From Multi to Single Stack Automata
Atig, Mohamed Faouzi
We investigate the issue of reducing the verification problem of multi-stack machines to the one for single-stack machines. For instance, elegant (and practically efficient) algorithms for bounded-context switch analysis of multi-pushdown systems have been recently defined based on reductions to the reachability problem of (single-stack) pushdown systems [10,18]. In this paper, we extend this view to both bounded-phase visibly pushdown automata (BVMPA) [16] and ordered multi-pushdown automata (OMPA) [1] by showing that each of their emptiness problem can be reduced to the one for a class of single-stack machines. For these reductions, we introduce effective generalized pushdown automata (EGPA) where operations on stacks are (1) pop the top symbol of the stack, and (2) push a word in some (effectively) given set of words L over the stack alphabet, assuming that L is in some class of languages for which checking whether L intersects regular languages is decidable. We show that the automata-based saturation procedure for computing the set of predecessors in standard pushdown automata can be extended to prove that for EGPA too the set of all predecessors of a regular set of configurations is an effectively constructible regular set. Our reductions from OMPA and BVMPA to EGPA, together with the reachability analysis procedure for EGPA, allow to provide conceptually simple algorithms for checking the emptiness problem for each of these models, and to significantly simplify the proofs for their 2ETIME upper bounds (matching their lower-bounds).
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/
Novel vehicle detection system based on stacked DoG kernel and AdaBoost
Kang, Hyun Ho; Lee, Seo Won; You, Sung Hyun
2018-01-01
This paper proposes a novel vehicle detection system that can overcome some limitations of typical vehicle detection systems using AdaBoost-based methods. The performance of the AdaBoost-based vehicle detection system is dependent on its training data. Thus, its performance decreases when the shape of a target differs from its training data, or the pattern of a preceding vehicle is not visible in the image due to the light conditions. A stacked Difference of Gaussian (DoG)–based feature extraction algorithm is proposed to address this issue by recognizing common characteristics, such as the shadow and rear wheels beneath vehicles—of vehicles under various conditions. The common characteristics of vehicles are extracted by applying the stacked DoG shaped kernel obtained from the 3D plot of an image through a convolution method and investigating only certain regions that have a similar patterns. A new vehicle detection system is constructed by combining the novel stacked DoG feature extraction algorithm with the AdaBoost method. Experiments are provided to demonstrate the effectiveness of the proposed vehicle detection system under different conditions. PMID:29513727
Magnetic resonance imaging of single rice kernels during cooking
Mohoric, A.; Vergeldt, F.J.; Gerkema, E.; Jager, de P.A.; Duynhoven, van J.P.M.; Dalen, van G.; As, van H.
2004-01-01
The RARE imaging method was used to monitor the cooking of single rice kernels in real time and with high spatial resolution in three dimensions. The imaging sequence is optimized for rapid acquisition of signals with short relaxation times using centered out RARE. Short scan time and high spatial
Deformation Induced Microtwins and Stacking Faults in Aluminum Single Crystal
Han, W. Z.; Cheng, G. M.; Li, S. X.; Wu, S. D.; Zhang, Z. F.
2008-09-01
Microtwins and stacking faults in plastically deformed aluminum single crystal were successfully observed by high-resolution transmission electron microscope. The occurrence of these microtwins and stacking faults is directly related to the specially designed crystallographic orientation, because they were not observed in pure aluminum single crystal or polycrystal before. Based on the new finding above, we propose a universal dislocation-based model to judge the preference or not for the nucleation of deformation twins and stacking faults in various face-centered-cubic metals in terms of the critical stress for dislocation glide or twinning by considering the intrinsic factors, such as stacking fault energy, crystallographic orientation, and grain size. The new finding of deformation induced microtwins and stacking faults in aluminum single crystal and the proposed model should be of interest to a broad community.
Single aflatoxin contaminated corn kernel analysis with fluorescence hyperspectral image
Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Ononye, Ambrose; Brown, Robert L.; Cleveland, Thomas E.
2010-04-01
Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.
Unvented single stack sanitary drainage system I
DEFF Research Database (Denmark)
Najman, Z.
This report forms the basis of the preparation of design recommendations. In the observation tables all single results from 147 tests of charging are dispersed on 53 test set-ups. At test set-ups in 1 till 4 floors height discharge pipes with dimensions of 100, 125, and 150 mm were tested with di...
Feasibility of detecting Aflatoxin B1 in single maize kernels using hyperspectral imaging
The feasibility of detecting Aflatoxin B1 (AFB1) in single maize kernel inoculated with Aspergillus flavus conidia in the field, as well as its spatial distribution in the kernels, was assessed using near-infrared hyperspectral imaging (HSI) technique. Firstly, an image mask was applied to a pixel-b...
Stacking fault tetrahedron induced plasticity in copper single crystal
International Nuclear Information System (INIS)
Zhang, Liang; Lu, Cheng; Tieu, Kiet; Su, Lihong; Zhao, Xing; Pei, Linqing
2017-01-01
Stacking fault tetrahedron (SFT) is the most common type of vacancy clustered defects in fcc metals and alloys, and can play an important role in the mechanical properties of metallic materials. In this study, molecular dynamics (MD) simulations were carried out to investigate the incipience of plasticity and the underlying atomic mechanisms in copper single crystals with SFT. Different deformation mechanisms of SFT were reported due to the crystal orientations and loading directions (compression and tension). The results showed that the incipient plasticity in crystals with SFT resulted from the heterogeneous dislocation nucleation from SFT, so the stress required for plastic deformation was less than that needed for perfect single crystals. Three crystal orientations ([1 0 0], [1 1 0] and [1 1 1]) were specified in this study because they can represent most of the typical deformation mechanisms of SFT. MD simulations revealed that the structural transformation of SFT was frequent under the applied loading; a metastable SFT structure and the collapse of SFT were usually observed. The structural transformation resulted in a different reduction of yield stress in compression and tension, and also caused a decreased or reversed compression/tension asymmetry. Compressive stress can result in the unfaulting of Frank loop in some crystal orientations. According to the elastic theory of dislocation, the process of unfaulting was closely related to the size of the dislocation loop and the stacking fault energy.
Development of nondestructive screening methods for single kernel characterization of wheat
DEFF Research Database (Denmark)
Nielsen, J.P.; Pedersen, D.K.; Munck, L.
2003-01-01
The development of nondestructive screening methods for single seed protein, vitreousness, density, and hardness index has been studied for single kernels of European wheat. A single kernel procedure was applied involving, image analysis, near-infrared transmittance (NIT) spectroscopy, laboratory...... predictability. However, by applying an averaging approach, in which single seed replicate measurements are mathematically simulated, a very good NIT prediction model was achieved. This suggests that the single seed NIT spectra contain hardness information, but that a single seed hardness method with higher...
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
In unsupervised classiﬁcation, kernel -means clustering method has been shown to perform better than conventional -means clustering method in ... 518501, India; Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur College of Engineering, Anantapur 515002, India ...
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
easily implemented and is suitable for large data sets, like those in data mining appli- cations. Experimental results show that, with a small loss of quality, the proposed method can significantly reduce the time taken than the conventional kernel k-means cluster- ing method. The proposed method is also compared with other ...
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
This approach has reduced both time complexity and memory requirements. However, the clustering result of this method will be very much deviated form that obtained using the conventional kernel k-means method. This is because of the fact that pseudo cluster centers in the input space may not represent the exact cluster ...
Directory of Open Access Journals (Sweden)
Manzar Ashtari
2012-02-01
Full Text Available Las imágenes por resonancia magnética pesadas en difusión son ampliamente utilizadaspara el estudio de las estructuras cerebrales dentro de la materia blanca del cerebro. Sinembargo, recuperar las orientaciones de los axones puede ser susceptible a errores por elruido dentro de la señal. Una regularización espacial puede mejorar la estimación, perodebe ser realizada cuidadosamente dado que puede remover información espacial ó introducirfalsas orientaciones. En este trabajo se investigaron las ventajas de aplicar un filtroanisotrópico basado en simples y múltiples kerneles de orientación de manojos de axones.Para esto, hemos calculado kerneles locales de difusión basados en modelos de tensoresde difusión y multi tensores de difusión. Mostraremos los beneficios de nuestra propuestaen 3 tipos diferentes de imágenes obtenidas por resonancia magnética pesada en difusión:Datos sintéticos, imágenes humanas tomadas en vivo, y datos obtenidos de un fantasmasimulador de difusión.Diffusion Weighted Magnetic Resonance Imaging is widely used to study the structure ofthe fiber pathways of white matter in the brain. However, the recovered axon orientationscan be prone to error because of the low signal to noise ratio. Spatial regularization canreduce the error, but it must be done carefully so that real spatial information is not removedand false orientations are not introduced. In this paper we investigate the advantagesof applying an anisotropic filter based on single and multiple axon bundle orientation kernels.To this end, we compute local diffusion kernels based on Diffusion Tensor and multiDiffusion Tensor models. We show the benefits of our approach to three different types ofDW-MRI data: synthetic, in vivo human, and acquired from a diffusion phantom.
Single determinant N-representability and the kernel energy method applied to water clusters.
Polkosnik, Walter; Massa, Lou
2017-10-24
The Kernel energy method (KEM) is a quantum chemical calculation method that has been shown to provide accurate energies for large molecules. KEM performs calculations on subsets of a molecule (called kernels) and so the computational difficulty of KEM calculations scales more softly than full molecule methods. Although KEM provides accurate energies those energies are not required to satisfy the variational theorem. In this article, KEM is extended to provide a full molecule single-determinant N-representable one-body density matrix. A kernel expansion for the one-body density matrix analogous to the kernel expansion for energy is defined. This matrix is converted to a normalized projector by an algorithm due to Clinton. The resulting single-determinant N-representable density matrix maps to a quantum mechanically valid wavefunction which satisfies the variational theorem. The process is demonstrated on clusters of three to twenty water molecules. The resulting energies are more accurate than the straightforward KEM energy results and all violations of the variational theorem are resolved. The N-representability studied in this article is applicable to the study of quantum crystallography. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
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.
Analysis of ergosterol in single kernel and ground grain by gas chromatography-mass spectrometry.
Dong, Yanhong; Steffenson, Brian J; Mirocha, Chester J
2006-06-14
A method for analyzing ergosterol in a single kernel and ground barley and wheat was developed using gas chromatography-mass spectrometry (GC-MS). Samples were saponified in methanolic KOH. Ergosterol was extracted by "one step" hexane extraction and subsequently silylated by N-trimethylsilylimidazole/trimethylchlorosilane (TMSI/TMCS) reagent at room temperature. The recoveries of ergosterol from ground barley were 96.6, 97.1, 97.1, 88.5, and 90.3% at the levels of 0.2, 1, 5, 10, and 20 microg/g (ppm), respectively. The recoveries from a single kernel were between 93.0 and 95.9%. The precision (coefficient of variance) of the method was in the range 0.8-12.3%. The method detection limit (MDL) and the method quantification limit (MQL) were 18.5 and 55.6 ng/g (ppb), respectively. The ergosterol analysis method developed can be used to handle 80 samples daily by one person, making it suitable for screening cereal cultivars for resistance to fungal infection. The ability for detecting low levels of ergosterol in a single kernel provides a tool to investigate early fungal invasion and to study mechanisms of resistance to fungal diseases.
Single-Event Transgene Product Levels Predict Levels in Genetically Modified Breeding Stacks.
Gampala, Satyalinga Srinivas; Fast, Brandon J; Richey, Kimberly A; Gao, Zhifang; Hill, Ryan; Wulfkuhle, Bryant; Shan, Guomin; Bradfisch, Greg A; Herman, Rod A
2017-09-13
The concentration of transgene products (proteins and double-stranded RNA) in genetically modified (GM) crop tissues is measured to support food, feed, and environmental risk assessments. Measurement of transgene product concentrations in breeding stacks of previously assessed and approved GM events is required by many regulatory authorities to evaluate unexpected transgene interactions that might affect expression. Research was conducted to determine how well concentrations of transgene products in single GM events predict levels in breeding stacks composed of these events. The concentrations of transgene products were compared between GM maize, soybean, and cotton breeding stacks (MON-87427 × MON-89034 × DAS-Ø15Ø7-1 × MON-87411 × DAS-59122-7 × DAS-40278-9 corn, DAS-81419-2 × DAS-44406-6 soybean, and DAS-21023-5 × DAS-24236-5 × SYN-IR102-7 × MON-88913-8 × DAS-81910-7 cotton) and their component single events (MON-87427, MON-89034, DAS-Ø15Ø7-1, MON-87411, DAS-59122-7, and DAS-40278-9 corn, DAS-81419-2, and DAS-44406-6 soybean, and DAS-21023-5, DAS-24236-5, SYN-IR102-7, MON-88913-8, and DAS-81910-7 cotton). Comparisons were made within a crop and transgene product across plant tissue types and were also made across transgene products in each breeding stack for grain/seed. Scatter plots were generated comparing expression in the stacks to their component events, and the percent of variability accounted for by the line of identity (y = x) was calculated (coefficient of identity, I 2 ). Results support transgene concentrations in single events predicting similar concentrations in breeding stacks containing the single events. Therefore, food, feed, and environmental risk assessments based on concentrations of transgene products in single GM events are generally applicable to breeding stacks composed of these events.
Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J
2014-01-01
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.
Directory of Open Access Journals (Sweden)
Yi-Hung Liu
2014-07-01
Full Text Available Electroencephalogram-based emotion recognition (EEG-ER has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI. However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP, and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68% and arousal (84.79% among all testing methods.
Single image super-resolution via an iterative reproducing kernel Hilbert space method.
Deng, Liang-Jian; Guo, Weihong; Huang, Ting-Zhu
2016-11-01
Image super-resolution, a process to enhance image resolution, has important applications in satellite imaging, high definition television, medical imaging, etc. Many existing approaches use multiple low-resolution images to recover one high-resolution image. In this paper, we present an iterative scheme to solve single image super-resolution problems. It recovers a high quality high-resolution image from solely one low-resolution image without using a training data set. We solve the problem from image intensity function estimation perspective and assume the image contains smooth and edge components. We model the smooth components of an image using a thin-plate reproducing kernel Hilbert space (RKHS) and the edges using approximated Heaviside functions. The proposed method is applied to image patches, aiming to reduce computation and storage. Visual and quantitative comparisons with some competitive approaches show the effectiveness of the proposed method.
Jiang, Fei; Ma, Yanyuan; Wang, Yuanjia
We propose a generalized partially linear functional single index risk score model for repeatedly measured outcomes where the index itself is a function of time. We fuse the nonparametric kernel method and regression spline method, and modify the generalized estimating equation to facilitate estimation and inference. We use local smoothing kernel to estimate the unspecified coefficient functions of time, and use B-splines to estimate the unspecified function of the single index component. The covariance structure is taken into account via a working model, which provides valid estimation and inference procedure whether or not it captures the true covariance. The estimation method is applicable to both continuous and discrete outcomes. We derive large sample properties of the estimation procedure and show different convergence rate of each component of the model. The asymptotic properties when the kernel and regression spline methods are combined in a nested fashion has not been studied prior to this work even in the independent data case.
Comparative Studies of Polymer Electrolyte Membrane Fuel Cell Stacks and Single Cells
2000-02-01
in the Catalyst Layer and Effects of Both Perfluorosulfonate Ionomer and PTFE-Loaded Carbon on the Catalyst Layer of Polymer Electrolyte Fuel Cells ...financial support of this project. 12 References 1. T. F. Fuller, "Is a Fuel Cell in Your Future?" 77K Electrochemical Society Interface (Fall...ARMY RESEARCH LABORATORY mm^ n Comparative Studies of Polymer Electrolyte Membrane Fuel Cell Stacks and Single Cells Deryn Chu and Rongzhong
Kernel PLS Estimation of Single-trial Event-related Potentials
Rosipal, Roman; Trejo, Leonard J.
2004-01-01
Nonlinear kernel partial least squaes (KPLS) regressior, is a novel smoothing approach to nonparametric regression curve fitting. We have developed a KPLS approach to the estimation of single-trial event related potentials (ERPs). For improved accuracy of estimation, we also developed a local KPLS method for situations in which there exists prior knowledge about the approximate latency of individual ERP components. To assess the utility of the KPLS approach, we compared non-local KPLS and local KPLS smoothing with other nonparametric signal processing and smoothing methods. In particular, we examined wavelet denoising, smoothing splines, and localized smoothing splines. We applied these methods to the estimation of simulated mixtures of human ERPs and ongoing electroencephalogram (EEG) activity using a dipole simulator (BESA). In this scenario we considered ongoing EEG to represent spatially and temporally correlated noise added to the ERPs. This simulation provided a reasonable but simplified model of real-world ERP measurements. For estimation of the simulated single-trial ERPs, local KPLS provided a level of accuracy that was comparable with or better than the other methods. We also applied the local KPLS method to the estimation of human ERPs recorded in an experiment on co,onitive fatigue. For these data, the local KPLS method provided a clear improvement in visualization of single-trial ERPs as well as their averages. The local KPLS method may serve as a new alternative to the estimation of single-trial ERPs and improvement of ERP averages.
High Power, Repetitive, Stacked Blumlein Pulse Generators Commuted by a Single Switching Element
Bhawalkar, Jayant Dilip
In this work, the stacked Blumlein pulsers developed at the University of Texas at Dallas were characterized and shown to be versatile sources of pulse power for a variety of applications. These devices consisted of several triaxial Blumleins stacked in series at one end. The lines were charged in parallel and synchronously commuted repetitively with a single switching element at the other end. In this way, relatively low charging voltages were multiplied to give a high discharge voltage across an arbitrary load without the need for complex Marx bank circuitry. Several pulser parameters such as the number of stacked Blumlein lines, line configuration, type of switching element, and the length of the lines, were varied and the waveform characteristics were observed and analyzed. It was shown that these devices are capable of generating fast rising waveforms with a wide range of peak voltage and current values. The generation of high power waveforms with pulse durations in the range of 80-600 ns was demonstrated without degradation of the voltage gains. The results of this work indicated that unlike generators based on stacked transmission lines, the effects of parasitic modes were not appreciable for the stacked Blumlein pulsers. Opportunities for tactically packaging these pulsers were also investigated and a significant reduction in their size and weight was demonstrated. For this, dielectric lifetime and Blumlein spacing studies were performed on small scale prototypes. In addition to production of intense X-ray pulses, the possible applications for these novel pulsers include driving magnetrons for high power microwave generation, pumping laser media, or powering e-beam diodes. They could also serve as compact, tabletop sources of high power pulses for various research experiments.
SCAP-82, Single Scattering, Albedo Scattering, Point-Kernel Analysis in Complex Geometry
International Nuclear Information System (INIS)
Disney, R.K.; Vogtman, S.E.
1987-01-01
1 - Description of problem or function: SCAP solves for radiation transport in complex geometries using the single or albedo scatter point kernel method. The program is designed to calculate the neutron or gamma ray radiation level at detector points located within or outside a complex radiation scatter source geometry or a user specified discrete scattering volume. Geometry is describable by zones bounded by intersecting quadratic surfaces within an arbitrary maximum number of boundary surfaces per zone. Anisotropic point sources are describable as pointwise energy dependent distributions of polar angles on a meridian; isotropic point sources may also be specified. The attenuation function for gamma rays is an exponential function on the primary source leg and the scatter leg with a build- up factor approximation to account for multiple scatter on the scat- ter leg. The neutron attenuation function is an exponential function using neutron removal cross sections on the primary source leg and scatter leg. Line or volumetric sources can be represented as a distribution of isotropic point sources, with un-collided line-of-sight attenuation and buildup calculated between each source point and the detector point. 2 - Method of solution: A point kernel method using an anisotropic or isotropic point source representation is used, line-of-sight material attenuation and inverse square spatial attenuation between the source point and scatter points and the scatter points and detector point is employed. A direct summation of individual point source results is obtained. 3 - Restrictions on the complexity of the problem: - The SCAP program is written in complete flexible dimensioning so that no restrictions are imposed on the number of energy groups or geometric zones. The geometric zone description is restricted to zones defined by boundary surfaces defined by the general quadratic equation or one of its degenerate forms. The only restriction in the program is that the total
National Aeronautics and Space Administration — This SBIR Phase I project aims to develop an innovative batch fabrication technique to create single crystal PMN-PT stack actuator deformable mirrors (DM) at low...
Automated assembling of single fuel cell units for use in a fuel cell stack
Jalba, C. K.; Muminovic, A.; Barz, C.; Nasui, V.
2017-05-01
The manufacturing of PEMFC stacks (POLYMER ELEKTROLYT MEMBRAN Fuel Cell) is nowadays still done by hand. Over hundreds of identical single components have to be placed accurate together for the construction of a fuel cell stack. Beside logistic problems, higher total costs and disadvantages in weight the high number of components produce a higher statistic interference because of faulty erection or material defects and summation of manufacturing tolerances. The saving of costs is about 20 - 25 %. Furthermore, the total weight of the fuel cells will be reduced because of a new sealing technology. Overall a one minute cycle time has to be aimed per cell at the manufacturing of these single components. The change of the existing sealing concept to a bonded sealing is one of the important requisites to get an automated manufacturing of single cell units. One of the important steps for an automated gluing process is the checking of the glue application by using of an image processing system. After bonding the single fuel cell the sealing and electrical function can be checked, so that only functional and high qualitative cells can get into further manufacturing processes.
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.
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
Open Computing Language (OpenCL) is a high-level language that enables software programmers to explore Field Programmable Gate Arrays (FPGAs) for application acceleration. The Intel FPGA software development kit (SDK) for OpenCL allows a user to specify applications at a high level and explore the performance of low-level hardware acceleration. In this report, we present the FPGA performance and power consumption results of the single-precision floating-point vector add OpenCL kernel using the Intel FPGA SDK for OpenCL on the Nallatech 385A FPGA board. The board features an Arria 10 FPGA. We evaluate the FPGA implementations using the compute unit duplication and kernel vectorization optimization techniques. On the Nallatech 385A FPGA board, the maximum compute kernel bandwidth we achieve is 25.8 GB/s, approximately 76% of the peak memory bandwidth. The power consumption of the FPGA device when running the kernels ranges from 29W to 42W.
The impact of base stacking on the conformations and electrostatics of single-stranded DNA.
Plumridge, Alex; Meisburger, Steve P; Andresen, Kurt; Pollack, Lois
2017-04-20
Single-stranded DNA (ssDNA) is notable for its interactions with ssDNA binding proteins (SSBs) during fundamentally important biological processes including DNA repair and replication. Previous work has begun to characterize the conformational and electrostatic properties of ssDNA in association with SSBs. However, the conformational distributions of free ssDNA have been difficult to determine. To capture the vast array of ssDNA conformations in solution, we pair small angle X-ray scattering with novel ensemble fitting methods, obtaining key parameters such as the size, shape and stacking character of strands with different sequences. Complementary ion counting measurements using inductively coupled plasma atomic emission spectroscopy are employed to determine the composition of the ion atmosphere at physiological ionic strength. Applying this combined approach to poly dA and poly dT, we find that the global properties of these sequences are very similar, despite having vastly different propensities for single-stranded helical stacking. These results suggest that a relatively simple mechanism for the binding of ssDNA to non-specific SSBs may be at play, which explains the disparity in binding affinities observed for these systems. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Wang, Xiaohong; Zhou, Yan'an; Zhang, Qian; Zhu, Yiming; Liu, Litian
2009-09-01
This paper presents a silicon-based air-breathing micro direct methanol fuel cell (μDMFC) stack with a shared anode plate and two air-breathing cathode plates. Three kinds of anode plates featured by different methanol transport methods are designed and simulated. Microfabrication technologies, including double-side lithography and bulk-micromachining, are used to fabricate both anode and cathode silicon plates on the same wafer simultaneously. Three μDMFC stacks with different kinds of anodes are assembled, and characterized with a single cell together. Simulation and experimental results show that the μDMFC stack with fuel transport in a shared model has the best performance, and this stack achieves a power of 2.52 mW which is almost double that of a single cell of 1.28 mW.
Galindo-de-la-Rosa, J; Arjona, N; Moreno-Zuria, A; Ortiz-Ortega, E; Guerra-Balcázar, M; Ledesma-García, J; Arriaga, L G
2017-06-15
The purpose of this work is to evaluate single and double-cell membraneless microfluidic fuel cells (MMFCs) that operate in the presence of simulated body fluids SBF, human serum and blood enriched with ethanol as fuels. The study was performed using the alcohol dehydrogenase enzyme immobilised by covalent binding through an array composed of carbon Toray paper as support and a layer of poly(methylene blue)/tetrabutylammonium bromide/Nafion and glutaraldehyde (3D bioanode electrode). The single MMFC was tested in a hybrid microfluidic fuel cell using Pt/C as the cathode. A cell voltage of 1.035V and power density of 3.154mWcm -2 were observed, which is the highest performance reported to date. The stability and durability were tested through chronoamperometry and polarisation/performance curves obtained at different days, which demonstrated a slow decrease in the power density on day 10 (14%) and day 20 (26%). Additionally, the cell was tested for ethanol oxidation in simulated body fluid (SBF) with ionic composition similar to human blood plasma. Those tests resulted in 0.93V of cell voltage and a power density close to 1.237mWcm -2 . The double cell MMFC (Stack) was tested using serum and human blood enriched with ethanol. The stack operated with blood in a serial connection showed an excellent cell performance (0.716mWcm -2 ), demonstrating the feasibility of employing human blood as energy source. Copyright © 2017 Elsevier B.V. All rights reserved.
DEFF Research Database (Denmark)
Winning, H.; Viereck, N.; Wollenweber, B.
2009-01-01
at terminal spikelet, during grain-filling or at both stages. Principal component trajectories of the total protein content and the protein fractions of flour as well as the H-1 NMR spectra of single wheat kernels, wheat flour, and wheat methanol extracts were analysed to elucidate the metabolic development...... indicating that protein metabolism is influenced by multiple drought events, the H-1 NMR spectra of the methanol extracts of flour from mature grains revealed that the amount of fumaric acid is particularly sensitive to water deficits....
Evaluating the pitch bias of CryoSat exploiting stacks of single look ehoes
Scagliola, Michele; Tagliani, Nicolas; Fornari, Marco; Bouzinac, Catherine; Parrinello, Tommaso
2014-05-01
CryoSat was launched on the 8th April 2010 and it is the first European ice mission dedicated to monitoring precise changes in the thickness of polar ice sheets and floating sea ice over a 3-year period. CryoSat carries an innovative radar altimeter called the Synthetic Aperture Interferometric Altimeter (SIRAL), that transmits pulses at a high pulse repetition frequency thus making the received echoes phase coherent and suitable for azimuth processing. The attitude information of the spacecraft is provided by star trackers, that have an internal accuracy of few arc-seconds. By analysis of the CryoSat products, two different studies [1, 2] verified the existence of a bias between the pitch reported by the star trackers and the actual pitch of CryoSat during its flight. However those studies, that use two different methods to evaluate the actual pitch, provided different values for the pitch bias. This poster is aimed at describing a further method to estimated the pitch with which the satellite is actually flying by analysis of the stacks of the single look echoes that are accumulated for a given location of sea surface during the Level1 processing. In fact, over ocean the power of the single look echoes for a given point is shaped by the along-track antenna pattern. As a consequence, estimating the angular direction of pointing of the antenna from the stack, an estimate of the pitch can be obtained. Finally, the bias evaluated starting from the pitch measured with the proposed method is compared with the pitch bias measured in [1, 2]. [1] Galin,N. and Wingham, D., Estimating Pitch Angle of CryoSat-2 using the Power Distribution of the Synthetic Aperture, presented at SAR Altimetry Expert Group Meeting, Southampton UK, June 25-27, 2013. [2] Smith, W.H.F. and Scharroo, R., Retracking range, SWH, sigma-naught, and attitude in CryoSat conventional ocean data. In proceedings of Ocean Surface Topography Science Team Meeting. San Diego, October 19-21, 2011.
Identifying fluorescently labeled single molecules in image stacks using machine learning.
Rifkin, Scott A
2011-01-01
In the past several years, a host of new technologies have made it possible to visualize single molecules within cells and organisms (Raj et al., Nat Methods 5:877-879, 2008; Paré et al., Curr Biol 19:2037-2042, 2009; Lu and Tsourkas, Nucleic Acids Res 37:e100, 2009; Femino et al., Science 280:585-590, 1998; Rodriguez et al., Semin Cell Dev Biol 18:202-208, 2007; Betzig et al., Science 313:1642-1645, 2006; Rust et al., Nat Methods 3:793-796, 2006; Fusco et al., Curr Biol 13:161-167, 2003). Many of these are based on fluorescence, either fluorescent proteins or fluorescent dyes coupled to a molecule of interest. In many applications, the fluorescent signal is limited to a few pixels, which poses a classic signal processing problem: how can actual signal be distinguished from background noise? In this chapter, I present a MATLAB (MathWorks (2010) MATLAB. Retrieved from http://www.mathworks.com) software suite designed to work with these single-molecule visualization technologies (Rifkin (2010) spotFinding Suite. http://www.biology.ucsd.edu/labs/rifkin/software.html). It takes images or image stacks from a fluorescence microscope as input and outputs locations of the molecules. Although the software was developed for the specific application of identifying single mRNA transcripts in fixed specimens, it is more general than this and can be used and/or customized for other applications that produce localized signals embedded in a potentially noisy background. The analysis pipeline consists of the following steps: (a) create a gold-standard dataset, (b) train a machine-learning algorithm to classify image features as signal or noise depending upon user defined statistics, (c) run the machine-learning algorithm on a new dataset to identify mRNA locations, and (d) visually inspect and correct the results.
International Nuclear Information System (INIS)
Eler, J.C.; Littlefield, L.G.; Tillery, M.I.
1979-01-01
A prototype particulate stack sampler (PPSS) has been developed to improve on the existing EPA Method 5 sampling apparatus. Its primary features are (1) higher sampling rate (56 1/min); (2) display (on demand) of all required variables and calculated values by a microcomputer-based calculating and display system; (3) continuous stack gas moisture determination; (4) a virtual impactor nozzle with 3 μm mass median diameter cutpoint which collects fine and coarse particle fractions on separate glass fiber filters; (5) a variable-area inlet to maintain isokinetic sampling conditions; and (6) conversion to stainless steel components from the glass specified by EPA Method 5. The basic sampling techniques of EPA Method 5 have been retained; however, versatility in the form of optional in-stack filters and general modernization of the stack sampler have been provided in the prototype design. Laboratory testing with monodisperse dye aerosols has shown the present variable inlet, virtual impactor nozzle to have a collection efficiency which is less than 77% and significant wall losses. This is primarily due to lack of symmetry in this rectangular jet impactor and short transition lengths dictated by physical design constraints (required passage of the nozzle through a 7.6 cm (3 in) diameter stack port). Electronic components have shown acceptable service in laboratory testing although no field testing of the prototype under a broad range of temperature, humidity, and SO 2 concentration has been undertaken
The coherent interlayer resistance of a single, rotated interface between two stacks of AB graphite
Energy Technology Data Exchange (ETDEWEB)
Habib, K. M. Masum, E-mail: khabib@ee.ucr.edu; Sylvia, Somaia S.; Neupane, Mahesh; Lake, Roger K., E-mail: rlake@ee.ucr.edu [Department of Electrical Engineering, University of California, Riverside, California 92521-0204 (United States); Ge, Supeng [Department of Physics and Astronomy, University of California, Riverside, California 92521-0204 (United States)
2013-12-09
The coherent, interlayer resistance of a misoriented, rotated interface between two stacks of AB graphite is determined for a variety of misorientation angles. The quantum-resistance of the ideal AB stack is on the order of 1 to 10 mΩ μm{sup 2}. For small rotation angles, the coherent interlayer resistance exponentially approaches the ideal quantum resistance at energies away from the charge neutrality point. Over a range of intermediate angles, the resistance increases exponentially with cell size for minimum size unit cells. Larger cell sizes, of similar angles, may not follow this trend. The energy dependence of the interlayer transmission is described.
The coherent interlayer resistance of a single, rotated interface between two stacks of AB graphite
International Nuclear Information System (INIS)
Habib, K. M. Masum; Sylvia, Somaia S.; Neupane, Mahesh; Lake, Roger K.; Ge, Supeng
2013-01-01
The coherent, interlayer resistance of a misoriented, rotated interface between two stacks of AB graphite is determined for a variety of misorientation angles. The quantum-resistance of the ideal AB stack is on the order of 1 to 10 mΩ μm 2 . For small rotation angles, the coherent interlayer resistance exponentially approaches the ideal quantum resistance at energies away from the charge neutrality point. Over a range of intermediate angles, the resistance increases exponentially with cell size for minimum size unit cells. Larger cell sizes, of similar angles, may not follow this trend. The energy dependence of the interlayer transmission is described
Emilie B. Henderson; Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Harold S.J. Zald
2014-01-01
Landscape management and conservation planning require maps of vegetation composition and structure over large regions. Species distribution models (SDMs) are often used for individual species, but projects mapping multiple species are rarer. We compare maps of plant community composition assembled by stacking results from many SDMs with multivariate maps constructed...
Single point aerosol sampling: Evaluation of mixing and probe performance in a nuclear stack
Energy Technology Data Exchange (ETDEWEB)
Rodgers, J.C.; Fairchild, C.I.; Wood, G.O. [Los Alamos National Laboratory, NM (United States)] [and others
1995-02-01
Alternative Reference Methodologies (ARMs) have been developed for sampling of radionuclides from stacks and ducts that differ from the methods required by the U.S. EPA. The EPA methods are prescriptive in selection of sampling locations and in design of sampling probes whereas the alternative methods are performance driven. Tests were conducted in a stack at Los Alamos National Laboratory to demonstrate the efficacy of the ARMs. Coefficients of variation of the velocity tracer gas, and aerosol particle profiles were determined at three sampling locations. Results showed numerical criteria placed upon the coefficients of variation by the ARMs were met at sampling stations located 9 and 14 stack diameters from flow entrance, but not at a location that is 1.5 diameters downstream from the inlet. Experiments were conducted to characterize the transmission of 10 {mu}m aerodynamic equivalent diameter liquid aerosol particles through three types of sampling probes. The transmission ratio (ratio of aerosol concentration at the probe exit plane to the concentration in the free stream) was 107% for a 113 L/min (4-cfm) anisokinetic shrouded probe, but only 20% for an isokinetic probe that follows the EPA requirements. A specially designed isokinetic probe showed a transmission ratio of 63%. The shrouded probe performance would conform to the ARM criteria; however, the isokinetic probes would not.
