Dziki, Urszula

2014-10-01

The physical properties of wheat kernel were determined and related to pulverizing performance by correlation analysis. Nineteen samples of wheat cultivars about similar level of protein content (11.2-12.8 % w.b.) and obtained from organic farming system were used for analysis. The kernel (moisture content 10 % w.b.) was pulverized by using the laboratory hammer mill equipped with round holes 1.0 mm screen. The specific grinding energy ranged from 120 kJkg(-1) to 159 kJkg(-1). On the basis of data obtained many of significant correlations (p kernel physical properties and pulverizing process of wheat kernel, especially wheat kernel hardness index (obtained on the basis of Single Kernel Characterization System) and vitreousness significantly and positively correlated with the grinding energy indices and the mass fraction of coarse particles (> 0.5 mm). Among the kernel mechanical properties determined on the basis of uniaxial compression test only the rapture force was correlated with the impact grinding results. The results showed also positive and significant relationships between kernel ash content and grinding energy requirements. On the basis of wheat physical properties the multiple linear regression was proposed for predicting the average particle size of pulverized kernel.

Evolution kernel for the Dirac field

International Nuclear Information System (INIS)

Baaquie, B.E.

1982-06-01

The evolution kernel for the free Dirac field is calculated using the Wilson lattice fermions. We discuss the difficulties due to which this calculation has not been previously performed in the continuum theory. The continuum limit is taken, and the complete energy eigenfunctions as well as the propagator are then evaluated in a new manner using the kernel. (author)

Gradient-based adaptation of general gaussian kernels.

Science.gov (United States)

Glasmachers, Tobias; Igel, Christian

2005-10-01

Gradient-based optimizing of gaussian kernel functions is considered. The gradient for the adaptation of scaling and rotation of the input space is computed to achieve invariance against linear transformations. This is done by using the exponential map as a parameterization of the kernel parameter manifold. By restricting the optimization to a constant trace subspace, the kernel size can be controlled. This is, for example, useful to prevent overfitting when minimizing radius-margin generalization performance measures. The concepts are demonstrated by training hard margin support vector machines on toy data.

MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions

Science.gov (United States)

Novosad, Philip; Reader, Andrew J.

2016-06-01

Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [18F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel

Different proportions of C/KCu7S4 hybrid structure for high-performance supercapacitors

Science.gov (United States)

Dai, Shuge; Xi, Yi; Hu, Chenguo; Yue, Xule; Cheng, Lu; Wang, Guo

2014-10-01

KCu7S4 has the channel structure and minor resistance. Its double larger channels ensure that the ions can well exchange with other's, at the same time, can shorten the ionic diffusion path and improve the ionic and electronic transport. So KCu7S4 shows good electrochemical property. The paper reports a novel and high performance supercapacitor based on hybrid carbon particles and KCu7S4 (C/KCu7S4) electrode. For the hybrid structure with different proportions of C and KCu7S4, the C/KCu7S4 (1:10) hybrid supercapacitor shows preferable electrochemical performance and large specific capacitance (469 mF cm-2) at high charge-discharge rate (2 mA), still retaining ∼95% of the capacitance over 5000 cycles by charge-discharge process at a fixed current of 10 mA. Three supercapacitor units in series can light 50 light-emitting diodes (LEDs) for 2.5 min, 10 LEDs for 4 min, one LED for 5.5 min. The much-increased capacity, rate capability, and cycling stability may be attributed to the superionic conductive KCu7S4 nanowires and C/KCu7S4 hybrid structure, which improve ionic and electronic transport, enhance the kinetics of redox reactions through the electrode system.

Analog forecasting with dynamics-adapted kernels

Science.gov (United States)

Zhao, Zhizhen; Giannakis, Dimitrios

2016-09-01

Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record of observations which most closely resembles the current initial data. Here, we introduce a suite of forecasting methods which improve traditional analog forecasting by combining ideas from kernel methods developed in harmonic analysis and machine learning and state-space reconstruction for dynamical systems. A key ingredient of our approach is to replace single-analog forecasting with weighted ensembles of analogs constructed using local similarity kernels. The kernels used here employ a number of dynamics-dependent features designed to improve forecast skill, including Takens’ delay-coordinate maps (to recover information in the initial data lost through partial observations) and a directional dependence on the dynamical vector field generating the data. Mathematically, our approach is closely related to kernel methods for out-of-sample extension of functions, and we discuss alternative strategies based on the Nyström method and the multiscale Laplacian pyramids technique. We illustrate these techniques in applications to forecasting in a low-order deterministic model for atmospheric dynamics with chaotic metastability, and interannual-scale forecasting in the North Pacific sector of a comprehensive climate model. We find that forecasts based on kernel-weighted ensembles have significantly higher skill than the conventional approach following a single analog.

Open Problem: Kernel methods on manifolds and metric spaces

DEFF Research Database (Denmark)

Feragen, Aasa; Hauberg, Søren

2016-01-01

Radial kernels are well-suited for machine learning over general geodesic metric spaces, where pairwise distances are often the only computable quantity available. We have recently shown that geodesic exponential kernels are only positive definite for all bandwidths when the input space has strong...... linear properties. This negative result hints that radial kernel are perhaps not suitable over geodesic metric spaces after all. Here, however, we present evidence that large intervals of bandwidths exist where geodesic exponential kernels have high probability of being positive definite over finite...... datasets, while still having significant predictive power. From this we formulate conjectures on the probability of a positive definite kernel matrix for a finite random sample, depending on the geometry of the data space and the spread of the sample....

Nonlinear Denoising and Analysis of Neuroimages With Kernel Principal Component Analysis and Pre-Image Estimation

DEFF Research Database (Denmark)

Rasmussen, Peter Mondrup; Abrahamsen, Trine Julie; Madsen, Kristoffer Hougaard

2012-01-01

We investigate the use of kernel principal component analysis (PCA) and the inverse problem known as pre-image estimation in neuroimaging: i) We explore kernel PCA and pre-image estimation as a means for image denoising as part of the image preprocessing pipeline. Evaluation of the denoising...... procedure is performed within a data-driven split-half evaluation framework. ii) We introduce manifold navigation for exploration of a nonlinear data manifold, and illustrate how pre-image estimation can be used to generate brain maps in the continuum between experimentally defined brain states/classes. We...

Kernel-based noise filtering of neutron detector signals

International Nuclear Information System (INIS)

Park, Moon Ghu; Shin, Ho Cheol; Lee, Eun Ki

2007-01-01

This paper describes recently developed techniques for effective filtering of neutron detector signal noise. In this paper, three kinds of noise filters are proposed and their performance is demonstrated for the estimation of reactivity. The tested filters are based on the unilateral kernel filter, unilateral kernel filter with adaptive bandwidth and bilateral filter to show their effectiveness in edge preservation. Filtering performance is compared with conventional low-pass and wavelet filters. The bilateral filter shows a remarkable improvement compared with unilateral kernel and wavelet filters. The effectiveness and simplicity of the unilateral kernel filter with adaptive bandwidth is also demonstrated by applying it to the reactivity measurement performed during reactor start-up physics tests

Towards spatial isolation design in a multi-core real-time kernel targeting safety-critical applications

DEFF Research Database (Denmark)

Li, Gang; Top, Søren

2013-01-01

. Partitioning can prevent fault propagation among mixed-criticality applications, if spatial and temporal isolation are adequately ensured. This paper focuses on the solution of spatial isolation in the HARTEX kernel on a multi-core platform in terms of memory, communication between applications and I/O sharing....... According to formulated isolation requirements, a simple partitioning multi-core hardware architecture is proposed using SoC and memory protection units, and the kernel is extended to support spatial isolation between the kernel and applications as well as between applications. Combined design of hardware...... and software can easily achieve this isolation. At last, the spatial isolation is evaluated using a statistical sampling method and its performance is tested in terms of task switch, system call and footprint....

A trace ratio maximization approach to multiple kernel-based dimensionality reduction.

Science.gov (United States)

Jiang, Wenhao; Chung, Fu-lai

2014-01-01

Most dimensionality reduction techniques are based on one metric or one kernel, hence it is necessary to select an appropriate kernel for kernel-based dimensionality reduction. Multiple kernel learning for dimensionality reduction (MKL-DR) has been recently proposed to learn a kernel from a set of base kernels which are seen as different descriptions of data. As MKL-DR does not involve regularization, it might be ill-posed under some conditions and consequently its applications are hindered. This paper proposes a multiple kernel learning framework for dimensionality reduction based on regularized trace ratio, termed as MKL-TR. Our method aims at learning a transformation into a space of lower dimension and a corresponding kernel from the given base kernels among which some may not be suitable for the given data. The solutions for the proposed framework can be found based on trace ratio maximization. The experimental results demonstrate its effectiveness in benchmark datasets, which include text, image and sound datasets, for supervised, unsupervised as well as semi-supervised settings. Copyright © 2013 Elsevier Ltd. All rights reserved.

Predictive Model Equations for Palm Kernel (Elaeis guneensis J ...

African Journals Online (AJOL)

Estimated error of ± 0.18 and ± 0.2 are envisaged while applying the models for predicting palm kernel and sesame oil colours respectively. Keywords: Palm kernel, Sesame, Palm kernel, Oil Colour, Process Parameters, Model. Journal of Applied Science, Engineering and Technology Vol. 6 (1) 2006 pp. 34-38 ...

Heat kernel analysis for Bessel operators on symmetric cones

DEFF Research Database (Denmark)

Möllers, Jan

2014-01-01

. The heat kernel is explicitly given in terms of a multivariable $I$-Bessel function on $Ω$. Its corresponding heat kernel transform defines a continuous linear operator between $L^p$-spaces. The unitary image of the $L^2$-space under the heat kernel transform is characterized as a weighted Bergmann space...

A multi-scale kernel bundle for LDDMM

DEFF Research Database (Denmark)

Sommer, Stefan Horst; Nielsen, Mads; Lauze, Francois Bernard

2011-01-01

The Large Deformation Diffeomorphic Metric Mapping framework constitutes a widely used and mathematically well-founded setup for registration in medical imaging. At its heart lies the notion of the regularization kernel, and the choice of kernel greatly affects the results of registrations...

Training Lp norm multiple kernel learning in the primal.

Science.gov (United States)

Liang, Zhizheng; Xia, Shixiong; Zhou, Yong; Zhang, Lei

2013-10-01

Some multiple kernel learning (MKL) models are usually solved by utilizing the alternating optimization method where one alternately solves SVMs in the dual and updates kernel weights. Since the dual and primal optimization can achieve the same aim, it is valuable in exploring how to perform Lp norm MKL in the primal. In this paper, we propose an Lp norm multiple kernel learning algorithm in the primal where we resort to the alternating optimization method: one cycle for solving SVMs in the primal by using the preconditioned conjugate gradient method and other cycle for learning the kernel weights. It is interesting to note that the kernel weights in our method can obtain analytical solutions. Most importantly, the proposed method is well suited for the manifold regularization framework in the primal since solving LapSVMs in the primal is much more effective than solving LapSVMs in the dual. In addition, we also carry out theoretical analysis for multiple kernel learning in the primal in terms of the empirical Rademacher complexity. It is found that optimizing the empirical Rademacher complexity may obtain a type of kernel weights. The experiments on some datasets are carried out to demonstrate the feasibility and effectiveness of the proposed method. Copyright © 2013 Elsevier Ltd. All rights reserved.

Stochastic subset selection for learning with kernel machines.

Science.gov (United States)

Rhinelander, Jason; Liu, Xiaoping P

2012-06-01

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

RTOS kernel in portable electrocardiograph

Science.gov (United States)

Centeno, C. A.; Voos, J. A.; Riva, G. G.; Zerbini, C.; Gonzalez, E. A.

2011-12-01

This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.

RTOS kernel in portable electrocardiograph

International Nuclear Information System (INIS)

Centeno, C A; Voos, J A; Riva, G G; Zerbini, C; Gonzalez, E A

2011-01-01

This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.

RKRD: Runtime Kernel Rootkit Detection

Science.gov (United States)

Grover, Satyajit; Khosravi, Hormuzd; Kolar, Divya; Moffat, Samuel; Kounavis, Michael E.

In this paper we address the problem of protecting computer systems against stealth malware. The problem is important because the number of known types of stealth malware increases exponentially. Existing approaches have some advantages for ensuring system integrity but sophisticated techniques utilized by stealthy malware can thwart them. We propose Runtime Kernel Rootkit Detection (RKRD), a hardware-based, event-driven, secure and inclusionary approach to kernel integrity that addresses some of the limitations of the state of the art. Our solution is based on the principles of using virtualization hardware for isolation, verifying signatures coming from trusted code as opposed to malware for scalability and performing system checks driven by events. Our RKRD implementation is guided by our goals of strong isolation, no modifications to target guest OS kernels, easy deployment, minimal infra-structure impact, and minimal performance overhead. We developed a system prototype and conducted a number of experiments which show that the per-formance impact of our solution is negligible.

Denoising by semi-supervised kernel PCA preimaging

DEFF Research Database (Denmark)

Hansen, Toke Jansen; Abrahamsen, Trine Julie; Hansen, Lars Kai

2014-01-01

Kernel Principal Component Analysis (PCA) has proven a powerful tool for nonlinear feature extraction, and is often applied as a pre-processing step for classification algorithms. In denoising applications Kernel PCA provides the basis for dimensionality reduction, prior to the so-called pre-imag...

Sentiment classification with interpolated information diffusion kernels

NARCIS (Netherlands)

Raaijmakers, S.

2007-01-01

Information diffusion kernels - similarity metrics in non-Euclidean information spaces - have been found to produce state of the art results for document classification. In this paper, we present a novel approach to global sentiment classification using these kernels. We carry out a large array of

Characterization of gamma irradiated peanut kernels stored one year under ambient and frozen conditions

International Nuclear Information System (INIS)

Chiou, R.Y.Y.; Shyu, S.L.; Tsai, C.L.

1991-01-01

Peanut kernels were gamma irradiated at 0, 2.5, 5.0, 10, and 20 KGy, and stored 1 yr at ambient and frozen (-14 degrees C) conditions. Irradiated peanuts lost germination capabilities during storage. Molds were detected only on peanuts irradiated with 2.5 KGy and stored at ambient temperature. Peanut oil in kernels stored at -14 degrees C was comparatively more stable than that in peanuts stored at ambient temperature. Oxidation of oil was not significantly changed by irradiation. Changes in fatty acid content varied slightly with exception of linoleic and linolenic acids which decreased with increased radiation depending on storage temperature. The SDS-PAGE protein patterns of peanuts revealed no noticeable variation of protein subunits resulting from irradiation and storage

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

Science.gov (United States)

Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

2006-06-06

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks

Directory of Open Access Journals (Sweden)

Chang Jeong-Ho

2006-06-01

Full Text Available Abstract Background To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. Results To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. Conclusion By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway

Linear and kernel methods for multivariate change detection

DEFF Research Database (Denmark)

Canty, Morton J.; Nielsen, Allan Aasbjerg

2012-01-01

), as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (nonlinear), may further enhance change signals relative to no-change background. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric...... normalization, and kernel PCA/MAF/MNF transformations are presented that function as transparent and fully integrated extensions of the ENVI remote sensing image analysis environment. The train/test approach to kernel PCA is evaluated against a Hebbian learning procedure. Matlab code is also available...... that allows fast data exploration and experimentation with smaller datasets. New, multiresolution versions of IR-MAD that accelerate convergence and that further reduce no-change background noise are introduced. Computationally expensive matrix diagonalization and kernel image projections are programmed...

Panel data specifications in nonparametric kernel regression

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...

Scuba: scalable kernel-based gene prioritization.

Science.gov (United States)

Zampieri, Guido; Tran, Dinh Van; Donini, Michele; Navarin, Nicolò; Aiolli, Fabio; Sperduti, Alessandro; Valle, Giorgio

2018-01-25

The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .

Physical properties of neem (Azadirachta indica seeds and kernels relevant in the design of processing machineries

Directory of Open Access Journals (Sweden)

A. A. Balami

2014-08-01

Full Text Available This study was undertaken to determine some physical properties of neem seeds and kernels such as axial dimensions, true density, bulk density, surface area, sphericity, moisture content, coefficient of friction, angle of repose and porosity that are relevant in the design of processing machineries. Results shows that the seeds and kernels have major, intermediate and minor diameter values of 13.641.15 mm, 6.800.41 mm and 5.540.44 mm and 9.321.13 mm, 4.320.63 mm and 3.380.35 mm respectively. The true density, bulk density, surface areas and the sphericity determined were 379.0015.16 kg/m3, 39.2930.001 kg/m3, 169.9331.15 mm2, 0.640.043 for the seed while for the kernel were 930.038.53 kg/m3, 534.8910.99 kg/m3, 70.6313.98 mm2, 0.610.044 respectively at an average moisture content of 13.80 % for the seed and 11.9 % for the kernel on wet basis. The coefficient of friction determined on three different structural surfaces namely plywood, glass and mild steel for the seeds were 0.38, 0.42 and 0.33 respectively at an angle of repose of 34.52o while that of the kernels were 0.49, 0.56 and 0.43 respectively at an angle of repose of 42.47°. The mean porosity obtained for the seed and kernel were 89.62 % and 42.47 % respectively.

Multiple kernel boosting framework based on information measure for classification

International Nuclear Information System (INIS)

Qi, Chengming; Wang, Yuping; Tian, Wenjie; Wang, Qun

2016-01-01

The performance of kernel-based method, such as support vector machine (SVM), is greatly affected by the choice of kernel function. Multiple kernel learning (MKL) is a promising family of machine learning algorithms and has attracted many attentions in recent years. MKL combines multiple sub-kernels to seek better results compared to single kernel learning. In order to improve the efficiency of SVM and MKL, in this paper, the Kullback–Leibler kernel function is derived to develop SVM. The proposed method employs an improved ensemble learning framework, named KLMKB, which applies Adaboost to learning multiple kernel-based classifier. In the experiment for hyperspectral remote sensing image classification, we employ feature selected through Optional Index Factor (OIF) to classify the satellite image. We extensively examine the performance of our approach in comparison to some relevant and state-of-the-art algorithms on a number of benchmark classification data sets and hyperspectral remote sensing image data set. Experimental results show that our method has a stable behavior and a noticeable accuracy for different data set.