International Nuclear Information System (INIS)
Wang, Quanlong; Bai, Qingshun; Chen, Jiaxuan; Guo, Yongbo; Xie, Wenkun
2015-01-01
Graphical abstract: In this paper, molecular dynamics simulation is performed to study the distribution of dislocation defects and local atomic crystal structure of single crystal copper. The stress distribution is investigated which is calculated by virial stress and analyzed by static pressure. The results are shown in (a)–(d). It is indicated that the compressive stress mainly spreads over the shear-slip zone, and the tensile stress is consisted in flank friction zone, shown in (a). The high tensile stress in subsurface is the source of stress, shown in (b). By the driven action of the stress source, the initial stair-rod dislocation nucleates. Then the dislocation climbs along four {1 1 1} planes under the stress driven action, shown in (d). Finally, the SFT is formed by the interaction of the compressive stress and the tensile stress which come from the shear-slip zone and friction zone, respectively. Besides, stair-rod dislocation, stacking faults and dislocation loop are also nucleated in the subsurface, shown in (c). Dislocation distribution, local atomic crystal structure state and stress-induced formation process of SFT by atomic. - Highlights: • A novel defect structure “stress-induced stacking fault tetrahedra” is revealed. • Atomic structural evolution and stress state distribution of the SFT are studied. • The stress-induced formation mechanism of the SFT is proposed. - Abstract: Stacking fault tetrahedra commonly existed in subsurface of deformed face center cubic metals, has great influence on machining precision and surface roughness in nano-cutting. Here we report, a stacking fault tetrahedra is formed in subsurface of workpiece during nano-cutting. The variation of cutting force and subsurface defects distribution are studied by using molecular dynamics simulation. The stress distribution is investigated which is calculated by virial stress and analyzed by static compression. The result shows that the cutting force has a rapidly
Energy Technology Data Exchange (ETDEWEB)
Kordatos, Apostolis [National Center for Scientific Research “Demokritos”, Athens, 15310 (Greece); Kelaidis, Nikolaos, E-mail: n.kelaidis@inn.demokritos.gr [National Center for Scientific Research “Demokritos”, Athens, 15310 (Greece); Giamini, Sigiava Aminalragia [National Center for Scientific Research “Demokritos”, Athens, 15310 (Greece); University of Athens, Department of Physics, Section of Solid State Physics, Athens, 15684 Greece (Greece); Marquez-Velasco, Jose [National Center for Scientific Research “Demokritos”, Athens, 15310 (Greece); National Technical University of Athens, Department of Physics, Athens, 15784 Greece (Greece); Xenogiannopoulou, Evangelia; Tsipas, Polychronis; Kordas, George; Dimoulas, Athanasios [National Center for Scientific Research “Demokritos”, Athens, 15310 (Greece)
2016-04-30
Highlights: • Growth of non-defective few layer graphene on Rh(1 1 1) substrates using an ambient- pressure CVD method. • Control of graphene stacking order via the cool-down rate. • Graphene is grown with a mainly AB-stacking geometry on single-crystalline Rhodium for a slow cool-down rate and non-AB for a very fast cool-down. • Good epitaxial orientation of the surface is presented through the RHEED data and confirmed with ARPES characterization for the lower cool-down rate, where graphene's ΓK direction a perfectly aligned with the ΓK direction of the Rh(1 1 1) single crystal. - Abstract: Graphene synthesis on single crystal Rh(1 1 1) catalytic substrates is performed by Chemical Vapor Deposition (CVD) at 1000 °C and atmospheric pressure. Raman analysis shows full substrate coverage with few layer graphene. It is found that the cool-down rate strongly affects the graphene stacking order. When lowered, the percentage of AB (Bernal) -stacked regions increases, leading to an almost full AB stacking order. When increased, the percentage of AB-stacked graphene regions decreases to a point where almost a full non AB-stacked graphene is grown. For a slow cool-down rate, graphene with AB stacking order and good epitaxial orientation with the substrate is achieved. This is indicated mainly by Raman characterization and confirmed by Reflection high-energy electron diffraction (RHEED) imaging. Additional Scanning Tunneling Microscopy (STM) topography data confirm that the grown graphene is mainly an AB-stacked structure. The electronic structure of the graphene/Rh(1 1 1) system is examined by Angle resolved Photo-Emission Spectroscopy (ARPES), where σ and π bands of graphene, are observed. Graphene's ΓK direction is aligned with the ΓK direction of the substrate, indicating no significant contribution from rotated domains.
International Nuclear Information System (INIS)
Kordatos, Apostolis; Kelaidis, Nikolaos; Giamini, Sigiava Aminalragia; Marquez-Velasco, Jose; Xenogiannopoulou, Evangelia; Tsipas, Polychronis; Kordas, George; Dimoulas, Athanasios
2016-01-01
Highlights: • Growth of non-defective few layer graphene on Rh(1 1 1) substrates using an ambient- pressure CVD method. • Control of graphene stacking order via the cool-down rate. • Graphene is grown with a mainly AB-stacking geometry on single-crystalline Rhodium for a slow cool-down rate and non-AB for a very fast cool-down. • Good epitaxial orientation of the surface is presented through the RHEED data and confirmed with ARPES characterization for the lower cool-down rate, where graphene's ΓK direction a perfectly aligned with the ΓK direction of the Rh(1 1 1) single crystal. - Abstract: Graphene synthesis on single crystal Rh(1 1 1) catalytic substrates is performed by Chemical Vapor Deposition (CVD) at 1000 °C and atmospheric pressure. Raman analysis shows full substrate coverage with few layer graphene. It is found that the cool-down rate strongly affects the graphene stacking order. When lowered, the percentage of AB (Bernal) -stacked regions increases, leading to an almost full AB stacking order. When increased, the percentage of AB-stacked graphene regions decreases to a point where almost a full non AB-stacked graphene is grown. For a slow cool-down rate, graphene with AB stacking order and good epitaxial orientation with the substrate is achieved. This is indicated mainly by Raman characterization and confirmed by Reflection high-energy electron diffraction (RHEED) imaging. Additional Scanning Tunneling Microscopy (STM) topography data confirm that the grown graphene is mainly an AB-stacked structure. The electronic structure of the graphene/Rh(1 1 1) system is examined by Angle resolved Photo-Emission Spectroscopy (ARPES), where σ and π bands of graphene, are observed. Graphene's ΓK direction is aligned with the ΓK direction of the substrate, indicating no significant contribution from rotated domains.
X-ray diffraction study of stacking faults in a single crystal of 2H SiC
International Nuclear Information System (INIS)
Pandey, D.; Krishna, P.
1977-01-01
The nature of random stacking faults in a heavily disordered single crystal of 2H SiC has been investigated by studying the broadening of x-ray diffraction maxima. The intensity distribution along the 10.1 reciprocal lattice row was recorded on a four-circle, computer-controlled single crystal diffractometer. The 10.1 reflections with 1 even were found to be considerably broadened showing that the stacking faults present are predominantly intrinsic faults ( both growth and deformation faults). A careful study of the half-width values of different 10.1 reflections revealed that the fault probabilities are large. Exact expressions for the diffracted intensity and the observable diffraction effects were obtained and these were then used to calculate the deformation and growth fault probabilities which were found to be 0.20 and 0.11 respectively. It is suggested that several deformation fault configurations result from a clustering of growth faults. The results obtained are compared with those obtained for 2H ZnS crystals. (author)
Investigation on stacked cascade multilevel inverter by employing single-phase transformers
Directory of Open Access Journals (Sweden)
Y. Suresh
2016-06-01
Full Text Available In the present paper a new version of multilevel inverter is investigated. This new version is based on hybrid association of commutation cells with H-bridge cells. The association allows a significant reduction of the volume of the capacitors. In fact, presented topology allows us to work on higher input voltage levels with the same power switches. This new version is generally called as SCMI (stacked cascade multilevel inverter. The proposed inverter has potential to generate high quality waveforms, reduction in switching frequency, capable to operate at higher voltage levels and finally utilizes minimum number of switching components. The presented version of SCMI is simulated in Matlab-simulink and further, experimental validation is carried out in the laboratory with prototype setup.
Mitigation of artifacts in rtm with migration kernel decomposition
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.
Directory of Open Access Journals (Sweden)
Guangjun Qiu
2018-03-01
Full Text Available The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR spectroscopy with a wavelength range of 1000–2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.
Qiu, Guangjun; Lü, Enli; Lu, Huazhong; Xu, Sai; Zeng, Fanguo; Shui, Qin
2018-03-28
The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000-2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.
Directory of Open Access Journals (Sweden)
Ali Sayyed
2015-09-01
Full Text Available The use of mobile nodes to collect data in a Wireless Sensor Network (WSN has gained special attention over the last years. Some researchers explore the use of Unmanned Aerial Vehicles (UAVs as mobile node for such data-collection purposes. Analyzing these works, it is apparent that mobile nodes used in such scenarios are typically equipped with at least two different radio interfaces. The present work presents a Dual-Stack Single-Radio Communication Architecture (DSSRCA, which allows a UAV to communicate in a bidirectional manner with a WSN and a Sink node. The proposed architecture was specifically designed to support different network QoS requirements, such as best-effort and more reliable communications, attending both UAV-to-WSN and UAV-to-Sink communications needs. DSSRCA was implemented and tested on a real UAV, as detailed in this paper. This paper also includes a simulation analysis that addresses bandwidth consumption in an environmental monitoring application scenario. It includes an analysis of the data gathering rate that can be achieved considering different UAV flight speeds. Obtained results show the viability of using a single radio transmitter for collecting data from the WSN and forwarding such data to the Sink node.
Sayyed, Ali; de Araújo, Gustavo Medeiros; Bodanese, João Paulo; Becker, Leandro Buss
2015-09-16
The use of mobile nodes to collect data in a Wireless Sensor Network (WSN) has gained special attention over the last years. Some researchers explore the use of Unmanned Aerial Vehicles (UAVs) as mobile node for such data-collection purposes. Analyzing these works, it is apparent that mobile nodes used in such scenarios are typically equipped with at least two different radio interfaces. The present work presents a Dual-Stack Single-Radio Communication Architecture (DSSRCA), which allows a UAV to communicate in a bidirectional manner with a WSN and a Sink node. The proposed architecture was specifically designed to support different network QoS requirements, such as best-effort and more reliable communications, attending both UAV-to-WSN and UAV-to-Sink communications needs. DSSRCA was implemented and tested on a real UAV, as detailed in this paper. This paper also includes a simulation analysis that addresses bandwidth consumption in an environmental monitoring application scenario. It includes an analysis of the data gathering rate that can be achieved considering different UAV flight speeds. Obtained results show the viability of using a single radio transmitter for collecting data from the WSN and forwarding such data to the Sink node.
Uno, Masatoshi; Tanaka, Koji
Series connections of energy-storage modules such as electric double-layer capacitors (EDLCs) and lithium-ion batteries result in voltage imbalance because of the nonuniform properties of individual modules. Conventional voltage equalizers based on traditional dc-dc converters require numerous switches and/or transformers, and therefore, their costs and complexity tend to increase. This paper proposes a novel single-switch equalization charger using multiple stacked buck-boost converters. The single-switch operation not only reduces the circuit complexity but also contributes to increasing the reliability. The fundamental operating principles and design procedures of key components are presented in detail. An experimental charge test using a 25W prototype of the proposed equalization charger was performed for four series-connected EDLC modules whose initial voltages were intentionally imbalanced. Experimental results demonstrated that the proposed equalization charger could charge the series-connected modules preferentially in the order of increasing module voltage and that all the modules could be charged up to a uniform voltage level.
Han, Taewon; O'Neal, Dennis L; Ortiz, Carlos A
2007-01-01
The ANSI/HPS-N13.1-1999 standard is based on the concept of obtaining a single point representative sample from a location where the velocity and contaminant profiles are relatively uniform. It is difficult to predict the level of mixing in an arbitrary stack or duct without experimental data to meet the ANSI/HPS N13.1-1999 requirements. The goal of this study was to develop experimental data for a range of conditions in "S" (S-shaped configuration) duct systems with different mixing elements and "S" systems having one or two mixing elements. Results were presented in terms of the coefficients of variation (COVs) for velocity, tracer gas, and 10-mum aerodynamic diameter (AD) aerosol particle profiles at different downstream locations for each mixing element. Five mixing elements were tested, including a 90 degrees elbow, a commercial static mixer, a Small-Horizontal Generic-Tee-Plenum (SH-GTP), a Small-Vertical Generic-Tee-Plenum (SV-GTP), and a Large-Horizontal Generic-Tee-Plenum (LH-GTP) system. The COVs for velocity, gas concentration, and aerosol particles for the three GTP systems were all determined to be less than 8%. Tests with two different sizes of GTPs were conducted, and the results showed the performance of the GTPs was relatively unaffected by either size or velocity as reflected by the Reynolds number. The pressure coefficients were 0.59, 0.57, and 0.65, respectively, for the SH-GTP, SV-GTP, and LH-GTP. The pressure drop for the GTPs was approximately twice that of the round elbow, but a factor of 5 less than a Type IV Air Blender. The GTP was developed to provide a sampling location less than 4-duct diameters downstream of a mixing element with low pressure drop condition. The object of the developmental effort was to provide a system that could be employed in new stack; however, the concept of GTPs could also be retrofitted onto existing system applications as well. Results from these tests show that the system performance is well within the ANSI
Testing pollen of single and stacked insect-resistant Bt-maize on in vitro reared honey bee larvae.
Directory of Open Access Journals (Sweden)
Harmen P Hendriksma
Full Text Available The ecologically and economic important honey bee (Apis mellifera is a key non-target arthropod species in environmental risk assessment (ERA of genetically modified (GM crops. Honey bee larvae are directly exposed to transgenic products by the consumption of GM pollen. But most ERA studies only consider responses of adult bees, although Bt-proteins primarily affect the larval phases of target organisms. We adopted an in vitro larvae rearing system, to assess lethal and sublethal effects of Bt-pollen consumption in a standardized eco-toxicological bioassay. The effects of pollen from two Bt-maize cultivars, one expressing a single and the other a total of three Bt-proteins, on the survival and prepupae weight of honey bee larvae were analyzed. The control treatments included pollen from three non-transgenic maize varieties and of Heliconia rostrata. Three days old larvae were fed the realistic exposure dose of 2 mg pollen within the semi-artificial diet. The larvae were monitored over 120 h, until the prepupal stage, where larvae terminate feeding and growing. Neither single nor stacked Bt-maize pollen showed an adverse effect on larval survival and the prepupal weight. In contrast, feeding of H. rostrata pollen caused significant toxic effects. The results of this study indicate that pollen of the tested Bt-varieties does not harm the development of in vitro reared A. mellifera larvae. To sustain the ecosystem service of pollination, Bt-impact on A. mellifera should always be a crucial part of regulatory biosafety assessments. We suggest that our approach of feeding GM pollen on in vitro reared honey bee larvae is well suited of becoming a standard bioassay in regulatory risk assessments schemes of GM crops.
Testing pollen of single and stacked insect-resistant Bt-maize on in vitro reared honey bee larvae.
Hendriksma, Harmen P; Härtel, Stephan; Steffan-Dewenter, Ingolf
2011-01-01
The ecologically and economic important honey bee (Apis mellifera) is a key non-target arthropod species in environmental risk assessment (ERA) of genetically modified (GM) crops. Honey bee larvae are directly exposed to transgenic products by the consumption of GM pollen. But most ERA studies only consider responses of adult bees, although Bt-proteins primarily affect the larval phases of target organisms. We adopted an in vitro larvae rearing system, to assess lethal and sublethal effects of Bt-pollen consumption in a standardized eco-toxicological bioassay. The effects of pollen from two Bt-maize cultivars, one expressing a single and the other a total of three Bt-proteins, on the survival and prepupae weight of honey bee larvae were analyzed. The control treatments included pollen from three non-transgenic maize varieties and of Heliconia rostrata. Three days old larvae were fed the realistic exposure dose of 2 mg pollen within the semi-artificial diet. The larvae were monitored over 120 h, until the prepupal stage, where larvae terminate feeding and growing. Neither single nor stacked Bt-maize pollen showed an adverse effect on larval survival and the prepupal weight. In contrast, feeding of H. rostrata pollen caused significant toxic effects. The results of this study indicate that pollen of the tested Bt-varieties does not harm the development of in vitro reared A. mellifera larvae. To sustain the ecosystem service of pollination, Bt-impact on A. mellifera should always be a crucial part of regulatory biosafety assessments. We suggest that our approach of feeding GM pollen on in vitro reared honey bee larvae is well suited of becoming a standard bioassay in regulatory risk assessments schemes of GM crops.
Muller, David
2012-02-01
Graphene can be produced by chemical vapor deposition (CVD) on copper substrates on up to meter scales [1, 2], making their polycrystallinity [3,4] almost unavoidable. By combining aberration-corrected scanning transmission electron microscopy and dark-field transmission electron microscopy, we image graphene grains and grain boundaries across six orders of magnitude. Atomic-resolution images of graphene grain boundaries reveal that different grains can stitch together via pentagon-heptagon pairs. We use diffraction-filtered electron imaging to map the shape and orientation of several hundred grains and boundaries over fields of view of a hundred microns. Single, double and multilayer graphene can be differentiated, and the stacking sequence and relative abundance of sequences can be directly imaged. These images reveal an intricate patchwork of grains with structural details depending strongly on growth conditions. The imaging techniques enabled studies of the structure, properties, and control of graphene grains and grain boundaries [5]. [4pt] [1] X. Li et al., Science 324, 1312 (2009).[0pt] [2] S. Bae et al., Nature Nanotechnol. 5, 574 (2010).[0pt] [3] J. M. Wofford, et al., Nano Lett., (2010).[0pt] [4] P. Y. Huang, et al., Nature 469, 389--392 (2011); arXiv:1009.4714, (2010)[0pt] [5] In collaboration with Pinshane Y. Huang, C. S. Ruiz-Vargas, A. M. van der Zande, A. W. Tsen, L. Brown, R. Hovden, F. Ghahari, W. S. Whitney, M.P. Levendorf, J. W. Kevek, S. Garg, J. S. Alden, C. J. Hustedt, Y. Zhu, N. Petrone, J. Hone, J. Park, P. L. McEuen
Ben Ali, Sina-Elisabeth; Madi, Zita Erika; Hochegger, Rupert; Quist, David; Prewein, Bernhard; Haslberger, Alexander G.; Brandes, Christian
2014-01-01
Genetic mutations must be avoided during the production and use of seeds. In the European Union (EU), Directive 2001/18/EC requires any DNA construct introduced via transformation to be stable. Establishing genetic stability is critical for the approval of genetically modified organisms (GMOs). In this study, genetic stability of two GMOs was examined using high resolution melting (HRM) analysis and real-time polymerase chain reaction (PCR) employing Scorpion primers for amplification. The genetic variability of the transgenic insert and that of the flanking regions in a single oilseed rape variety (GT73) and a stacked maize (MON88017 × MON810) was studied. The GT73 and the 5' region of MON810 showed no instabilities in the examined regions. However; two out of 100 analyzed samples carried a heterozygous point mutation in the 3' region of MON810 in the stacked variety. These results were verified by direct sequencing of the amplified PCR products as well as by sequencing of cloned PCR fragments. The occurrence of the mutation suggests that the 5' region is more suitable than the 3' region for the quantification of MON810. The identification of the single nucleotide polymorphism (SNP) in a stacked event is in contrast to the results of earlier studies of the same MON810 region in a single event where no DNA polymorphism was found. PMID:25365178
International Nuclear Information System (INIS)
Kim, Yanghoo; Kim, Yong Min; Koh, Ji-Yeon; Lee, Tae-Ho; Woo, Wan Chuck; Han, Heung Nam
2016-01-01
Single crystal elastic constants of austenite and ferrite phases in high-nitrogen duplex stainless steel were evaluated by an elastic self-consistent model combined with an optimization process using in-situ neutron diffraction data. The optimized elastic constants were validated by the indentation moduli of each phase obtained by nanoindentation. In addition, the stacking fault energy of austenite was evaluated based on the neutron diffraction profile and the single crystal elastic constants and was subsequently correlated with the observed deformation microstructure.
Indian Academy of Sciences (India)
generally, any fiber product) is not uniquely defined: it is only defined up to unique isomorphism. ..... Fiber product. Given two morphisms f1 : F1 ! G, f2 : F2 ! G, we define a new stack. F1 آG F2 (with projections to F1 and F2) as follows. The objects are triples ًX1; X2; ق ..... In fact, any Artin stack F can be defined in this fashion.
System effects in sample self-stacking CZE: Single analyte peak splitting of salt-containing samples
Czech Academy of Sciences Publication Activity Database
Malá, Zdeňka; Gebauer, Petr; Boček, Petr
2009-01-01
Roč. 30, č. 5 (2009), s. 866-874 ISSN 0173-0835 R&D Projects: GA ČR GA203/08/1536; GA AV ČR IAA400310609; GA AV ČR IAA400310703 Institutional research plan: CEZ:AV0Z40310501 Keywords : CZE * peak splitting * self-stacking Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 3.077, year: 2009
Dauer, Joseph; Hulting, Andrew; Carlson, Dale; Mankin, Luke; Harden, John; Mallory-Smith, Carol
2018-02-01
Provisia™ rice (PV), a non-genetically engineered (GE) quizalofop-resistant rice, will provide growers with an additional option for weed management to use in conjunction with Clearfield ® rice (CL) production. Modeling compared the impact of stacking resistance traits versus single traits in rice on introgression of the resistance trait to weedy rice (also called red rice). Common weed management practices were applied to 2-, 3- and 4-year crop rotations, and resistant and multiple-resistant weedy rice seeds, seedlings and mature plants were tracked for 15 years. Two-year crop rotations resulted in resistant weedy rice after 2 years with abundant populations (exceeding 0.4 weedy rice plants m -2 ) occurring after 7 years. When stacked trait rice was rotated with soybeans in a 3-year rotation and with soybeans and CL in a 4-year rotation, multiple-resistance occurred after 2-5 years with abundant populations present in 4-9 years. When CL rice, PV rice, and soybeans were used in 3- and 4-year rotations, the median time of first appearance of multiple-resistance was 7-11 years and reached abundant levels in 10-15 years. Maintaining separate CL and PV rice systems, in rotation with other crops and herbicides, minimized the evolution of multiple herbicide-resistant weedy rice through gene flow compared to stacking herbicide resistance traits. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.
Indian Academy of Sciences (India)
truct the 'moduli stack', that captures all the information that we would like in a fine moduli space. ..... the fine moduli space), it has the property that for any family W of vector bundles (i.e. W is a vector bundle over B ...... the etale topology is finer: V is a 'small enough open subset' because the square root can be defined on it.
International Nuclear Information System (INIS)
Sandoval, Miguel A.; Fuentes, Rosalba; Walsh, Frank C.; Nava, José L.; Ponce de León, Carlos
2016-01-01
Highlights: • Computational fluid dynamic simulations in a filter-press stack of three cells. • The fluid velocity was different in each cell due to local turbulence. • The upper cell link pipe of the filter press cell acts as a fluid mixer. • The fluid behaviour tends towards a continuous mixing flow pattern. • Close agreement between simulations and experimental data was achieved. - Abstract: Computational fluid dynamics (CFD) simulations were carried out for single-phase flow in a pre-pilot filter press flow reactor with a stack of three cells. Velocity profiles and streamlines were obtained by solving the Reynolds-Averaged Navier-Stokes (RANS) equations with a standard k − ε turbulence model. The flow behaviour shows the appearance of jet flow at the entrance to each cell. At lengths from 12 to 15 cm along the cells channels, a plug flow pattern is developed at all mean linear flow rates studied here, 1.2 ≤ u ≤ 2.1 cm s −1 . The magnitude of the velocity profiles in each cell was different, due to the turbulence generated by the change of flow direction in the last fluid manifold. Residence time distribution (RTD) simulations indicated that the fluid behaviour tends towards a continuous mixing flow pattern, owing to flow at the output of each cell across the upper cell link pipe, which acts as a mixer. Close agreement between simulations and experimental RTD was obtained.
The Direct FuelCell™ stack engineering
Doyon, J.; Farooque, M.; Maru, H.
FuelCell Energy (FCE) has developed power plants in the size range of 300 kW to 3 MW for distributed power generation. Field-testing of the sub-megawatt plants is underway. The FCE power plants are based on its Direct FuelCell™ (DFC) technology. This is so named because of its ability to generate electricity directly from a hydrocarbon fuel, such as natural gas, by reforming it inside the fuel cell stack itself. All FCE products use identical 8000 cm 2 cell design, approximately 350-400 cells per stack, external gas manifolds, and similar stack compression systems. The difference lies in the packaging of the stacks inside the stack module. The sub-megawatt system stack module contains a single horizontal stack whereas the MW-class stack module houses four identical vertical stacks. The commonality of the design, internal reforming features, and atmospheric operation simplify the system design, reduce cost, improve efficiency, increase reliability and maintainability. The product building-block stack design has been advanced through three full-size stack operations at company's headquarters in Danbury, CT. The initial proof-of-concept of the full-size stack design was verified in 1999, followed by a 1.5 year of endurance verification in 2000-2001, and currently a value-engineered stack version is in operation. This paper discusses the design features, important engineering solutions implemented, and test results of FCE's full-size DFC stacks.
DEFF Research Database (Denmark)
Pedersen, Niels Falsig; Sakai, Shigeki
2000-01-01
We demonstrate analytically that parametric excitation of certain plasma resonance modes in Bi2Sr2CaCu2Ox single crystals is possible. The model we use is that of a Josephson stack, and the fundamental mechanism is that of half harmonic generation in time and space when a threshold of the applied...... rf signal is exceeded. The phenomenon is important as a diagnostic tool for the investigation of plasma resonance in Bi2Sr2CaCu2Ox-like materials, as well as being a basis for making high-T-c high-frequency parametric amplifiers. It has same unique features of space and time nonlinear behavior....
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`.
Londo, Jason P; Bollman, Michael A; Sagers, Cynthia L; Lee, E Henry; Watrud, Lidia S
2011-08-01
• Transgenic plants can offer agricultural benefits, but the escape of transgenes is an environmental concern. In this study we tested the hypothesis that glyphosate drift and herbivory selective pressures can change the rate of transgene flow between the crop Brassica napus (canola), and weedy species and contribute to the potential for increased transgene escape risk and persistence outside of cultivation. • We constructed plant communities containing single transgenic B. napus genotypes expressing glyphosate herbicide resistance (CP4 EPSPS), lepidopteran insect resistance (Cry1Ac), or both traits ('stacked'), plus nontransgenic B. napus, Brassica rapa and Brassica nigra. Two different selective pressures, a sublethal glyphosate dose and lepidopteran herbivores (Plutella xylostella), were applied and rates of transgene flow and transgenic seed production were measured. • Selective treatments differed in the degree in which they affected gene flow and production of transgenic hybrid seed. Most notably, glyphosate-drift increased the incidence of transgenic seeds on nontransgenic B. napus by altering flowering phenology and reproductive function. • The findings of this study indicate that transgenic traits may be transmitted to wild populations and may increase in frequency in weedy populations through the direct and indirect effects of selection pressures on gene flow. No claim to original US government works. New Phytologist © 2011 New Phytologist Trust.
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
Kroah-Hartman, Greg
2009-01-01
Linux Kernel in a Nutshell covers the entire range of kernel tasks, starting with downloading the source and making sure that the kernel is in sync with the versions of the tools you need. In addition to configuration and installation steps, the book offers reference material and discussions of related topics such as control of kernel options at runtime.
National Aeronautics and Space Administration — This SBIR Phase I project aims to develop a new manufacturing approach for deformable mirrors (DMs) by batch fabricating the stack actuator array. The innovation...
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
[Study of genetic models of maize kernel traits].
Zhang, H W; Kong, F L
2000-01-01
Two sets of NCII mating design including 21 different maize inbreds were used to study the genetic models of five maize kernel traits--kernel length, width, ratio of kernel length and width, kernel thickness and weight per 100 kernels. Ten generations including P1, P2, F1, F2, B1, B2 and their reciprocal crosses RF1, RF2, RB1, RB2 were obtained. Three years' data were obtained and analyzed using mainly two methods: (1) precision identification for single cross and (2) mixed liner model MINQUE approach for diallel design. Method 1 showed that kernel traits were primarily controlled by maternal dominance, endosperm additive and dominance effect (maternal dominance > endosperm additive > endosperm dominance). Cytoplasmic effect was detected in one of the two crosses studied. Method 2 revealed that in the total variance of kernel traits, maternal genotypic effect contributed more than 60%, endosperm genotypic effect contributed less than 40%. Cytoplasmic effect only existed in kernel length and 100 kernel weight, with the range of 10% to 30%. The results indicated that kernel genetic performance was quite largely controlled by maternal genotypic effect.
Analog forecasting with dynamics-adapted kernels
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.
On the "stacking fault" in copper
Fransens, J.R.; Pleiter, F
2003-01-01
The results of a perturbed gamma-gamma angular correlations experiment on In-111 implanted into a properly cut single crystal of copper show that the defect known in the literature as "stacking fault" is not a planar faulted loop but a stacking fault tetrahedron with a size of 10-50 Angstrom.
Multiple Segmentation of Image Stacks
DEFF Research Database (Denmark)
Smets, Jonathan; Jaeger, Manfred
2014-01-01
We propose a method for the simultaneous construction of multiple image segmentations by combining a recently proposed “convolution of mixtures of Gaussians” model with a multi-layer hidden Markov random field structure. The resulting method constructs for a single image several, alternative...... segmentations that capture different structural elements of the image. We also apply the method to collections of images with identical pixel dimensions, which we call image stacks. Here it turns out that the method is able to both identify groups of similar images in the stack, and to provide segmentations...
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
Robust visual tracking via speedup multiple kernel ridge regression
Qian, Cheng; Breckon, Toby P.; Li, Hui
2015-09-01
Most of the tracking methods attempt to build up feature spaces to represent the appearance of a target. However, limited by the complex structure of the distribution of features, the feature spaces constructed in a linear manner cannot characterize the nonlinear structure well. We propose an appearance model based on kernel ridge regression for visual tracking. Dense sampling is fulfilled around the target image patches to collect the training samples. In order to obtain a kernel space in favor of describing the target appearance, multiple kernel learning is introduced into the selection of kernels. Under the framework, instead of a single kernel, a linear combination of kernels is learned from the training samples to create a kernel space. Resorting to the circulant property of a kernel matrix, a fast interpolate iterative algorithm is developed to seek coefficients that are assigned to these kernels so as to give an optimal combination. After the regression function is learned, all candidate image patches gathered are taken as the input of the function, and the candidate with the maximal response is regarded as the object image patch. Extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art tracking methods.
Multidimensional kernel estimation
Milosevic, Vukasin
2015-01-01
Kernel estimation is one of the non-parametric methods used for estimation of probability density function. Its first ROOT implementation, as part of RooFit package, has one major issue, its evaluation time is extremely slow making in almost unusable. The goal of this project was to create a new class (TKNDTree) which will follow the original idea of kernel estimation, greatly improve the evaluation time (using the TKTree class for storing the data and creating different user-controlled modes of evaluation) and add the interpolation option, for 2D case, with the help of the new Delaunnay2D class.
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...
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...
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Hansen, P. Reinhard; Lunde, Asger
2009-01-01
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...
Directory of Open Access Journals (Sweden)
Hailun Wang
2017-01-01
Full Text Available Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.
Perumal, Packiyaraj; Karuppiah, Chelladurai; Liao, Wei-Cheng; Liou, Yi-Rou; Liao, Yu-Ming; Chen, Yang-Fang
2017-08-30
Integrating different dimentional materials on vertically stacked p-n hetero-junctions have facinated a considerable scrunity and can open up excellent feasibility with various functionalities in opto-electronic devices. Here, we demonstrate that vertically stacked p-GaN/SiO 2 /n-MoS 2 /Graphene heterostructures enable to exhibit prominent dual opto-electronic characteristics, including efficient photo-detection and light emission, which represents the emergence of a new class of devices. The photoresponsivity was found to achieve as high as ~10.4 AW -1 and the detectivity and external quantum efficiency were estimated to be 1.1 × 10 10 Jones and ~30%, respectively. These values are superier than most reported hererojunction devices. In addition, this device exhibits as a self-powered photodetector, showing a high responsivity and fast response speed. Moreover, the device demonstrates the light emission with low turn-on voltage (~1.0 V) which can be realized by electron injection from graphene electrode and holes from GaN film into monolayer MoS 2 layer. These results indicate that with a suitable choice of band alignment, the vertical stacking of materials with different dimentionalities could be significant potential for integration of highly efficient heterostructures and open up feasible pathways towards integrated nanoscale multi-functional optoelectronic devices for a variety of applications.
Pepple, Ken
2011-01-01
OpenStack was created with the audacious goal of being the ubiquitous software choice for building public and private cloud infrastructures. In just over a year, it's become the most talked-about project in open source. This concise book introduces OpenStack's general design and primary software components in detail, and shows you how to start using it to build cloud infrastructures. If you're a developer, technologist, or system administrator familiar with cloud offerings such as Rackspace Cloud or Amazon Web Services, Deploying OpenStack shows you how to obtain and deploy OpenStack softwar
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...... returns measured over 5 or 10 minutes intervals. We show the new estimator is substantially more precise....
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger
2011-01-01
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 error of certain types and can also handle non-synchronous trading. It is the first estimator...... returns measured over 5 or 10 min intervals. We show that the new estimator is substantially more precise....
Steerability of Hermite Kernel
Czech Academy of Sciences Publication Activity Database
Yang, Bo; Flusser, Jan; Suk, Tomáš
2013-01-01
Roč. 27, č. 4 (2013), 1354006-1-1354006-25 ISSN 0218-0014 R&D Projects: GA ČR GAP103/11/1552 Institutional support: RVO:67985556 Keywords : Hermite polynomials * Hermite kernel * steerability * adaptive filtering Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.558, year: 2013 http://library.utia.cas.cz/separaty/2013/ZOI/yang-0394387.pdf
Cheng, Nan; Shang, Ying; Xu, Yuancong; Zhang, Li; Luo, Yunbo; Huang, Kunlun; Xu, Wentao
2017-05-15
Stacked genetically modified organisms (GMO) are becoming popular for their enhanced production efficiency and improved functional properties, and on-site detection of stacked GMO is an urgent challenge to be solved. In this study, we developed a cascade system combining event-specific tag-labeled multiplex LAMP with a DNAzyme-lateral flow biosensor for reliable detection of stacked events (DP305423× GTS 40-3-2). Three primer sets, both event-specific and soybean species-specific, were newly designed for the tag-labeled multiplex LAMP system. A trident-like lateral flow biosensor displayed amplified products simultaneously without cross contamination, and DNAzyme enhancement improved the sensitivity effectively. After optimization, the limit of detection was approximately 0.1% (w/w) for stacked GM soybean, which is sensitive enough to detect genetically modified content up to a threshold value established by several countries for regulatory compliance. The entire detection process could be shortened to 120min without any large-scale instrumentation. This method may be useful for the in-field detection of DP305423× GTS 40-3-2 soybean on a single kernel basis and on-site screening tests of stacked GM soybean lines and individual parent GM soybean lines in highly processed foods. Copyright © 2017 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Yulin Jian
2017-06-01
Full Text Available A novel classification model, named the quantum-behaved particle swarm optimization (QPSO-based weighted multiple kernel extreme learning machine (QWMK-ELM, is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs. The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM, kernel extreme learning machine (KELM, k-nearest neighbors (KNN, support vector machine (SVM, multi-layer perceptron (MLP, radical basis function neural network (RBFNN, and probabilistic neural network (PNN. The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.
Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong
2017-06-19
A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.
Radez, Dan
2015-01-01
If you need to get started with OpenStack or want to learn more, then this book is your perfect companion. If you're comfortable with the Linux command line, you'll gain confidence in using OpenStack.
Khedher, Omar
2015-01-01
This book is intended for system administrators, cloud engineers, and system architects who want to deploy a cloud based on OpenStack in a mid- to large-sized IT infrastructure. If you have a fundamental understanding of cloud computing and OpenStack and want to expand your knowledge, then this book is an excellent checkpoint to move forward.
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 ...