Factorization and the synthesis of optimal feedback kernels for differential-delay systems

Science.gov (United States)

Milman, Mark M.; Scheid, Robert E.

1987-01-01

A combination of ideas from the theories of operator Riccati equations and Volterra factorizations leads to the derivation of a novel, relatively simple set of hyperbolic equations which characterize the optimal feedback kernel for the finite-time regulator problem for autonomous differential-delay systems. Analysis of these equations elucidates the underlying structure of the feedback kernel and leads to the development of fast and accurate numerical methods for its computation. Unlike traditional formulations based on the operator Riccati equation, the gain is characterized by means of classical solutions of the derived set of equations. This leads to the development of approximation schemes which are analogous to what has been accomplished for systems of ordinary differential equations with given initial conditions.

Evaluation of cancer detection efficiency by means of hybrid and inverse filter in chest radiography

International Nuclear Information System (INIS)

Kim, Youn Young; Kim, Tae Young; Kim, Hyun Ji; Kim, Jung Min; Park, Min Seock

2013-01-01

The purpose of this study is to evaluate usefulness of Hybrid image and Inverse image about detection of tumor shadow in chest radiography using ROC analysis. Original images of 60 cases are selected from Standards digital image date base issued by the Japanese Society of Radiological Technology. Through computer language of C, Inverse images of 60 cases and Hybrid image of 30 cases are made. The continues reading experiment was conducted. In the case of inverse image were observed by 5 radiographer and 2 radiologist. In the case of In case of Hybrid image were observed by 3 student radiographer and 2 experienced radiographer. ROC curve are constructed using ROCKIT Program made by Metz. In Inverse image, a Az of average ROC curve was increases from 0.742 of original image to 0.775 of inverse image. In normal cases, the effect of the detrimental is same to that of the beneficial, however In abnormal cases, the beneficial effect is greater than detrimental effect. However in Hybrid image, a Az of average ROC curve was decreases from 0.5253 of original image to 0.4868 of Hybrid image. In Normal cases, the effect of the detrimental is greater than that of the Beneficial, however In abnormal cases, the Beneficial effect is greater than detrimental effect. The inverse image can be more positively considered for the detecting of tumor than the hybrid image

Biasing anisotropic scattering kernels for deep-penetration Monte Carlo calculations

International Nuclear Information System (INIS)

Carter, L.L.; Hendricks, J.S.

1983-01-01

The exponential transform is often used to improve the efficiency of deep-penetration Monte Carlo calculations. This technique is usually implemented by biasing the distance-to-collision kernel of the transport equation, but leaving the scattering kernel unchanged. Dwivedi obtained significant improvements in efficiency by biasing an isotropic scattering kernel as well as the distance-to-collision kernel. This idea is extended to anisotropic scattering, particularly the highly forward Klein-Nishina scattering of gamma rays

STUDI SIMULASI MENGGUNAKAN FUZZY C-MEANS DALAM MENGKLASIFIKASI KONSTRUK TES

Directory of Open Access Journals (Sweden)

Rukli Rukli

2013-01-01

Full Text Available Tulisan ini memperkenalkan metode fuzzy c-means dalam mengklasifikasi konstruk tes. Untuk memverifikasi sifat unidimensional suatu tes biasanya menggunakan analisis faktor sebagai bagian dari statistik parametrik dengan beberapa persyaratan yang ketat sedangkan metode fuzzy c-means termasuk metode heuristik yang tidak memerlukan persyaratan yang ketat. Studi simulasi penelitian ini menggunakan dua metode yakni analisis faktor menggunakan program SPSS dan fuzzy c-means menggunakan program Matlab. Data simulasi menggunakan tipe data dikotomi dan politomi yang dibangkitkan lewat prog-ram Microsoft Office Excel dengan desain 2 kategori, yakni: tiga butir soal dengan banyak peserta tes 10, dan 30 butir soal dengan banyak peserta tes 100. Hasil simulasi menunjukkan bahwa metode fuzzy c-means lebih memberikan gambaran pengelompokan secara deskriptif dan dinamis pada semua desain yang telah dibuat dalam memverifikasi unidimensional pada suatu tes. Kata kunci: fuzzy c-means, analisis faktor, unidimensional _____________________________________________________________ SIMULATION STUDY USING FUZZY C-MEANS FOR CLASIFYING TEST CONSTRUCTION Abstract This paper introduces the fuzzy c-means method for classifying the test constructs. To verify the unidimensional a test typically uses factor analysis as part of parametric statistics with some strict requirements, while fuzzy c-means methods including method heuristic that do not require strict require-ments. Simulation comparison between the method of factor analysis using SPSS program and fuzzy c-means methods using Matlab. Simulation data using data type dichotomy and politomus generated through Microsoft Office Excel programs each with a number of 3 items using the number of participants 10 tests, while the number of 30 test items using the number as many as 100 participants. The simulation results show that the fuzzy c-means method provides a more descriptive and dyna-mic grouping of all the designs that

The integral first collision kernel method for gamma-ray skyshine analysis[Skyshine; Gamma-ray; First collision kernel; Monte Carlo calculation

Energy Technology Data Exchange (ETDEWEB)

Sheu, R.-D.; Chui, C.-S.; Jiang, S.-H. E-mail: shjiang@mx.nthu.edu.tw

2003-12-01

A simplified method, based on the integral of the first collision kernel, is presented for performing gamma-ray skyshine calculations for the collimated sources. The first collision kernels were calculated in air for a reference air density by use of the EGS4 Monte Carlo code. These kernels can be applied to other air densities by applying density corrections. The integral first collision kernel (IFCK) method has been used to calculate two of the ANSI/ANS skyshine benchmark problems and the results were compared with a number of other commonly used codes. Our results were generally in good agreement with others but only spend a small fraction of the computation time required by the Monte Carlo calculations. The scheme of the IFCK method for dealing with lots of source collimation geometry is also presented in this study.

Over-Sampling Codebook-Based Hybrid Minimum Sum-Mean-Square-Error Precoding for Millimeter-Wave 3D-MIMO

KAUST Repository

Mao, Jiening

2018-05-23

Abstract: Hybrid precoding design is challenging for millimeter-wave (mmWave) massive MIMO. Most prior hybrid precoding schemes are designed to maximize the sum spectral efficiency (SSE), while seldom investigate the bit-error-rate (BER). Therefore, this letter designs an over-sampling codebook (OSC)-based hybrid minimum sum-mean-square-error (min-SMSE) precoding to optimize the BER. Specifically, given the effective baseband channel consisting of the real channel and analog precoding, we first design the digital precoder/combiner based on min-SMSE criterion to optimize the BER. To further reduce the SMSE between the transmit and receive signals, we propose an OSC-based joint analog precoder/combiner (JAPC) design. Simulation results show that the proposed scheme can achieve the better performance than its conventional counterparts.

Over-Sampling Codebook-Based Hybrid Minimum Sum-Mean-Square-Error Precoding for Millimeter-Wave 3D-MIMO

KAUST Repository

Mao, Jiening; Gao, Zhen; Wu, Yongpeng; Alouini, Mohamed-Slim

2018-01-01

Hybrid precoding design is challenging for millimeter-wave (mmWave) massive MIMO. Most prior hybrid precoding schemes are designed to maximize the sum spectral efficiency (SSE), while seldom investigate the bit-error-rate (BER). Therefore, this letter designs an over-sampling codebook (OSC)-based hybrid minimum sum-mean-square-error (min-SMSE) precoding to optimize the BER. Specifically, given the effective baseband channel consisting of the real channel and analog precoding, we first design the digital precoder/combiner based on min-SMSE criterion to optimize the BER. To further reduce the SMSE between the transmit and receive signals, we propose an OSC-based joint analog precoder/combiner (JAPC) design. Simulation results show that the proposed scheme can achieve the better performance than its conventional counterparts.

[Determination of acidity and vitamin C in apples using portable NIR analyzer].

Science.gov (United States)

Yang, Fan; Li, Ya-Ting; Gu, Xuan; Ma, Jiang; Fan, Xing; Wang, Xiao-Xuan; Zhang, Zhuo-Yong

2011-09-01

Near infrared (NIR) spectroscopy technology based on a portable NIR analyzer, combined with kernel Isomap algorithm and generalized regression neural network (GRNN) has been applied to establishing quantitative models for prediction of acidity and vitamin C in six kinds of apple samples. The obtained results demonstrated that the fitting and the predictive accuracy of the models with kernel Isomap algorithm were satisfactory. The correlation between actual and predicted values of calibration samples (R(c)) obtained by the acidity model was 0.999 4, and for prediction samples (R(p)) was 0.979 9. The root mean square error of prediction set (RMSEP) was 0.055 8. For the vitamin C model, R(c) was 0.989 1, R(p) was 0.927 2, and RMSEP was 4.043 1. Results proved that the portable NIR analyzer can be a feasible tool for the determination of acidity and vitamin C in apples.