Composition Kernel: A Software Solution for Constructing a Multi-OS Embedded System
Directory of Open Access Journals (Sweden)
Kinebuchi Yuki
2010-01-01
Full Text Available Abstract Modern high-end embedded systems require both predictable real-time scheduling and high-level abstraction interface to their OS kernels. Since these features are difficult to be balanced by a single OS, some methods that accommodate multiple different versions of OS kernels, typically real-time OS and general purpose OS, into a single device have been proposed. The hybrid kernel, one of those methods, executes a general purpose OS kernel as a task of real-time OS which can support those features with reasonable engineering effort. However when adapting the approach to various combinations of OS kernels, which is required in the real-world embedded system design, the engineering effort of modifying the kernel becomes not negligible. This article introduce a method called a composition kernel which uses a thin abstraction layer for accommodating kernels without making direct dependencies between them. The authors developed the abstraction layer on an SH-4A processor and executed kernels on top of it. The amount of modifications to the kernels was significantly smaller than that in related work, while introducing only negligible verhead to the performance of the kernels.
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...... information to be automatically incorporated in registrations and promises to improve the standard framework in several aspects. We present the mathematical foundations of LDDKBM and derive the KB-EPDiff evolution equations, which provide optimal warps in this new framework. To illustrate the resulting...
Clustering via Kernel Decomposition
DEFF Research Database (Denmark)
Have, Anna Szynkowiak; Girolami, Mark A.; Larsen, Jan
2006-01-01
Methods for spectral clustering have been proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this work it is proposed that the affinity matrix is created based on the elements of a non-parametric density estimator. This matrix is then decomposed to obtain...... posterior probabilities of class membership using an appropriate form of nonnegative matrix factorization. The troublesome selection of hyperparameters such as kernel width and number of clusters can be obtained using standard cross-validation methods as is demonstrated on a number of diverse data sets....
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.
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...
Classification Using the Zipfian Kernel
Czech Academy of Sciences Publication Activity Database
Jiřina, Marcel; Jiřina jr., M.
2015-01-01
Roč. 32, č. 2 (2015), s. 305-326 ISSN 0176-4268 R&D Projects: GA TA ČR TA01010490 Institutional support: RVO:67985807 Keywords : kernel machine * Zipfian kernel * multivariate data * correlation dimension * harmonic series * classification Subject RIV: JC - Computer Hardware ; Software Impact factor: 1.147, year: 2015
Model Selection in Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter
, 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...... applicable, and we recommend their use instead of the popular polynomial kernels in general settings, in which no information on the data-generating process is available....
Viscosity kernel of molecular fluids
DEFF Research Database (Denmark)
Puscasu, Ruslan; Todd, Billy; Daivis, Peter
2010-01-01
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......The wave-vector dependent shear viscosities for butane and freely jointed chains have been determined. The transverse momentum density and stress autocorrelation functions have been determined by equilibrium molecular dynamics in both atomic and molecular hydrodynamic formalisms. The density......, 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...
Model selection in kernel ridge regression
DEFF Research Database (Denmark)
Exterkate, Peter
2013-01-01
. 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...... 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 applicable. Therefore, their use is recommended instead of the popular polynomial kernels in general settings, where no information...
Implementation of kernels on the Maestro processor
Suh, Jinwoo; Kang, D. I. D.; Crago, S. P.
Currently, most microprocessors use multiple cores to increase performance while limiting power usage. Some processors use not just a few cores, but tens of cores or even 100 cores. One such many-core microprocessor is the Maestro processor, which is based on Tilera's TILE64 processor. The Maestro chip is a 49-core, general-purpose, radiation-hardened processor designed for space applications. The Maestro processor, unlike the TILE64, has a floating point unit (FPU) in each core for improved floating point performance. The Maestro processor runs at 342 MHz clock frequency. On the Maestro processor, we implemented several widely used kernels: matrix multiplication, vector add, FIR filter, and FFT. We measured and analyzed the performance of these kernels. The achieved performance was up to 5.7 GFLOPS, and the speedup compared to single tile was up to 49 using 49 tiles.
Directory of Open Access Journals (Sweden)
A. Herz
2016-03-01
Full Text Available Self-assembly of ultrathin Au, W, and Au-W bilayer thin films is investigated using a rapid thermal annealing technique in an inert ambient. The solid-state dewetting of Au films is briefly revisited in order to emphasize the role of initial film thickness. W films deposited onto SiO2 evolve into needle-like nanocrystals rather than forming particle-like agglomerates upon annealing at elevated temperatures. Transmission electron microscopy reveals that such nanocrystals actually consist of tungsten (VI oxide (WO3 which is related to an anisotropic oxide crystal growth out of the thin film. The evolution of W films is highly sensitive to the presence of any residual oxygen. Combination of both the dewetting of Au and the oxide crystal growth of WO3 is realized by using various bilayer film configurations of the immiscible Au and W. At low temperature, Au dewetting is initiated while oxide crystal growth is still suppressed. Depending on the stacking sequence of the Au-W bilayer thin film, W acts either as a substrate or as a passivation layer for the dewetting of Au. Being the ground layer, W changes the wettability of Au which clearly modifies its initial state for the dewetting. Being the top layer, W prevents Au from dewetting regardless of Au film thickness. Moreover, regular pattern formation of Au-WO3 nanoparticles is observed at high temperature demonstrating how bilayer thin film dewetting can create unique nanostructure arrangements.
Bruemmer, David J [Idaho Falls, ID
2009-11-17
A robot platform includes perceptors, locomotors, and a system controller. The system controller executes a robot intelligence kernel (RIK) that includes a multi-level architecture and a dynamic autonomy structure. The multi-level architecture includes a robot behavior level for defining robot behaviors, that incorporate robot attributes and a cognitive level for defining conduct modules that blend an adaptive interaction between predefined decision functions and the robot behaviors. The dynamic autonomy structure is configured for modifying a transaction capacity between an operator intervention and a robot initiative and may include multiple levels with at least a teleoperation mode configured to maximize the operator intervention and minimize the robot initiative and an autonomous mode configured to minimize the operator intervention and maximize the robot initiative. Within the RIK at least the cognitive level includes the dynamic autonomy structure.
Mixture Density Mercer Kernels: A Method to Learn Kernels
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...
Signaling in Early Maize Kernel Development.
Doll, Nicolas M; Depège-Fargeix, Nathalie; Rogowsky, Peter M; Widiez, Thomas
2017-03-06
Developing the next plant generation within the seed requires the coordination of complex programs driving pattern formation, growth, and differentiation of the three main seed compartments: the embryo (future plant), the endosperm (storage compartment), representing the two filial tissues, and the surrounding maternal tissues. This review focuses on the signaling pathways and molecular players involved in early maize kernel development. In the 2 weeks following pollination, functional tissues are shaped from single cells, readying the kernel for filling with storage compounds. Although the overall picture of the signaling pathways regulating embryo and endosperm development remains fragmentary, several types of molecular actors, such as hormones, sugars, or peptides, have been shown to be involved in particular aspects of these developmental processes. These molecular actors are likely to be components of signaling pathways that lead to transcriptional programming mediated by transcriptional factors. Through the integrated action of these components, multiple types of information received by cells or tissues lead to the correct differentiation and patterning of kernel compartments. In this review, recent advances regarding the four types of molecular actors (hormones, sugars, peptides/receptors, and transcription factors) involved in early maize development are presented. Copyright © 2017 The Author. Published by Elsevier Inc. All rights reserved.
Unsupervised multiple kernel learning for heterogeneous data integration.
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.
Energy Technology Data Exchange (ETDEWEB)
Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory
2009-01-01
Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.
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...
Proteome analysis of the almond kernel (Prunus dulcis).
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.
On Stack Reconstruction Problem
Directory of Open Access Journals (Sweden)
V. D. Аkeliev
2009-01-01
Full Text Available The paper describes analytical investigations that study relation of fuel combustion regimes with concentration values of sulphur anhydride in flue gases and acid dew point. Coefficients of convective heat transfer at internal and external surfaces of stacks have been determined in the paper. The paper reveals the possibility to reconstruct stacks while using gas discharging channel made of composite material on the basis of glass-reinforced plastic which permits to reduce thermo-stressed actions on reinforced concrete and increase volume of released gases due to practically two-fold reduction of gas-dynamic pressure losses along the pipe length.
Moses, E.I.
1992-12-01
A laser pulse stacking method is disclosed. A problem with the prior art has been the generation of a series of laser beam pulses where the outer and inner regions of the beams are generated so as to form radially non-synchronous pulses. Such pulses thus have a non-uniform cross-sectional area with respect to the outer and inner edges of the pulses. The present invention provides a solution by combining the temporally non-uniform pulses in a stacking effect to thus provide a more uniform temporal synchronism over the beam diameter. 2 figs.
Heat kernels and critical limits
Pickrell, Doug
2007-01-01
This paper is an exposition of several questions linking heat kernel measures on infinite dimensional Lie groups, limits associated with critical Sobolev exponents, and Feynmann-Kac measures for sigma models.
Multineuron spike train analysis with R-convolution linear combination kernel.
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.
DEFF Research Database (Denmark)
2009-01-01
po_stack® er et reolsystem, hvis enkle elementer giver stor flexibilitet, variation og skulpturel virkning. Elementerne stables og forskydes frit, så reolens rum kan vendes til begge sider, være åbne eller lukkede og farvekombineres ubegrænset. Reolen kan let ombygges, udvides eller opdeles, når ...
Myers, Colton
2015-01-01
If you are a system administrator who manages multiple servers, then you know how difficult it is to keep your infrastructure in line. If you've been searching for an easier way, this book is for you. No prior experience with SaltStack is required.
Design and development of an automated uranium pellet stacking system
International Nuclear Information System (INIS)
Reiss, B.S.; Nokleby, S.B.
2010-01-01
A novel design for an automated uranium pellet stacking system is presented. This system is designed as a drop-in solution to the current production line to enhance the fuel pellet stacking process. The three main goals of this system are to reduce worker exposure to radiation to as low as reasonable achievable (ALARA), improve product quality, and increase productivity. The proposed system will reduce the potential for human error. This single automated system will replace the two existing pellet stacking stations while increasing the total output, eliminating pellet stacking as a bottleneck in the fuel bundle assembly process. (author)
Option Valuation with Volatility Components, Fat Tails, and Non-Monotonic Pricing Kernels
DEFF Research Database (Denmark)
Babaoglu, Kadir; Christoffersen, Peter; Heston, Steven L.
We nest multiple volatility components, fat tails and a U-shaped pricing kernel in a single option model and compare their contribution to describing returns and option data. All three features lead to statistically significant model improvements. A U-shaped pricing kernel is economically most im...
Yang, Yan-Zhuo; Ding, Shuo; Wang, Yong; Li, Cui-Ling; Shen, Yun; Meeley, Robert; McCarty, Donald R; Tan, Bao-Cai
2017-06-01
Vitamin B 6 , an essential cofactor for a range of biochemical reactions and a potent antioxidant, plays important roles in plant growth, development, and stress tolerance. Vitamin B 6 deficiency causes embryo lethality in Arabidopsis ( Arabidopsis thaliana ), but the specific role of vitamin B 6 biosynthesis in endosperm development has not been fully addressed, especially in monocot crops, where endosperm constitutes the major portion of the grain. Through molecular characterization of a small kernel2 ( smk2 ) mutant in maize, we reveal that vitamin B 6 has differential effects on embryogenesis and endosperm development in maize. The B 6 vitamer pyridoxal 5'-phosphate (PLP) is drastically reduced in both the smk2 embryo and the endosperm. However, whereas embryogenesis of the smk2 mutant is arrested at the transition stage, endosperm formation is nearly normal. Cloning reveals that Smk2 encodes the glutaminase subunit of the PLP synthase complex involved in vitamin B 6 biosynthesis de novo. Smk2 partially complements the Arabidopsis vitamin B 6 -deficient mutant pdx2.1 and Saccharomyces cerevisiae pyridoxine auxotrophic mutant MML21. Smk2 is constitutively expressed in the maize plant, including developing embryos. Analysis of B 6 vitamers indicates that the endosperm accumulates a large amount of pyridoxamine 5'-phosphate (PMP). These results indicate that vitamin B 6 is essential to embryogenesis but has a reduced role in endosperm development in maize. The vitamin B 6 required for seed development is synthesized in the seed, and the endosperm accumulates PMP probably as a storage form of vitamin B 6 . © 2017 American Society of Plant Biologists. All Rights Reserved.
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...
Integral equations with contrasting kernels
Directory of Open Access Journals (Sweden)
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.
Energy Expenditure of Sport Stacking
Murray, Steven R.; Udermann, Brian E.; Reineke, David M.; Battista, Rebecca A.
2009-01-01
Sport stacking is an activity taught in many physical education programs. The activity, although very popular, has been studied minimally, and the energy expenditure for sport stacking is unknown. Therefore, the purposes of this study were to determine the energy expenditure of sport stacking in elementary school children and to compare that value…
Locati, Fabio Alessandro
2015-01-01
If you are an OpenStack administrator or developer, or wish to build solutions to protect your OpenStack environment, then this book is for you. Experience of Linux administration and familiarity with different OpenStack components is assumed.
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...
Kernel learning algorithms for face recognition
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
Batched Triangular Dense Linear Algebra Kernels for Very Small Matrix Sizes on GPUs
Charara, Ali
2017-03-06
Batched dense linear algebra kernels are becoming ubiquitous in scientific applications, ranging from tensor contractions in deep learning to data compression in hierarchical low-rank matrix approximation. Within a single API call, these kernels are capable of simultaneously launching up to thousands of similar matrix computations, removing the expensive overhead of multiple API calls while increasing the occupancy of the underlying hardware. A challenge is that for the existing hardware landscape (x86, GPUs, etc.), only a subset of the required batched operations is implemented by the vendors, with limited support for very small problem sizes. We describe the design and performance of a new class of batched triangular dense linear algebra kernels on very small data sizes using single and multiple GPUs. By deploying two-sided recursive formulations, stressing the register usage, maintaining data locality, reducing threads synchronization and fusing successive kernel calls, the new batched kernels outperform existing state-of-the-art implementations.
RTOS kernel in portable electrocardiograph
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.
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 squ...... 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....
Metabolisable energy values of whole palm kernel and palm kernel ...
African Journals Online (AJOL)
4.12 Kcal/kg DM. 4.36 and 4.13 Kcal/kg DM, respectively were the corresponding values for broiler chickens. No interaction between ingredients and birds was found but there were interactions among the bioavailable energy systems and the bird types. Keywords: Metabolisable energy, palm kernel layers, broilers.
Spine labeling in axial magnetic resonance imaging via integral kernels.
Miles, Brandon; Ben Ayed, Ismail; Hojjat, Seyed-Parsa; Wang, Michael H; Li, Shuo; Fenster, Aaron; Garvin, Gregory J
2016-12-01
This study investigates a fast integral-kernel algorithm for classifying (labeling) the vertebra and disc structures in axial magnetic resonance images (MRI). The method is based on a hierarchy of feature levels, where pixel classifications via non-linear probability product kernels (PPKs) are followed by classifications of 2D slices, individual 3D structures and groups of 3D structures. The algorithm further embeds geometric priors based on anatomical measurements of the spine. Our classifier requires evaluations of computationally expensive integrals at each pixel, and direct evaluations of such integrals would be prohibitively time consuming. We propose an efficient computation of kernel density estimates and PPK evaluations for large images and arbitrary local window sizes via integral kernels. Our method requires a single user click for a whole 3D MRI volume, runs nearly in real-time, and does not require an intensive external training. Comprehensive evaluations over T1-weighted axial lumbar spine data sets from 32 patients demonstrate a competitive structure classification accuracy of 99%, along with a 2D slice classification accuracy of 88%. To the best of our knowledge, such a structure classification accuracy has not been reached by the existing spine labeling algorithms. Furthermore, we believe our work is the first to use integral kernels in the context of medical images. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Use of impedance tagging to monitor fuel cell stack performance
Silva, Gregory
Fuel cells are electrochemical device that are traditionally assembled in stacks to perform meaningful work. Monitoring the state of the stack is vitally important to ensure that it is operating efficiently and that constituent cells are not failing for one of a several common reasons including membrane dehydration, gas diffusion layer flooding, reactant starvation, and physical damage. Current state-of-the-art monitoring systems are costly and require at least one connection per cell on the stack, which introduces reliability concerns for stacks consisting of hundreds of cells. This thesis presents a novel approach for diagnosing problems in a fuel cell stack that attempts to reduce the cost and complexity of monitoring cells in a stack. The proposed solution modifies the electrochemical impedance spectroscopy (EIS) response of each cell in the stack by connecting an electrical tag in parallel with each cell. This approach allows the EIS response of the entire stack to identify and locate problems in the stack. Capacitors were chosen as tags because they do not interfere with normal stack operation and because they can generate distinct stack EIS responses. An experiment was performed in the Center for Automation Technologies an Systems (CATS) fuel cell laboratory at Rensselaer Polytechnic Institute (RPI) to perform EIS measurements on a single cell with and without capacitor tags to investigate the proposed solution. The EIS data collected from this experiment was used to create a fuel cell model to investigate the proposed solution under ideal conditions. This thesis found that, although the concept shows some promise in simulations, significant obstacles to implementing the proposed solution. Observed EIS response when the capacitor tags were connected did not match the expected EIS response. Constraints on the capacitor tags found by the model impose significant manufacturing challenges to the proposed solution. Further development of the proposed solution is
Stack Caching Using Split Data Caches
DEFF Research Database (Denmark)
Nielsen, Carsten; Schoeberl, Martin
2015-01-01
In most embedded and general purpose architectures, stack data and non-stack data is cached together, meaning that writing to or loading from the stack may expel non-stack data from the data cache. Manipulation of the stack has a different memory access pattern than that of non-stack data, showing...... higher temporal and spatial locality. We propose caching stack and non-stack data separately and develop four different stack caches that allow this separation without requiring compiler support. These are the simple, window, and prefilling with and without tag stack caches. The performance of the stack...
Reducing Kernel Development Complexity in Distributed Environments
Lèbre , Adrien; Lottiaux , Renaud; Focht , Erich; Morin , Christine
2008-01-01
Setting up generic and fully transparent distributed services for clusters implies complex and tedious kernel developments. More flexible approaches such as user-space libraries are usually preferred with the drawback of requiring application recompilation. A second approach consists in using specific kernel modules (such as FUSE in Gnu/Linux system) to transfer kernel complexity into user space. In this paper, we present a new way to design and implement kernel distributed services for clust...
Oops! What about a Million Kernel Oopses?
Guo , Lisong; Senna Tschudin , Peter; Kono , Kenji; Muller , Gilles; Lawall , Julia
2013-01-01
When a failure occurs in the Linux kernel, the kernel emits an "oops", summarizing the execution context of the failure. Kernel oopses describe real Linux errors, and thus can help prioritize debugging efforts and motivate the design of tools to improve the reliability of Linux code. Nevertheless, the information is only meaningful if it is representative and can be interpreted correctly. In this paper, we study a repository of kernel oopses collected over 8 months by Red Hat. We consider the...
Derivative Kernels: Numerics and Applications.
Hosseini, Mahdi S; Plataniotis, Konstantinos N
2017-10-01
A generalized framework for numerical differentiation (ND) is proposed for constructing a finite impulse response (FIR) filter in closed form. The framework regulates the frequency response of ND filters for arbitrary derivative-order and cutoff frequency selected parameters relying on interpolating power polynomials and maximally flat design techniques. Compared with the state-of-the-art solutions, such as Gaussian kernels, the proposed ND filter is sharply localized in the Fourier domain with ripple-free artifacts. Here, we construct 2D MaxFlat kernels for image directional differentiation to calculate image differentials for arbitrary derivative order, cutoff level and steering angle. The resulted kernel library renders a new solution capable of delivering discrete approximation of gradients, Hessian, and higher-order tensors in numerous applications. We tested the utility of this library on three different imaging applications with main focus on the unsharp masking. The reported results highlight the high efficiency of the 2D MaxFlat kernel and its versatility with respect to robustness and parameter control accuracy.
Veto-Consensus Multiple Kernel Learning
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
Paramecium: An Extensible Object-Based Kernel
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
GRIM : Leveraging GPUs for Kernel integrity monitoring
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
Directory of Open Access Journals (Sweden)
Fengtao Wang
2018-01-01
Full Text Available Rotating machinery vibration signals are nonstationary and nonlinear under complicated operating conditions. It is meaningful to extract optimal features from raw signal and provide accurate fault diagnosis results. In order to resolve the nonlinear problem, an enhancement deep feature extraction method based on Gaussian radial basis kernel function and autoencoder (AE is proposed. Firstly, kernel function is employed to enhance the feature learning capability, and a new AE is designed termed kernel AE (KAE. Subsequently, a deep neural network is constructed with one KAE and multiple AEs to extract inherent features layer by layer. Finally, softmax is adopted as the classifier to accurately identify different bearing faults, and error backpropagation algorithm is used to fine-tune the model parameters. Aircraft engine intershaft bearing vibration data are used to verify the method. The results confirm that the proposed method has a better feature extraction capability, requires fewer iterations, and has a higher accuracy than standard methods using a stacked AE.
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.
Directory of Open Access Journals (Sweden)
Violeta Andjelković
2011-06-01
Full Text Available Molecular and metabolic response of plants to a combination of two abiotic stresses is unique and cannot be directly extrapolated from the response of plants to each of the stresses individually. cDNA macroarray has become a useful tool to analyze expression profiles and compare the similarities and differences of various expression patterns. A macroarray of approximately 2,500 maize (Zea mays L. cDNAs was used for transcriptome profiling in response to single and simultaneous application of water and high temperature stress of maize developing kernels at 15 days after pollination. All stress treatments (water stress-WS, heat stress-HS and their combined application-CS induced changes in expression of 106 transcripts with 54 up-regulated and 52 down-regulated. There were 11 up-regulated and 15 down-regulated transcripts in common for all three stresses. Although these common transcripts showed existence of a mutual mechanism in stress response, the 23 transcripts induced only in CS indicate that plants responded in a different manner when exposed to simultaneous effects of both stresses. A glimpse of functions regulated under WS, HS and CS is provided, and also the common and different responses between individual and simultaneous stresses.A resposta molecular e metabólica de plantas a uma combinação de dois estresses abióticos é singular, e não pode ser diretamente extrapolada da resposta das plantas a cada um dos estresses individualmente. O macroarranjo do cDNA, tornou-se uma ferramenta útil para analisar os perfís de expressão e comparar as similaridades e diferenças de vários padrões de expressão. Um macroarranjo de 2.500 cDNAs de milho (Zea mays L. foi usado para traçar um perfil de transcriptoma em resposta ao stress ocasionado por uma única e simultânea aplicação de água e alta temperatura em espigas em desenvolvimento, 15 dias após a polinização. Todos os tratamentos de stress (stress de água - SA, stress de calor
Energy Technology Data Exchange (ETDEWEB)
Palmer, J.; Parkins, L.; Shaw, P.; Watkins, R. [Databuild, Birmingham (United Kingdom)
1994-12-31
The adequate ventilation of houses is essential for both the occupants and the building fabric. As air-tightness standards increase, background infiltration levels decrease and extra ventilation has to be designed into the building. Passive stack ventilation has many advantages - particularly when employed in low cost housing schemes -but it is essential that it performs satisfactorily. This paper give the results from monitoring two passive stack ventilation schemes. One scheme was a retrofit into refurbished local authority houses in which a package of energy efficiency measures had been taken and condensation had been a problem. The other series of tests were conducted on a new installation in a Housing Association development. Nine houses were monitored each of which had at least two passive vents. The results show air flow rates by the passive ducts equivalent to approximately 1 room air change per hour. The air flow in the ducts was influenced by both, internal to external temperature difference and wind speed and direction. (author)
Asymmetric Flexible Supercapacitor Stack
Directory of Open Access Journals (Sweden)
Leela Mohana Reddy A
2008-01-01
Full Text Available AbstractElectrical double layer supercapacitor is very significant in the field of electrical energy storage which can be the solution for the current revolution in the electronic devices like mobile phones, camera flashes which needs flexible and miniaturized energy storage device with all non-aqueous components. The multiwalled carbon nanotubes (MWNTs have been synthesized by catalytic chemical vapor deposition technique over hydrogen decrepitated Mischmetal (Mm based AB3alloy hydride. The polymer dispersed MWNTs have been obtained by insitu polymerization and the metal oxide/MWNTs were synthesized by sol-gel method. Morphological characterizations of polymer dispersed MWNTs have been carried out using scanning electron microscopy (SEM, transmission electron microscopy (TEM and HRTEM. An assymetric double supercapacitor stack has been fabricated using polymer/MWNTs and metal oxide/MWNTs coated over flexible carbon fabric as electrodes and nafion®membrane as a solid electrolyte. Electrochemical performance of the supercapacitor stack has been investigated using cyclic voltammetry, galvanostatic charge-discharge, and electrochemical impedance spectroscopy.
Pareto-path multitask multiple kernel learning.
Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C
2015-01-01
A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.
Radiation-Tolerant Intelligent Memory Stack - RTIMS
Ng, Tak-kwong; Herath, Jeffrey A.
2011-01-01
This innovation provides reconfigurable circuitry and 2-Gb of error-corrected or 1-Gb of triple-redundant digital memory in a small package. RTIMS uses circuit stacking of heterogeneous components and radiation shielding technologies. A reprogrammable field-programmable gate array (FPGA), six synchronous dynamic random access memories, linear regulator, and the radiation mitigation circuits are stacked into a module of 42.7 42.7 13 mm. Triple module redundancy, current limiting, configuration scrubbing, and single- event function interrupt detection are employed to mitigate radiation effects. The novel self-scrubbing and single event functional interrupt (SEFI) detection allows a relatively soft FPGA to become radiation tolerant without external scrubbing and monitoring hardware
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.
Multi-board kernel communication using socket programming for embedded applications
Mishra, Ashish; Girdhar, Neha; Krishnia, Nikita
2016-03-01
It is often seen in large application projects, there is a need to communicate between two different processors or two different kernels. The aim of this paper is to communicate between two different kernels and use efficient method to do so. The TCP/IP protocol is implemented to communicate between two boards via the Ethernet port and use lwIP (lightweight IP) stack, which is a smaller independent implementation of the TCP/IP stack suitable for use in embedded systems. While retaining TCP/IP functionality, lwIP stack reduces the use of memory and even size of the code. In this process of communication we made Raspberry pi as an active client and Field programmable gate array(FPGA) board as a passive server and they are allowed to communicate via Ethernet. Three applications based on TCP/IP client-server network communication have been implemented. The Echo server application is used to communicate between two different kernels of two different boards. Socket programming is used as it is independent of platform and programming language used. TCP transmit and receive throughput test applications are used to measure maximum throughput of the transmission of data. These applications are based on communication to an open source tool called iperf. It is used to measure the throughput transmission rate by sending or receiving some constant piece of data to the client or server according to the test application.
Three-Dimensional Sensitivity Kernels of Z/H Amplitude Ratios of Surface and Body Waves
Bao, X.; Shen, Y.
2017-12-01
The ellipticity of Rayleigh wave particle motion, or Z/H amplitude ratio, has received increasing attention in inversion for shallow Earth structures. Previous studies of the Z/H ratio assumed one-dimensional (1D) velocity structures beneath the receiver, ignoring the effects of three-dimensional (3D) heterogeneities on wave amplitudes. This simplification may introduce bias in the resulting models. Here we present 3D sensitivity kernels of the Z/H ratio to Vs, Vp, and density perturbations, based on finite-difference modeling of wave propagation in 3D structures and the scattering-integral method. Our full-wave approach overcomes two main issues in previous studies of Rayleigh wave ellipticity: (1) the finite-frequency effects of wave propagation in 3D Earth structures, and (2) isolation of the fundamental mode Rayleigh waves from Rayleigh wave overtones and converted Love waves. In contrast to the 1D depth sensitivity kernels in previous studies, our 3D sensitivity kernels exhibit patterns that vary with azimuths and distances to the receiver. The laterally-summed 3D sensitivity kernels and 1D depth sensitivity kernels, based on the same homogeneous reference model, are nearly identical with small differences that are attributable to the single period of the 1D kernels and a finite period range of the 3D kernels. We further verify the 3D sensitivity kernels by comparing the predictions from the kernels with the measurements from numerical simulations of wave propagation for models with various small-scale perturbations. We also calculate and verify the amplitude kernels for P waves. This study shows that both Rayleigh and body wave Z/H ratios provide vertical and lateral constraints on the structure near the receiver. With seismic arrays, the 3D kernels afford a powerful tool to use the Z/H ratios to obtain accurate and high-resolution Earth models.
Stacked Switched Capacitor Energy Buffer Architecture
Chen, Minjie; Perreault, David J.; Afridi, Khurram
2012-01-01
Electrolytic capacitors are often used for energy buffering applications, including buffering between single-phase ac and dc. While these capacitors have high energy density compared to film and ceramic capacitors, their life is limited. This paper presents a stacked switched capacitor (SSC) energy buffer architecture and some of its topological embodiments, which when used with longer life film capacitors overcome this limitation while achieving effective energy densities comparable to elect...
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....
Judge, Gary
2013-01-01
Get to grips with a new technology, understand what it is and what it can do for you, and then get to work with the most important features and tasks. A fast-paced, example-based approach guide for learning BlueStacks.This book is for anyone with a Mac or PC who wants to run Android apps on their computer. Whether you want to play games that are freely available for Android but not your computer, or you want to try apps before you install them on a physical device or use it as a development tool, this book will show you how. No previous experience is needed as this is written in plain English
RKRD: Runtime Kernel Rootkit Detection
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.
Kernel Bayesian ART and ARTMAP.
Masuyama, Naoki; Loo, Chu Kiong; Dawood, Farhan
2018-02-01
Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.
Assessing Elementary Algebra with STACK
Sangwin, Christopher J.
2007-01-01
This paper concerns computer aided assessment (CAA) of mathematics in which a computer algebra system (CAS) is used to help assess students' responses to elementary algebra questions. Using a methodology of documentary analysis, we examine what is taught in elementary algebra. The STACK CAA system, http://www.stack.bham.ac.uk/, which uses the CAS…
HPC Software Stack Testing Framework
Energy Technology Data Exchange (ETDEWEB)
2017-07-27
The HPC Software stack testing framework (hpcswtest) is used in the INL Scientific Computing Department to test the basic sanity and integrity of the HPC Software stack (Compilers, MPI, Numerical libraries and Applications) and to quickly discover hard failures, and as a by-product it will indirectly check the HPC infrastructure (network, PBS and licensing servers).
Nonlinear Deep Kernel Learning for Image Annotation.
Jiu, Mingyuan; Sahbi, Hichem
2017-02-08
Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.
A framework for dense triangular matrix kernels on various manycore architectures
Charara, Ali
2017-06-06
We present a new high-performance framework for dense triangular Basic Linear Algebra Subroutines (BLAS) kernels, ie, triangular matrix-matrix multiplication (TRMM) and triangular solve (TRSM), on various manycore architectures. This is an extension of a previous work on a single GPU by the same authors, presented at the EuroPar\\'16 conference, in which we demonstrated the effectiveness of recursive formulations in enhancing the performance of these kernels. In this paper, the performance of triangular BLAS kernels on a single GPU is further enhanced by implementing customized in-place CUDA kernels for TRMM and TRSM, which are called at the bottom of the recursion. In addition, a multi-GPU implementation of TRMM and TRSM is proposed and we show an almost linear performance scaling, as the number of GPUs increases. Finally, the algorithmic recursive formulation of these triangular BLAS kernels is in fact oblivious to the targeted hardware architecture. We, therefore, port these recursive kernels to homogeneous x86 hardware architectures by relying on the vendor optimized BLAS implementations. Results reported on various hardware architectures highlight a significant performance improvement against state-of-the-art implementations. These new kernels are freely available in the KAUST BLAS (KBLAS) open-source library at https://github.com/ecrc/kblas.
A Heterogeneous Multi-core Architecture with a Hardware Kernel for Control Systems
DEFF Research Database (Denmark)
Li, Gang; Guan, Wei; Sierszecki, Krzysztof
2012-01-01
Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints. This pa......Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints......). Second, a heterogeneous multi-core architecture is investigated, focusing on its performance in relation to hard real-time constraints and predictable behavior. Third, the hardware implementation of HARTEX is designated to support the heterogeneous multi-core architecture. This hardware kernel has...... several advantages over a similar kernel implemented in software: higher-speed processing capability, parallel computation, and separation between the kernel itself and the applications being run. A microbenchmark has been used to compare the hardware kernel with the software kernel, and compare...
Electrochemical Detection in Stacked Paper Networks.
Liu, Xiyuan; Lillehoj, Peter B
2015-08-01
Paper-based electrochemical biosensors are a promising technology that enables rapid, quantitative measurements on an inexpensive platform. However, the control of liquids in paper networks is generally limited to a single sample delivery step. Here, we propose a simple method to automate the loading and delivery of liquid samples to sensing electrodes on paper networks by stacking multiple layers of paper. Using these stacked paper devices (SPDs), we demonstrate a unique strategy to fully immerse planar electrodes by aqueous liquids via capillary flow. Amperometric measurements of xanthine oxidase revealed that electrochemical sensors on four-layer SPDs generated detection signals up to 75% higher compared with those on single-layer paper devices. Furthermore, measurements could be performed with minimal user involvement and completed within 30 min. Due to its simplicity, enhanced automation, and capability for quantitative measurements, stacked paper electrochemical biosensors can be useful tools for point-of-care testing in resource-limited settings. © 2015 Society for Laboratory Automation and Screening.
Optimizing Multiple Kernel Learning for the Classification of UAV Data
Directory of Open Access Journals (Sweden)
Caroline M. Gevaert
2016-12-01
Full Text Available Unmanned Aerial Vehicles (UAVs are capable of providing high-quality orthoimagery and 3D information in the form of point clouds at a relatively low cost. Their increasing popularity stresses the necessity of understanding which algorithms are especially suited for processing the data obtained from UAVs. The features that are extracted from the point cloud and imagery have different statistical characteristics and can be considered as heterogeneous, which motivates the use of Multiple Kernel Learning (MKL for classification problems. In this paper, we illustrate the utility of applying MKL for the classification of heterogeneous features obtained from UAV data through a case study of an informal settlement in Kigali, Rwanda. Results indicate that MKL can achieve a classification accuracy of 90.6%, a 5.2% increase over a standard single-kernel Support Vector Machine (SVM. A comparison of seven MKL methods indicates that linearly-weighted kernel combinations based on simple heuristics are competitive with respect to computationally-complex, non-linear kernel combination methods. We further underline the importance of utilizing appropriate feature grouping strategies for MKL, which has not been directly addressed in the literature, and we propose a novel, automated feature grouping method that achieves a high classification accuracy for various MKL methods.
Annular feed air breathing fuel cell stack
Wilson, Mahlon S.
1996-01-01
A stack of polymer electrolyte fuel cells is formed from a plurality of unit cells where each unit cell includes fuel cell components defining a periphery and distributed along a common axis, where the fuel cell components include a polymer electrolyte membrane, an anode and a cathode contacting opposite sides of the membrane, and fuel and oxygen flow fields contacting the anode and the cathode, respectively, wherein the components define an annular region therethrough along the axis. A fuel distribution manifold within the annular region is connected to deliver fuel to the fuel flow field in each of the unit cells. In a particular embodiment, a single bolt through the annular region clamps the unit cells together. In another embodiment, separator plates between individual unit cells have an extended radial dimension to function as cooling fins for maintaining the operating temperature of the fuel cell stack.