Improving the Bandwidth Selection in Kernel Equating

Science.gov (United States)

Andersson, Björn; von Davier, Alina A.

2014-01-01

We investigate the current bandwidth selection methods in kernel equating and propose a method based on Silverman's rule of thumb for selecting the bandwidth parameters. In kernel equating, the bandwidth parameters have previously been obtained by minimizing a penalty function. This minimization process has been criticized by practitioners…

Point kernels and superposition methods for scatter dose calculations in brachytherapy

International Nuclear Information System (INIS)

Carlsson, A.K.

2000-01-01

Point kernels have been generated and applied for calculation of scatter dose distributions around monoenergetic point sources for photon energies ranging from 28 to 662 keV. Three different approaches for dose calculations have been compared: a single-kernel superposition method, a single-kernel superposition method where the point kernels are approximated as isotropic and a novel 'successive-scattering' superposition method for improved modelling of the dose from multiply scattered photons. An extended version of the EGS4 Monte Carlo code was used for generating the kernels and for benchmarking the absorbed dose distributions calculated with the superposition methods. It is shown that dose calculation by superposition at and below 100 keV can be simplified by using isotropic point kernels. Compared to the assumption of full in-scattering made by algorithms currently in clinical use, the single-kernel superposition method improves dose calculations in a half-phantom consisting of air and water. Further improvements are obtained using the successive-scattering superposition method, which reduces the overestimates of dose close to the phantom surface usually associated with kernel superposition methods at brachytherapy photon energies. It is also shown that scatter dose point kernels can be parametrized to biexponential functions, making them suitable for use with an effective implementation of the collapsed cone superposition algorithm. (author)

Online learning control using adaptive critic designs with sparse kernel machines.

Science.gov (United States)

Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

2013-05-01

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

P.I.X.S.C.A.N.: a micro-CT scanner for small animal based on hybrid pixel detectors; PIXSCAN: micro-tomodensitrometre a pixels hybrides pour le petit animal

Energy Technology Data Exchange (ETDEWEB)

Khoury, R

2008-03-15

Since more than a dozen years, efforts were led in the field of X-ray tomography for small animals, principally for the improvement of spatial resolution and the diminution of the absorbed dose. The C.P.P.M. developed the micro-CT P.I.X.S.C.A.N. based on the hybrid pixel detector X.P.A.D.2. In this context, my thesis work consists in studying the demonstrator P.I.X.S.C.A.N./X.P.A.D.2 and the contribution of the hybrid pixels in the imaging of small animals. A fast analytical simulation, FastSimu, was developed. An extrapolation of the performance of the demonstrator P.I.X.S.C.A.N, as well as the validation of the results obtained with the measured data, were led by means of the analytical simulator FastSimu. The demonstrator P.I.X.S.C.A.N./X.P.A.D.2 allowed to obtain reconstructed images with a rather good quality for a relatively weak absorbed dose. Its spatial resolution is degraded by the high number of defective pixels of the detector X.P.A.D.2. Beyond this study, a new version of the demonstrator P.I.X.S.C.A.N./X.P.A.D.2 is under construction. This latter, characterized by two and a half times smaller pixels and about no defective pixels will bring a considerable improvement on spatial resolution. (author)

Some physical and mechanical properties of palm kernel shell (PKS ...

African Journals Online (AJOL)

In this study, some of the mechanical and physical properties of palm kernel shells (PKS) were evaluated. These are moisture content, 7.8325 ± 0.6672%; true density, 1.254 ± 5.292 x 10-3 g/cm3; bulk density, 1.1248g/cm3; mean rupture force along width, and thickness were 3174.52 ± 270.70N and 2806.94 ± 498.45N for ...

PHOSPHOLIPIDS FROM PUMPKIN (Cucurbita moschata (Duch. Poir SEED KERNEL OIL AND THEIR FATTY ACID COMPOSITION

Directory of Open Access Journals (Sweden)

Tri Joko Raharjo

2011-07-01

Full Text Available The phospholipids (PL of pumpkin (Cucurbita moschata (Duch Poir seed kernel and their fatty acid composition were investigated. The crude oil was obtained by maceration with isopropanol followed by steps of extraction yielded polar lipids. The quantitative determination of PLs content of the dried pumpkin seed kernel and their polar lipids were calculated based on the elemental phosphorus (P contents which was determined by means of spectrophotometric methods. PL classes were separated from polar lipids via column chromatography. The fatty acid composition of individual PL was identified by gas chromatography-mass spectrometry (GC-MS. The total of PL in the pumpkin seed kernels was 1.27% which consisted of phosphatidylcholine (PC, phosphatidylserine (PS and phosphatidyletanolamine (PE. The predominant fatty acids of PL were oleic and palmitic acid in PC and PE while PS's fatty acid were dominantly consisted of oleic acid and linoleic acid.

Results from ORNL Characterization of Nominal 350 (micro)m LEUCO Kernels (LEU03) from the BWXT G73V-20-69303 Composite

International Nuclear Information System (INIS)

Kercher, Andrew K.; Hunn, John D.

2006-01-01

Measurements were made using optical microscopy to determine the size and shape of the LEU03 kernels. Hg porosimetry was performed to measure density. The results are summarized in Table 1-1. Values in the table are for the composite and are calculated at 95% confidence from the measured values of a random riffled sample. The LEu03 kernel composite met all the specifications in Table 1-1. The BWXT results for measuring the same kernel properties are given in Table 1-2. BWXT characterization methods were significantly different from ORNL methods, which resulted in slight differences in the reported results. BWXT performed manual microscopy measurements for mean diameter (100 particles measured along 2 axes) and aspect ratio (100 particles measured); ORNL used automated image acquisition and analysis (3847 particles measured along 180 axes). Diameter measurements were in good agreement. The narrower confidence interval in the ORNL results for average mean diameter is due to the greater number of particles measured. The critical limits for mean diameter reported at ORNL and BWXT are similar, because ORNL measured a larger standard deviation (10.46 (micro)m vs. 8.70 (micro)m). Aspect ratio satisfied the specification with greater margin in the ORNL results mostly because of the larger sample size resulting in a lower uncertainty in the binomial distribution statistical calculation. ORNL measured 11 out of 3847 kernels exceeding the control limit (1.05); BWXT measured 1 out of 100 particles exceeding the control limit. BWXT used the aspect ratio of perpendicular diameters in a random image plane, where one diameter was a maximum or a minimum. ORNL used the aspect ratio of the absolute maximum and minimum diameters in a random image plane. The ORNL technique can be expected to yield higher measured aspect ratios. Hand tabling was performed at ORNL prior to characterization by repeatedly pouring a small fraction of the kernels in a pan and tilting the pan so that rounder

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search.

Science.gov (United States)

Villagra, Andrea; Alba, Enrique; Leguizamón, Guillermo

2016-01-01

This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology.

A multi-label learning based kernel automatic recommendation method for support vector machine.

Science.gov (United States)

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

Evaluation of date kernel oils and their effects on wheat flour characteristics

Directory of Open Access Journals (Sweden)

Elsakr, A. S.

1991-06-01

Full Text Available Date kernels constitute about 13% of the whole date fruit, and cotain moderate amounts of oils with relatively high caloric values. Agwa soft and Aprimi dry date kernel oils were used in this work. Results showed that Aprimi date kernel oils have high levels of both caproic acid and stearic acid of 23.04 and 18.40% respectively, while Agwa kernel oils have high levels of caprylic acid and capric acid of 22.31 and 12.20%. Moreover Agwa oils achieved higher amounts of unsaponifiable matters, sterols and tocopherols. The addition of soft and dry date kernel oils did not improve the wheat flour strength but raised its resistance to extensibility.

Las pepitas de dátiles constituyen alrededor del 13% del fruto entero, y contienen cantidades moderadas de aceite con valores calóricos relativamente altos. En este trabajo se han utilizado aceites de pepitas de dátiles Agwa blandas y Aprimi secas. Los resultados mostraron que los aceites de pepitas de dátiles Aprimi tienen altos niveles de ácidos caproico y esteárico, 23’04 y 18'40% respectivamente, mientras que los aceites de pepitas Agwa tienen niveles altos de ácidos caprílico y cáprico, 22'31 y 12'20%. Por otra parte los aceites Agwa alcanzaron valores elevados de materia insaponificable, esteroles y tocoferoles. La adición de aceites de pepitas de dátiles secas y blandas no mejoró el poder de la harina de trigo pero incrementó su resistencia a la extensibilidad.

LINEAR KERNEL SUPPORT VECTOR MACHINES FOR MODELING PORE-WATER PRESSURE RESPONSES

Directory of Open Access Journals (Sweden)

KHAMARUZAMAN W. YUSOF

2017-08-01

Full Text Available Pore-water pressure responses are vital in many aspects of slope management, design and monitoring. Its measurement however, is difficult, expensive and time consuming. Studies on its predictions are lacking. Support vector machines with linear kernel was used here to predict the responses of pore-water pressure to rainfall. Pore-water pressure response data was collected from slope instrumentation program. Support vector machine meta-parameter calibration and model development was carried out using grid search and k-fold cross validation. The mean square error for the model on scaled test data is 0.0015 and the coefficient of determination is 0.9321. Although pore-water pressure response to rainfall is a complex nonlinear process, the use of linear kernel support vector machine can be employed where high accuracy can be sacrificed for computational ease and time.

Using the Intel Math Kernel Library on Peregrine | High-Performance

Science.gov (United States)

Computing | NREL the Intel Math Kernel Library on Peregrine Using the Intel Math Kernel Library on Peregrine Learn how to use the Intel Math Kernel Library (MKL) with Peregrine system software. MKL architectures. Core math functions in MKL include BLAS, LAPACK, ScaLAPACK, sparse solvers, fast Fourier

A facile synthesis of C{sub 60}-organosilicon hybrid polymers: Considering their tunable optical properties for spin-on-silicon hardmask materials

Energy Technology Data Exchange (ETDEWEB)

Choi, Jin-Kyu; Dao, Tung Duy; Kim, Ye-Seul; Jeong, Hyun-Dam, E-mail: hdjeong@chonnam.ac.kr

2016-09-15

Organic-inorganic hybrid materials with high refractive index have attracted considerable attention for many optoelectronic applications, including spin-on-type hardmask for ArF lithography (193 nm). In this study, we demonstrate the synthesis of a C{sub 60}-embedded organosilicon hybrid polymer, C{sub 60}-embedded poly-xylene-hexamethyltrisiloxane hybrid (C{sub 60}-PXS), of tunable optical properties. C{sub 60} was covalently bonded to the PXS backbone through Pt-catalyzed hydrosilylation, in which the PXS was formed possibly by unexpected transition metal-catalyzed benzylic C−H silylation and oxygenation of the o-xylene. The C{sub 60}-PXS thin films fabricated using a spin-coating method showed much higher refractive index by 5–22% according to the curing temperatures, than the PXS thin films containing no C{sub 60}. In particular, the C{sub 60}-PXS thin film cured at 350 °C showed the refractive index (n) and extinction coefficient (k) at 193 nm to be 1.61 and 0.29 that are very close to the optimum values for the Si-hardmask. This implies the high applicability of the C{sub 60}-embedded organosilicon hybrid polymer, C{sub 60}-PXS, for the spin-on Si-hardmask in ArF lithography. - Highlights: • A facile synthetic route for C{sub 60}-embedded organosilicon hybrid polymer was presented. • The hybrid polymer showed much higher refractive index than the polymer without C{sub 60}. • The hybrid polymer is highly applicable for Si-hardmask in terms of optical properties. • It is believed that the properties of the hybrid polymer can be further optimized.

MAX Phase Modified SiC Composites for Ceramic-Metal Hybrid Cladding Tubes

International Nuclear Information System (INIS)

Jung, Yang-Il; Kim, Sun-Han; Park, Dong-Jun; Park, Jeong-Hwan; Park, Jeong-Yong; Kim, Hyun-Gil; Koo, Yang-Hyun

2015-01-01

A metal-ceramic hybrid cladding consists of an inner zirconium tube, and an outer SiC fiber-matrix SiC ceramic composite with surface coating as shown in Fig. 1 (left-hand side). The inner zirconium allows the matrix to remain fully sealed even if the ceramic matrix cracks through. The outer SiC composite can increase the safety margin by taking the merits of the SiC itself. In addition, the outermost layer prevents the dissolution of SiC during normal operation. On the other hand, a ceramic-metal hybrid cladding consists of an outer zirconium tube, and an inner SiC ceramic composite as shown in Fig. 1 (right-hand side). The outer zirconium protects the fuel rod from a corrosion during reactor operation, as in the present fuel claddings. The inner SiC composite, additionally, is designed to resist the severe oxidation under a postulated accident condition of a high-temperature steam environment. Reaction-bonded SiC was fabricated by modifying the matrix as the MAX phase. The formation of Ti 3 SiC 2 was investigated depending on the compositions of the preform and melt. In most cases, TiSi 2 was the preferential phase because of its lowest melting point in the Ti-Si-C system. The evidence of Ti 3 SiC 2 was the connection with the pressurizing

Assessment of vertebral artery stents using 16-slice multi-detector row CT angiography in vivo evaluation: Comparison of a medium-smooth kernel and a sharp kernel

International Nuclear Information System (INIS)

Yoo, Won Jong; Lim, Yeon Soo; Ahn, Kook Jin; Choi, Byung Gil; Kim, Ji Young; Kim, Sung Hoon

2009-01-01

Objectives: To assess the lumen visibility of extracranial vertebral artery stents examined with 16-slice multi-detector row computed tomography (MDCT) angiography in vivo using a medium-smooth kernel (B30s) and a sharp kernel (B60s), and to compare these with digital subtraction angiography (DSA) after stent placement. Methods: Twenty stents from 20 patients (14 men, 6 women; mean age, 62.7 ± 10.1 years) who underwent CT angiography (CTA) with 16-slice MDCT were retrospectively analyzed. In CT angiograms using a B30s and a B60s, the lumen diameters and CT attenuations of the stented vessels were measured three times by three observers, and artificial luminal narrowing (ALN) was calculated. To assess measurement reliability on CT angiograms, the intraclass correlation coefficient (ICC) was used. DSA served as the reference standard for the in-stent luminal measurements on CT angiography. The median interval between CT angiography and DSA was 1 day (range 1-10). Results: For interobserver reliability, intraclass correlation coefficients for the lumen diameters on CT angiograms with a B30s and a B60s were 0.90 and 0.96, respectively. The lumen diameters on CT angiograms using a B30s were consistently smaller than that on CT angiograms using a B60s (p < 0.01). The mean ALN was 37 ± 7% on CT angiograms using a B30s and 25 ± 9% on CT angiograms using a B60s. The mean CT attenuation in in-stent lumen was 347 ± 55 HU on CT angiograms using a B30s and 295 ± 46 HU on CT angiograms using a B60s. The ALN and CT attenuation within the stented vessels between CT angiograms using a B30s and a B60s was significant (p < 0.01). Conclusions: 16-slice MDCT using a sharp kernel allows good visualization of the stented vessels and is useful in the assessment of vertebral artery stent patency after stent placement.

Unsupervised multiple kernel learning for heterogeneous data integration.

Science.gov (United States)

Mariette, Jérôme; Villa-Vialaneix, Nathalie

2018-03-15

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

Kernel bundle EPDiff

DEFF Research Database (Denmark)

Sommer, Stefan Horst; Lauze, Francois Bernard; Nielsen, Mads

2011-01-01

In the LDDMM framework, optimal warps for image registration are found as end-points of critical paths for an energy functional, and the EPDiff equations describe the evolution along such paths. The Large Deformation Diffeomorphic Kernel Bundle Mapping (LDDKBM) extension of LDDMM allows scale space...

Proteome analysis of the almond kernel (Prunus dulcis).

Science.gov (United States)

Li, Shugang; Geng, Fang; Wang, Ping; Lu, Jiankang; Ma, Meihu

2016-08-01

Almond (Prunus dulcis) is a popular tree nut worldwide and offers many benefits to human health. However, the importance of almond kernel proteins in the nutrition and function in human health requires further evaluation. The present study presents a systematic evaluation of the proteins in the almond kernel using proteomic analysis. The nutrient and amino acid content in almond kernels from Xinjiang is similar to that of American varieties; however, Xinjiang varieties have a higher protein content. Two-dimensional electrophoresis analysis demonstrated a wide distribution of molecular weights and isoelectric points of almond kernel proteins. A total of 434 proteins were identified by LC-MS/MS, and most were proteins that were experimentally confirmed for the first time. Gene ontology (GO) analysis of the 434 proteins indicated that proteins involved in primary biological processes including metabolic processes (67.5%), cellular processes (54.1%), and single-organism processes (43.4%), the main molecular function of almond kernel proteins are in catalytic activity (48.0%), binding (45.4%) and structural molecule activity (11.9%), and proteins are primarily distributed in cell (59.9%), organelle (44.9%), and membrane (22.8%). Almond kernel is a source of a wide variety of proteins. This study provides important information contributing to the screening and identification of almond proteins, the understanding of almond protein function, and the development of almond protein products. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

Science.gov (United States)

Han, Yina; Yang, Kunde; Ma, Yuanliang; Liu, Guizhong

2014-01-01

Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

Control Transfer in Operating System Kernels

Science.gov (United States)

1994-05-13

microkernel system that runs less code in the kernel address space. To realize the performance benefit of allocating stacks in unmapped kseg0 memory, the...review how I modified the Mach 3.0 kernel to use continuations. Because of Mach’s message-passing microkernel structure, interprocess communication was...critical control transfer paths, deeply- nested call chains are undesirable in any case because of the function call overhead. 4.1.3 Microkernel Operating

Bivariate discrete beta Kernel graduation of mortality data.