Theory of reproducing kernels and applications
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...
kFOIL: Learning simple relational kernels
Landwehr, Niels; Passerini, Andrea; De Raedt, Luc; Frasconi, Paolo
2006-01-01
A novel and simple combination of inductive logic programming with kernel methods is presented. The kFOIL algorithm integrates the well-known inductive logic programming system FOIL with kernel methods. The feature space is constructed by leveraging FOIL search for a set of relevant clauses. The search is driven by the performance obtained by a support vector machine based on the resulting kernel. In this way, kFOIL implements a dynamic propositionalization approach. Both classification an...
Convergence of barycentric coordinates to barycentric kernels
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.
Environmental assessment of phosphogypsum stacks
International Nuclear Information System (INIS)
Odat, M.; Al-Attar, L.; Raja, G.; Abdul Ghany, B.
2008-03-01
Phosphogypsum is one of the most important by-products of phosphate fertilizer industry. It is kept in large stacks to the west of Homs city. Storing Phosphogypsum as open stacks exposed to various environmental effects, wind and rain, may cause pollution of the surrounding ecosystem (soil, plant, water and air). This study was carried out in order to assess the environmental impact of Phosphogypsum stacks on the surrounding ecosystem. The obtained results show that Phosphogypsum stacks did not increase the concentration of radionuclides, i.e. Radon-222 and Radium-226, the external exposed dose of gamma rays, as well as the concentration of heavy metals in the components of the ecosystem, soil, plant, water and air, as their concentrations did not exceed the permissible limits. However, the concentration of fluorine in the upper layer of soil, located to the east of the Phosphogypsum stacks, increased sufficiently, especially in the dry period of the year. Also, the concentration of fluoride in plants growing up near-by the Phosphogypsum stacks was too high, exceeded the permissible levels. This was reflected in poising plants and animals, feeding on the plants. Consequently, increasing the concentration of fluoride in soil and plants is the main impact of Phosphogypsum stacks on the surrounding ecosystem. Minimising this effect could be achieved by establishing a 50 meter wide protection zone surrounding the Phosphogypsum stacks, which has to be planted with non palatable trees, such as pine and cypress, forming wind barriers. Increasing the concentrations of heavy metals and fluoride in infiltrated water around the stacks was high; hence cautions must be taken to prevent its usage in any application or disposal in adjacent rivers and leaks.(author)
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
Hilbertian kernels and spline functions
Atteia, M
1992-01-01
In this monograph, which is an extensive study of Hilbertian approximation, the emphasis is placed on spline functions theory. The origin of the book was an effort to show that spline theory parallels Hilbertian Kernel theory, not only for splines derived from minimization of a quadratic functional but more generally for splines considered as piecewise functions type. Being as far as possible self-contained, the book may be used as a reference, with information about developments in linear approximation, convex optimization, mechanics and partial differential equations.
PieceStack: Toward Better Understanding of Stacked Graphs.
Wu, Tongshuang; Wu, Yingcai; Shi, Conglei; Qu, Huamin; Cui, Weiwei
2016-02-24
Stacked graphs have been widely adopted in various fields, because they are capable of hierarchically visualizing a set of temporal sequences as well as their aggregation. However, because of visual illusion issues, connections between overly-detailed individual layers and overly-generalized aggregation are intercepted. Consequently, information in this area has yet to be fully excavated. Thus, we present PieceStack in this paper, to reveal the relevance of stacked graphs in understanding intrinsic details of their displayed shapes. This new visual analytic design interprets the ways through which aggregations are generated with individual layers by interactively splitting and re-constructing the stacked graphs. A clustering algorithm is designed to partition stacked graphs into sub-aggregated pieces based on trend similarities of layers. We then visualize the pieces with augmented encoding to help analysts decompose and explore the graphs with respect to their interests. Case studies and a user study are conducted to demonstrate the usefulness of our technique in understanding the formation of stacked graphs.
Simulation Of Networking Protocols On Software Emulated Network Stack
Directory of Open Access Journals (Sweden)
Hrushikesh Nimkar
2015-08-01
Full Text Available With the increasing number and complexity of network based applications the need to easy configuration development and integration of network applications has taken a high precedence. Trivial activities such as configuration can be carried out efficiently if network services are software based rather than hardware based. Project aims at enabling the network engineers to easily include network functionalities into hisher configuration and define hisher own network stack without using the kernel network stack. Having thought of this we have implemented two functionalities UPNP and MDNS. The multicast Domain Name System MDNS resolves host names to IP addresses within small ad-hoc networks and without having need of special DNS server and its configuration. MDNS application provides every host with functionality to register itself to the router make a multicast DNS request and its resolution. To make adding network devices and networked programs to a network as easy as it is to plug in a piece of hardware into a PC we make use of UPnP. The devices and programs find out about the network setup and other networked devices and programs through discovery and advertisements of services and configure themselves accordingly. UPNP application provides every host with functionality of discovering services of other hosts and serving requests on demand. To implement these applications we have used snabbswitch framework which an open source virtualized ethernet networking stack.
Thyristor stack for pulsed inductive plasma generation.
Teske, C; Jacoby, J; Schweizer, W; Wiechula, J
2009-03-01
A thyristor stack for pulsed inductive plasma generation has been developed and tested. The stack design includes a free wheeling diode assembly for current reversal. Triggering of the device is achieved by a high side biased, self supplied gate driver unit using gating energy derived from a local snubber network. The structure guarantees a hard firing gate pulse for the required high dI/dt application. A single fiber optic command is needed to achieve a simultaneous turn on of the thyristors. The stack assembly is used for switching a series resonant circuit with a ringing frequency of 30 kHz. In the prototype pulsed power system described here an inductive discharge has been generated with a pulse duration of 120 micros and a pulse energy of 50 J. A maximum power transfer efficiency of 84% and a peak power of 480 kW inside the discharge were achieved. System tests were performed with a purely inductive load and an inductively generated plasma acting as a load through transformer action at a voltage level of 4.1 kV, a peak current of 5 kA, and a current switching rate of 1 kA/micros.
7 CFR 51.1403 - Kernel color classification.
2010-01-01
... models of pecan kernels, illustrate the color intensities implied by the terms “golden,” “light brown... Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color...
Sentiment classification with interpolated information diffusion kernels
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
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.
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)
Modelling Issues in Kernel Ridge Regression
P. Exterkate (Peter)
2011-01-01
textabstractKernel 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
Kernel method for corrections to scaling.
Harada, Kenji
2015-07-01
Scaling analysis, in which one infers scaling exponents and a scaling function in a scaling law from given data, is a powerful tool for determining universal properties of critical phenomena in many fields of science. However, there are corrections to scaling in many cases, and then the inference problem becomes ill-posed by an uncontrollable irrelevant scaling variable. We propose a new kernel method based on Gaussian process regression to fix this problem generally. We test the performance of the new kernel method for some example cases. In all cases, when the precision of the example data increases, inference results of the new kernel method correctly converge. Because there is no limitation in the new kernel method for the scaling function even with corrections to scaling, unlike in the conventional method, the new kernel method can be widely applied to real data in critical phenomena.
Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection
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.
Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System
Directory of Open Access Journals (Sweden)
Chunmei Liu
2016-01-01
Full Text Available This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour.
Bayesian Kernel Mixtures for Counts.
Canale, Antonio; Dunson, David B
2011-12-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online.
Karmeshu; Gupta, Varun; Kadambari, K V
2011-06-01
A single neuronal model incorporating distributed delay (memory)is proposed. The stochastic model has been formulated as a Stochastic Integro-Differential Equation (SIDE) which results in the underlying process being non-Markovian. A detailed analysis of the model when the distributed delay kernel has exponential form (weak delay) has been carried out. The selection of exponential kernel has enabled the transformation of the non-Markovian model to a Markovian model in an extended state space. For the study of First Passage Time (FPT) with exponential delay kernel, the model has been transformed to a system of coupled Stochastic Differential Equations (SDEs) in two-dimensional state space. Simulation studies of the SDEs provide insight into the effect of weak delay kernel on the Inter-Spike Interval(ISI) distribution. A measure based on Jensen-Shannon divergence is proposed which can be used to make a choice between two competing models viz. distributed delay model vis-á-vis LIF model. An interesting feature of the model is that the behavior of (CV(t))((ISI)) (Coefficient of Variation) of the ISI distribution with respect to memory kernel time constant parameter η reveals that neuron can switch from a bursting state to non-bursting state as the noise intensity parameter changes. The membrane potential exhibits decaying auto-correlation structure with or without damped oscillatory behavior depending on the choice of parameters. This behavior is in agreement with empirically observed pattern of spike count in a fixed time window. The power spectral density derived from the auto-correlation function is found to exhibit single and double peaks. The model is also examined for the case of strong delay with memory kernel having the form of Gamma distribution. In contrast to fast decay of damped oscillations of the ISI distribution for the model with weak delay kernel, the decay of damped oscillations is found to be slower for the model with strong delay kernel.
RPLsh: An Interactive Shell for Stack-based Numerical Computation
Rauch, Kevin P.
RPL shell or RPLsh, is an interactive numerical shell designed to combine the convenience of a hand-held calculator with the computational power and advanced numerical functionality of a workstation. The user interface is modelled after stack-based scientific calculators such as those made by Hewlett-Packard RPL is the name of the Forth-like programming language used in the HP 48 series), but includes many features not found in hand-held devices, such as a multi-threaded kernel with job control, integrated extended precision arithmetic, a large library of special functions, and a dynamic, resizable window display. As a native C/C++ application, it is over 1000 times faster than HP 48 emulators (e.g. Emu48 ) in simple benchmarks; for extended precision numerical analysis, its performance can exceed that of Mathematica by similar amounts. Current development focuses on interactive user functionality, with comprehensive programming and debugging support to follow.
Time-predictable Stack Caching
DEFF Research Database (Denmark)
Abbaspourseyedi, Sahar
completely. Thus, in systems with hard deadlines the worst-case execution time (WCET) of the real-time software running on them needs to be bounded. Modern architectures use features such as pipelining and caches for improving the average performance. These features, however, make the WCET analysis more...... addresses, provides an opportunity to predict and tighten the WCET of accesses to data in caches. In this thesis, we introduce the time-predictable stack cache design and implementation within a time-predictable processor. We introduce several optimizations to our design for tightening the WCET while...... keeping the timepredictability of the design intact. Moreover, we provide a solution for reducing the cost of context switching in a system using the stack cache. In design of these caches, we use custom hardware and compiler support for delivering time-predictable stack data accesses. Furthermore...
Glassy carbon based supercapacitor stacks
Energy Technology Data Exchange (ETDEWEB)
Baertsch, M.; Braun, A.; Koetz, R.; Haas, O. [Paul Scherrer Inst. (PSI), Villigen (Switzerland)
1997-06-01
Considerable effort is being made to develop electrochemical double layer capacitors (EDLC) that store relatively large quantities of electrical energy and possess at the same time a high power density. Our previous work has shown that glassy carbon is suitable as a material for capacitor electrodes concerning low resistance and high capacity requirements. We present the development of bipolar electrochemical glassy carbon capacitor stacks of up to 3 V. Bipolar stacks are an efficient way to meet the high voltage and high power density requirements for traction applications. Impedance and cyclic voltammogram measurements are reported here and show the frequency response of a 1, 2, and 3 V stack. (author) 3 figs., 1 ref..
A Visual Approach to Investigating Shared and Global Memory Behavior of CUDA Kernels
Rosen, Paul
2013-06-01
We present an approach to investigate the memory behavior of a parallel kernel executing on thousands of threads simultaneously within the CUDA architecture. Our top-down approach allows for quickly identifying any significant differences between the execution of the many blocks and warps. As interesting warps are identified, we allow further investigation of memory behavior by visualizing the shared memory bank conflicts and global memory coalescence, first with an overview of a single warp with many operations and, subsequently, with a detailed view of a single warp and a single operation. We demonstrate the strength of our approach in the context of a parallel matrix transpose kernel and a parallel 1D Haar Wavelet transform kernel. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.
Mandaji, Marcos; Rübensam, Gabriel; Hoff, Rodrigo Barcellos; Hillebrand, Sandro; Carrilho, Emanuel; Kist, Tarso Ledur
2009-05-01
In a previous work [M. Mandaji, et al., this issue] a sample stacking method was theoretically modeled and experimentally demonstrated for analytes with low dpK(a)/dT (analytes carrying carboxylic groups) and BGEs with high dpH/dT (high pH-temperature-coefficients). In that work, buffer pH was modulated with temperature, inducing electrophoretic mobility changes in the analytes. In the present work, the opposite conditions are studied and tested, i.e. analytes with high dpK(a)/dT and BGEs that exhibit low dpH/dT. It is well known that organic bases such as amines, imidazoles, and benzimidazoles exhibit high dpK(a)/dT. Temperature variations induce instantaneous changes on the basicity of these and other basic groups. Therefore, the electrophoretic velocity of some analytes changes abruptly when temperature variations are applied along the capillary. This is true only if BGE pH remains constant or if it changes in the opposite direction of pK(a) of the analyte. The presence of hot and cold sections along the capillary also affects local viscosity, conductivity, and electric field strength. The effect of these variables on electrophoretic velocity and band stacking efficacy was also taken into account in the theoretical model presented. Finally, this stacking method is demonstrated for lysine partially derivatized with naphthalene-2,3-dicarboxaldehyde. In this case, the amino group of the lateral chain was left underivatized and only the alpha amino group was derivatized. Therefore, the basicity of the lateral amino group, and consequently the electrophoretic mobility, was modulated with temperature while the pH of the buffer used remained unchanged.
Mandaji, Marcos; Rübensam, Gabriel; Hoff, Rodrigo Barcellos; Hillebrand, Sandro; Carrilho, Emanuel; Kist, Tarso Ledur
2009-05-01
The possibility to compress analyte bands at the beginning of CE runs has many advantages. Analytes at low concentration can be analyzed with high signal-to-noise ratios by using the so-called sample stacking methods. Moreover, sample injections with very narrow initial band widths (small initial standard deviations) are sometimes useful, especially if high resolutions among the bands are required in the shortest run time. In the present work, a method of sample stacking is proposed and demonstrated. It is based on BGEs with high thermal sensitive pHs (high dpH/dT) and analytes with low dpK(a)/dT. High thermal sensitivity means that the working pK(a) of the BGE has a high dpK(a)/dT in modulus. For instance, Tris and Ethanolamine have dpH/dT=-0.028/ degrees C and -0.029/ degrees C, respectively, whereas carboxylic acids have low dpK(a)/dT values, i.e. in the -0.002/ degrees C to+0.002/ degrees C range. The action of cooling and heating sections along the capillary during the runs affects also the local viscosity, conductivity, and electric field strength. The effect of these variables on electrophoretic velocity and band compression is theoretically calculated using a simple model. Finally, this stacking method was demonstrated for amino acids derivatized with naphthalene-2,3-dicarboxaldehyde and fluorescamine using a temperature difference of 70 degrees C between two neighbor sections and Tris as separation buffer. In this case, the BGE has a high pH thermal coefficient whereas the carboxylic groups of the analytes have low pK(a) thermal coefficients. The application of these dynamic thermal gradients increased peak height by a factor of two (and decreased the standard deviations of peaks by a factor of two) of aspartic acid and glutamic acid derivatized with naphthalene-2,3-dicarboxaldehyde and serine derivatized with fluorescamine. The effect of thermal compression of bands was not observed when runs were accomplished using phosphate buffer at pH 7 (negative
Simulating Small-Scale Object Stacking Using Stack Stability
DEFF Research Database (Denmark)
Kronborg Thomsen, Kasper; Kraus, Martin
2015-01-01
This paper presents an extension system to a closed-source, real-time physics engine for improving structured stacking behavior with small-scale objects such as wooden toy bricks. The proposed system was implemented and evaluated. The tests showed that the system is able to simulate several common...
Optimizing memory-bound SYMV kernel on GPU hardware accelerators
Abdelfattah, Ahmad
2013-01-01
Hardware accelerators are becoming ubiquitous high performance scientific computing. They are capable of delivering an unprecedented level of concurrent execution contexts. High-level programming language extensions (e.g., CUDA), profiling tools (e.g., PAPI-CUDA, CUDA Profiler) are paramount to improve productivity, while effectively exploiting the underlying hardware. We present an optimized numerical kernel for computing the symmetric matrix-vector product on nVidia Fermi GPUs. Due to its inherent memory-bound nature, this kernel is very critical in the tridiagonalization of a symmetric dense matrix, which is a preprocessing step to calculate the eigenpairs. Using a novel design to address the irregular memory accesses by hiding latency and increasing bandwidth, our preliminary asymptotic results show 3.5x and 2.5x fold speedups over the similar CUBLAS 4.0 kernel, and 7-8% and 30% fold improvement over the Matrix Algebra on GPU and Multicore Architectures (MAGMA) library in single and double precision arithmetics, respectively. © 2013 Springer-Verlag.
Distance Based Multiple Kernel ELM: A Fast Multiple Kernel Learning Approach
Directory of Open Access Journals (Sweden)
Chengzhang Zhu
2015-01-01
Full Text Available We propose a distance based multiple kernel extreme learning machine (DBMK-ELM, which provides a two-stage multiple kernel learning approach with high efficiency. Specifically, DBMK-ELM first projects multiple kernels into a new space, in which new instances are reconstructed based on the distance of different sample labels. Subsequently, an l2-norm regularization least square, in which the normal vector corresponds to the kernel weights of a new kernel, is trained based on these new instances. After that, the new kernel is utilized to train and test extreme learning machine (ELM. Extensive experimental results demonstrate the superior performance of the proposed DBMK-ELM in terms of the accuracy and the computational cost.
Pressurized electrolysis stack with thermal expansion capability
Bourgeois, Richard Scott
2015-07-14
The present techniques provide systems and methods for mounting an electrolyzer stack in an outer shell so as to allow for differential thermal expansion of the electrolyzer stack and shell. Generally, an electrolyzer stack may be formed from a material with a high coefficient of thermal expansion, while the shell may be formed from a material having a lower coefficient of thermal expansion. The differences between the coefficients of thermal expansion may lead to damage to the electrolyzer stack as the shell may restrain the thermal expansion of the electrolyzer stack. To allow for the differences in thermal expansion, the electrolyzer stack may be mounted within the shell leaving a space between the electrolyzer stack and shell. The space between the electrolyzer stack and the shell may be filled with a non-conductive fluid to further equalize pressure inside and outside of the electrolyzer stack.
3D-accelerated, stack-of-spirals acquisitions and reconstruction of arterial spin labeling MRI.
Chang, Yulin V; Vidorreta, Marta; Wang, Ze; Detre, John A
2017-10-01
The goal of this study was to develop a 3D acceleration and reconstruction method to improve image quality and resolution of background-suppressed arterial spin-labeled perfusion MRI. Accelerated acquisition was implemented in all three k-space dimensions in a stack-of-spirals readout using variable density spirals and partition undersampling. A single 3D self-consistent parallel imaging (SPIRiT) kernel was calibrated and iteratively applied to reconstruct each imaging volume. Whole-brain (including cerebellum) perfusion imaging was obtained at 3-mm isotropic resolution (nominal) using single- and 2-shot acquisitions and at 2-mm isotropic resolution (nominal) using four-shot acquisitions, achieving effective acceleration factors between 5.5 and 6.6. The signal-to-noise (SNR) performance of 3D SPIRiT was evaluated. The temporal SNR (tSNR) of the cerebral blood flow (CBF) maps and the gray/white matter CBF ratios were quantified. The readout of the arterial spin labeling (ASL) sequence was significantly shortened with acceleration. The CBF values were consistent between accelerated and fully sampled ASL. With shorter spiral interleaves and shorter echo trains, the accelerated images demonstrated reduced blurring and signal dropout in regions with high susceptibility gradients, resulting in improved image quality and increased gray/white matter CBF ratios. The shortened readout was accompanied by a corresponding decrease in tSNR. The 3D acceleration and reconstruction allow a rapid whole-brain readout that improved the quality of ASL perfusion imaging. Magn Reson Med 78:1405-1419, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.
NLO corrections to the Kernel of the BKP-equations
International Nuclear Information System (INIS)
Bartels, J.; Lipatov, L.N.; Vacca, G.P.
2012-01-01
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→3 kernel, computed in the tree approximation.
21 CFR 176.350 - Tamarind seed kernel powder.
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... the provisions of this section. (a) Tamarind seed kernel powder is the ground kernel of tamarind seed...
Higher-order Gaussian kernel in bootstrap boosting algorithm ...
African Journals Online (AJOL)
The bootstrap boosting algorithm is a bias reduction scheme. The adoption of higher-order Gaussian kernel in a bootstrap boosting algorithm in kernel density estimation was investigated. The algorithm used the higher-order. Gaussian kernel instead of the regular fixed kernels. A comparison of the scheme with existing ...
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...
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 ...
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 ...
Higher-Order Hybrid Gaussian Kernel in Meshsize Boosting Algorithm
African Journals Online (AJOL)
In this paper, we shall use higher-order hybrid Gaussian kernel in a meshsize boosting algorithm in kernel density estimation. Bias reduction is guaranteed in this scheme like other existing schemes but uses the higher-order hybrid Gaussian kernel instead of the regular fixed kernels. A numerical verification of this scheme ...
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.
Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine
Directory of Open Access Journals (Sweden)
Xiaoou Li
2014-07-01
Full Text Available In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI systems. The presented BCI approach included three stages: (1 a pre-processing step was performed to improve the general signal quality of the EEG; (2 the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3 a multiple kernel learning support vector machine (MKL-SVM based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI, especially for mental task classification and identifying suitable brain impairment candidates.
Digital signal processing with kernel methods
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...
National Aeronautics and Space Administration — This data set includes the complete set of Hayabusa SPICE data files (kernel files'') for the surveying and collection phases of the mission. The SPICE data files,...
Ensemble Approach to Building Mercer Kernels
National Aeronautics and Space Administration — 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...
Multiple Kernel Spectral Regression for Dimensionality Reduction
Directory of Open Access Journals (Sweden)
Bing Liu
2013-01-01
Full Text Available Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL into SR for dimensionality reduction. The proposed approach (termed MKL-SR seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm.
National Aeronautics and Space Administration — This data set includes the complete set of MESSENGER SPICE data files (''kernel files''), which can be accessed using SPICE software. The SPICE data contains...
National Aeronautics and Space Administration — This data set includes the complete set of Cassini SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contains geometric...
National Aeronautics and Space Administration — This data set includes the complete set of NEAR SPICE data files (kernel files'), which can be accessed using SPICE software. The SPICE data contain geometric and...
National Aeronautics and Space Administration — This data set includes the complete set of Stardust SPICE data files (kernel files'), which can be accessed using SPICE software. The SPICE data contains geometric...
National Aeronautics and Space Administration — This data set includes the MSL SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contain geometric and other ancillary...
Stack semantics of type theory
DEFF Research Database (Denmark)
Coquand, Thierry; Mannaa, Bassel; Ruch, Fabian
2017-01-01
We give a model of dependent type theory with one univalent universe and propositional truncation interpreting a type as a stack, generalizing the groupoid model of type theory. As an application, we show that countable choice cannot be proved in dependent type theory with one univalent universe...
Multilayer Piezoelectric Stack Actuator Characterization
Sherrit, Stewart; Jones, Christopher M.; Aldrich, Jack B.; Blodget, Chad; Bao, Xioaqi; Badescu, Mircea; Bar-Cohen, Yoseph
2008-01-01
Future NASA missions are increasingly seeking to use actuators for precision positioning to accuracies of the order of fractions of a nanometer. For this purpose, multilayer piezoelectric stacks are being considered as actuators for driving these precision mechanisms. In this study, sets of commercial PZT stacks were tested in various AC and DC conditions at both nominal and extreme temperatures and voltages. AC signal testing included impedance, capacitance and dielectric loss factor of each actuator as a function of the small-signal driving sinusoidal frequency, and the ambient temperature. DC signal testing includes leakage current and displacement as a function of the applied DC voltage. The applied DC voltage was increased to over eight times the manufacturers' specifications to investigate the correlation between leakage current and breakdown voltage. Resonance characterization as a function of temperature was done over a temperature range of -180C to +200C which generally exceeded the manufacturers' specifications. In order to study the lifetime performance of these stacks, five actuators from one manufacturer were driven by a 60volt, 2 kHz sine-wave for ten billion cycles. The tests were performed using a Lab-View controlled automated data acquisition system that monitored the waveform of the stack electrical current and voltage. The measurements included the displacement, impedance, capacitance and leakage current and the analysis of the experimental results will be presented.
Open stack thermal battery tests
Energy Technology Data Exchange (ETDEWEB)
Long, Kevin N. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Roberts, Christine C. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Grillet, Anne M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Headley, Alexander J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Fenton, Kyle [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wong, Dennis [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ingersoll, David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-04-17
We present selected results from a series of Open Stack thermal battery tests performed in FY14 and FY15 and discuss our findings. These tests were meant to provide validation data for the comprehensive thermal battery simulation tools currently under development in Sierra/Aria under known conditions compared with as-manufactured batteries. We are able to satisfy this original objective in the present study for some test conditions. Measurements from each test include: nominal stack pressure (axial stress) vs. time in the cold state and during battery ignition, battery voltage vs. time against a prescribed current draw with periodic pulses, and images transverse to the battery axis from which cell displacements are computed. Six battery configurations were evaluated: 3, 5, and 10 cell stacks sandwiched between 4 layers of the materials used for axial thermal insulation, either Fiberfrax Board or MinK. In addition to the results from 3, 5, and 10 cell stacks with either in-line Fiberfrax Board or MinK insulation, a series of cell-free “control” tests were performed that show the inherent settling and stress relaxation based on the interaction between the insulation and heat pellets alone.
Holst, Glendon
2016-12-01
Serial section electron microscopy (SSEM) image stacks generated using high throughput microscopy techniques are an integral tool for investigating brain connectivity and cell morphology. FIB or 3View scanning electron microscopes easily generate gigabytes of data. In order to produce analyzable 3D dataset from the imaged volumes, efficient and reliable image segmentation is crucial. Classical manual approaches to segmentation are time consuming and labour intensive. Semiautomatic seeded watershed segmentation algorithms, such as those implemented by ilastik image processing software, are a very powerful alternative, substantially speeding up segmentation times. We have used ilastik effectively for small EM stacks – on a laptop, no less; however, ilastik was unable to carve the large EM stacks we needed to segment because its memory requirements grew too large – even for the biggest workstations we had available. For this reason, we refactored the carving module of ilastik to scale it up to large EM stacks on large workstations, and tested its efficiency. We modified the carving module, building on existing blockwise processing functionality to process data in manageable chunks that can fit within RAM (main memory). We review this refactoring work, highlighting the software architecture, design choices, modifications, and issues encountered.
Bandwidth Selection for Weighted Kernel Density Estimation
Wang, Bin; Wang, Xiaofeng
2007-01-01
In the this paper, the authors propose to estimate the density of a targeted population with a weighted kernel density estimator (wKDE) based on a weighted sample. Bandwidth selection for wKDE is discussed. Three mean integrated squared error based bandwidth estimators are introduced and their performance is illustrated via Monte Carlo simulation. The least-squares cross-validation method and the adaptive weight kernel density estimator are also studied. The authors also consider the boundary...
Some Remarks on the Symmetry Kernel Test
Baszczyńska, Aleksandra
2013-01-01
The paper presents chosen statistical tests used to verify the hypothesis of the symmetry of random variable’s distribution. Detailed analysis of the symmetry kernel test is made. The properties of the regarded symmetry kernel test are compared with the other symmetry tests using Monte Carlo methods. The symmetry tests are used, as an example, in analysis of the distribution of the Human Development Index (HDI). W pracy przedstawiono wybrane statystyczne testy wykorzystywane w ...
Methane Steam Reforming over an Ni-YSZ Solid Oxide Fuel Cell Anode in Stack Configuration
DEFF Research Database (Denmark)
Mogensen, David; Grunwaldt, Jan-Dierk; Hendriksen, Peter Vang
2014-01-01
The kinetics of catalytic steam reforming of methane over an Ni-YSZ anode of a solid oxide fuel cell (SOFC) have been investigated with the cell placed in a stack configuration. In order to decrease the degree of conversion, a single cell stack with reduced area was used. Measurements were...
On the Inclusion Relation of Reproducing Kernel Hilbert Spaces
Zhang, Haizhang; Zhao, Liang
2011-01-01
To help understand various reproducing kernels used in applied sciences, we investigate the inclusion relation of two reproducing kernel Hilbert spaces. Characterizations in terms of feature maps of the corresponding reproducing kernels are established. A full table of inclusion relations among widely-used translation invariant kernels is given. Concrete examples for Hilbert-Schmidt kernels are presented as well. We also discuss the preservation of such a relation under various operations of ...
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 ...
Development and durability of SOFC stacks
Energy Technology Data Exchange (ETDEWEB)
Beeaff, D.; Dinesen, A.R.; Mikkelsen, Lars; Nielsen, Karsten A.; Solvang, M.; Hendriksen, Peter V.
2004-12-01
The present project is a part of the Danish SOFC programme, which has the overall aim of establishing a Danish production of SOFC - cells, stacks and systems for economical and environmentally friendly power production. The aim of the present project was to develop and demonstrate (on a small scale, few cells, few thousand hours) a durable, thermally cyclable stack with high performance at 750 deg. C. Good progress towards this target has been made and demonstrated at the level of stack-elements (one cell between two interconnects) or small stacks (3 5 cells). Three different stacks or stack-elements have been operated for periods exceeding 3000 hr. The work has covered development of stack-components (seals, interconnects, coatings, contact layers), establishment of procedures for stack assembly and initiation, and detailed electrical characterisation with the aims of identifying performance limiting factors as well as long term durability. Further, post test investigations have been carried out to identify possible degradation mechanisms. (BA)
Annular feed air breathing fuel cell stack
Wilson, Mahlon S.; Neutzler, Jay K.
1997-01-01
A stack of polymer electrolyte fuel cells is formed from a plurality of unit cells where each unit cell includes fuel cell components defining a periphery and distributed along a common axis, where the fuel cell components include a polymer electrolyte membrane, an anode and a cathode contacting opposite sides of the membrane, and fuel and oxygen flow fields contacting the anode and the cathode, respectively, wherein the components define an annular region therethrough along the axis. A fuel distribution manifold within the annular region is connected to deliver fuel to the fuel flow field in each of the unit cells. The fuel distribution manifold is formed from a hydrophilic-like material to redistribute water produced by fuel and oxygen reacting at the cathode. In a particular embodiment, a single bolt through the annular region clamps the unit cells together. In another embodiment, separator plates between individual unit cells have an extended radial dimension to function as cooling fins for maintaining the operating temperature of the fuel cell stack.
Salt Concentration Differences Alter Membrane Resistance in Reverse Electrodialysis Stacks
Geise, Geoffrey M.
2014-01-14
Membrane ionic resistance is usually measured by immersing the membrane in a salt solution at a single, fixed concentration. While salt concentration is known to affect membrane resistance when the same concentration is used on both sides of the membrane, little is known about membrane resistance when the membrane is placed between solutions of different concentrations, such as in a reverse electrodialysis (RED) stack. Ionic resistance measurements obtained using Selemion CMV and AMV that separated sodium chloride and ammonium bicarbonate solutions of different concentrations were greater than those measured using only the high-concentration solution. Measured RED stack resistances showed good agreement with resistances calculated using an equivalent series resistance model, where the membranes accounted for 46% of the total stack resistance. The high area resistance of the membranes separating different salt concentration solutions has implications for modeling and optimizing membranes used in RED systems.
Flexibly imposing periodicity in kernel independent FMM: A multipole-to-local operator approach
Yan, Wen; Shelley, Michael
2018-02-01
An important but missing component in the application of the kernel independent fast multipole method (KIFMM) is the capability for flexibly and efficiently imposing singly, doubly, and triply periodic boundary conditions. In most popular packages such periodicities are imposed with the hierarchical repetition of periodic boxes, which may give an incorrect answer due to the conditional convergence of some kernel sums. Here we present an efficient method to properly impose periodic boundary conditions using a near-far splitting scheme. The near-field contribution is directly calculated with the KIFMM method, while the far-field contribution is calculated with a multipole-to-local (M2L) operator which is independent of the source and target point distribution. The M2L operator is constructed with the far-field portion of the kernel function to generate the far-field contribution with the downward equivalent source points in KIFMM. This method guarantees the sum of the near-field & far-field converge pointwise to results satisfying periodicity and compatibility conditions. The computational cost of the far-field calculation observes the same O (N) complexity as FMM and is designed to be small by reusing the data computed by KIFMM for the near-field. The far-field calculations require no additional control parameters, and observes the same theoretical error bound as KIFMM. We present accuracy and timing test results for the Laplace kernel in singly periodic domains and the Stokes velocity kernel in doubly and triply periodic domains.
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.
Anato, F M; Sinzogan, A A C; Offenberg, J; Adandonon, A; Wargui, R B; Deguenon, J M; Ayelo, P M; Vayssières, J-F; Kossou, D K
2017-06-01
Weaver ants, Oecophylla spp., are known to positively affect cashew, Anacardium occidentale L., raw nut yield, but their effects on the kernels have not been reported. We compared nut size and the proportion of marketable kernels between raw nuts collected from trees with and without ants. Raw nuts collected from trees with weaver ants were 2.9% larger than nuts from control trees (i.e., without weaver ants), leading to 14% higher proportion of marketable kernels. On trees with ants, the kernel: raw nut ratio from nuts damaged by formic acid was 4.8% lower compared with nondamaged nuts from the same trees. Weaver ants provided three benefits to cashew production by increasing yields, yielding larger nuts, and by producing greater proportions of marketable kernel mass. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Kernel based orthogonalization for change detection in hyperspectral images
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
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 MNF analyses handle 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. An example shows the successful application of (kernel PCA and) kernel MNF analysis to change detection in HyMap data covering a small agricultural area near Lake Waging-Taching, Bavaria, in Southern Germany. In the change detection...
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo
2016-01-01
The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
Directory of Open Access Journals (Sweden)
Jaime Cuevas
2017-01-01
Full Text Available The phenomenon of genotype × environment (G × E interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects ( u that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP and Gaussian (Gaussian kernel, GK. The other model has the same genetic component as the first model ( u plus an extra component, f, that captures random effects between environments that were not captured by the random effects u . We used five CIMMYT data sets (one maize and four wheat that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u .
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.
Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo
2017-01-05
The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.
Pattern Classification of Signals Using Fisher Kernels
Directory of Open Access Journals (Sweden)
Yashodhan Athavale
2012-01-01
Full Text Available The intention of this study is to gauge the performance of Fisher kernels for dimension simplification and classification of time-series signals. Our research work has indicated that Fisher kernels have shown substantial improvement in signal classification by enabling clearer pattern visualization in three-dimensional space. In this paper, we will exhibit the performance of Fisher kernels for two domains: financial and biomedical. The financial domain study involves identifying the possibility of collapse or survival of a company trading in the stock market. For assessing the fate of each company, we have collected financial time-series composed of weekly closing stock prices in a common time frame, using Thomson Datastream software. The biomedical domain study involves knee signals collected using the vibration arthrometry technique. This study uses the severity of cartilage degeneration for classifying normal and abnormal knee joints. In both studies, we apply Fisher Kernels incorporated with a Gaussian mixture model (GMM for dimension transformation into feature space, which is created as a three-dimensional plot for visualization and for further classification using support vector machines. From our experiments we observe that Fisher Kernel usage fits really well for both kinds of signals, with low classification error rates.