Science.gov (United States)

Mazza, Angelo; Punzo, Antonio

2015-07-01

Various parametric/nonparametric techniques have been proposed in literature to graduate mortality data as a function of age. Nonparametric approaches, as for example kernel smoothing regression, are often preferred because they do not assume any particular mortality law. Among the existing kernel smoothing approaches, the recently proposed (univariate) discrete beta kernel smoother has been shown to provide some benefits. Bivariate graduation, over age and calendar years or durations, is common practice in demography and actuarial sciences. In this paper, we generalize the discrete beta kernel smoother to the bivariate case, and we introduce an adaptive bandwidth variant that may provide additional benefits when data on exposures to the risk of death are available; furthermore, we outline a cross-validation procedure for bandwidths selection. Using simulations studies, we compare the bivariate approach proposed here with its corresponding univariate formulation and with two popular nonparametric bivariate graduation techniques, based on Epanechnikov kernels and on P-splines. To make simulations realistic, a bivariate dataset, based on probabilities of dying recorded for the US males, is used. Simulations have confirmed the gain in performance of the new bivariate approach with respect to both the univariate and the bivariate competitors.

Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach

Directory of Open Access Journals (Sweden)

Ayad Hendalianpour

2016-11-01

Full Text Available Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with the objects in other clusters. The K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms are the most popular clustering algorithms for their easy implementation and fast work, but in some cases we cannot use these algorithms. Regarding this, in this paper, a hybrid model for customer clustering is presented that is applicable in five banks of Fars Province, Shiraz, Iran. In this way, the fuzzy relation among customers is defined by using their features described in linguistic and quantitative variables. As follows, the customers of banks are grouped according to K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Clustering (FRC algorithm. The aim of this paper is to show how to choose the best clustering algorithms based on density-based clustering and present a new clustering algorithm for both crisp and fuzzy variables. Finally, we apply the proposed approach to five datasets of customer's segmentation in banks. The result of the FCR shows the accuracy and high performance of FRC compared other clustering methods.

A framework for optimal kernel-based manifold embedding of medical image data.

Science.gov (United States)

Zimmer, Veronika A; Lekadir, Karim; Hoogendoorn, Corné; Frangi, Alejandro F; Piella, Gemma

2015-04-01

Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, the kernel function is generally chosen as Gaussian or polynomial and such standard kernels might not always be optimal for a given image dataset or application. In this paper, we present a study on the effect of the kernel functions in nonlinear manifold embedding of medical image data. To this end, we first carry out a literature review on existing advanced kernels developed in the statistics, machine learning, and signal processing communities. In addition, we implement kernel-based formulations of well-known nonlinear dimensional reduction techniques such as Isomap and Locally Linear Embedding, thus obtaining a unified framework for manifold embedding using kernels. Subsequently, we present a method to automatically choose a kernel function and its associated parameters from a pool of kernel candidates, with the aim to generate the most optimal manifold embeddings. Furthermore, we show how the calculated selection measures can be extended to take into account the spatial relationships in images, or used to combine several kernels to further improve the embedding results. Experiments are then carried out on various synthetic and phantom datasets for numerical assessment of the methods. Furthermore, the workflow is applied to real data that include brain manifolds and multispectral images to demonstrate the importance of the kernel selection in the analysis of high-dimensional medical images. Copyright © 2014 Elsevier Ltd. All rights reserved.

Measurement of Weight of Kernels in a Simulated Cylindrical Fuel Compact for HTGR

International Nuclear Information System (INIS)

Kim, Woong Ki; Lee, Young Woo; Kim, Young Min; Kim, Yeon Ku; Eom, Sung Ho; Jeong, Kyung Chai; Cho, Moon Sung; Cho, Hyo Jin; Kim, Joo Hee

2011-01-01

The TRISO-coated fuel particle for the high temperature gas-cooled reactor (HTGR) is composed of a nuclear fuel kernel and outer coating layers. The coated particles are mixed with graphite matrix to make HTGR fuel element. The weight of fuel kernels in an element is generally measured by the chemical analysis or a gamma-ray spectrometer. Although it is accurate to measure the weight of kernels by the chemical analysis, the samples used in the analysis cannot be put again in the fabrication process. Furthermore, radioactive wastes are generated during the inspection procedure. The gamma-ray spectrometer requires an elaborate reference sample to reduce measurement errors induced from the different geometric shape of test sample from that of reference sample. X-ray computed tomography (CT) is an alternative to measure the weight of kernels in a compact nondestructively. In this study, X-ray CT is applied to measure the weight of kernels in a cylindrical compact containing simulated TRISO-coated particles with ZrO 2 kernels. The volume of kernels as well as the number of kernels in the simulated compact is measured from the 3-D density information. The weight of kernels was calculated from the volume of kernels or the number of kernels. Also, the weight of kernels was measured by extracting the kernels from a compact to review the result of the X-ray CT application

3-D waveform tomography sensitivity kernels for anisotropic media

KAUST Repository

Djebbi, Ramzi

2014-01-01

The complications in anisotropic multi-parameter inversion lie in the trade-off between the different anisotropy parameters. We compute the tomographic waveform sensitivity kernels for a VTI acoustic medium perturbation as a tool to investigate this ambiguity between the different parameters. We use dynamic ray tracing to efficiently handle the expensive computational cost for 3-D anisotropic models. Ray tracing provides also the ray direction information necessary for conditioning the sensitivity kernels to handle anisotropy. The NMO velocity and η parameter kernels showed a maximum sensitivity for diving waves which results in a relevant choice of those parameters in wave equation tomography. The δ parameter kernel showed zero sensitivity; therefore it can serve as a secondary parameter to fit the amplitude in the acoustic anisotropic inversion. Considering the limited penetration depth of diving waves, migration velocity analysis based kernels are introduced to fix the depth ambiguity with reflections and compute sensitivity maps in the deeper parts of the model.

A Fourier-series-based kernel-independent fast multipole method

International Nuclear Information System (INIS)

Zhang Bo; Huang Jingfang; Pitsianis, Nikos P.; Sun Xiaobai

2011-01-01

We present in this paper a new kernel-independent fast multipole method (FMM), named as FKI-FMM, for pairwise particle interactions with translation-invariant kernel functions. FKI-FMM creates, using numerical techniques, sufficiently accurate and compressive representations of a given kernel function over multi-scale interaction regions in the form of a truncated Fourier series. It provides also economic operators for the multipole-to-multipole, multipole-to-local, and local-to-local translations that are typical and essential in the FMM algorithms. The multipole-to-local translation operator, in particular, is readily diagonal and does not dominate in arithmetic operations. FKI-FMM provides an alternative and competitive option, among other kernel-independent FMM algorithms, for an efficient application of the FMM, especially for applications where the kernel function consists of multi-physics and multi-scale components as those arising in recent studies of biological systems. We present the complexity analysis and demonstrate with experimental results the FKI-FMM performance in accuracy and efficiency.

Numerical Evaluation of the "Dual-Kernel Counter-flow" Matric Convolution Integral that Arises in Discrete/Continuous (D/C) Control Theory

Science.gov (United States)

Nixon, Douglas D.

2009-01-01

Discrete/Continuous (D/C) control theory is a new generalized theory of discrete-time control that expands the concept of conventional (exact) discrete-time control to create a framework for design and implementation of discretetime control systems that include a continuous-time command function generator so that actuator commands need not be constant between control decisions, but can be more generally defined and implemented as functions that vary with time across sample period. Because the plant/control system construct contains two linear subsystems arranged in tandem, a novel dual-kernel counter-flow convolution integral appears in the formulation. As part of the D/C system design and implementation process, numerical evaluation of that integral over the sample period is required. Three fundamentally different evaluation methods and associated algorithms are derived for the constant-coefficient case. Numerical results are matched against three available examples that have closed-form solutions.

Resummed memory kernels in generalized system-bath master equations

International Nuclear Information System (INIS)

Mavros, Michael G.; Van Voorhis, Troy

2014-01-01

Generalized master equations provide a concise formalism for studying reduced population dynamics. Usually, these master equations require a perturbative expansion of the memory kernels governing the dynamics; in order to prevent divergences, these expansions must be resummed. Resummation techniques of perturbation series are ubiquitous in physics, but they have not been readily studied for the time-dependent memory kernels used in generalized master equations. In this paper, we present a comparison of different resummation techniques for such memory kernels up to fourth order. We study specifically the spin-boson Hamiltonian as a model system bath Hamiltonian, treating the diabatic coupling between the two states as a perturbation. A novel derivation of the fourth-order memory kernel for the spin-boson problem is presented; then, the second- and fourth-order kernels are evaluated numerically for a variety of spin-boson parameter regimes. We find that resumming the kernels through fourth order using a Padé approximant results in divergent populations in the strong electronic coupling regime due to a singularity introduced by the nature of the resummation, and thus recommend a non-divergent exponential resummation (the “Landau-Zener resummation” of previous work). The inclusion of fourth-order effects in a Landau-Zener-resummed kernel is shown to improve both the dephasing rate and the obedience of detailed balance over simpler prescriptions like the non-interacting blip approximation, showing a relatively quick convergence on the exact answer. The results suggest that including higher-order contributions to the memory kernel of a generalized master equation and performing an appropriate resummation can provide a numerically-exact solution to system-bath dynamics for a general spectral density, opening the way to a new class of methods for treating system-bath dynamics

The dipole form of the gluon part of the BFKL kernel

International Nuclear Information System (INIS)

Fadin, V.S.; Fiore, R.; Grabovsky, A.V.; Papa, A.

2007-01-01

The dipole form of the gluon part of the color singlet BFKL kernel in the next-to-leading order (NLO) is obtained in the coordinate representation by direct transfer from the momentum representation, where the kernel was calculated before. With this paper the transformation of the NLO BFKL kernel to the dipole form, started a few months ago with the quark part of the kernel, is completed

An OSKit-Based Implementation of Least Privilege Separation Kernel Memory Partitioning

Science.gov (United States)

2007-06-01

Fiasco is developed at TU Dresden. It is compatible with the x86 L4 microkernel . It is a real-time preemptive kernel written in C++. Fiasco’s...adherence to the specification of the L4 microkernel makes it a desirable choice. The downside is Fiasco is too specialized for what is needed by the

Lupin kernel fiber consumption modifies fecal microbiota in healthy men as determined by rRNA gene fluorescent in situ hybridization

NARCIS (Netherlands)

Smith, Stuart C.; Choy, Rachel; Johnson, Stuart K.; Hall, Ramon S.; Wildeboer-Veloo, Alida C. M.; Welling, Gjalt W.

Background: Changes in the composition of gastrointestinal microbiota by dietary interventions using pro- and prebiotics provide opportunity for improving health and preventing disease. However, the capacity of lupin kernel fiber (LKFibre), a novel legume-derived food ingredient, to act as a

Range-separated time-dependent density-functional theory with a frequency-dependent second-order Bethe-Salpeter correlation kernel

Energy Technology Data Exchange (ETDEWEB)

Rebolini, Elisa, E-mail: elisa.rebolini@kjemi.uio.no; Toulouse, Julien, E-mail: julien.toulouse@upmc.fr [Laboratoire de Chimie Théorique, Sorbonne Universités, UPMC Univ Paris 06, CNRS, 4 place Jussieu, F-75005 Paris (France)

2016-03-07

We present a range-separated linear-response time-dependent density-functional theory (TDDFT) which combines a density-functional approximation for the short-range response kernel and a frequency-dependent second-order Bethe-Salpeter approximation for the long-range response kernel. This approach goes beyond the adiabatic approximation usually used in linear-response TDDFT and aims at improving the accuracy of calculations of electronic excitation energies of molecular systems. A detailed derivation of the frequency-dependent second-order Bethe-Salpeter correlation kernel is given using many-body Green-function theory. Preliminary tests of this range-separated TDDFT method are presented for the calculation of excitation energies of the He and Be atoms and small molecules (H{sub 2}, N{sub 2}, CO{sub 2}, H{sub 2}CO, and C{sub 2}H{sub 4}). The results suggest that the addition of the long-range second-order Bethe-Salpeter correlation kernel overall slightly improves the excitation energies.

Modelling of HTR (High Temperature Reactor Pebble-Bed 10 MW to Determine Criticality as A Variations of Enrichment and Radius of the Fuel (Kernel With the Monte Carlo Code MCNP4C

Directory of Open Access Journals (Sweden)

Hammam Oktajianto

2014-12-01

Full Text Available Gas-cooled nuclear reactor is a Generation IV reactor which has been receiving significant attention due to many desired characteristics such as inherent safety, modularity, relatively low cost, short construction period, and easy financing. High temperature reactor (HTR pebble-bed as one of type of gas-cooled reactor concept is getting attention. In HTR pebble-bed design, radius and enrichment of the fuel kernel are the key parameter that can be chosen freely to determine the desired value of criticality. This paper models HTR pebble-bed 10 MW and determines an effective of enrichment and radius of the fuel (Kernel to get criticality value of reactor. The TRISO particle coated fuel particle which was modelled explicitly and distributed in the fuelled region of the fuel pebbles using a Simple-Cubic (SC lattice. The pebble-bed balls and moderator balls distributed in the core zone using a Body-Centred Cubic lattice with assumption of a fresh fuel by the fuel enrichment was 7-17% at 1% range and the size of the fuel radius was 175-300 µm at 25 µm ranges. The geometrical model of the full reactor is obtained by using lattice and universe facilities provided by MCNP4C. The details of model are discussed with necessary simplifications. Criticality calculations were conducted by Monte Carlo transport code MCNP4C and continuous energy nuclear data library ENDF/B-VI. From calculation results can be concluded that an effective of enrichment and radius of fuel (Kernel to achieve a critical condition was the enrichment of 15-17% at a radius of 200 µm, the enrichment of 13-17% at a radius of 225 µm, the enrichments of 12-15% at radius of 250 µm, the enrichments of 11-14% at a radius of 275 µm and the enrichment of 10-13% at a radius of 300 µm, so that the effective of enrichments and radii of fuel (Kernel can be considered in the HTR 10 MW. Keywords—MCNP4C, HTR, enrichment, radius, criticality 

Fast Interrupt Priority Management in Operating System Kernels

Science.gov (United States)

1993-05-01

We present results for the Mach 3.0 microkernel operating system, although the technique is applicable to other kernel architectures, both micro and...protection in the Mach 3.0 microkernel for several different processor architectures. For example, on the Omron Luna88k, we observed a 50% reduction in...general interrupt mask raise/lower pair within the Mach 3.0 microkernel on a variety of architectures. DTIC QUALM i.N1’R%.*1IMD 5 k81tltC Avail andl

Upport vector machines for nonlinear kernel ARMA system identification.

Science.gov (United States)

Martínez-Ramón, Manel; Rojo-Alvarez, José Luis; Camps-Valls, Gustavo; Muñioz-Marí, Jordi; Navia-Vázquez, Angel; Soria-Olivas, Emilio; Figueiras-Vidal, Aníbal R

2006-11-01

Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA2K) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based system identification nonlinear models is presented, based on the use of composite Mercer's kernels. This general class can improve model flexibility by emphasizing the input-output cross information (SVM-ARMA4K), which leads to straightforward and natural combinations of implicit and explicit ARMA models (SVR-ARMA2K and SVR-ARMA4K). Capabilities of these different SVM-based system identification schemes are illustrated with two benchmark problems.

A new discrete dipole kernel for quantitative susceptibility mapping.

Science.gov (United States)

Milovic, Carlos; Acosta-Cabronero, Julio; Pinto, José Miguel; Mattern, Hendrik; Andia, Marcelo; Uribe, Sergio; Tejos, Cristian

2018-09-01

Most approaches for quantitative susceptibility mapping (QSM) are based on a forward model approximation that employs a continuous Fourier transform operator to solve a differential equation system. Such formulation, however, is prone to high-frequency aliasing. The aim of this study was to reduce such errors using an alternative dipole kernel formulation based on the discrete Fourier transform and discrete operators. The impact of such an approach on forward model calculation and susceptibility inversion was evaluated in contrast to the continuous formulation both with synthetic phantoms and in vivo MRI data. The discrete kernel demonstrated systematically better fits to analytic field solutions, and showed less over-oscillations and aliasing artifacts while preserving low- and medium-frequency responses relative to those obtained with the continuous kernel. In the context of QSM estimation, the use of the proposed discrete kernel resulted in error reduction and increased sharpness. This proof-of-concept study demonstrated that discretizing the dipole kernel is advantageous for QSM. The impact on small or narrow structures such as the venous vasculature might by particularly relevant to high-resolution QSM applications with ultra-high field MRI - a topic for future investigations. The proposed dipole kernel has a straightforward implementation to existing QSM routines. Copyright © 2018 Elsevier Inc. All rights reserved.

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations

Directory of Open Access Journals (Sweden)

Zhengbin Liu

2016-08-01

Full Text Available Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis. In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits.

Scientific opinion on the acute health risks related to the presence of cyanogenic glycosides in raw apricot kernels and products derived from raw apricot kernels

DEFF Research Database (Denmark)

Petersen, Annette

of kernels promoted (10 and 60 kernels/day for the general population and cancer patients, respectively), exposures exceeded the ARfD 17–413 and 3–71 times in toddlers and adults, respectively. The estimated maximum quantity of apricot kernels (or raw apricot material) that can be consumed without exceeding...

Kernel Function Tuning for Single-Layer Neural Networks

Czech Academy of Sciences Publication Activity Database

Vidnerová, Petra; Neruda, Roman

-, accepted 28.11. 2017 (2018) ISSN 2278-0149 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : single-layer neural networks * kernel methods * kernel function * optimisation Subject RIV: IN - Informatics, Computer Science http://www.ijmerr.com/

Broken rice kernels and the kinetics of rice hydration and texture during cooking.