The Classification of Diabetes Mellitus Using Kernel k-means
Alamsyah, M.; Nafisah, Z.; Prayitno, E.; Afida, A. M.; Imah, E. M.
2018-01-01
Diabetes Mellitus is a metabolic disorder which is characterized by chronicle hypertensive glucose. Automatics detection of diabetes mellitus is still challenging. This study detected diabetes mellitus by using kernel k-Means algorithm. Kernel k-means is an algorithm which was developed from k-means algorithm. Kernel k-means used kernel learning that is able to handle non linear separable data; where it differs with a common k-means. The performance of kernel k-means in detecting diabetes mellitus is also compared with SOM algorithms. The experiment result shows that kernel k-means has good performance and a way much better than SOM.
OS X and iOS Kernel Programming
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
Lightweight Stacks of Direct Methanol Fuel Cells
Narayanan, Sekharipuram; Valdez, Thomas
2004-01-01
An improved design concept for direct methanol fuel cells makes it possible to construct fuel-cell stacks that can weigh as little as one-third as much as do conventional bipolar fuel-cell stacks of equal power. The structural-support components of the improved cells and stacks can be made of relatively inexpensive plastics. Moreover, in comparison with conventional bipolar fuel-cell stacks, the improved fuel-cell stacks can be assembled, disassembled, and diagnosed for malfunctions more easily. These improvements are expected to bring portable direct methanol fuel cells and stacks closer to commercialization. In a conventional bipolar fuel-cell stack, the cells are interspersed with bipolar plates (also called biplates), which are structural components that serve to interconnect the cells and distribute the reactants (methanol and air). The cells and biplates are sandwiched between metal end plates. Usually, the stack is held together under pressure by tie rods that clamp the end plates. The bipolar stack configuration offers the advantage of very low internal electrical resistance. However, when the power output of a stack is only a few watts, the very low internal resistance of a bipolar stack is not absolutely necessary for keeping the internal power loss acceptably low.
Discriminating oat and groat kernels from other grains using near infrared spectroscopy
Oat and groats can be discriminated from other grains such as barley, wheat, rye, and triticale (non-oats) using near infrared spectroscopy. The two instruments tested were the manual version of the ARS-USDA Single Kernel Near Infrared (SKNIR) and the automated QualySense QSorter Explorer high-speed...
Option Valuation with Volatility Components, Fat Tails, and Nonlinear Pricing Kernels
DEFF Research Database (Denmark)
Babaoglu, Kadir Gokhan; Christoffersen, Peter; Heston, Steven
We nest multiple volatility components, fat tails and a U-shaped pricing kernel in a single option model and compare their contribution to describing returns and option data. All three features lead to statistically significant model improvements. A second volatility factor is economically most i...
Diversity of maize kernels from a breeding program for protein quality III: Ionome profiling
Densities of single and multiple macro- and micronutrients have been estimated in mature kernels of 1,348 accessions in 13 maize genotypes. The germplasm belonged to stiff stalk (SS) and non-stiff stalk (NS) heterotic groups (HG) with one (S1) to four (S4) years of inbreeding (IB), or open pollinati...
Two axiomatizations of the kernel of TU games: bilateral and converse reduced game properties
Driessen, Theo; Hu, C.-C.
We provide two axiomatic characterizations of the kernel of TU games by means of both bilateral consistency and converse consistency with respect to two types of two-person reduced games. According to the first type, the worth of any single player in the two-person reduced game is derived from the
ZPEG: a hybrid DPCM-DCT based approach for compression of Z-stack images.
Khire, Sourabh; Cooper, Lee; Park, Yuna; Carter, Alexis; Jayant, Nikil; Saltz, Joel
2012-01-01
Modern imaging technology permits obtaining images at varying depths along the thickness, or the Z-axis of the sample being imaged. A stack of multiple such images is called a Z-stack image. The focus capability offered by Z-stack images is critical for many digital pathology applications. A single Z-stack image may result in several hundred gigabytes of data, and needs to be compressed for archival and distribution purposes. Currently, the existing methods for compression of Z-stack images such as JPEG and JPEG 2000 compress each focal plane independently, and do not take advantage of the Z-signal redundancy. It is possible to achieve additional compression efficiency over the existing methods, by exploiting the high Z-signal correlation during image compression. In this paper, we propose a novel algorithm for compression of Z-stack images, which we term as ZPEG. ZPEG extends the popular discrete-cosine transform (DCT) based image encoder to compress Z-stack images. This is achieved by decorrelating the neighboring layers of the Z-stack image using differential pulse-code modulation (DPCM). PSNR measurements, as well as subjective evaluations by experts indicate that ZPEG can encode Z-stack images at a higher quality as compared to JPEG, JPEG 2000 and JP3D at compression ratios below 50∶1.
Eucalyptus-Palm Kernel Oil Blends: A Complete Elimination of Diesel in a 4-Stroke VCR Diesel Engine
Directory of Open Access Journals (Sweden)
Srinivas Kommana
2015-01-01
Full Text Available Fuels derived from biomass are mostly preferred as alternative fuels for IC engines as they are abundantly available and renewable in nature. The objective of the study is to identify the parameters that influence gross indicated fuel conversion efficiency and how they are affected by the use of biodiesel relative to petroleum diesel. Important physicochemical properties of palm kernel oil and eucalyptus blend were experimentally evaluated and found within acceptable limits of relevant standards. As most of vegetable oils are edible, growing concern for trying nonedible and waste fats as alternative to petrodiesel has emerged. In present study diesel fuel is completely replaced by biofuels, namely, methyl ester of palm kernel oil and eucalyptus oil in various blends. Different blends of palm kernel oil and eucalyptus oil are prepared on volume basis and used as operating fuel in single cylinder 4-stroke variable compression ratio diesel engine. Performance and emission characteristics of these blends are studied by varying the compression ratio. In the present experiment methyl ester extracted from palm kernel oil is considered as ignition improver and eucalyptus oil is considered as the fuel. The blends taken are PKE05 (palm kernel oil 95 + eucalyptus 05, PKE10 (palm kernel oil 90 + eucalyptus 10, and PKE15 (palm kernel 85 + eucalyptus 15. The results obtained by operating with these fuels are compared with results of pure diesel; finally the most preferable combination and the preferred compression ratio are identified.
Solid Oxide Fuel Cell Stack Diagnostics
DEFF Research Database (Denmark)
Mosbæk, Rasmus Rode; Barfod, Rasmus Gottrup
. An operating stack is subject to compositional gradients in the gaseous reactant streams, and temperature gradients across each cell and across the stack, which complicates detailed analysis. Several experimental stacks from Topsoe Fuel Cell A/S were characterized using Electrochemical Impedance Spectroscopy...... (EIS). The stack measurement geometry was optimized for EIS by careful selection of the placement of current feeds and voltage probes in order to minimize measurement errors. It was demonstrated that with the improved placement of current feeds and voltage probes it is possible to separate the loss...... in the hydrogen fuel gas supplied to the stack. EIS was used to examine the long-term behavior and monitor the evolution of the impedance of each of the repeating units and the whole stack. The observed impedance was analyzed in detail for one of the repeating units and the whole stack and the losses reported...
Phenolic constituents of shea (Vitellaria paradoxa) kernels.
Maranz, Steven; Wiesman, Zeev; Garti, Nissim
2003-10-08
Analysis of the phenolic constituents of shea (Vitellaria paradoxa) kernels by LC-MS revealed eight catechin compounds-gallic acid, catechin, epicatechin, epicatechin gallate, gallocatechin, epigallocatechin, gallocatechin gallate, and epigallocatechin gallate-as well as quercetin and trans-cinnamic acid. The mean kernel content of the eight catechin compounds was 4000 ppm (0.4% of kernel dry weight), with a 2100-9500 ppm range. Comparison of the profiles of the six major catechins from 40 Vitellaria provenances from 10 African countries showed that the relative proportions of these compounds varied from region to region. Gallic acid was the major phenolic compound, comprising an average of 27% of the measured total phenols and exceeding 70% in some populations. Colorimetric analysis (101 samples) of total polyphenols extracted from shea butter into hexane gave an average of 97 ppm, with the values for different provenances varying between 62 and 135 ppm of total polyphenols.
Kernel Method for Nonlinear Granger Causality
Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano
2008-04-01
Important information on the structure of complex systems can be obtained by measuring to what extent the individual components exchange information among each other. The linear Granger approach, to detect cause-effect relationships between time series, has emerged in recent years as a leading statistical technique to accomplish this task. Here we generalize Granger causality to the nonlinear case using the theory of reproducing kernel Hilbert spaces. Our method performs linear Granger causality in the feature space of suitable kernel functions, assuming arbitrary degree of nonlinearity. We develop a new strategy to cope with the problem of overfitting, based on the geometry of reproducing kernel Hilbert spaces. Applications to coupled chaotic maps and physiological data sets are presented.
The scalar field kernel in cosmological spaces
Energy Technology Data Exchange (ETDEWEB)
Koksma, Jurjen F; Prokopec, Tomislav [Institute for Theoretical Physics (ITP) and Spinoza Institute, Utrecht University, Postbus 80195, 3508 TD Utrecht (Netherlands); Rigopoulos, Gerasimos I [Helsinki Institute of Physics, University of Helsinki, PO Box 64, FIN-00014 (Finland)], E-mail: J.F.Koksma@phys.uu.nl, E-mail: T.Prokopec@phys.uu.nl, E-mail: gerasimos.rigopoulos@helsinki.fi
2008-06-21
We construct the quantum-mechanical evolution operator in the functional Schroedinger picture-the kernel-for a scalar field in spatially homogeneous FLRW spacetimes when the field is (a) free and (b) coupled to a spacetime-dependent source term. The essential element in the construction is the causal propagator, linked to the commutator of two Heisenberg picture scalar fields. We show that the kernels can be expressed solely in terms of the causal propagator and derivatives of the causal propagator. Furthermore, we show that our kernel reveals the standard light cone structure in FLRW spacetimes. We finally apply the result to Minkowski spacetime, to de Sitter spacetime and calculate the forward time evolution of the vacuum in a general FLRW spacetime.
Fast Generation of Sparse Random Kernel Graphs.
Hagberg, Aric; Lemons, Nathan
2015-01-01
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most (n(logn)2). As a practical example we show how to generate samples of power-law degree distribution graphs with tunable assortativity.
Robust C-Loss Kernel Classifiers.
Xu, Guibiao; Hu, Bao-Gang; Principe, Jose C
2018-03-01
The correntropy-induced loss (C-loss) function has the nice property of being robust to outliers. In this paper, we study the C-loss kernel classifier with the Tikhonov regularization term, which is used to avoid overfitting. After using the half-quadratic optimization algorithm, which converges much faster than the gradient optimization algorithm, we find out that the resulting C-loss kernel classifier is equivalent to an iterative weighted least square support vector machine (LS-SVM). This relationship helps explain the robustness of iterative weighted LS-SVM from the correntropy and density estimation perspectives. On the large-scale data sets which have low-rank Gram matrices, we suggest to use incomplete Cholesky decomposition to speed up the training process. Moreover, we use the representer theorem to improve the sparseness of the resulting C-loss kernel classifier. Experimental results confirm that our methods are more robust to outliers than the existing common classifiers.
Slip-stacking Dynamics for High-Power Proton Beams at Fermilab
Energy Technology Data Exchange (ETDEWEB)
Eldred, Jeffrey Scott [Indiana Univ., Bloomington, IN (United States)
2015-12-01
Slip-stacking is a particle accelerator configuration used to store two particle beams with different momenta in the same ring. The two beams are longitudinally focused by two radiofrequency (RF) cavities with a small frequency difference between them. Each beam is synchronized to one RF cavity and perturbed by the other RF cavity. Fermilab uses slip-stacking in the Recycler so as to double the power of the 120 GeV proton beam in the Main Injector. This dissertation investigates the dynamics of slip-stacking beams analytically, numerically and experimentally. In the analytic analysis, I find the general trajectory of stable slip-stacking particles and identify the slip-stacking parametric resonances. In the numerical analysis, I characterize the stable phase-space area and model the particle losses. In particular, I evaluate the impact of upgrading the Fermilab Booster cycle-rate from 15 Hz to 20 Hz as part of the Proton Improvement Plan II (PIP-II). The experimental analysis is used to verify my approach to simulating slip-stacking loss. I design a study for measuring losses from the longitudinal single-particle dynamics of slip-stacking as a function of RF cavity voltage and RF frequency separation. I further propose the installation of a harmonic RF cavity and study the dynamics of this novel slip-stacking configuration. I show the harmonic RF cavity cancels out parametric resonances in slip-stacking, reduces emittance growth during slip-stacking, and dramatically enhances the stable phase-space area. The harmonic cavity is expected to reduce slip-stacking losses to far exceed PIP-II requirements. These results raise the possibility of extending slip-stacking beyond the PIP-II era.
Tunable infrared plasmonic devices using graphene/insulator stacks
Yan, Hugen; Li, Xuesong; Chandra, Bhupesh; Tulevski, George; Wu, Yanqing; Freitag, Marcus; Zhu, Wenjuan; Avouris, Phaedon; Xia, Fengnian
2012-05-01
The collective oscillation of carriers--the plasmon--in graphene has many desirable properties, including tunability and low loss. However, in single-layer graphene, the dependence on carrier concentration of both the plasmonic resonance frequency and magnitude is relatively weak, limiting its applications in photonics. Here, we demonstrate transparent photonic devices based on graphene/insulator stacks, which are formed by depositing alternating wafer-scale graphene sheets and thin insulating layers, then patterning them together into photonic-crystal-like structures. We show experimentally that the plasmon in such stacks is unambiguously non-classical. Compared with doping in single-layer graphene, distributing carriers into multiple graphene layers effectively enhances the plasmonic resonance frequency and magnitude, which is different from the effect in a conventional semiconductor superlattice and is a direct consequence of the unique carrier density scaling law of the plasmonic resonance of Dirac fermions. Using patterned graphene/insulator stacks, we demonstrate widely tunable far-infrared notch filters with 8.2 dB rejection ratios and terahertz linear polarizers with 9.5 dB extinction ratios. An unpatterned stack consisting of five graphene layers shields 97.5% of electromagnetic radiation at frequencies below 1.2 THz. This work could lead to the development of transparent mid- and far-infrared photonic devices such as detectors, modulators and three-dimensional metamaterial systems.
Geometric Patterns for Neighboring Bases Near the Stacked State in Nucleic Acid Strands.
Sedova, Ada; Banavali, Nilesh K
2017-03-14
Structural variation in base stacking has been analyzed frequently in isolated double helical contexts for nucleic acids, but not as often in nonhelical geometries or in complex biomolecular environments. In this study, conformations of two neighboring bases near their stacked state in any environment are comprehensively characterized for single-strand dinucleotide (SSD) nucleic acid crystal structure conformations. An ensemble clustering method is used to identify a reduced set of representative stacking geometries based on pairwise distances between select atoms in consecutive bases, with multiple separable conformational clusters obtained for categories divided by nucleic acid type (DNA/RNA), SSD sequence, stacking face orientation, and the presence or absence of a protein environment. For both DNA and RNA, SSD conformations are observed that are either close to the A-form, or close to the B-form, or intermediate between the two forms, or further away from either form, illustrating the local structural heterogeneity near the stacked state. Among this large variety of distinct conformations, several common stacking patterns are observed between DNA and RNA, and between nucleic acids in isolation or in complex with proteins, suggesting that these might be stable stacking orientations. Noncanonical face/face orientations of the two bases are also observed for neighboring bases in the same strand, but their frequency is much lower, with multiple SSD sequences across categories showing no occurrences of such unusual stacked conformations. The resulting reduced set of stacking geometries is directly useful for stacking-energy comparisons between empirical force fields, prediction of plausible localized variations in single-strand structures near their canonical states, and identification of analogous stacking patterns in newly solved nucleic acid containing structures.
Ensemble-based forecasting at Horns Rev: Ensemble conversion and kernel dressing
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
For management and trading purposes, information on short-term wind generation (from few hours to few days ahead) is even more crucial at large offshore wind farms, since they concentrate a large capacity at a single location. The most complete information that can be provided today consists....... 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...
Reproducing kernel method with Taylor expansion for linear Volterra integro-differential equations
Directory of Open Access Journals (Sweden)
Azizallah Alvandi
2017-06-01
Full Text Available This research aims of the present a new and single algorithm for linear integro-differential equations (LIDE. To apply the reproducing Hilbert kernel method, there is made an equivalent transformation by using Taylor series for solving LIDEs. Shown in series form is the analytical solution in the reproducing kernel space and the approximate solution $ u_{N} $ is constructed by truncating the series to $ N $ terms. It is easy to prove the convergence of $ u_{N} $ to the analytical solution. The numerical solutions from the proposed method indicate that this approach can be implemented easily which shows attractive features.
Kernel principal component analysis for change detection
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Morton, J.C.
2008-01-01
Principal component analysis (PCA) is often used to detect change over time in remotely sensed images. A commonly used technique consists of finding the projections along the two eigenvectors for data consisting of two variables which represent the same spectral band covering the same geographical...... 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...
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
We discuss nonparametric regression models for panel data. A fully nonparametric panel data specification that uses the time variable and the individual identifier as additional (categorical) explanatory variables is considered to be the most suitable. We use this estimator and conventional...... 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...... found the estimates of the fully nonparametric panel data model to be more reliable....
Fluidization calculation on nuclear fuel kernel coating
International Nuclear Information System (INIS)
Sukarsono; Wardaya; Indra-Suryawan
1996-01-01
The fluidization of nuclear fuel kernel coating was calculated. The bottom of the reactor was in the from of cone on top of the cone there was a cylinder, the diameter of the cylinder for fluidization was 2 cm and at the upper part of the cylinder was 3 cm. Fluidization took place in the cone and the first cylinder. The maximum and the minimum velocity of the gas of varied kernel diameter, the porosity and bed height of varied stream gas velocity were calculated. The calculation was done by basic program
Kernel abortion in maize. II. Distribution of 14C among kernel carboydrates
International Nuclear Information System (INIS)
Hanft, J.M.; Jones, R.J.
1986-01-01
This study was designed to compare the uptake and distribution of 14 C among fructose, glucose, sucrose, and starch in the cob, pedicel, and endosperm tissues of maize (Zea mays L.) kernels induced to abort by high temperature with those that develop normally. Kernels cultured in vitro at 309 and 35 0 C were transferred to [ 14 C]sucrose media 10 days after pollination. Kernels cultured at 35 0 C aborted prior to the onset of linear dry matter accumulation. Significant uptake into the cob, pedicel, and endosperm of radioactivity associated with the soluble and starch fractions of the tissues was detected after 24 hours in culture on atlageled media. After 8 days in culture on [ 14 C]sucrose media, 48 and 40% of the radioactivity associated with the cob carbohydrates was found in the reducing sugars at 30 and 35 0 C, respectively. Of the total carbohydrates, a higher percentage of label was associated with sucrose and lower percentage with fructose and glucose in pedicel tissue of kernels cultured at 35 0 C compared to kernels cultured at 30 0 C. These results indicate that sucrose was not cleaved to fructose and glucose as rapidly during the unloading process in the pedicel of kernels induced to abort by high temperature. Kernels cultured at 35 0 C had a much lower proportion of label associated with endosperm starch (29%) than did kernels cultured at 30 0 C (89%). Kernels cultured at 35 0 C had a correspondingly higher proportion of 14 C in endosperm fructose, glucose, and sucrose
On methods to increase the security of the Linux kernel
International Nuclear Information System (INIS)
Matvejchikov, I.V.
2014-01-01
Methods to increase the security of the Linux kernel for the implementation of imposed protection tools have been examined. The methods of incorporation into various subsystems of the kernel on the x86 architecture have been described [ru
Comparative Analysis of Kernel Methods for Statistical Shape Learning
National Research Council Canada - National Science Library
Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen
2006-01-01
.... In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding...
Vertically stacked nanocellulose tactile sensor.
Jung, Minhyun; Kim, Kyungkwan; Kim, Bumjin; Lee, Kwang-Jae; Kang, Jae-Wook; Jeon, Sanghun
2017-11-16
Paper-based electronic devices are attracting considerable attention, because the paper platform has unique attributes such as flexibility and eco-friendliness. Here we report on what is claimed to be the firstly fully integrated vertically-stacked nanocellulose-based tactile sensor, which is capable of simultaneously sensing temperature and pressure. The pressure and temperature sensors are operated using different principles and are stacked vertically, thereby minimizing the interference effect. For the pressure sensor, which utilizes the piezoresistance principle under pressure, the conducting electrode was inkjet printed on the TEMPO-oxidized-nanocellulose patterned with micro-sized pyramids, and the counter electrode was placed on the nanocellulose film. The pressure sensor has a high sensitivity over a wide range (500 Pa-3 kPa) and a high durability of 10 4 loading/unloading cycles. The temperature sensor combines various materials such as poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS), silver nanoparticles (AgNPs) and carbon nanotubes (CNTs) to form a thermocouple on the upper nanocellulose layer. The thermoelectric-based temperature sensors generate a thermoelectric voltage output of 1.7 mV for a temperature difference of 125 K. Our 5 × 5 tactile sensor arrays show a fast response, negligible interference, and durable sensing performance.
Symbol recognition with kernel density matching.
Zhang, Wan; Wenyin, Liu; Zhang, Kun
2006-12-01
We propose a novel approach to similarity assessment for graphic symbols. Symbols are represented as 2D kernel densities and their similarity is measured by the Kullback-Leibler divergence. Symbol orientation is found by gradient-based angle searching or independent component analysis. Experimental results show the outstanding performance of this approach in various situations.
Analytic properties of the Virasoro modular kernel
Energy Technology Data Exchange (ETDEWEB)
Nemkov, Nikita [Moscow Institute of Physics and Technology (MIPT), Dolgoprudny (Russian Federation); Institute for Theoretical and Experimental Physics (ITEP), Moscow (Russian Federation); National University of Science and Technology MISIS, The Laboratory of Superconducting metamaterials, Moscow (Russian Federation)
2017-06-15
On the space of generic conformal blocks the modular transformation of the underlying surface is realized as a linear integral transformation. We show that the analytic properties of conformal block implied by Zamolodchikov's formula are shared by the kernel of the modular transformation and illustrate this by explicit computation in the case of the one-point toric conformal block. (orig.)
42 Variability Bugs in the Linux Kernel
DEFF Research Database (Denmark)
Abal, Iago; Brabrand, Claus; Wasowski, Andrzej
2014-01-01
, serving to evaluate tool implementations of feature-sensitive analyses by testing them on real bugs. We present a qualitative study of 42 variability bugs collected from bug-fixing commits to the Linux kernel repository. We analyze each of the bugs, and record the results in a database. In addition, we...
40 Variability Bugs in the Linux Kernel
DEFF Research Database (Denmark)
Abal Rivas, Iago; Brabrand, Claus; Wasowski, Andrzej
2014-01-01
is a requirement for goal-oriented research, serving to evaluate tool implementations of feature-sensitive analyses by testing them on real bugs. We present a qualitative study of 40 variability bugs collected from bug-fixing commits to the Linux kernel repository. We investigate each of the 40 bugs, recording...
Flexible Scheduling in Multimedia Kernels: An Overview
Jansen, P.G.; Scholten, Johan; Laan, Rene; Chow, W.S.
1999-01-01
Current Hard Real-Time (HRT) kernels have their timely behaviour guaranteed on the cost of a rather restrictive use of the available resources. This makes current HRT scheduling techniques inadequate for use in a multimedia environment where we can make a considerable profit by a better and more
Analytic continuation of weighted Bergman kernels
Czech Academy of Sciences Publication Activity Database
Engliš, Miroslav
2010-01-01
Roč. 94, č. 6 (2010), s. 622-650 ISSN 0021-7824 R&D Projects: GA AV ČR IAA100190802 Keywords : Bergman kernel * analytic continuation * Toeplitz operator Subject RIV: BA - General Mathematics Impact factor: 1.450, year: 2010 http://www.sciencedirect.com/science/article/pii/S0021782410000942
A synthesis of empirical plant dispersal kernels
Czech Academy of Sciences Publication Activity Database
Bullock, J. M.; González, L. M.; Tamme, R.; Götzenberger, Lars; White, S. M.; Pärtel, M.; Hooftman, D. A. P.
2017-01-01
Roč. 105, č. 1 (2017), s. 6-19 ISSN 0022-0477 Institutional support: RVO:67985939 Keywords : dispersal kernel * dispersal mode * probability density function Subject RIV: EH - Ecology, Behaviour OBOR OECD: Ecology Impact factor: 5.813, year: 2016
Graph Bundling by Kernel Density Estimation
Hurter, C.; Ersoy, O.; Telea, A.
We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved
Evaluation of different combinations of palm kernel cake - and cotton ...
African Journals Online (AJOL)
... sole palm kernel cake based diets than those fed combinations of palm kernel cake and cottonseed cake. It is concluded that palm kernel cake alone (without any combination with cottonseed cake) is adequate as protein source in compounding protein supplements for West African Dwarf goats for profitable performance.
Enhanced gluten properties in soft kernel durum wheat
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...
determination of bio-energy potential of palm kernel shell
African Journals Online (AJOL)
88888888
2012-11-03
Nov 3, 2012 ... Palm Kernel Shell (PKS) is an economically and environmentally sustainable raw material for ... oil and palm kernel oil production, palm oil fibre, effluent, kernel shell and empty fruit bunch are re- garded as wastes. According to Luangkiattikhun et ... use as concrete reinforcement in construction indus-.
Dense Medium Machine Processing Method for Palm Kernel/ Shell ...
African Journals Online (AJOL)
ADOWIE PERE
ABSTRACT: A machine processing method for the separation of cracked palm kernel from the shells using ... Cracked palm kernel is a mixture of kernels, broken shells, dusts and other impurities. In order to produce ... Received 31 September 2017, received in revised form 18 October 2017, accepted 29 November 2017.
An Investigation of Kernel Data Attacks and Countermeasures
2017-02-14
demonstrate that attackers can stealthily subvert various kernel security mechanism s and develop a new keylogger , which is more stealthy than existing... keyloggers .. By classifying kernel data into different categories and handling them separately, we propose a defense mechanism and evaluate its...a computer system. 15. SUBJECT TERMS Kernel Data, Rootkit, Keylogger , Countermeasure 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF a. REPORT b
Behavior of a nuclear power plant ventilation stack for wind loads
International Nuclear Information System (INIS)
Venkatachalapathy, V.
2012-01-01
This paper describes behavior of self supporting tall reinforced concrete (RC) ventilation stack of a nuclear power plant (NPP) for wind loads. Since the static and equivalent dynamic wind loads are inter-dependant on overall size of the stack, proper sizing of the stack geometry is important for reducing wind loads. The present study investigated the influence of engineered backfill soil on lateral response of ventilation stack. Ignoring backfill soil stiffness up to ground height does not allow to predict actual critical wind velocity causing across wind oscillation. The results show that proposed modification in the stack geometry modeled using 2D beam-spring elements is economical than that of single tapered geometry. Shaft diameter reduced in the proposed geometry indicates that there is a scope for overall space savings in the NPP layout. (author)
A method of measuring stacked objects volume based on laser sensing
Zhao, Qijie; Wu, Yijing; Li, Xianfa; Xu, Jiao; Meng, Qingxu
2017-10-01
Stacked objects volume measurement is now widely used in the fields of enterprise material management. It is significant to improve the efficiency of enterprise management and to reduce the cost of management and operation. The method based on laser sensing is one of the key methods to measure the stacked objects volume. This paper presents a laser sensing measurement method of stacked objects based on bottom plane extraction and real-time calibration. A calibration method for a laser scanning sensor and inertial measurement sensor is proposed. Three-dimensional reconstructions of stacked objects and volume calculations are carried out after acquisition and processing of point clouds. Volume measurement experiments of the single box and stacked boxes have been conducted respectively. Experimental results show that the measurement method is feasible and valid with good accuracy.
A small mono-polar direct methanol fuel cell stack with passive operation
Chan, Y. H.; Zhao, T. S.; Chen, R.; Xu, C.
A passive direct methanol fuel cell (DMFC) stack that consists of six unit cells was designed, fabricated, and tested. The stack was tested with different methanol concentrations under ambient conditions. It was found that the stack performance increased when the methanol concentration inside the fuel tank was increased from 2.0 to 6.0 M. The improved performance is primarily due to the increased cell temperature as a result of the exothermic reaction between the permeated methanol and oxygen on the cathode. Moreover, the increased cell temperature enhanced the water evaporation rate on the air-breathing cathode, which significantly reduced water flooding on the cathode and further improved the stack performance. This passive DMFC stack, providing 350 mW at 1.8 V, was successfully applied to power a seagull display kit. The seagull display kit can continuously run for about 4 h on a single charge of 25 cm 3 4.0-M methanol solution.
Learning a peptide-protein binding affinity predictor with kernel ridge regression.
Giguère, Sébastien; Marchand, Mario; Laviolette, François; Drouin, Alexandre; Corbeil, Jacques
2013-03-05
The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it's approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding
Physically Connected Stacked Patch Antenna Design with 100% Bandwidth
Klionovski, Kirill
2017-11-01
Typically, stacked patch antennas are parasitically coupled and provide larger bandwidth than a single patch antenna. Here, we show a stacked patch antenna design where square patches with semi-circular cutouts are physically connected to each other. This arrangement provides 100% bandwidth from 23.9–72.2 GHz with consistent high gain (5 dBi or more) across the entire bandwidth. In another variation, a single patch loaded with a superstrate provides 83.5% bandwidth from 25.6–62.3 GHz. The mechanism of bandwidth enhancement is explained through electromagnetic simulations. Measured reflection coefficient, radiation patterns and gain results confirm the extremely wideband performance of the design.
Generalized data stacking programming model with applications
Directory of Open Access Journals (Sweden)
Hala Samir Elhadidy
2016-09-01
Full Text Available Recent researches have shown that, everywhere in various sciences the systems are following stacked-based stored change behavior when subjected to events or varying environments “on and above” their normal situations. This paper presents a generalized data stack programming (GDSP model which is developed to describe the system changes under varying environment. These changes which are captured with different ways such as sensor reading are stored in matrices. Extraction algorithm and identification technique are proposed to extract the different layers between images and identify the stack class the object follows; respectively. The general multi-stacking network is presented including the interaction between various stack-based layering of some applications. The experiments prove that the concept of stack matrix gives average accuracy of 99.45%.
Wheat kernel dimensions: how do they contribute to kernel weight at ...
Indian Academy of Sciences (India)
2011-12-02
Dec 2, 2011 ... Wheat kernel dimensions: how do they contribute to kernel weight at an individual QTL level? FA CUI1, 2†, ANMING DING1†, JUN LI1, 3†, CHUNHUA ZHAO1†, XINGFENG LI1, DESHUN FENG1,. XIUQIN WANG4, LIN WANG1, 5, JURONG GAO1 and HONGGANG WANG1∗. 1State Key Laboratory of Crop ...
Flexural characteristics of a stack leg
International Nuclear Information System (INIS)
Cook, J.
1979-06-01
A 30 MV tandem Van de Graaff accelerator is at present under construction at Daresbury Laboratory. The insulating stack of the machine is of modular construction, each module being 860 mm in length. Each live section stack module contains 8 insulating legs mounted between bulkhead rings. The design, fabrication (from glass discs bonded to stainless steel discs using an epoxy film adhesive) and testing of the stack legs is described. (U.K.)
Hydrogen Embrittlement And Stacking-Fault Energies
Parr, R. A.; Johnson, M. H.; Davis, J. H.; Oh, T. K.
1988-01-01
Embrittlement in Ni/Cu alloys appears related to stacking-fault porbabilities. Report describes attempt to show a correlation between stacking-fault energy of different Ni/Cu alloys and susceptibility to hydrogen embrittlement. Correlation could lead to more fundamental understanding and method of predicting susceptibility of given Ni/Cu alloy form stacking-fault energies calculated from X-ray diffraction measurements.
Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism
Directory of Open Access Journals (Sweden)
Xinran Zhou
2014-01-01
Full Text Available To apply the single hidden-layer feedforward neural networks (SLFN to identify time-varying system, online regularized extreme learning machine (ELM with forgetting mechanism (FORELM and online kernelized ELM with forgetting mechanism (FOKELM are presented in this paper. The FORELM updates the output weights of SLFN recursively by using Sherman-Morrison formula, and it combines advantages of online sequential ELM with forgetting mechanism (FOS-ELM and regularized online sequential ELM (ReOS-ELM; that is, it can capture the latest properties of identified system by studying a certain number of the newest samples and also can avoid issue of ill-conditioned matrix inversion by regularization. The FOKELM tackles the problem of matrix expansion of kernel based incremental ELM (KB-IELM by deleting the oldest sample according to the block matrix inverse formula when samples occur continually. The experimental results show that the proposed FORELM and FOKELM have better stability than FOS-ELM and have higher accuracy than ReOS-ELM in nonstationary environments; moreover, FORELM and FOKELM have time efficiencies superiority over dynamic regression extreme learning machine (DR-ELM under certain conditions.
Directory of Open Access Journals (Sweden)
Yongliang Lin
2016-10-01
Full Text Available In this paper, we propose a multiple kernel relevance vector machine (RVM method based on the adaptive cloud particle swarm optimization (PSO algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection problem can be solved by adjusting the kernel weight, which determines the single kernel contribution of the final kernel mapping. The weights and parameters of the multi-kernel function were optimized using the PSO algorithm. In addition, the convergence speed of the PSO algorithm was increased using cloud theory. To ensure the stability of the prediction model, the result of a five-fold cross-validation method was used as the fitness of the PSO algorithm. To verify the results, receiver operating characteristic curves (ROC and landslide dot density (LDD were used. The results show that the model that used a heterogeneous kernel (a combination of two different kernel functions had a larger area under the ROC curve (0.7616 and a lower prediction error ratio (0.28% than did the other types of kernel models employed in this study. In addition, both the sum of two high susceptibility zone LDDs (6.71/100 km2 and the sum of two low susceptibility zone LDDs (0.82/100 km2 demonstrated that the landslide susceptibility map based on the heterogeneous kernel model was closest to the historical landslide distribution. In conclusion, the results obtained in this study can provide very useful information for disaster prevention and land-use planning in the study area.
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.
1974-01-01
Stacks of SPS Dipole Magnets ready for installation in the tunnel. The SPS uses a separated function lattice with dipoles for bending and quadrupoles for focusing. The 6.2 m long normal conducting dipoles are of H-type with coils that are bent-up at the ends. There are two types, B1 (total of 360) and B2 (384). Both are for a maximum field of 1.8 Tesla and have the same outer dimensions (450x800 mm2 vxh) but with different gaps (B1: 39x129 mm2, B2: 52x92 mm2) tailored to the beam size. The yoke, made of 1.5 mm thick laminations, consists of an upper and a lower half joined together in the median plane once the coils have been inserted.
California dreaming?[PEM stacks
Energy Technology Data Exchange (ETDEWEB)
Crosse, J.
2002-06-01
Hyundai's Santa Fe FCEV will be on sale by the end of 2002. Hyundai uses PEM stacks that are manufactured by International Fuel Cells (IFC), a division of United Technologies. Santa Fe is equipped with a 65 kW electric powertrain of Enova systems and Shell's new gasoline reformer called Hydrogen Source. Eugene Jang, Senior Engineer - Fuel Cell and Materials at Hyundai stated that the compressor related losses on IFC system are below 3%. The maximum speed offered by the vehicle is estimated as 123km/hr while the petrol equivalent fuel consumption is quoted between 5.6L/100 km and 4.8L/100 km. Santa Fe is a compact vehicle offering better steering response and a pleasant drive. (author)
Paul, Subir; Nagesh Kumar, D.
2018-04-01
Hyperspectral (HS) data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. In the present study, Mutual Information (MI) based Segmented Stacked Autoencoder (S-SAE) approach for spectral-spatial classification of the HS data is proposed to reduce the complexity and computational time compared to Stacked Autoencoder (SAE) based feature extraction. A non-parametric dependency measure (MI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of both linear and nonlinear inter-band dependency for spectral segmentation of the HS bands. Then morphological profiles are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, Support Vector Machine (SVM) with Gaussian kernel and Random Forest (RF) are used for classification of the three most popularly used HS datasets. Results of the numerical experiments carried out in this study have shown that SVM with a Gaussian kernel is providing better results for the Pavia University and Botswana datasets whereas RF is performing better for Indian Pines dataset. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches.
Utilizing Temporal Information in fMRI Decoding: Classifier Using Kernel Regression Methods
Chu, Carlton; Mourão-Miranda, Janaina; Chiu, Yu-Chin; Kriegeskorte, Nikolaus; Tan, Geoffrey; Ashburner, John
2011-01-01
This paper describes a general kernel regression approach to predict experimental conditions from activity patterns acquired with functional magnetic resonance image (fMRI). The standard approach is to use classifiers that predict conditions from activity patterns. Our approach involves training different regression machines for each experimental condition, so that a predicted temporal profile is computed for each condition. A decision function is then used to classify the responses from the testing volumes into the corresponding category, by comparing the predicted temporal profile elicited by each event, against a canonical haemodynamic response function. This approach utilizes the temporal information in the fMRI signal and maintains more training samples in order to improve the classification accuracy over an existing strategy. This paper also introduces efficient techniques of temporal compaction, which operate directly on kernel matrices for kernel classification algorithms such as the support vector machine (SVM). Temporal compacting can convert the kernel computed from each fMRI volume directly into the kernel computed from beta-maps, average of volumes or spatial-temporal kernel. The proposed method was applied to three different datasets. The first one is a block-design experiment with three conditions of image stimuli. The method outperformed the SVM classifiers of three different types of temporal compaction in single-subject leave-one-block-out cross-validation. Our method achieved 100% classification accuracy for six of the subjects and an average of 94% accuracy across all 16 subjects, exceeding the best SVM classification result, which was 83% accuracy (p=0.008). The second dataset is also a block-design experiment with two conditions of visual attention (left or right). Our method yielded 96% accuracy and SVM yielded 92% (p=0.005). The third dataset is from a fast event-related experiment with two categories of visual objects. Our method achieved
Kernel Methods for Machine Learning with Life Science Applications
DEFF Research Database (Denmark)
Abrahamsen, Trine Julie
Kernel methods refer to a family of widely used nonlinear algorithms for machine learning tasks like classification, regression, and feature extraction. By exploiting the so-called kernel trick straightforward extensions of classical linear algorithms are enabled as long as the data only appear...... models to kernel learning, and means for restoring the generalizability in both kernel Principal Component Analysis and the Support Vector Machine are proposed. Viability is proved on a wide range of benchmark machine learning data sets....... as innerproducts in the model formulation. This dissertation presents research on improving the performance of standard kernel methods like kernel Principal Component Analysis and the Support Vector Machine. Moreover, the goal of the thesis has been two-fold. The first part focuses on the use of kernel Principal...
Quantitative review of degradation and lifetime of solid oxide cells and stacks
DEFF Research Database (Denmark)
Skafte, Theis Løye; Hjelm, Johan; Blennow, Peter
2016-01-01
A comprehensive review of degradation and lifetime for solid oxide cells and stacks hasbeen conducted. Based on more than 50 parameters from 150 publications and 1 000 000hours of accumulated testing, this paper presents a quantitative analysis of the currentinternational status of degradation...... updating by thecommunity is encouraged. Furthermore, the commonly reported test parameters anddegradation indicators are discussed. The difficulty in standardizing testing due tovariations in cell and stack design, materials and intended purpose of the system isacknowledged. A standardization of reporting...... of long-term single-cell- and stack-tests isproposed....
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
2016-08-26
Aug 26, 2016 ... Department of Computer Science and Engineering, Srinivasa Ramanujan Institute of Technology, Anantapur 515701, India; Department of Computer Science and Engineering, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal 518501, India; Department of Computer Science and ...
Detection of Fusarium in single wheat kernels using spectral Imaging
Polder, G.; Heijden, van der G.W.A.M.; Waalwijk, C.; Young, I.T.
2005-01-01
Fusarium head blight (FHB) is a harmful fungal disease that occurs in small grains. Non-destructive detection of this disease is traditionally done using spectroscopy or image processing. In this paper the combination of these two in the form of spectral imaging is evaluated. Transmission spectral
Stacking technology for a space constrained microsystem
DEFF Research Database (Denmark)
Heschel, Matthias; Kuhmann, Jochen Friedrich; Bouwstra, Siebe
1998-01-01
In this paper we present a stacking technology for an integrated packaging of an intelligent transducer which is formed by a micromachined silicon transducer and an integrated circuit chip. Transducer and circuitry are stacked on top of each other with an intermediate chip in between. The bonding...
Vector Fields and Flows on Differentiable Stacks
DEFF Research Database (Denmark)
A. Hepworth, Richard
2009-01-01
This paper introduces the notions of vector field and flow on a general differentiable stack. Our main theorem states that the flow of a vector field on a compact proper differentiable stack exists and is unique up to a uniquely determined 2-cell. This extends the usual result on the existence...... of vector fields....
40 CFR 61.44 - Stack sampling.
2010-07-01
... 40 Protection of Environment 8 2010-07-01 2010-07-01 false Stack sampling. 61.44 Section 61.44 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL... Firing § 61.44 Stack sampling. (a) Sources subject to § 61.42(b) shall be continuously sampled, during...
Learning OpenStack networking (Neutron)
Denton, James
2014-01-01
If you are an OpenStack-based cloud operator with experience in OpenStack Compute and nova-network but are new to Neutron networking, then this book is for you. Some networking experience is recommended, and a physical network infrastructure is required to provide connectivity to instances and other network resources configured in the book.
Project W-420 stack monitoring system upgrades
International Nuclear Information System (INIS)
CARPENTER, K.E.
1999-01-01
This project will execute the design, procurement, construction, startup, and turnover activities for upgrades to the stack monitoring system on selected Tank Waste Remediation System (TWRS) ventilation systems. In this plan, the technical, schedule, and cost baselines are identified, and the roles and responsibilities of project participants are defined for managing the Stack Monitoring System Upgrades, Project W-420
Hydrodynamic Modelling and Experimental Analysis of FE-DMFC Stacks
Kablou, Yashar
Direct methanol fuel cells (DMFCs) present some unique features such as having liquid fuel, quick refueling process, compact design and high energy density. These characteristics make them incredibly suitable as a promising power source for portable electronic applications, such as cell phones or laptop computers. Despite of these positive aspects, the commercial development of DMFCs has nevertheless been hindered by some important issues such as, carbon dioxide formation at the anode compartment and, methanol crossover through the membrane. Many researchers have tried to model the two-phase flow behavior inside the DMFC anode compartment using the "homogenous flow modelling" approach, which has proven to be inaccurate specially when dealing with DMFC stacks. On the other hand, several strategies to prevent methanol crossover have been suggested in the literature, including the use of a flowing electrolyte between the DMFC anode and cathode compartments. Preliminary tests on flowing electrolyte direct methanol fuel cells (FE-DMFCs) have shown promising results; however, further investigation should be carried out on the stack level. In the first part of this study, a quasi two-dimensional numerical model was developed, to predict the two-phase flow behavior within the DMFC anode compartment, both in single cell and stack levels. Various types of flow modelling approaches and void fraction correlations were utilized to estimate the pressure drop across the anode compartment. It was found that the "separated flow modelling" approach, as well as CISE correlation for void fraction (developed at the CISE labs in Milan), yield the best results. In the second part, a five-cell FE-DMFC stack unit with a parallel serpentine flow bed design and U-type manifold configuration, was developed and tested at various operating conditions. It was found that, the flowing electrolyte effectively reduced methanol crossover and, improved the stack performance.
Kernel-based tests for joint independence
DEFF Research Database (Denmark)
Pfister, Niklas; Bühlmann, Peter; Schölkopf, Bernhard
2018-01-01
if the $d$ variables are jointly independent, as long as the kernel is characteristic. Based on an empirical estimate of dHSIC, we define three different non-parametric hypothesis tests: a permutation test, a bootstrap test and a test based on a Gamma approximation. We prove that the permutation test......We investigate the problem of testing whether $d$ random variables, which may or may not be continuous, are jointly (or mutually) independent. Our method builds on ideas of the two variable Hilbert-Schmidt independence criterion (HSIC) but allows for an arbitrary number of variables. We embed...... the $d$-dimensional joint distribution and the product of the marginals into a reproducing kernel Hilbert space and define the $d$-variable Hilbert-Schmidt independence criterion (dHSIC) as the squared distance between the embeddings. In the population case, the value of dHSIC is zero if and only...
Wilson Dslash Kernel From Lattice QCD Optimization
Energy Technology Data Exchange (ETDEWEB)
Joo, Balint [Jefferson Lab, Newport News, VA; Smelyanskiy, Mikhail [Parallel Computing Lab, Intel Corporation, California, USA; Kalamkar, Dhiraj D. [Parallel Computing Lab, Intel Corporation, India; Vaidyanathan, Karthikeyan [Parallel Computing Lab, Intel Corporation, India
2015-07-01
Lattice Quantum Chromodynamics (LQCD) is a numerical technique used for calculations in Theoretical Nuclear and High Energy Physics. LQCD is traditionally one of the first applications ported to many new high performance computing architectures and indeed LQCD practitioners have been known to design and build custom LQCD computers. Lattice QCD kernels are frequently used as benchmarks (e.g. 168.wupwise in the SPEC suite) and are generally well understood, and as such are ideal to illustrate several optimization techniques. In this chapter we will detail our work in optimizing the Wilson-Dslash kernels for Intel Xeon Phi, however, as we will show the technique gives excellent performance on regular Xeon Architecture as well.
Status of MCFC stack technology at IHI
Energy Technology Data Exchange (ETDEWEB)
Hosaka, M.; Morita, T.; Matsuyama, T.; Otsubo, M. [Ishikawajima-Harima Heavy Industries Co., Ltd., Tokyo (Japan)
1996-12-31
The molten carbonate fuel cell (MCFC) is a promising option for highly efficient power generation possible to enlarge. IHI has been studying parallel flow MCFC stacks with internal manifolds that have a large electrode area of 1m{sup 2}. IHI will make two 250 kW stacks for MW plant, and has begun to make cell components for the plant. To improve the stability of stack, soft corrugated plate used in the separator has been developed, and a way of gathering current from stacks has been studied. The DC output potential of the plant being very high, the design of electric insulation will be very important. A 20 kW short stack test was conducted in 1995 FY to certificate some of the improvements and components of the MW plant. These activities are presented below.
A Kernel for Protein Secondary Structure Prediction
Guermeur , Yann; Lifchitz , Alain; Vert , Régis
2004-01-01
http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10338&mode=toc; International audience; Multi-class support vector machines have already proved efficient in protein secondary structure prediction as ensemble methods, to combine the outputs of sets of classifiers based on different principles. In this chapter, their implementation as basic prediction methods, processing the primary structure or the profile of multiple alignments, is investigated. A kernel devoted to the task is in...
Searching and Indexing Genomic Databases via Kernelization
Directory of Open Access Journals (Sweden)
Travis eGagie
2015-02-01
Full Text Available The rapid advance of DNA sequencing technologies has yielded databases of thousands of genomes. To search and index these databases effectively, it is important that we take advantage of the similarity between those genomes. Several authors have recently suggested searching or indexing only one reference genome and the parts of the other genomes where they differ. In this paper we survey the twenty-year history of this idea and discuss its relation to kernelization in parameterized complexity.
Kernel based subspace projection of hyperspectral images
DEFF Research Database (Denmark)
Larsen, Rasmus; Nielsen, Allan Aasbjerg; Arngren, Morten
In hyperspectral image analysis an exploratory approach to analyse the image data is to conduct subspace projections. As linear projections often fail to capture the underlying structure of the data, we present kernel based subspace projections of PCA and Maximum Autocorrelation Factors (MAF......). The MAF projection exploits the fact that interesting phenomena in images typically exhibit spatial autocorrelation. The analysis is based on nearinfrared hyperspectral images of maize grains demonstrating the superiority of the kernelbased MAF method....
Multiple Kernel Learning with Data Augmentation
2016-11-22
et al., 2010; Sun et al., 2010). Particularly, Sun et al. (2010) developed an efficient method based on sequential minimal optimization (SMO). The...http://www.robots.ox.ac.uk/~vgg/data/ flowers /17/ 58 Multiple Kernel Learning with Data Augmentation Algorithm 2 MKL with Data Augmentation approach for...Maria-Elena Nilsback and Andrew Zisserman. A visual vocabulary for flower classification. In Com- puter Vision and Pattern Recognition, 2006 IEEE Computer
Multiple Kernel Spectral Regression for Dimensionality Reduction
Liu, Bing; Xia, Shixiong; Zhou, Yong
2013-01-01
Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR) solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL) into SR for dimensionality...
Searching and Indexing Genomic Databases via Kernelization.
Gagie, Travis; Puglisi, Simon J
2015-01-01
The rapid advance of DNA sequencing technologies has yielded databases of thousands of genomes. To search and index these databases effectively, it is important that we take advantage of the similarity between those genomes. Several authors have recently suggested searching or indexing only one reference genome and the parts of the other genomes where they differ. In this paper, we survey the 20-year history of this idea and discuss its relation to kernelization in parameterized complexity.
Scalar contribution to the BFKL kernel
International Nuclear Information System (INIS)
Gerasimov, R. E.; Fadin, V. S.
2010-01-01
The contribution of scalar particles to the kernel of the Balitsky-Fadin-Kuraev-Lipatov (BFKL) equation is calculated. A great cancellation between the virtual and real parts of this contribution, analogous to the cancellation in the quark contribution in QCD, is observed. The reason of this cancellation is discovered. This reason has a common nature for particles with any spin. Understanding of this reason permits to obtain the total contribution without the complicated calculations, which are necessary for finding separate pieces.
Weighted Bergman Kernels for Logarithmic Weights
Czech Academy of Sciences Publication Activity Database
Engliš, Miroslav
2010-01-01
Roč. 6, č. 3 (2010), s. 781-813 ISSN 1558-8599 R&D Projects: GA AV ČR IAA100190802 Keywords : Bergman kernel * Toeplitz operator * logarithmic weight * pseudodifferential operator Subject RIV: BA - General Mathematics Impact factor: 0.462, year: 2010 http://www.intlpress.com/site/pub/pages/journals/items/pamq/content/vols/0006/0003/a008/
Heat kernels and zeta functions on fractals
International Nuclear Information System (INIS)
Dunne, Gerald V
2012-01-01
On fractals, spectral functions such as heat kernels and zeta functions exhibit novel features, very different from their behaviour on regular smooth manifolds, and these can have important physical consequences for both classical and quantum physics in systems having fractal properties. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical in honour of Stuart Dowker's 75th birthday devoted to ‘Applications of zeta functions and other spectral functions in mathematics and physics’. (paper)
Nanocomposite Materials of Alternately Stacked C60 Monolayer and Graphene
International Nuclear Information System (INIS)
Ishikawa, M.; Miura, K.; Kamiya, S.; Yoshimoto, S.; Suzuki, M.; Kuwahara, D.; Sasaki, N.
2010-01-01
We synthesized the novel nanocomposite consisting alternately of a stacked single graphene sheet and a C 60 monolayer by using the graphite intercalation technique in which alkylamine molecules help intercalate large C 60 molecules into the graphite. Moreover, it is found that the intercalated C 60 molecules can rotate in between single graphene sheets by using C 13 NMR measurements. This preparation method provides a general way for intercalating huge fullerene molecules into graphite, which will lead to promising materials with novel mechanical, physical, and electrical properties.
Nanocomposite Materials of Alternately Stacked C60 Monolayer and Graphene
Directory of Open Access Journals (Sweden)
Makoto Ishikawa
2010-01-01
Full Text Available We synthesized the novel nanocomposite consisting alternately of a stacked single graphene sheet and a C60 monolayer by using the graphite intercalation technique in which alkylamine molecules help intercalate large C60 molecules into the graphite. Moreover, it is found that the intercalated C60 molecules can rotate in between single graphene sheets by using C13 NMR measurements. This preparation method provides a general way for intercalating huge fullerene molecules into graphite, which will lead to promising materials with novel mechanical, physical, and electrical properties.
Multiple kernel learning for dimensionality reduction.
Lin, Yen-Yu; Liu, Tyng-Luh; Fuh, Chiou-Shann
2011-06-01
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting data representations are typically high-dimensional and assume diverse forms. Hence, finding a way of transforming them into a unified space of lower dimension generally facilitates the underlying tasks such as object recognition or clustering. To this end, the proposed approach (termed MKL-DR) generalizes the framework of multiple kernel learning for dimensionality reduction, and distinguishes itself with the following three main contributions: first, our method provides the convenience of using diverse image descriptors to describe useful characteristics of various aspects about the underlying data. Second, it extends a broad set of existing dimensionality reduction techniques to consider multiple kernel learning, and consequently improves their effectiveness. Third, by focusing on the techniques pertaining to dimensionality reduction, the formulation introduces a new class of applications with the multiple kernel learning framework to address not only the supervised learning problems but also the unsupervised and semi-supervised ones.
Slip-stacking Dynamics and the 20 Hz Booster
Energy Technology Data Exchange (ETDEWEB)
Eldred, Jeffery; Zwaska, Robert
2015-03-01
Slip-stacking is an accumulation technique used at Fermilab since 2004 which nearly doubles the proton intensity. The Proton Improvement Plan II intensity upgrades require a reduction in slip-stacking losses by approximately a factor of 2. We study the single-particle dynamics that determine the stability of slip-stacking particles. We introduce universal area factors to calculate the available phase space area for any set of beam parameters without individual simulation. We show the particle loss as a function of time. We calculate the injection efficiency as a function of longitudinal emittance and aspect-ratio. We demonstrate that the losses from RF single particle dynamics can be reduced by a factor of 4-10 (depending on beam parameters) by upgrading the Fermilab Booster from a 15-Hz cycle-rate to a 20-Hz cycle-rate. We recommend a change in injection scheme to eliminate the need for a greater momentum aperture in the Fermilab Recycler.
Exploiting graph kernels for high performance biomedical relation extraction.
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
Wantha, Channarong
2018-02-01
This paper reports on the experimental and simulation studies of the influence of stack geometries and different mean pressures on the cold end temperature of the stack in the thermoacoustic refrigeration system. The stack geometry was tested, including spiral stack, circular pore stack and pin array stack. The results of this study show that the mean pressure of the gas in the system has a significant impact on the cold end temperature of the stack. The mean pressure of the gas in the system corresponds to thermal penetration depth, which results in a better cold end temperature of the stack. The results also show that the cold end temperature of the pin array stack decreases more than that of the spiral stack and circular pore stack geometry by approximately 63% and 70%, respectively. In addition, the thermal area and viscous area of the stack are analyzed to explain the results of such temperatures of thermoacoustic stacks.
Akiyama, Hiroshi; Sakata, Kozue; Makiyma, Daiki; Nakamura, Kosuke; Teshima, Reiko; Nakashima, Akie; Ogawa, Asako; Yamagishi, Toru; Futo, Satoshi; Oguchi, Taichi; Mano, Junichi; Kitta, Kazumi
2011-01-01
In many countries, the labeling of grains, feed, and foodstuff is mandatory if the genetically modified (GM) organism content exceeds a certain level of approved GM varieties. We previously developed an individual kernel detection system consisting of grinding individual kernels, DNA extraction from the individually ground kernels, GM detection using multiplex real-time PCR, and GM event detection using multiplex qualitative PCR to analyze the precise commingling level and varieties of GM maize in real sample grains. We performed the interlaboratory study of the DNA extraction with multiple ground samples, multiplex real-time PCR detection, and multiplex qualitative PCR detection to evaluate its applicability, practicality, and ruggedness for the individual kernel detection system of GM maize. DNA extraction with multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR were evaluated by five laboratories in Japan, and all results from these laboratories were consistent with the expected results in terms of the commingling level and event analysis. Thus, the DNA extraction with multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR for the individual kernel detection system is applicable and practicable in a laboratory to regulate the commingling level of GM maize grain for GM samples, including stacked GM maize.
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.
Spam comments prediction using stacking with ensemble learning
Mehmood, Arif; On, Byung-Won; Lee, Ingyu; Ashraf, Imran; Choi, Gyu Sang
2018-01-01
Illusive comments of product or services are misleading for people in decision making. The current methodologies to predict deceptive comments are concerned for feature designing with single training model. Indigenous features have ability to show some linguistic phenomena but are hard to reveal the latent semantic meaning of the comments. We propose a prediction model on general features of documents using stacking with ensemble learning. Term Frequency/Inverse Document Frequency (TF/IDF) features are inputs to stacking of Random Forest and Gradient Boosted Trees and the outputs of the base learners are encapsulated with decision tree to make final training of the model. The results exhibits that our approach gives the accuracy of 92.19% which outperform the state-of-the-art method.
Energy Technology Data Exchange (ETDEWEB)
Le, Anh Dinh; Zhou, Biao [Department of Mechanical, Automotive and Materials Engineering, University of Windsor, 401 Sunset Ave., Windsor, ON (Canada)
2010-08-15
In this study, the simulation of a fuel cell stack is performed by applying a general numerical model with VOF method that has been successfully applied to single PEMFC model to investigate the fluid dynamics, mass transport, flooding phenomenon and the effects of liquid water on the stack performance. The performance of three single cells in series connection in the fuel cell stack is examined according to the presence of liquid water in different single cells. The distributions of fluid flow, species concentration and the current density are presented to illustrate the effects of liquid water on the performance of each single cell. The numerical results locate that the low distributions of species in the flooding cell certainly degrade the performance of this cell. Moreover, it can be seen that the performance of the flooding cell will significantly affect the whole stack performance since the values of average current density must be identical in all single cells. (author)
Black Hole Spectroscopy with Coherent Mode Stacking.
Yang, Huan; Yagi, Kent; Blackman, Jonathan; Lehner, Luis; Paschalidis, Vasileios; Pretorius, Frans; Yunes, Nicolás
2017-04-21
The measurement of multiple ringdown modes in gravitational waves from binary black hole mergers will allow for testing the fundamental properties of black holes in general relativity and to constrain modified theories of gravity. To enhance the ability of Advanced LIGO/Virgo to perform such tasks, we propose a coherent mode stacking method to search for a chosen target mode within a collection of multiple merger events. We first rescale each signal so that the target mode in each of them has the same frequency and then sum the waveforms constructively. A crucial element to realize this coherent superposition is to make use of a priori information extracted from the inspiral-merger phase of each event. To illustrate the method, we perform a study with simulated events targeting the ℓ=m=3 ringdown mode of the remnant black holes. We show that this method can significantly boost the signal-to-noise ratio of the collective target mode compared to that of the single loudest event. Using current estimates of merger rates, we show that it is likely that advanced-era detectors can measure this collective ringdown mode with one year of coincident data gathered at design sensitivity.
Smith, Joshua Wyatt; The ATLAS collaboration
2016-01-01
The ATLAS experiment explores new hardware and software platforms that, in the future, may be more suited to its data intensive workloads. One such alternative hardware platform is the ARM architecture, which is designed to be extremely power efficient and is found in most smartphones and tablets. CERN openlab recently installed a small cluster of ARM 64-bit evaluation prototype servers. Each server is based on a single-socket ARM 64-bit system on a chip, with 32 Cortex-A57 cores. In total, each server has 128 GB RAM connected with four fast memory channels. This paper reports on the port of the ATLAS software stack onto these new prototype ARM64 servers. This included building the "external" packages that the ATLAS software relies on. Patches were needed to introduce this new architecture into the build as well as patches that correct for platform specific code that caused failures on non-x86 architectures. These patches were applied such that porting to further platforms will need no or only very little adj...
Lithiation-induced shuffling of atomic stacks
Nie, Anmin
2014-09-10
In rechargeable lithium-ion batteries, understanding the atomic-scale mechanism of Li-induced structural evolution occurring at the host electrode materials provides essential knowledge for design of new high performance electrodes. Here, we report a new crystalline-crystalline phase transition mechanism in single-crystal Zn-Sb intermetallic nanowires upon lithiation. Using in situ transmission electron microscopy, we observed that stacks of atomic planes in an intermediate hexagonal (h-)LiZnSb phase are "shuffled" to accommodate the geometrical confinement stress arising from lamellar nanodomains intercalated by lithium ions. Such atomic rearrangement arises from the anisotropic lithium diffusion and is accompanied by appearance of partial dislocations. This transient structure mediates further phase transition from h-LiZnSb to cubic (c-)Li2ZnSb, which is associated with a nearly "zero-strain" coherent interface viewed along the [001]h/[111]c directions. This study provides new mechanistic insights into complex electrochemically driven crystalline-crystalline phase transitions in lithium-ion battery electrodes and represents a noble example of atomic-level structural and interfacial rearrangements.
Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.
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.
P- and S-wave Receiver Function Imaging with Scattering Kernels
Hansen, S. M.; Schmandt, B.
2017-12-01
Full waveform inversion provides a flexible approach to the seismic parameter estimation problem and can account for the full physics of wave propagation using numeric simulations. However, this approach requires significant computational resources due to the demanding nature of solving the forward and adjoint problems. This issue is particularly acute for temporary passive-source seismic experiments (e.g. PASSCAL) that have traditionally relied on teleseismic earthquakes as sources resulting in a global scale forward problem. Various approximation strategies have been proposed to reduce the computational burden such as hybrid methods that embed a heterogeneous regional scale model in a 1D global model. In this study, we focus specifically on the problem of scattered wave imaging (migration) using both P- and S-wave receiver function data. The proposed method relies on body-wave scattering kernels that are derived from the adjoint data sensitivity kernels which are typically used for full waveform inversion. The forward problem is approximated using ray theory yielding a computationally efficient imaging algorithm that can resolve dipping and discontinuous velocity interfaces in 3D. From the imaging perspective, this approach is closely related to elastic reverse time migration. An energy stable finite-difference method is used to simulate elastic wave propagation in a 2D hypothetical subduction zone model. The resulting synthetic P- and S-wave receiver function datasets are used to validate the imaging method. The kernel images are compared with those generated by the Generalized Radon Transform (GRT) and Common Conversion Point stacking (CCP) methods. These results demonstrate the potential of the kernel imaging approach to constrain lithospheric structure in complex geologic environments with sufficiently dense recordings of teleseismic data. This is demonstrated using a receiver function dataset from the Central California Seismic Experiment which shows several
Density of oxidation-induced stacking faults in damaged silicon
Kuper, F.G.; Hosson, J.Th.M. De; Verwey, J.F.
1986-01-01
A model for the relation between density and length of oxidation-induced stacking faults on damaged silicon surfaces is proposed, based on interactions of stacking faults with dislocations and neighboring stacking faults. The model agrees with experiments.
A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control
Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.
2014-01-01
Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569
Kernel-Based Sensor Fusion With Application to Audio-Visual Voice Activity Detection
Dov, David; Talmon, Ronen; Cohen, Israel
2016-12-01
In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences. Recent studies have approached this problem using kernel methods, by relying particularly on a product of kernels constructed separately for each view. From a graph theory point of view, we analyze this fusion approach in a discrete setting. More specifically, based on a statistical model for the connectivity between data points, we propose an algorithm for the selection of the kernel bandwidth, a parameter, which, as we show, has important implications on the robustness of this fusion approach to interferences. Then, we consider the fusion of audio-visual speech signals measured by a single microphone and by a video camera pointed to the face of the speaker. Specifically, we address the task of voice activity detection, i.e., the detection of speech and non-speech segments, in the presence of structured interferences such as keyboard taps and office noise. We propose an algorithm for voice activity detection based on the audio-visual signal. Simulation results show that the proposed algorithm outperforms competing fusion and voice activity detection approaches. In addition, we demonstrate that a proper selection of the kernel bandwidth indeed leads to improved performance.
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...... 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......- tor transform outperform the linear methods as well as kernel principal components in producing interesting projections of the data....
International Nuclear Information System (INIS)
Liu, Xiaowei; Zhang, Bo; Zhang, Yufeng; He, Hong; Li, Jianmin; Wang, Shibo; Yuan, Zhenyu; Deng, Huichao
2010-01-01
An air-breathing 10-cell micro direct methanol fuel cell (µDMFC) stack with four anode feeding patterns is designed, fabricated and tested. For a better understanding of the operational characteristics of both the single cell and the stack, a two-dimensional numerical model is established and calculated. Employing micro-stamping technology, the current collectors of each single cell are microfabricated on the stainless steel plate with a thickness of 300 µm. The single µDMFC is first tested under various operating parameters. On the basis of the simulation and experimental observation of the single cell performance, the µDMFC stack performance is thoroughly analyzed with different anode feeding patterns. The results indicate that the µDMFC stack with pattern B can ensure the uniform performance of each single cell and generate the highest power output. With pattern B, further experiments are carried out to investigate the influence of the anode flow rate on the stack performance. As a result, the µDMFC stack achieves the best performance with the maximum power density of about 24.75 mW cm −2 at 5.0 ml min −1 . Finally, the stack is successfully applied to two electronic devices of different rated power
Liu, Xiaowei; Zhang, Bo; Zhang, Yufeng; He, Hong; Li, Jianmin; Wang, Shibo; Yuan, Zhenyu; Deng, Huichao
2010-10-01
An air-breathing 10-cell micro direct methanol fuel cell (µDMFC) stack with four anode feeding patterns is designed, fabricated and tested. For a better understanding of the operational characteristics of both the single cell and the stack, a two-dimensional numerical model is established and calculated. Employing micro-stamping technology, the current collectors of each single cell are microfabricated on the stainless steel plate with a thickness of 300 µm. The single µDMFC is first tested under various operating parameters. On the basis of the simulation and experimental observation of the single cell performance, the µDMFC stack performance is thoroughly analyzed with different anode feeding patterns. The results indicate that the µDMFC stack with pattern B can ensure the uniform performance of each single cell and generate the highest power output. With pattern B, further experiments are carried out to investigate the influence of the anode flow rate on the stack performance. As a result, the µDMFC stack achieves the best performance with the maximum power density of about 24.75 mW cm-2 at 5.0 ml min-1. Finally, the stack is successfully applied to two electronic devices of different rated power.
Tunable electro-optic filter stack
Energy Technology Data Exchange (ETDEWEB)
Fontecchio, Adam K.; Shriyan, Sameet K.; Bellingham, Alyssa
2017-09-05
A holographic polymer dispersed liquid crystal (HPDLC) tunable filter exhibits switching times of no more than 20 microseconds. The HPDLC tunable filter can be utilized in a variety of applications. An HPDLC tunable filter stack can be utilized in a hyperspectral imaging system capable of spectrally multiplexing hyperspectral imaging data acquired while the hyperspectral imaging system is airborne. HPDLC tunable filter stacks can be utilized in high speed switchable optical shielding systems, for example as a coating for a visor or an aircraft canopy. These HPDLC tunable filter stacks can be fabricated using a spin coating apparatus and associated fabrication methods.
Dynamical stability of slip-stacking particles
Energy Technology Data Exchange (ETDEWEB)
Eldred, Jeffrey; Zwaska, Robert
2014-09-01
We study the stability of particles in slip-stacking configuration, used to nearly double proton beam intensity at Fermilab. We introduce universal area factors to calculate the available phase space area for any set of beam parameters without individual simulation. We find perturbative solutions for stable particle trajectories. We establish Booster beam quality requirements to achieve 97% slip-stacking efficiency. We show that slip-stacking dynamics directly correspond to the driven pendulum and to the system of two standing-wave traps moving with respect to each other.
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...... kernels and did not give acceptable results because of high misclassification. However by using a predefined threshold and classifying entire kernels based on the number of correctly predicted pixels, improved results were achieved (sensitivity and specificity of 0.75 and 0.97). Object-wise classification...... was performed using two methods for feature extraction — score histograms and mean spectra. The model based on score histograms performed better for hard kernel classification (sensitivity and specificity of 0.93 and 0.97), while that of mean spectra gave better results for medium kernels (sensitivity...
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...... Analysis (KPCA) that only keeps features contributing mostly to image reconstruction, KECA selects the CKD that contribute mostly to the Rényi entropy of the image. These CKD are discriminative as they relate to the density distribution of the histogram of image attributes. We report superior performance...
Ray, Sibdas; Das, Aniruddha
2015-06-01
Reaction of 2-ethoxymethyleneamino-2-cyanoacetamide with primary alkyl amines in acetonitrile solvent affords 1-substituted-5-aminoimidazole-4-carboxamides. Single crystal X-ray diffraction studies of these imidazole compounds show that there are both anti-parallel and syn-parallel π-π stackings between two imidazole units in parallel-displaced (PD) conformations and the distance between two π-π stacked imidazole units depends mainly on the anti/ syn-parallel nature and to some extent on the alkyl group attached to N-1 of imidazole; molecules with anti-parallel PD-stacking arrangements of the imidazole units have got vertical π-π stacking distance short enough to impart stabilization whereas the imidazole unit having syn-parallel stacking arrangement have got much larger π-π stacking distances. DFT studies on a pair of anti-parallel imidazole units of such an AICA lead to curves for 'π-π stacking stabilization energy vs. π-π stacking distance' which have got similarity with the 'Morse potential energy diagram for a diatomic molecule' and this affords to find out a minimum π-π stacking distance corresponding to the maximum stacking stabilization energy between the pair of imidazole units. On the other hand, a DFT calculation based curve for 'π-π stacking stabilization energy vs. π-π stacking distance' of a pair of syn-parallel imidazole units is shown to have an exponential nature.
Visualization of nonlinear kernel models in neuroimaging by sensitivity maps
DEFF Research Database (Denmark)
Rasmussen, Peter Mondrup; Hansen, Lars Kai; Madsen, Kristoffer Hougaard
show that the performance of linear models is reduced for certain scan labelings/categorizations in this data set, while the nonlinear models provide more flexibility. We show that the sensitivity map can be used to visualize nonlinear versions of kernel logistic regression, the kernel Fisher...... discriminant, and the SVM, and conclude that the sensitivity map is a versatile and computationally efficient tool for visualization of nonlinear kernel models in neuroimaging...
Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression
Peter Exterkate; Patrick J.F. Groenen; Christiaan Heij; Dick van Dijk
2011-01-01
textabstractThis paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predictive regression model is based on a shrinkage estimator to avoid overfitting. We extend the kernel ridge regression methodology to enable its use for economic time-series forecasting, by ...