Science.gov (United States)

Saleh, Mohammed; Meullenet, Jean-Francois

2013-05-01

During rice milling and processing, broken kernels are inevitably present, although to date it has been unclear as to how the presence of broken kernels affects rice hydration and cooked rice texture. Therefore, this work intended to study the effect of broken kernels in a rice sample on rice hydration and texture during cooking. Two medium-grain and two long-grain rice cultivars were harvested, dried and milled, and the broken kernels were separated from unbroken kernels. Broken rice kernels were subsequently combined with unbroken rice kernels forming treatments of 0, 40, 150, 350 or 1000 g kg(-1) broken kernels ratio. Rice samples were then cooked and the moisture content of the cooked rice, the moisture uptake rate, and rice hardness and stickiness were measured. As the amount of broken rice kernels increased, rice sample texture became increasingly softer (P hardness was negatively correlated to the percentage of broken kernels in rice samples. Differences in the proportions of broken rice in a milled rice sample play a major role in determining the texture properties of cooked rice. Variations in the moisture migration kinetics between broken and unbroken kernels caused faster hydration of the cores of broken rice kernels, with greater starch leach-out during cooking affecting the texture of the cooked rice. The texture of cooked rice can be controlled, to some extent, by varying the proportion of broken kernels in milled rice. © 2012 Society of Chemical Industry.

The effect of STDP temporal kernel structure on the learning dynamics of single excitatory and inhibitory synapses.

Directory of Open Access Journals (Sweden)

Yotam Luz

Full Text Available Spike-Timing Dependent Plasticity (STDP is characterized by a wide range of temporal kernels. However, much of the theoretical work has focused on a specific kernel - the "temporally asymmetric Hebbian" learning rules. Previous studies linked excitatory STDP to positive feedback that can account for the emergence of response selectivity. Inhibitory plasticity was associated with negative feedback that can balance the excitatory and inhibitory inputs. Here we study the possible computational role of the temporal structure of the STDP. We represent the STDP as a superposition of two processes: potentiation and depression. This allows us to model a wide range of experimentally observed STDP kernels, from Hebbian to anti-Hebbian, by varying a single parameter. We investigate STDP dynamics of a single excitatory or inhibitory synapse in purely feed-forward architecture. We derive a mean-field-Fokker-Planck dynamics for the synaptic weight and analyze the effect of STDP structure on the fixed points of the mean field dynamics. We find a phase transition along the Hebbian to anti-Hebbian parameter from a phase that is characterized by a unimodal distribution of the synaptic weight, in which the STDP dynamics is governed by negative feedback, to a phase with positive feedback characterized by a bimodal distribution. The critical point of this transition depends on general properties of the STDP dynamics and not on the fine details. Namely, the dynamics is affected by the pre-post correlations only via a single number that quantifies its overlap with the STDP kernel. We find that by manipulating the STDP temporal kernel, negative feedback can be induced in excitatory synapses and positive feedback in inhibitory. Moreover, there is an exact symmetry between inhibitory and excitatory plasticity, i.e., for every STDP rule of inhibitory synapse there exists an STDP rule for excitatory synapse, such that their dynamics is identical.

Local coding based matching kernel method for image classification.

Directory of Open Access Journals (Sweden)

Yan Song

Full Text Available This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increased storage requirements. We show that a unified visual matching framework can be developed to encompass both BoV and kernel based metrics, in which local kernel plays an important role between feature pairs or between features and their reconstruction. Generally, local kernels are defined using Euclidean distance or its derivatives, based either explicitly or implicitly on an assumption of Gaussian noise. However, local features such as SIFT and HoG often follow a heavy-tailed distribution which tends to undermine the motivation behind Euclidean metrics. Motivated by recent advances in feature coding techniques, a novel efficient local coding based matching kernel (LCMK method is proposed. This exploits the manifold structures in Hilbert space derived from local kernels. The proposed method combines advantages of both BoV and kernel based metrics, and achieves a linear computational complexity. This enables efficient and scalable visual matching to be performed on large scale image sets. To evaluate the effectiveness of the proposed LCMK method, we conduct extensive experiments with widely used benchmark datasets, including 15-Scenes, Caltech101/256, PASCAL VOC 2007 and 2011 datasets. Experimental results confirm the effectiveness of the relatively efficient LCMK method.

Sorption Kinetics for the Removal of Cadmium and Zinc onto Palm Kernel Shell Based Activated Carbon

Directory of Open Access Journals (Sweden)

Muhammad Muhammad

2010-12-01

Full Text Available The kinetics and mechanism of cadmium and zinc adsorption on palm kernel shell based activated carbons (PKSAC have been studied. A series of batch laboratory studies were conducted in order to investigate the suitability of palm kernel shell based activated carbon (PKSAC for the removal of cadmium (cadmium ions and zinc (zinc ions from their aqueous solutions. All batch experiments were carried out at pH 7.0 and a constant temperature of 30+-1°C using an incubator shaker that operated at 150 rpm. The kinetics investigated includes the pseudo first order, the pseudo-second order and the intraparticle diffusion models. The pseudo-second order model correlate excellently the experimental data, suggesting that chemisorption processes could be the rate-limiting step. Keywords: adsorption, cadmium, kinetics, palm kernel shell, zinc

Multivariate realised kernels

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole; Hansen, Peter Reinhard; Lunde, Asger

We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...

Process for producing metal oxide kernels and kernels so obtained

International Nuclear Information System (INIS)

Lelievre, Bernard; Feugier, Andre.

1974-01-01

The process desbribed is for producing fissile or fertile metal oxide kernels used in the fabrication of fuels for high temperature nuclear reactors. This process consists in adding to an aqueous solution of at least one metallic salt, particularly actinide nitrates, at least one chemical compound capable of releasing ammonia, in dispersing drop by drop the solution thus obtained into a hot organic phase to gel the drops and transform them into solid particles. These particles are then washed, dried and treated to turn them into oxide kernels. The organic phase used for the gel reaction is formed of a mixture composed of two organic liquids, one acting as solvent and the other being a product capable of extracting the anions from the metallic salt of the drop at the time of gelling. Preferably an amine is used as product capable of extracting the anions. Additionally, an alcohol that causes a part dehydration of the drops can be employed as solvent, thus helping to increase the resistance of the particles [fr

Ideal Gas Resonance Scattering Kernel Routine for the NJOY Code

International Nuclear Information System (INIS)

Rothenstein, W.

1999-01-01

In a recent publication an expression for the temperature-dependent double-differential ideal gas scattering kernel is derived for the case of scattering cross sections that are energy dependent. Some tabulations and graphical representations of the characteristics of these kernels are presented in Ref. 2. They demonstrate the increased probability that neutron scattering by a heavy nuclide near one of its pronounced resonances will bring the neutron energy nearer to the resonance peak. This enhances upscattering, when a neutron with energy just below that of the resonance peak collides with such a nuclide. A routine for using the new kernel has now been introduced into the NJOY code. Here, its principal features are described, followed by comparisons between scattering data obtained by the new kernel, and the standard ideal gas kernel, when such comparisons are meaningful (i.e., for constant values of the scattering cross section a 0 K). The new ideal gas kernel for variable σ s 0 (E) at 0 K leads to the correct Doppler-broadened σ s T (E) at temperature T

Effects of inter-fullerene π-band mixings in the photoexcitation of hybrid plasmons in the C60@C240 molecule

Science.gov (United States)

de, Rume; Madjet, Mohamed; Chakraborty, Himadri

2013-05-01

We perform a detailed study of the ground state electronic structure of a two-layer fullerene onion molecule C60@C240. Calculations are carried out in a quantum mechanical framework of local density approximation (LDA) where the onion's ion-core of sixty C4+ ions from C60 and two hundred and forty of those from C240 is smeared into a classical jellium distribution. Significant inter-fullerene mixing between the bands of single-node radial symmetry, the π-bands, is found. We then compute the photoionization from all the levels of the system using a time-dependent version of LDA at photon energies where the ionization is dominated by the inter-layer hybridization of collective plasmon resonances. It is determined, by comparing the isolated fullerene cross sections with the cross section of the onion system for both π and σ (having nodeless radial waves) symmetry, that the π-band mixing is predominantly responsible for the production of plasmon hybrids. Supported by NSF and DOE.

Geodesic exponential kernels: When Curvature and Linearity Conflict

DEFF Research Database (Denmark)

Feragen, Aase; Lauze, François; Hauberg, Søren

2015-01-01

manifold, the geodesic Gaussian kernel is only positive definite if the Riemannian manifold is Euclidean. This implies that any attempt to design geodesic Gaussian kernels on curved Riemannian manifolds is futile. However, we show that for spaces with conditionally negative definite distances the geodesic...

Real time kernel performance monitoring with SystemTap

CERN Multimedia

CERN. Geneva

2018-01-01

SystemTap is a dynamic method of monitoring and tracing the operation of a running Linux kernel. In this talk I will present a few practical use cases where SystemTap allowed me to turn otherwise complex userland monitoring tasks in simple kernel probes.

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search