Parameter optimization in the regularized kernel minimum noise fraction transformation
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack
2012-01-01
Based on the original, linear minimum noise fraction (MNF) transformation and kernel principal component analysis, a kernel version of the MNF transformation was recently introduced. Inspired by we here give a simple method for finding optimal parameters in a regularized version of kernel MNF...... analysis. We consider the model signal-to-noise ratio (SNR) as a function of the kernel parameters and the regularization parameter. In 2-4 steps of increasingly refined grid searches we find the parameters that maximize the model SNR. An example based on data from the DLR 3K camera system is given....
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......) dimensional feature space via the kernel function and then performing a linear analysis in that space. An example shows the successful application of kernel MAF analysis to change detection in HyMap data covering a small agricultural area near Lake Waging-Taching, Bavaria, Germany....
Visualization of nonlinear kernel models in neuroimaging by sensitivity maps
DEFF Research Database (Denmark)
Rasmussen, P.M.; Madsen, Kristoffer H; Lund, T.E.
on visualization of such nonlinear kernel models. Specifically, we investigate the sensitivity map as a technique for generation of global summary maps of kernel classification methods. We illustrate the performance of the sensitivity map on functional magnetic resonance (fMRI) data based on visual stimuli. We...... show that the performance of linear models is reduced for certain scan labelings/categorizations in this data set, while the nonlinear models provide more flexibility. We show that the sensitivity map can be used to visualize nonlinear versions of kernel logistic regression, the kernel Fisher...
Wearable solar cells by stacking textile electrodes.
Pan, Shaowu; Yang, Zhibin; Chen, Peining; Deng, Jue; Li, Houpu; Peng, Huisheng
2014-06-10
A new and general method to produce flexible, wearable dye-sensitized solar cell (DSC) textiles by the stacking of two textile electrodes has been developed. A metal-textile electrode that was made from micrometer-sized metal wires was used as a working electrode, while the textile counter electrode was woven from highly aligned carbon nanotube fibers with high mechanical strengths and electrical conductivities. The resulting DSC textile exhibited a high energy conversion efficiency that was well maintained under bending. Compared with the woven DSC textiles that are based on wire-shaped devices, this stacked DSC textile unexpectedly exhibited a unique deformation from a rectangle to a parallelogram, which is highly desired in portable electronics. This lightweight and wearable stacked DSC textile is superior to conventional planar DSCs because the energy conversion efficiency of the stacked DSC textile was independent of the angle of incident light. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
STACKING FAULT ENERGY IN HIGH MANGANESE ALLOYS
Directory of Open Access Journals (Sweden)
Eva Mazancová
2009-04-01
Full Text Available Stacking fault energy of high manganese alloys (marked as TWIP and TRIPLEX is an important parameter determining deformation mechanism type realized in above mentioned alloys. Stacking fault energy level can be asserted with a gliding of partial and/or full dislocations, b gliding mechanism and twinning deformation process in connection with increasing of fracture deformation level (deformation elongation and with increasing of simultaneously realized work hardening proces., c gliding mechanism and deformation induced e-martensite formation. In contribution calculated stacking fault energies are presented for various chemical compositions of high manganese alloys. Stacking fault energy dependences on manganese, carbon, iron and alluminium contents are presented. Results are confronted with some accessible papers.The aim of work is to deepen knowledge of presented data. The TWIP and TRIPLEX alloys can be held for promissing new automotive materials.
Stack-Based Typed Assembly Language
National Research Council Canada - National Science Library
Morrisett, Greg
1998-01-01
.... This paper also formalizes the typing connection between CPS based compilation and stack based compilation and illustrates how STAL can formally model calling conventions by specifying them as formal translations of source function types to STAL types.
Characterization of Piezoelectric Stacks for Space Applications
Sherrit, Stewart; Jones, Christopher; Aldrich, Jack; Blodget, Chad; Bao, Xiaoqi; Badescu, Mircea; Bar-Cohen, Yoseph
2008-01-01
Future NASA missions are increasingly seeking to actuate mechanisms to precision levels in the nanometer range and below. Co-fired multilayer piezoelectric stacks offer the required actuation precision that is needed for such mechanisms. To obtain performance statistics and determine reliability for extended use, sets of commercial PZT stacks were tested in various AC and DC conditions at both nominal and high temperatures and voltages. In order to study the lifetime performance of these stacks, five actuators were driven sinusoidally for up to ten billion cycles. An automated data acquisition system was developed and implemented to monitor each stack's electrical current and voltage waveforms over the life of the test. As part of the monitoring tests, the displacement, impedance, capacitance and leakage current were measured to assess the operation degradation. This paper presents some of the results of this effort.
The stack on software and sovereignty
Bratton, Benjamin H
2016-01-01
A comprehensive political and design theory of planetary-scale computation proposing that The Stack -- an accidental megastructure -- is both a technological apparatus and a model for a new geopolitical architecture.
Valdez, Thomas I.; Firdosy, S.; Koel, B. E.; Narayanan, S. R.
2005-01-01
Dissolution of ruthenium was observed in the 80-cell stack. Duration testing was performed in single cell MEAs to determine the pathway of cell degradation. EDAX analysis on each of the single cell MEAs has shown that the Johnson Matthey commercial catalyst is stable in DMFC operation for 250 hours, no ruthenium dissolution was observed. Changes in the hydrophobicity of the cathode backing papers was minimum. Electrode polarization analysis revealed that the MEA performance loss is attributed to changes in the cathode catalyst layer. Ruthenium migration does not seem to occur during cell operation but can occur when methanol is absent from the anode compartment, the cathode compartment has access to air, and the cells in the stack are electrically connected to a load (Shunt Currents). The open-to-air cathode stack design allowed for: a) The MEAs to have continual access to oxygen; and b) The stack to sustain shunt currents. Ruthenium dissolution in a DMFC stack can be prevented by: a) Developing an internally manifolded stacks that seal reactant compartments when not in operation; b) Bringing the cell voltages to zero quickly when not in operation; and c) Limiting the total number of cells to 25 in an effort to limit shunt currents.
Bialas, David; Zitzler-Kunkel, André; Kirchner, Eva; Schmidt, David; Würthner, Frank
2016-01-01
Exciton coupling is of fundamental importance and determines functional properties of organic dyes in (opto-)electronic and photovoltaic devices. Here we show that strong exciton coupling is not limited to the situation of equal chromophores as often assumed. Quadruple dye stacks were obtained from two bis(merocyanine) dyes with same or different chromophores, respectively, which dimerize in less-polar solvents resulting in the respective homo- and heteroaggregates. The structures of the quadruple dye stacks were assigned by NMR techniques and unambiguously confirmed by single-crystal X-ray analysis. The heteroaggregate stack formed from the bis(merocyanine) bearing two different chromophores exhibits remarkably different ultraviolet/vis absorption bands compared with those of the homoaggregate of the bis(merocyanine) comprising two identical chromophores. Quantum chemical analysis based on an extension of Kasha's exciton theory appropriately describes the absorption properties of both types of stacks revealing strong exciton coupling also between different chromophores within the heteroaggregate. PMID:27680284
Methane Steam Reforming over an Ni-YSZ Solid Oxide Fuel Cell Anode in Stack Configuration
Directory of Open Access Journals (Sweden)
D. Mogensen
2014-01-01
Full Text Available The kinetics of catalytic steam reforming of methane over an Ni-YSZ anode of a solid oxide fuel cell (SOFC have been investigated with the cell placed in a stack configuration. In order to decrease the degree of conversion, a single cell stack with reduced area was used. Measurements were performed in the temperature range 600–800°C and the partial pressures of all reactants and products were varied. The obtained rates could be well fitted with a power law expression (r ∝PCH40.7. A simple model is presented which is capable of predicting the methane conversion in a stack configuration from intrinsic kinetics of the anode support material. The predictions are compared with the stack measurements presented here, and good agreement is observed.
Stacking for Cosmic Magnetism with SKA Surveys
Stil, J. M.; Keller, B. W.
2015-01-01
Stacking polarized radio emission in SKA surveys provides statistical information on large samples that is not accessible otherwise due to limitations in sensitivity, source statistics in small fields, and averaging over frequency (including Faraday synthesis). Polarization is a special case because one obvious source of stacking targets is the Stokes I source catalog, possibly in combination with external catalogs, for example an SKA HI survey or a non-radio survey. We point out the signific...
Environmental Modeling Framework using Stacked Gaussian Processes
Abdelfatah, Kareem; Bao, Junshu; Terejanu, Gabriel
2016-01-01
A network of independently trained Gaussian processes (StackedGP) is introduced to obtain predictions of quantities of interest with quantified uncertainties. The main applications of the StackedGP framework are to integrate different datasets through model composition, enhance predictions of quantities of interest through a cascade of intermediate predictions, and to propagate uncertainties through emulated dynamical systems driven by uncertain forcing variables. By using analytical first an...
Generalized data stacking programming model with applications
Hala Samir Elhadidy; Rawya Yehia Rizk; Hassen Taher Dorrah
2016-01-01
Recent researches have shown that, everywhere in various sciences the systems are following stacked-based stored change behavior when subjected to events or varying environments “on and above” their normal situations. This paper presents a generalized data stack programming (GDSP) model which is developed to describe the system changes under varying environment. These changes which are captured with different ways such as sensor reading are stored in matrices. Extraction algorithm and identif...
Representations of stack triangulations in the plane
Selig, Thomas
2013-01-01
Stack triangulations appear as natural objects when defining an increasing family of triangulations by successive additions of vertices. We consider two different probability distributions for such objects. We represent, or "draw" these random stack triangulations in the plane $\\R^2$ and study the asymptotic properties of these drawings, viewed as random compact metric spaces. We also look at the occupation measure of the vertices, and show that for these two distributions it converges to som...
Álvarez, Aitor; Sierra, Basilio; Arruti, Andoni; López-Gil, Juan-Miguel; Garay-Vitoria, Nestor
2015-01-01
In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level multi-classifier system known as stacking generalization by means of an integration of an estimation of distribution algorithm (EDA) in the first layer to select the optimal subset from the standard base classifiers. The good performance of the proposed new paradigm was demonstrated over different configurations and datasets. First, several CSS stacking classifiers were constructed on the RekEmozio dataset, using some specific standard base classifiers and a total of 123 spectral, quality and prosodic features computed using in-house feature extraction algorithms. These initial CSS stacking classifiers were compared to other multi-classifier systems and the employed standard classifiers built on the same set of speech features. Then, new CSS stacking classifiers were built on RekEmozio using a different set of both acoustic parameters (extended version of the Geneva Minimalistic Acoustic Parameter Set (eGeMAPS)) and standard classifiers and employing the best meta-classifier of the initial experiments. The performance of these two CSS stacking classifiers was evaluated and compared. Finally, the new paradigm was tested on the well-known Berlin Emotional Speech database. We compared the performance of single, standard stacking and CSS stacking systems using the same parametrization of the second phase. All of the classifications were performed at the categorical level, including the six primary emotions plus the neutral one. PMID:26712757
Fixed kernel regression for voltammogram feature extraction
International Nuclear Information System (INIS)
Acevedo Rodriguez, F J; López-Sastre, R J; Gil-Jiménez, P; Maldonado Bascón, S; Ruiz-Reyes, N
2009-01-01
Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals
Index-free Heat Kernel Coefficients
van de Ven, Anton E. M.
1997-01-01
Using index-free notation, we present the diagonal values of the first five heat kernel coefficients associated with a general Laplace-type operator on a compact Riemannian space without boundary. The fifth coefficient appears here for the first time. For a flat space with a gauge connection, the sixth coefficient is given too. Also provided are the leading terms for any coefficient, both in ascending and descending powers of the Yang-Mills and Riemann curvatures, to the same order as require...
Localized Multiple Kernel Learning A Convex Approach
2016-11-22
1/2 . Theorem 9 (CLMKL Generalization Error Bounds) Assume that km(x, x) ≤ B, ∀m ∈ NM , x ∈ X . Suppose the loss function ℓ is L- Lipschitz and...mathematical foundation (e.g., Schölkopf and Smola, 2002). The performance of such algorithms, however, crucially depends on the involved kernel function ...approaches to localized MKL (reviewed in Section 1.1) optimize non-convex objective functions . This puts their generalization ability into doubt. Indeed
Learning Rotation for Kernel Correlation Filter
Hamdi, Abdullah
2017-08-11
Kernel Correlation Filters have shown a very promising scheme for visual tracking in terms of speed and accuracy on several benchmarks. However it suffers from problems that affect its performance like occlusion, rotation and scale change. This paper tries to tackle the problem of rotation by reformulating the optimization problem for learning the correlation filter. This modification (RKCF) includes learning rotation filter that utilizes circulant structure of HOG feature to guesstimate rotation from one frame to another and enhance the detection of KCF. Hence it gains boost in overall accuracy in many of OBT50 detest videos with minimal additional computation.
Kernel-based machine learning techniques for infrasound signal classification
Tuma, Matthias; Igel, Christian; Mialle, Pierrick
2014-05-01
Infrasound monitoring is one of four remote sensing technologies continuously employed by the CTBTO Preparatory Commission. The CTBTO's infrasound network is designed to monitor the Earth for potential evidence of atmospheric or shallow underground nuclear explosions. Upon completion, it will comprise 60 infrasound array stations distributed around the globe, of which 47 were certified in January 2014. Three stages can be identified in CTBTO infrasound data processing: automated processing at the level of single array stations, automated processing at the level of the overall global network, and interactive review by human analysts. At station level, the cross correlation-based PMCC algorithm is used for initial detection of coherent wavefronts. It produces estimates for trace velocity and azimuth of incoming wavefronts, as well as other descriptive features characterizing a signal. Detected arrivals are then categorized into potentially treaty-relevant versus noise-type signals by a rule-based expert system. This corresponds to a binary classification task at the level of station processing. In addition, incoming signals may be grouped according to their travel path in the atmosphere. The present work investigates automatic classification of infrasound arrivals by kernel-based pattern recognition methods. It aims to explore the potential of state-of-the-art machine learning methods vis-a-vis the current rule-based and task-tailored expert system. To this purpose, we first address the compilation of a representative, labeled reference benchmark dataset as a prerequisite for both classifier training and evaluation. Data representation is based on features extracted by the CTBTO's PMCC algorithm. As classifiers, we employ support vector machines (SVMs) in a supervised learning setting. Different SVM kernel functions are used and adapted through different hyperparameter optimization routines. The resulting performance is compared to several baseline classifiers. All
Nuclear Magnetic Shieldings of Stacked Aromatic and Antiaromatic Molecules.
Sundholm, Dage; Rauhalahti, Markus; Özcan, Nergiz; Mera-Adasme, Raúl; Kussmann, Jörg; Luenser, Arne; Ochsenfeld, Christian
2017-05-09
Nuclear magnetic shieldings have been calculated at the density functional theory (DFT) level for stacks of benzene, hexadehydro[12]annulene, dodecadehydro[18]annulene, and hexabenzocoronene. The magnetic shieldings due to the ring currents in the adjacent molecules have been estimated by calculating nucleus independent molecular shieldings for the monomer in the atomic positions of neighbor molecules. The calculations show that the independent shielding model works reasonably well for the 1 H NMR shieldings of benzene and hexadehydro[12]annulene, whereas for the larger molecules and for the 13 C NMR shieldings the interaction between the molecules leads to shielding effects that are at least of the same size as the ring current contributions from the adjacent molecules. A better agreement is obtained when the nearest neighbors are also considered at full quantum mechanical (QM) level. The calculations suggest that the nearest solvent molecules must be included in the quantum mechanical system, at least when estimating solvent shifts at the molecular mechanics (MM) level. Current density calculations show that the stacking does not significantly affect the ring current strengths of the individual molecules, whereas the shape of the ring current for a single molecule differs from that of the stacked molecules.
Multibands tunneling in AAA-stacked trilayer graphene
Redouani, Ilham; Jellal, Ahmed; Bahaoui, Abdelhadi; Bahlouli, Hocine
2018-04-01
We study the electronic transport through np and npn junctions for AAA-stacked trilayer graphene. Two kinds of gates are considered where the first is a single gate and the second is a double gate. After obtaining the solutions for the energy spectrum, we use the transfer matrix method to determine the three transmission probabilities for each individual cone τ = 0 , ± 1 . We show that the quasiparticles in AAA-stacked trilayer graphene are not only chiral but also labeled by an additional cone index τ. The obtained bands are composed of three Dirac cones that depend on the chirality indexes. We show that there is perfect transmission for normal or near normal incidence, which is a manifestation of the Klein tunneling effect. We analyze also the corresponding total conductance, which is defined as the sum of the conductance channels in each individual cone. Our results are numerically discussed and compared with those obtained for ABA- and ABC-stacked trilayer graphene.
A Time-predictable Stack Cache
DEFF Research Database (Denmark)
Abbaspour, Sahar; Brandner, Florian; Schoeberl, Martin
2013-01-01
Real-time systems need time-predictable architectures to support static worst-case execution time (WCET) analysis. One architectural feature, the data cache, is hard to analyze when different data areas (e.g., heap allocated and stack allocated data) share the same cache. This sharing leads to le...... of a cache for stack allocated data. Our port of the LLVM C++ compiler supports the management of the stack cache. The combination of stack cache instructions and the hardware implementation of the stack cache is a further step towards timepredictable architectures.......Real-time systems need time-predictable architectures to support static worst-case execution time (WCET) analysis. One architectural feature, the data cache, is hard to analyze when different data areas (e.g., heap allocated and stack allocated data) share the same cache. This sharing leads to less...... precise results of the cache analysis part of the WCET analysis. Splitting the data cache for different data areas enables composable data cache analysis. The WCET analysis tool can analyze the accesses to these different data areas independently. In this paper we present the design and implementation...
Detailed Electrochemical Characterisation of Large SOFC Stacks
DEFF Research Database (Denmark)
Mosbæk, Rasmus Rode; Hjelm, Johan; Barfod, R.
2012-01-01
As solid oxide fuel cell (SOFC) technology is moving closer to a commercial break through, lifetime limiting factors, determination of the limits of safe operation and methods to measure the “state-of-health” of operating cells and stacks are becoming of increasing interest. This requires applica...... out at a range of ac perturbation amplitudes in order to investigate linearity of the response and the signal-to-noise ratio. Separation of the measured impedance into series and polarisation resistances was possible....... to analyse in detail. Today one is forced to use mathematical modelling to extract information about existing gradients and cell resistances in operating stacks, as mature techniques for local probing are not available. This type of spatially resolved information is essential for model refinement...... and validation, and helps to further the technological stack development. Further, more detailed information obtained from operating stacks is essential for developing appropriate process monitoring and control protocols for stack and system developers. An experimental stack with low ohmic resistance from Topsoe...
Zhang, David; Corlet, Aurélie; Fouilloux, Stephane
2008-06-01
Real-time Polymerase Chain Reaction (PCR) based assays are widely used to estimate the content of genetically modified (GM) materials in food, feed and seed. It has been known that the genetic structures of the analyte can significantly influence the GM content expressed by the haploid genome (HG) % estimated using real-time PCR assays; this kind of influence is also understood as the impact of biological factors. The influence was first simulated at theoretical level using maize as a model. We then experimentally assessed the impact of biological factors on quantitative results, analysing by quantitative real-time PCR six maize MON 810 hybrid kernels with different genetic structures: (1) hemizygous from transgenic male parent, (2) hemizygous from transgenic female parent and (3) homozygous at the transgenic locus. The results obtained in the present study showed clear influences of biological factors on GM DNA quantification: 1% of GM materials by weight (wt) for the three genetic structures contained 0.39, 0.55 and 1.0% of GM DNA by HG respectively, from quantitative real-time PCR analyses. The relationships between GM wt% and GM HG% can be empirically established as: (1) in the case of the presence of a single GM trait: GM HG% = GM wt% x (0.5 +/- 0.167Y), where Y is the endosperm DNA content (%) in the total DNA of a maize kernel, (2) in the case of the presence of multiple GM traits: GM HG% = N x GM wt% x (0.5 +/- 0.167Y), where N is the number of GM traits (stacked or not) present in an unknown sample. This finding can be used by stakeholders related to GMO for empirical prediction from one unit of expression to another in the monitoring of seed and grain production chains. Practical equations have also been suggested for haploid copy number calculations, using hemizygous GM materials for calibration curves.
vanDijk, P; Wit, HP; Segenhout, JM
1997-01-01
Wiener kernel analysis was used to characterize the auditory pathway from tympanic membrane to single primary auditory nerve fibers in the European edible frog, Rana esculenta. Nerve fiber signals were recorded in response to white Gaussian noise. By cross-correlating the noise stimulus and the
Index-free heat kernel coefficients
van de Ven, Anton E. M.
1998-08-01
Using index-free notation, we present the diagonal values 0264-9381/15/8/014/img1 of the first five heat kernel coefficients 0264-9381/15/8/014/img2 associated with a general Laplace-type operator on a compact Riemannian space without boundary. The fifth coefficient 0264-9381/15/8/014/img3 appears here for the first time. For the special case of a flat space, but with a gauge connection, the sixth coefficient is given too. Also provided are the leading terms for any coefficient, both in ascending and descending powers of the Yang-Mills and Riemann curvatures, to the same order as required for the fourth coefficient. These results are obtained by directly solving the relevant recursion relations, working in the Fock-Schwinger gauge and Riemann normal coordinates. Our procedure is thus non-covariant, but we show that for any coefficient the `gauged', respectively `curved', version is found from the corresponding `non-gauged', respectively `flat', coefficient by making some simple covariant substitutions. These substitutions being understood, the coefficients retain their `flat' form and size. In this sense the fifth and sixth coefficient have only 26 and 75 terms, respectively, allowing us to write them down. Using index-free notation also clarifies the general structure of the heat kernel coefficients. In particular, in flat space we find that from the fifth coefficient onward, certain scalars are absent. This may be relevant for the anomalies of quantum field theories in ten or more dimensions.
The Kernel Estimation in Biosystems Engineering
Directory of Open Access Journals (Sweden)
Esperanza Ayuga Téllez
2008-04-01
Full Text Available In many fields of biosystems engineering, it is common to find works in which statistical information is analysed that violates the basic hypotheses necessary for the conventional forecasting methods. For those situations, it is necessary to find alternative methods that allow the statistical analysis considering those infringements. Non-parametric function estimation includes methods that fit a target function locally, using data from a small neighbourhood of the point. Weak assumptions, such as continuity and differentiability of the target function, are rather used than "a priori" assumption of the global target function shape (e.g., linear or quadratic. In this paper a few basic rules of decision are enunciated, for the application of the non-parametric estimation method. These statistical rules set up the first step to build an interface usermethod for the consistent application of kernel estimation for not expert users. To reach this aim, univariate and multivariate estimation methods and density function were analysed, as well as regression estimators. In some cases the models to be applied in different situations, based on simulations, were defined. Different biosystems engineering applications of the kernel estimation are also analysed in this review.
Kernelized rank learning for personalized drug recommendation.
He, Xiao; Folkman, Lukas; Borgwardt, Karsten
2018-03-08
Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patient's molecular profile. These comprehensive screens differ from the clinical setting in which (1) medical records only contain the response of a patient to very few drugs, (2) drugs are recommended by doctors based on their expert judgment, and (3) selecting the most promising therapy is often more important than accurately predicting the sensitivity to all potential drugs. Current regression models for drug sensitivity prediction fail to account for these three properties. We present a machine learning approach, named Kernelized Rank Learning (KRL), that ranks drugs based on their predicted effect per cell line (patient), circumventing the difficult problem of precisely predicting the sensitivity to the given drug. Our approach outperforms several state-of-the-art predictors in drug recommendation, particularly if the training dataset is sparse, and generalizes to patient data. Our work phrases personalized drug recommendation as a new type of machine learning problem with translational potential to the clinic. The Python implementation of KRL and scripts for running our experiments are available at https://github.com/BorgwardtLab/Kernelized-Rank-Learning. xiao.he@bsse.ethz.ch, lukas.folkman@bsse.ethz.ch. Supplementary data are available at Bioinformatics online.
Scientific Computing Kernels on the Cell Processor
Energy Technology Data Exchange (ETDEWEB)
Williams, Samuel W.; Shalf, John; Oliker, Leonid; Kamil, Shoaib; Husbands, Parry; Yelick, Katherine
2007-04-04
The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. As a result, the high performance computing community is examining alternative architectures that address the limitations of modern cache-based designs. In this work, we examine the potential of using the recently-released STI Cell processor as a building block for future high-end computing systems. Our work contains several novel contributions. First, we introduce a performance model for Cell and apply it to several key scientific computing kernels: dense matrix multiply, sparse matrix vector multiply, stencil computations, and 1D/2D FFTs. The difficulty of programming Cell, which requires assembly level intrinsics for the best performance, makes this model useful as an initial step in algorithm design and evaluation. Next, we validate the accuracy of our model by comparing results against published hardware results, as well as our own implementations on a 3.2GHz Cell blade. Additionally, we compare Cell performance to benchmarks run on leading superscalar (AMD Opteron), VLIW (Intel Itanium2), and vector (Cray X1E) architectures. Our work also explores several different mappings of the kernels and demonstrates a simple and effective programming model for Cell's unique architecture. Finally, we propose modest microarchitectural modifications that could significantly increase the efficiency of double-precision calculations. Overall results demonstrate the tremendous potential of the Cell architecture for scientific computations in terms of both raw performance and power efficiency.
Delimiting areas of endemism through kernel interpolation.
Oliveira, Ubirajara; Brescovit, Antonio D; Santos, Adalberto J
2015-01-01
We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE), based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units.
Delimiting areas of endemism through kernel interpolation.
Directory of Open Access Journals (Sweden)
Ubirajara Oliveira
Full Text Available We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE, based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units.
Start-Stop Test Procedures on the PEMFC Stack Level
DEFF Research Database (Denmark)
Mitzel, Jens; Nygaard, Frederik; Veltzé, Sune
The test is addressed to investigate the influence on stack durability of a long stop followed by a restart of a stack. Long stop should be defined as a stop in which the anodic compartment is fully filled by air due to stack leakages. In systems, leakage level of the stack is low and time to fil...
Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.
2014-10-01
In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.
Predictive Model Equations for Palm Kernel (Elaeis guneensis J ...
African Journals Online (AJOL)
A 3-factor experimental design was used to determine the influence of moisture content, roasting duration and temperature on palm kernel and sesame oil colours. Four levels each of these parameters were used. The data obtained were used to develop prediction models for palm kernel and sesame oil colours. Coefficient ...
Evaluation of enzyme supplementation of palm kernel meal-based ...
African Journals Online (AJOL)
Journal of Agriculture, Forestry and the Social Sciences ... The results of this study showed that broilers can tolerate 20% inclusion rate of palm kernel meal in their rations without enzyme supplementation and partially replacing maize with palm kernel meal at that level of inclusion can reduce the cost of production of ...
Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter; Groenen, Patrick J.F.; Heij, Christiaan
This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predi...
Efficient methods for robust classification under uncertainty in kernel matrices
Ben-Tal, A.; Bhadra, S.; Bhattacharyya, C.; Nemirovski, A.
2012-01-01
In this paper we study the problem of designing SVM classifiers when the kernel matrix, K , is affected by uncertainty. Specifically K is modeled as a positive affine combination of given positive semi definite kernels, with the coefficients ranging in a norm-bounded uncertainty set. We treat the
Design and construction of palm kernel cracking and separation ...
African Journals Online (AJOL)
Design and construction of palm kernel cracking and separation machines. ... Username, Password, Remember me, or Register. DOWNLOAD FULL TEXT Open Access DOWNLOAD FULL TEXT Subscription or Fee Access. Design and construction of palm kernel cracking and separation machines. JO Nordiana, K ...
Homotopy deform method for reproducing kernel space for ...
Indian Academy of Sciences (India)
In this paper, the combination of homotopy deform method (HDM) and simplified reproducing kernel method (SRKM) is introduced for solving the boundary value problems (BVPs) of nonlinear differential equations. The solution methodology is based on Adomian decomposition and reproducing kernel method (RKM).
Replacement Value of Palm Kernel Meal for Maize on Carcass ...
African Journals Online (AJOL)
This study was conducted to evaluate the effect of replacing maize with palm kernel meal on nutrient composition, fatty acid profile and sensory qualities of the meat of turkeys fed the dietary treatments. Six dietary treatments were formulated using palm kernel meal to replace maize at 0, 20, 40, 60, 80 and 100 percent.
Effect of Palm Kernel Cake Replacement and Enzyme ...
African Journals Online (AJOL)
A feeding trial which lasted for twelve weeks was conducted to study the performance of finisher pigs fed five different levels of palm kernel cake replacement for maize (0%, 40%, 40%, 60%, 60%) in a maize-palm kernel cake based ration with or without enzyme supplementation. It was a completely randomized design ...
Extracting Feature Model Changes from the Linux Kernel Using FMDiff
Dintzner, N.J.R.; Van Deursen, A.; Pinzger, M.
2014-01-01
The Linux kernel feature model has been studied as an example of large scale evolving feature model and yet details of its evolution are not known. We present here a classification of feature changes occurring on the Linux kernel feature model, as well as a tool, FMDiff, designed to automatically
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...
High-power asymptotics of some weighted harmonic Bergman kernels
Czech Academy of Sciences Publication Activity Database
Engliš, Miroslav
2016-01-01
Roč. 271, č. 5 (2016), s. 1243-1261 ISSN 0022-1236 Institutional support: RVO:67985840 Keywords : Bergman kernel * harmonic Bergman kernel * asymptotic expansion Subject RIV: BA - General Mathematics Impact factor: 1.254, year: 2016 http://www.sciencedirect.com/science/article/pii/S0022123616301513
Efficient Kernel-based 2DPCA for Smile Stages Recognition
Directory of Open Access Journals (Sweden)
Fitri Damayanti
2012-03-01
Full Text Available Recently, an approach called two-dimensional principal component analysis (2DPCA has been proposed for smile stages representation and recognition. The essence of 2DPCA is that it computes the eigenvectors of the so-called image covariance matrix without matrix-to-vector conversion so the size of the image covariance matrix are much smaller, easier to evaluate covariance matrix, computation cost is reduced and the performance is also improved than traditional PCA. In an effort to improve and perfect the performance of smile stages recognition, in this paper, we propose efficient Kernel based 2DPCA concepts. The Kernelization of 2DPCA can be benefit to develop the nonlinear structures in the input data. This paper discusses comparison of standard Kernel based 2DPCA and efficient Kernel based 2DPCA for smile stages recognition. The results of experiments show that Kernel based 2DPCA achieve better performance in comparison with the other approaches. While the use of efficient Kernel based 2DPCA can speed up the training procedure of standard Kernel based 2DPCA thus the algorithm can achieve much more computational efficiency and remarkably save the memory consuming compared to the standard Kernel based 2DPCA.
Nutritional evaluation of palm kernel meal types: 1. Proximate ...
African Journals Online (AJOL)
Studies were conducted to determine the proximate composition and metabolizable energy values of palm kernel meal (PKM) types. The PKM types studied were obtained from Okomu, Presco and Envoy Oil Mills and were either mechanically or solvent extracted using different varieties of palm kernels. Samples of PKM ...
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...
Screening of the kernels of Pentadesma butyracea from various ...
African Journals Online (AJOL)
Gwla10
Pentadesma butyracea Sabine (Clusiaceae) is a ligneous forest species of multipurpose uses. It is widely distributed in Africa from Guinea-Bissau to the West of the Democratic Republic of Congo. This study screened the kernel of P. butyracea on the basis of their physico-chemical properties. Six types of kernels were ...
A relationship between Gel'fand-Levitan and Marchenko kernels
International Nuclear Information System (INIS)
Kirst, T.; Von Geramb, H.V.; Amos, K.A.
1989-01-01
An integral equation which relates the output kernels of the Gel'fand-Levitan and Marchenko inverse scattering equations is specified. Structural details of this integral equation are studied when the S-matrix is a rational function, and the output kernels are separable in terms of Bessel, Hankel and Jost solutions. 4 refs
Boundary singularity of Poisson and harmonic Bergman kernels
Czech Academy of Sciences Publication Activity Database
Engliš, Miroslav
2015-01-01
Roč. 429, č. 1 (2015), s. 233-272 ISSN 0022-247X R&D Projects: GA AV ČR IAA100190802 Institutional support: RVO:67985840 Keywords : harmonic Bergman kernel * Poisson kernel * pseudodifferential boundary operators Subject RIV: BA - General Mathematics Impact factor: 1.014, year: 2015 http://www.sciencedirect.com/science/article/pii/S0022247X15003170
Real time kernel performance monitoring with SystemTap
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.
Commutators of Integral Operators with Variable Kernels on Hardy ...
Indian Academy of Sciences (India)
Home; Journals; Proceedings – Mathematical Sciences; Volume 115; Issue 4. Commutators of Integral Operators with Variable Kernels on Hardy Spaces. Pu Zhang Kai Zhao. Volume 115 Issue 4 November 2005 pp 399-410 ... Keywords. Singular and fractional integrals; variable kernel; commutator; Hardy space.
Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression
P. Exterkate (Peter); P.J.F. Groenen (Patrick); C. Heij (Christiaan); D.J.C. van Dijk (Dick)
2011-01-01
textabstractThis paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of
On sensitivity kernels for 'wave-equation' transmission tomography
Hoop, Maarten V. de; Hilst, R.D. van der
2004-01-01
We combine seismological scattering theory with the theory of distributions to study some properties of sensitivity kernels for finite frequency seismic delay times. The theory to be used for calculating the kernels depends on the way the measurements are made. For example, the sensitivity to the
Commutators of integral operators with variable kernels on Hardy ...
Indian Academy of Sciences (India)
Home; Journals; Proceedings – Mathematical Sciences; Volume 115; Issue 4. Commutators of Integral Operators with Variable Kernels on Hardy Spaces. Pu Zhang Kai Zhao. Volume 115 Issue 4 November 2005 pp 399-410 ... Keywords. Singular and fractional integrals; variable kernel; commutator; Hardy space.
3-D waveform tomography sensitivity kernels for anisotropic media
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.
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....
Compactly Supported Basis Functions as Support Vector Kernels for Classification.
Wittek, Peter; Tan, Chew Lim
2011-10-01
Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.
Triso coating development progress for uranium nitride kernels
Energy Technology Data Exchange (ETDEWEB)
Jolly, Brian C. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Lindemer, Terrence [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Terrani, Kurt A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2015-08-01
In support of fully ceramic matrix (FCM) fuel development [1-2], coating development work is ongoing at the Oak Ridge National Laboratory (ORNL) to produce tri-structural isotropic (TRISO) coated fuel particles with UN kernels [3]. The nitride kernels are used to increase fissile density in these SiC-matrix fuel pellets with details described elsewhere [4]. The advanced gas reactor (AGR) program at ORNL used fluidized bed chemical vapor deposition (FBCVD) techniques for TRISO coating of UCO (two phase mixture of UO2 and UCx) kernels [5]. Similar techniques were employed for coating of the UN kernels, however significant changes in processing conditions were required to maintain acceptable coating properties due to physical property and dimensional differences between the UCO and UN kernels (Table 1).
EmuStack: An OpenStack-Based DTN Network Emulation Platform (Extended Version
Directory of Open Access Journals (Sweden)
Haifeng Li
2016-01-01
Full Text Available With the advancement of computing and network virtualization technology, the networking research community shows great interest in network emulation. Compared with network simulation, network emulation can provide more relevant and comprehensive details. In this paper, EmuStack, a large-scale real-time emulation platform for Delay Tolerant Network (DTN, is proposed. EmuStack aims at empowering network emulation to become as simple as network simulation. Based on OpenStack, distributed synchronous emulation modules are developed to enable EmuStack to implement synchronous and dynamic, precise, and real-time network emulation. Meanwhile, the lightweight approach of using Docker container technology and network namespaces allows EmuStack to support a (up to hundreds of nodes large-scale topology with only several physical nodes. In addition, EmuStack integrates the Linux Traffic Control (TC tools with OpenStack for managing and emulating the virtual link characteristics which include variable bandwidth, delay, loss, jitter, reordering, and duplication. Finally, experiences with our initial implementation suggest the ability to run and debug experimental network protocol in real time. EmuStack environment would bring qualitative change in network research works.
Levitation characteristics of HTS tape stacks
Energy Technology Data Exchange (ETDEWEB)
Pokrovskiy, S. V.; Ermolaev, Y. S.; Rudnev, I. A. [National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow (Russian Federation)
2015-03-15
Due to the considerable development of the technology of second generation high-temperature superconductors and a significant improvement in their mechanical and transport properties in the last few years it is possible to use HTS tapes in the magnetic levitation systems. The advantages of tapes on a metal substrate as compared with bulk YBCO material primarily in the strength, and the possibility of optimizing the convenience of manufacturing elements of levitation systems. In the present report presents the results of the magnetic levitation force measurements between the stack of HTS tapes containing of tapes and NdFeB permanent magnet in the FC and ZFC regimes. It was found a non- linear dependence of the levitation force from the height of the array of stack in both modes: linear growth at small thickness gives way to flattening and constant at large number of tapes in the stack. Established that the levitation force of stacks comparable to that of bulk samples. The numerical calculations using finite element method showed that without the screening of the applied field the levitation force of the bulk superconductor and the layered superconductor stack with a critical current of tapes increased by the filling factor is exactly the same, and taking into account the screening force slightly different.
Forced Air-Breathing PEMFC Stacks
Directory of Open Access Journals (Sweden)
K. S. Dhathathreyan
2012-01-01
Full Text Available Air-breathing fuel cells have a great potential as power sources for various electronic devices. They differ from conventional fuel cells in which the cells take up oxygen from ambient air by active or passive methods. The air flow occurs through the channels due to concentration and temperature gradient between the cell and the ambient conditions. However developing a stack is very difficult as the individual cell performance may not be uniform. In order to make such a system more realistic, an open-cathode forced air-breathing stacks were developed by making appropriate channel dimensions for the air flow for uniform performance in a stack. At CFCT-ARCI (Centre for Fuel Cell Technology-ARC International we have developed forced air-breathing fuel cell stacks with varying capacity ranging from 50 watts to 1500 watts. The performance of the stack was analysed based on the air flow, humidity, stability, and so forth, The major advantage of the system is the reduced number of bipolar plates and thereby reduction in volume and weight. However, the thermal management is a challenge due to the non-availability of sufficient air flow to remove the heat from the system during continuous operation. These results will be discussed in this paper.
Contemporary sample stacking in analytical electrophoresis.
Malá, Zdena; Šlampová, Andrea; Křivánková, Ludmila; Gebauer, Petr; Boček, Petr
2015-01-01
This contribution is a methodological review of the publications about the topic from the last 2 years. Therefore, it is primarily organized according to the methods and procedures used in surveyed papers and the origin and type of sample and specification of analytes form the secondary structure. The introductory part about navigation in the architecture of stacking brings a brief characterization of the various stacking methods, with the description of mutual links to each other and important differences among them. The main body of the article brings a survey of publications organized according to main principles of stacking and then according to the origin and type of the sample. Provided that the paper cited gave explicitly the relevant data, information about the BGE(s) used, procedure, detector employed, and reached LOD and/or concentration effect is given. The papers where the procedure used is a combination of diverse fragments and parts of various stacking techniques are mentioned in a special section on combined techniques. The concluding remarks in the final part of the review evaluate present state of art and the trends of sample stacking in CE. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Enhanced dynamical stability with harmonic slip stacking
Directory of Open Access Journals (Sweden)
Jeffrey Eldred
2016-10-01
Full Text Available We develop a configuration of radio-frequency (rf cavities to dramatically improve the performance of slip stacking. Slip stacking is an accumulation technique used at Fermilab to nearly double proton intensity by maintaining two beams of different momenta in the same storage ring. The two particle beams are longitudinally focused in the Recycler by two 53 MHz 100 kV rf cavities with a small frequency difference between them. We propose an additional 106 MHz 20 kV rf cavity with a frequency at the double the average of the upper and lower main rf frequencies. We show the harmonic rf cavity cancels out the resonances generated between the two main rf cavities and we derive the relationship between the harmonic rf voltage and the main rf voltage. We find the area factors that can be used to calculate the available phase space area for any set of beam parameters without individual simulation. We establish Booster beam quality requirements to achieve 99% slip stacking efficiency. We measure the longitudinal distribution of the Booster beam and use it to generate a realistic beam model for slip stacking simulation. We demonstrate that the harmonic rf cavity can not only reduce particle loss during slip stacking, but also reduce the final longitudinal emittance.
Performance and Emission of VCR-CI Engine with palm kernel and eucalyptus blends
Directory of Open Access Journals (Sweden)
Srinivas kommana
2016-09-01
Full Text Available This study aims at complete replacement of conventional diesel fuel by biodiesel. In order to achieve that, palm kernel oil and eucalyptus oil blend has been chosen. Eucalyptus oil was blended with methyl ester of palm kernel oil in 5%, 10% and 15% by volume. Tests were conducted with diesel fuel and blends on a 4 stroke VCR diesel engine for comparative analysis with 220 bar injection pressure and 19:1 compression ratio. All the test fuels were used in computerized 4 stroke single cylinder variable compression ratio engine at five different loads (0, 6, 12, 18 and 24 N m. Present investigation depicts the improved combustion and reduced emissions for the PKO85% + EuO15% blend when compared to diesel at full load conditions.
A weighted string kernel for protein fold recognition.
Nojoomi, Saghi; Koehl, Patrice
2017-08-25
Alignment-free methods for comparing protein sequences have proved to be viable alternatives to approaches that first rely on an alignment of the sequences to be compared. Much work however need to be done before those methods provide reliable fold recognition for proteins whose sequences share little similarity. We have recently proposed an alignment-free method based on the concept of string kernels, SeqKernel (Nojoomi and Koehl, BMC Bioinformatics, 2017, 18:137). In this previous study, we have shown that while Seqkernel performs better than standard alignment-based methods, its applications are potentially limited, because of biases due mostly to sequence length effects. In this study, we propose improvements to SeqKernel that follows two directions. First, we developed a weighted version of the kernel, WSeqKernel. Second, we expand the concept of string kernels into a novel framework for deriving information on amino acids from protein sequences. Using a dataset that only contains remote homologs, we have shown that WSeqKernel performs remarkably well in fold recognition experiments. We have shown that with the appropriate weighting scheme, we can remove the length effects on the kernel values. WSeqKernel, just like any alignment-based sequence comparison method, depends on a substitution matrix. We have shown that this matrix can be optimized so that sequence similarity scores correlate well with structure similarity scores. Starting from no information on amino acid similarity, we have shown that we can derive a scoring matrix that echoes the physico-chemical properties of amino acids. We have made progress in characterizing and parametrizing string kernels as alignment-based methods for comparing protein sequences, and we have shown that they provide a framework for extracting sequence information from structure.
3-D sensitivity kernels of the Rayleigh wave ellipticity
Maupin, Valérie
2017-10-01
The ellipticity of the Rayleigh wave at the surface depends on the seismic structure beneath and in the vicinity of the seismological station where it is measured. We derive here the expression and compute the 3-D kernels that describe this dependence with respect to S-wave velocity, P-wave velocity and density. Near-field terms as well as coupling to Love waves are included in the expressions. We show that the ellipticity kernels are the difference between the amplitude kernels of the radial and vertical components of motion. They show maximum values close to the station, but with a complex pattern, even when smoothing in a finite-frequency range is used to remove the oscillatory pattern present in mono-frequency kernels. In order to follow the usual data processing flow, we also compute and analyse the kernels of the ellipticity averaged over incoming wave backazimuth. The kernel with respect to P-wave velocity has the simplest lateral variation and is in good agreement with commonly used 1-D kernels. The kernels with respect to S-wave velocity and density are more complex and we have not been able to find a good correlation between the 3-D and 1-D kernels. Although it is clear that the ellipticity is mostly sensitive to the structure within half-a-wavelength of the station, the complexity of the kernels within this zone prevents simple approximations like a depth dependence times a lateral variation to be useful in the inversion of the ellipticity.
A high-performance aluminum-feed microfluidic fuel cell stack
Wang, Yifei; Leung, Dennis Y. C.
2016-12-01
In this paper, a six-cell microfluidic fuel cell (MFC) stack is demonstrated. Low-cost aluminum is fed directly to the stack, which produces hydrogen fuel on site, through the Al-H2O reaction. This design is not only cost-efficient, but also eliminates the need for hydrogen storage. Unlike the conventional MFC stacks which generally require complex electrolyte distribution and management, the present Al-feed MFC stack requires only a single electrolyte stream, flowing successively through individual cells, which is finally utilized for hydrogen generation. In this manner, the whole system is greatly simplified while the operational robustness is also improved. With 2 M sodium hydroxide solution as electrolyte and kitchen foil Al as fuel, the present six-cell stack (in series) exhibits an open circuit voltage of nearly 6 V and a peak power density of 180.6 mWcm-2 at room temperature. In addition, an energy density of 1 Whg-1(Al) is achieved, which is quite high and comparable with its proton exchange membrane-based counterparts. Finally, pumpless operation of the present stack, together with its practical applications are successfully demonstrated, including lightening LED lights, driving an electric fan, and cell phone charging.
A method for manufacturing kernels of metallic oxides and the thus obtained kernels
International Nuclear Information System (INIS)
Lelievre Bernard; Feugier, Andre.
1973-01-01
A method is described for manufacturing fissile or fertile metal oxide kernels, consisting in adding at least a chemical compound capable of releasing ammonia to an aqueous solution of actinide nitrates dispersing the thus obtained solution dropwise in a hot organic phase so as to gelify the drops and transform them into solid particles, washing drying and treating said particles so as to transform them into oxide kernels. Such a method is characterized in that the organic phase used in the gel-forming reactions comprises a mixture of two organic liquids, one of which acts as a solvent, whereas the other is a product capable of extracting the metal-salt anions from the drops while the gel forming reaction is taking place. This can be applied to the so-called high temperature nuclear reactors [fr
Learning molecular energies using localized graph kernels
Ferré, Grégoire; Haut, Terry; Barros, Kipton
2017-03-01
Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.
Heat kernel methods for Lifshitz theories
Barvinsky, Andrei O.; Blas, Diego; Herrero-Valea, Mario; Nesterov, Dmitry V.; Pérez-Nadal, Guillem; Steinwachs, Christian F.
2017-06-01
We study the one-loop covariant effective action of Lifshitz theories using the heat kernel technique. The characteristic feature of Lifshitz theories is an anisotropic scaling between space and time. This is enforced by the existence of a preferred foliation of space-time, which breaks Lorentz invariance. In contrast to the relativistic case, covariant Lifshitz theories are only invariant under diffeomorphisms preserving the foliation structure. We develop a systematic method to reduce the calculation of the effective action for a generic Lifshitz operator to an algorithm acting on known results for relativistic operators. In addition, we present techniques that drastically simplify the calculation for operators with special properties. We demonstrate the efficiency of these methods by explicit applications.
Modified kernel-based nonlinear feature extraction.
Energy Technology Data Exchange (ETDEWEB)
Ma, J. (Junshui); Perkins, S. J. (Simon J.); Theiler, J. P. (James P.); Ahalt, S. (Stanley)
2002-01-01
Feature Extraction (FE) techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of Kernel-based nonlinear FE ( H E ) algorithms has attracted much attention due to their high performance. However, a serious limitation that is inherent in these algorithms -- the maximal number of features extracted by them is limited by the number of classes involved -- dramatically degrades their flexibility. Here we propose a modified version of those KFE algorithms (MKFE), This algorithm is developed from a special form of scatter-matrix, whose rank is not determined by the number of classes involved, and thus breaks the inherent limitation in those KFE algorithms. Experimental results suggest that MKFE algorithm is .especially useful when the training set is small.
Heat kernel method and its applications
Avramidi, Ivan G
2015-01-01
The heart of the book is the development of a short-time asymptotic expansion for the heat kernel. This is explained in detail and explicit examples of some advanced calculations are given. In addition some advanced methods and extensions, including path integrals, jump diffusion and others are presented. The book consists of four parts: Analysis, Geometry, Perturbations and Applications. The first part shortly reviews of some background material and gives an introduction to PDEs. The second part is devoted to a short introduction to various aspects of differential geometry that will be needed later. The third part and heart of the book presents a systematic development of effective methods for various approximation schemes for parabolic differential equations. The last part is devoted to applications in financial mathematics, in particular, stochastic differential equations. Although this book is intended for advanced undergraduate or beginning graduate students in, it should also provide a useful reference ...
Progress of MCFC stack technology at Toshiba
Energy Technology Data Exchange (ETDEWEB)
Hori, M.; Hayashi, T.; Shimizu, Y. [Toshiba Corp., Tokyo (Japan)
1996-12-31
Toshiba is working on the development of MCFC stack technology; improvement of cell characteristics, and establishment of separator technology. For the cell technology, Toshiba has concentrated on both the restraints of NiO cathode dissolution and electrolyte loss from cells, which are the critical issues to extend cell life in MCFC, and great progress has been made. On the other hand, recognizing that the separator is one of key elements in accomplishing reliable and cost-competitive MCFC stacks, Toshiba has been accelerating the technology establishment and verification of an advanced type separator. A sub-scale stack with such a separator was provided for an electric generating test, and has been operated for more than 10,000 hours. This paper presents several topics obtained through the technical activities in the MCFC field at Toshiba.
Celluclast 1.5L pretreatment enhanced aroma of palm kernels and oil after kernel roasting.
Zhang, Wencan; Zhao, Fangju; Yang, Tiankui; Zhao, Feifei; Liu, Shaoquan
2017-12-01
The aroma of palm kernel oil (PKO) affects its applications. Little information is available on how enzymatic modification of palm kernels (PK) affects PK and PKO aroma after kernel roasting. Celluclast (cellulase) pretreatment of PK resulted in a 2.4-fold increment in the concentration of soluble sugars, with glucose being increased by 6.0-fold. Higher levels of 1.7-, 1.8- and 1.9-fold of O-heterocyclic volatile compounds were found in the treated PK after roasting at 180 °C for 8, 14 and 20 min respectively relative to the corresponding control, with furfural, 5-methyl-2-furancarboxaldehyde, 2-furanmethanol and maltol in particularly higher amounts. Volatile differences between PKOs from control and treated PK were also found, though less obvious owing to the aqueous extraction process. Principal component analysis based on aroma-active compounds revealed that upon the proceeding of roasting, the differentiation between control and treated PK was enlarged while that of corresponding PKOs was less clear-cut. Celluclast pretreatment enabled the medium roasted PK to impart more nutty, roasty and caramelic odor and the corresponding PKO to impart more caramelic but less roasty and burnt notes. Celluclast pretreatment of PK followed by roasting may be a promising new way of improving PKO aroma. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.
Design Handbook for a Stack Foundation
Tuominen, Vilma
2011-01-01
This thesis was made for Citec Engineering Oy Ab as a handbook and as a design tool for concrete structure designers. Handbook is about the Wärtsilä Power Plant stack structure, which is a base for about 40 meters high stack pipe. The purpose is to make a calculation base to support the design work, which helps the designer to check the right dimensions of the structure. Thesis is about to be for the concrete designers and also other designers and authorities. As an example I have used an...
Simple model of stacking-fault energies
DEFF Research Database (Denmark)
Stokbro, Kurt; Jacobsen, Lærke Wedel
1993-01-01
A simple model for the energetics of stacking faults in fcc metals is constructed. The model contains third-nearest-neighbor pairwise interactions and a term involving the fourth moment of the electronic density of states. The model is in excellent agreement with recently published local-density ......A simple model for the energetics of stacking faults in fcc metals is constructed. The model contains third-nearest-neighbor pairwise interactions and a term involving the fourth moment of the electronic density of states. The model is in excellent agreement with recently published local...
Stochastic subset selection for learning with kernel machines.
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.
Deep Restricted Kernel Machines Using Conjugate Feature Duality.
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.
Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.
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.
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.
Training Lp norm multiple kernel learning in the primal.
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.
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.
Gilkey-de Witt heat kernel expansion and zero modes
International Nuclear Information System (INIS)
Alonso-Izquierdo, A.; Mateos Guilarte, J.
2013-01-01
In this paper we propose a generalization of the Gilkey-de Witt heat kernel expansion, designed to provide us with a precise estimation of the heat trace of non-negative Schr¨odinger type differential operators with non-trivial kernel over all the domain of its “inverse temperature” variable β. We apply this modified approach to compute effectively the one-loop kink mass shift for some models whose kink fluctuation operator spectrum is unknown and the only alternative to estimate this magnitude is the use of the heat kernel expansion techniques.
Visualization of nonlinear kernel models in neuroimaging by sensitivity maps
DEFF Research Database (Denmark)
Rasmussen, P.M.; Madsen, Kristoffer H; Lund, T.E.
There is significant current interest in decoding mental states from neuroimages. In this context kernel methods, e.g., support vector machines (SVM) are frequently adopted to learn statistical relations between patterns of brain activation and experimental conditions. In this paper we focus...... on visualization of such nonlinear kernel models. Specifically, we investigate the sensitivity map as a technique for generation of global summary maps of kernel classification methods. We illustrate the performance of the sensitivity map on functional magnetic resonance (fMRI) data based on visual stimuli. We...
Flour quality and kernel hardness connection in winter wheat
Directory of Open Access Journals (Sweden)
Szabó B. P.
2016-12-01
Full Text Available Kernel hardness is controlled by friabilin protein and it depends on the relation between protein matrix and starch granules. Friabilin is present in high concentration in soft grain varieties and in low concentration in hard grain varieties. The high gluten, hard wheat our generally contains about 12.0–13.0% crude protein under Mid-European conditions. The relationship between wheat protein content and kernel texture is usually positive and kernel texture influences the power consumption during milling. Hard-textured wheat grains require more grinding energy than soft-textured grains.
A Truly Jitter-Free Real-Time Kernel
DEFF Research Database (Denmark)
Marian, Nicolae; Jiang, Peng
2008-01-01
Hardware-Software co-design is a powerful method nowadays for the embedded system development. Reducing time to the market, more accuracy and interactivity with the whole system by the co-design developments are available. The paper considers and investigates a co-design solution applied to a real-time...... kernel (RTK) named HARTEX. The objective was to even more improve the timing performances of the kernel in terms of minimized, constant overhead (jitter free), in an application transparent manner. The co-design solution partitions the kernel between a pure software part, with constant overhead...
Robust Learning With Kernel Mean $p$-Power Error Loss.
Chen, Badong; Xing, Lei; Wang, Xin; Qin, Jing; Zheng, Nanning
2017-07-25
Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecond order statistical measure in kernel space, called the kernel mean-p power error (KMPE), including the correntropic loss (C-Loss) as a special case. Some basic properties of KMPE are presented. In particular, we apply the KMPE to extreme learning machine (ELM) and principal component analysis (PCA), and develop two robust learning algorithms, namely ELM-KMPE and PCA-KMPE. Experimental results on synthetic and benchmark data show that the developed algorithms can achieve better performance when compared with some existing methods.
Contemporary sample stacking in analytical electrophoresis
Czech Academy of Sciences Publication Activity Database
Šlampová, Andrea; Malá, Zdeňka; Pantůčková, Pavla; Gebauer, Petr; Boček, Petr
2013-01-01
Roč. 34, č. 1 (2013), s. 3-18 ISSN 0173-0835 R&D Projects: GA ČR GAP206/10/1219 Institutional support: RVO:68081715 Keywords : biological samples * stacking * trace analysis * zone electrophoresis Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 3.161, year: 2013
SRS reactor stack plume marking tests
International Nuclear Information System (INIS)
Petry, S.F.
1992-03-01
Tests performed in 105-K in 1987 and 1988 demonstrated that the stack plume can successfully be made visible (i.e., marked) by introducing smoke into the stack breech. The ultimate objective of these tests is to provide a means during an emergency evacuation so that an evacuee can readily identify the stack plume and evacuate in the opposite direction, thus minimizing the potential of severe radiation exposure. The EPA has also requested DOE to arrange for more tests to settle a technical question involving the correct calculation of stack downwash. New test canisters were received in 1988 designed to produce more smoke per unit time; however, these canisters have not been evaluated, because normal ventilation conditions have not been reestablished in K Area. Meanwhile, both the authorization and procedure to conduct the tests have expired. The tests can be performed during normal reactor operation. It is recommended that appropriate authorization and procedure approval be obtained to resume testing after K Area restart
Testing of Electrodes, Cells and Short Stacks
DEFF Research Database (Denmark)
Hauch, Anne; Mogensen, Mogens Bjerg
2017-01-01
The present contribution describes the electrochemical testing and characterization of electrodes, cells, and short stacks. To achieve the maximum insight and results from testing of electrodes and cells, it is obviously necessary to have a good understanding of the fundamental principles...
Stack Gas Scrubber Makes the Grade
Chemical and Engineering News, 1975
1975-01-01
Describes a year long test of successful sulfur dioxide removal from stack gas with a calcium oxide slurry. Sludge disposal problems are discussed. Cost is estimated at 0.6 mill per kwh not including sludge removal. A flow diagram and equations are included. (GH)
OpenStack Object Storage (Swift) essentials
Kapadia, Amar; Varma, Sreedhar
2015-01-01
If you are an IT administrator and you want to enter the world of cloud storage using OpenStack Swift, then this book is ideal for you. Basic knowledge of Linux and server technology is beneficial to get the most out of the book.
Stacked spheres and lower bound theorem
Indian Academy of Sciences (India)
BASUDEB DATTA
2011-11-20
Nov 20, 2011 ... Preliminaries. Lower bound theorem. On going work. Definitions. An n-simplex is a convex hull of n + 1 affinely independent points. (called vertices) in some Euclidean space R. N . Stacked spheres and lower bound theorem. Basudeb Datta. Indian Institute of Science. 2 / 27 ...
Contemporary sample stacking in analytical electrophoresis
Czech Academy of Sciences Publication Activity Database
Malá, Zdeňka; Šlampová, Andrea; Křivánková, Ludmila; Gebauer, Petr; Boček, Petr
2015-01-01
Roč. 36, č. 1 (2015), s. 15-35 ISSN 0173-0835 R&D Projects: GA ČR(CZ) GA13-05762S Institutional support: RVO:68081715 Keywords : biological samples * stacking * trace analysis * zone electrophoresis Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 2.482, year: 2015
The data type variety of stack algebras
Bergstra, J.A.; Tucker, J.V.
1995-01-01
We define and study the class of all stack algebras as the class of all minimal algebras in a variety defined by an infinite recursively enumerable set of equations. Among a number of results, we show that the initial model of the variety is computable, that its equational theory is decidable,
Photoswitchable Intramolecular H-Stacking of Perylenebisimide
Wang, Jiaobing; Kulago, Artem; Browne, Wesley R.; Feringa, Ben L.
2010-01-01
Dynamic control over the formation of H- or J-type aggregates of chromophores is of fundamental importance for developing responsive organic optoelectronic materials. In this study, the first example of photoswitching between a nonstacked and an intramolecularly H-stacked arrangement of
40 CFR 61.53 - Stack sampling.
2010-07-01
... 40 Protection of Environment 8 2010-07-01 2010-07-01 false Stack sampling. 61.53 Section 61.53 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL... sampling. (a) Mercury ore processing facility. (1) Unless a waiver of emission testing is obtained under...
40 CFR 61.33 - Stack sampling.
2010-07-01
... 40 Protection of Environment 8 2010-07-01 2010-07-01 false Stack sampling. 61.33 Section 61.33 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL... sampling. (a) Unless a waiver of emission testing is obtained under § 61.13, each owner or operator...
OpenStack cloud computing cookbook
Jackson, Kevin
2013-01-01
A Cookbook full of practical and applicable recipes that will enable you to use the full capabilities of OpenStack like never before.This book is aimed at system administrators and technical architects moving from a virtualized environment to cloud environments with familiarity of cloud computing platforms. Knowledge of virtualization and managing linux environments is expected.
Biochemical and molecular characterization of Avena indolines and their role in kernel texture.
Gazza, Laura; Taddei, Federica; Conti, Salvatore; Gazzelloni, Gloria; Muccilli, Vera; Janni, Michela; D'Ovidio, Renato; Alfieri, Michela; Redaelli, Rita; Pogna, Norberto E
2015-02-01
Among cereals, Avena sativa is characterized by an extremely soft endosperm texture, which leads to some negative agronomic and technological traits. On the basis of the well-known softening effect of puroindolines in wheat kernel texture, in this study, indolines and their encoding genes are investigated in Avena species at different ploidy levels. Three novel 14 kDa proteins, showing a central hydrophobic domain with four tryptophan residues and here named vromindoline (VIN)-1,2 and 3, were identified. Each VIN protein in diploid oat species was found to be synthesized by a single Vin gene whereas, in hexaploid A. sativa, three Vin-1, three Vin-2 and two Vin-3 genes coding for VIN-1, VIN-2 and VIN-3, respectively, were described and assigned to the A, C or D genomes based on similarity to their counterparts in diploid species. Expression of oat vromindoline transgenes in the extra-hard durum wheat led to accumulation of vromindolines in the endosperm and caused an approximate 50 % reduction of grain hardness, suggesting a central role for vromindolines in causing the extra-soft texture of oat grain. Further, hexaploid oats showed three orthologous genes coding for avenoindolines A and B, with five or three tryptophan residues, respectively, but very low amounts of avenoindolines were found in mature kernels. The present results identify a novel protein family affecting cereal kernel texture and would further elucidate the phylogenetic evolution of Avena genus.
Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels
Directory of Open Access Journals (Sweden)
Taiyong Li
2016-12-01
Full Text Available Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD, adaptive particle swarm optimization (APSO, and relevance vector machine (RVM—namely, EEMD-APSO-RVM—to predict crude oil price based on the “decomposition and ensemble” framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE, mean absolute percent error (MAPE, and directional statistic (Dstat, showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price.
Energy Technology Data Exchange (ETDEWEB)
Dimits, A M; Wang, C; Caflisch, R; Cohen, B I; Huang, Y
2008-08-06
We investigate the accuracy of and assumptions underlying the numerical binary Monte-Carlo collision operator due to Nanbu [K. Nanbu, Phys. Rev. E 55 (1997)]. The numerical experiments that resulted in the parameterization of the collision kernel used in Nanbu's operator are argued to be an approximate realization of the Coulomb-Lorentz pitch-angle scattering process, for which an analytical solution for the collision kernel is available. It is demonstrated empirically that Nanbu's collision operator quite accurately recovers the effects of Coulomb-Lorentz pitch-angle collisions, or processes that approximate these (such interspecies Coulomb collisions with very small mass ratio) even for very large values of the collisional time step. An investigation of the analytical solution shows that Nanbu's parameterized kernel is highly accurate for small values of the normalized collision time step, but loses some of its accuracy for larger values of the time step. Careful numerical and analytical investigations are presented, which show that the time dependence of the relaxation of a temperature anisotropy by Coulomb-Lorentz collisions has a richer structure than previously thought, and is not accurately represented by an exponential decay with a single decay rate. Finally, a practical collision algorithm is proposed that for small-mass-ratio interspecies Coulomb collisions improves on the accuracy of Nanbu's algorithm.
Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP
Directory of Open Access Journals (Sweden)
Jeffrey B. Endelman
2011-11-01
Full Text Available Many important traits in plant breeding are polygenic and therefore recalcitrant to traditional marker-assisted selection. Genomic selection addresses this complexity by including all markers in the prediction model. A key method for the genomic prediction of breeding values is ridge regression (RR, which is equivalent to best linear unbiased prediction (BLUP when the genetic covariance between lines is proportional to their similarity in genotype space. This additive model can be broadened to include epistatic effects by using other kernels, such as the Gaussian, which represent inner products in a complex feature space. To facilitate the use of RR and nonadditive kernels in plant breeding, a new software package for R called rrBLUP has been developed. At its core is a fast maximum-likelihood algorithm for mixed models with a single variance component besides the residual error, which allows for efficient prediction with unreplicated training data. Use of the rrBLUP software is demonstrated through several examples, including the identification of optimal crosses based on superior progeny value. In cross-validation tests, the prediction accuracy with nonadditive kernels was significantly higher than RR for wheat ( L. grain yield but equivalent for several maize ( L. traits.
Mandaris, Dwi; Moonen, Dominicus Johannes Guilielmus; Schuurmans, Jaap; Leferink, Frank
2017-01-01
A Comparison of 4 different types of antennas, - a biconical (dipole like-type) antenna, a single log periodic dipole antenna (LPDA), a dual-stacked log periodic antenna (dual-stacked LPDA, HL043E) and Extended-Double Ridge Guide (Ext-DRG) Horn antenna has been performed. The aims are to obtain an
Verification of helical tomotherapy delivery using autoassociative kernel regression
International Nuclear Information System (INIS)
Seibert, Rebecca M.; Ramsey, Chester R.; Garvey, Dustin R.; Wesley Hines, J.; Robison, Ben H.; Outten, Samuel S.
2007-01-01
Quality assurance (QA) is a topic of major concern in the field of intensity modulated radiation therapy (IMRT). The standard of practice for IMRT is to perform QA testing for individual patients to verify that the dose distribution will be delivered to the patient. The purpose of this study was to develop a new technique that could eventually be used to automatically evaluate helical tomotherapy treatments during delivery using exit detector data. This technique uses an autoassociative kernel regression (AAKR) model to detect errors in tomotherapy delivery. AAKR is a novel nonparametric model that is known to predict a group of correct sensor values when supplied a group of sensor values that is usually corrupted or contains faults such as machine failure. This modeling scheme is especially suited for the problem of monitoring the fluence values found in the exit detector data because it is able to learn the complex detector data relationships. This scheme still applies when detector data are summed over many frames with a low temporal resolution and a variable beam attenuation resulting from patient movement. Delivery sequences from three archived patients (prostate, lung, and head and neck) were used in this study. Each delivery sequence was modified by reducing the opening time for random individual multileaf collimator (MLC) leaves by random amounts. The error and error-free treatments were delivered with different phantoms in the path of the beam. Multiple autoassociative kernel regression (AAKR) models were developed and tested by the investigators using combinations of the stored exit detector data sets from each delivery. The models proved robust and were able to predict the correct or error-free values for a projection, which had a single MLC leaf decrease its opening time by less than 10 msec. The model also was able to determine machine output errors. The average uncertainty value for the unfaulted projections ranged from 0.4% to 1.8% of the detector
Effects of kernel weight and source-limitation on wheat grain yield ...
African Journals Online (AJOL)
Also, source levels were manipulated through 50% spikelet removal at anthesis to evaluate cultivar source/sink limitations to kernel growth. The results depicted that grain yield, kernel number per spike and 1000 kernel weight were reduced by 24.1%, 9.2% and 23.7% in warmer environment, respectively. Hence, kernel ...
Systematic approach in optimizing numerical memory-bound kernels on GPU
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.
International Nuclear Information System (INIS)
Mori, Shinichiro; Kanematsu, Nobuyuki; Asakura, Hiroshi; Sharp, Gregory C.; Kumagai, Motoki; Dobashi, Suguru; Nakajima, Mio; Yamamoto, Naoyoshi; Kandatsu, Susumu; Baba, Masayuki
2011-01-01
Purpose: We compared four-dimensional (4D) layer-stacking and conventional carbon ion beam distribution in the treatment of lung cancer between ungated and gated respiratory strategies using 4DCT data sets. Methods and Materials: Twenty lung patients underwent 4DCT imaging under free-breathing conditions. Using planning target volumes (PTVs) at respective respiratory phases, two types of compensating bolus were designed, a full single respiratory cycle for the ungated strategy and an approximately 30% duty cycle for the exhalation-gated strategy. Beams were delivered to the PTVs for the ungated and gated strategies, PTV(ungated) and PTV(gated), respectively, which were calculated by combining the respective PTV(Tn)s by layer-stacking and conventional irradiation. Carbon ion beam dose distribution was calculated as a function of respiratory phase by applying a compensating bolus to 4DCT. Accumulated dose distributions were calculated by applying deformable registration. Results: With the ungated strategy, accumulated dose distributions were satisfactorily provided to the PTV, with D95 values for layer-stacking and conventional irradiation of 94.0% and 96.2%, respectively. V20 for the lung and Dmax for the spinal cord were lower with layer-stacking than with conventional irradiation, whereas Dmax for the skin (14.1 GyE) was significantly lower (21.9 GyE). In addition, dose conformation to the GTV/PTV with layer-stacking irradiation was better with the gated than with the ungated strategy. Conclusions: Gated layer-stacking irradiation allows the delivery of a carbon ion beam to a moving target without significant degradation of dose conformity or the development of hot spots.
Toussaint, Michel; Pernet, Kurt; Steens, Marc; Haan, Jurn; Sheers, Nicole
2016-01-01
Air stacking improves cough effectiveness in people with Duchenne muscular dystrophy (DMD) and respiratory muscle weakness. However, it is not known whether air stacking is more effective via a resuscitator bag or a home ventilator. This prospective randomized study investigated the effect of air stacking via a volume-cycled home ventilator versus via a resuscitator bag in participants with DMD. Maximum insufflation capacity and peak expiratory flow during spontaneous (cough peak flow) and air stacking-assisted cough maneuvers (air stacking-assisted cough peak flow) were measured. Fifty-two adult DMD subjects receiving noninvasive ventilation were included in the study: 27 participants performed air stacking via their home ventilator (home-ventilator group; age = 25.3 ± 5.1 y; forced vital capacity (FVC) = 809 ± 555 mL), and 25 participants used a resuscitator bag (resuscitator-bag group; age = 24.7 ± 5.7 y, FVC = 807 ± 495 mL). Following a single training session, air stacking could be performed successfully by 89% (home ventilator) and 88% (resuscitator bag) of participants. There were comparable maximum insufflation capacities (1,481 mL for the home-ventilator group vs 1,344 mL for the resuscitator-bag group, P = .33) and mean air stacking-assisted cough peak flow values (199 L/min for the home-ventilator group vs 186 L/min for the resuscitator-bag group, P = .33) between techniques. Air stacking-assisted cough peak flow increased significantly compared with baseline in both groups (mean increase: +51% [home ventilator] vs +49% [resuscitator bag], P bag. Both methods achieved mean air stacking-assisted cough peak flow values of >160 L/min. Provision of an inexpensive resuscitator bag can effectively improve cough capacity, and it is simple to use, which may improve access to respiratory care in people with DMD. Copyright © 2016 by Daedalus Enterprises.
NEW HORIZONS SPICE KERNELS V1.0
National Aeronautics and Space Administration — This data set includes the complete set of New Horizons (NH) SPICE data files (kernel files'), which can be accessed using SPICE software. The SPICE data contain...
Kernel based collaborative recommender system for e-purchasing
Indian Academy of Sciences (India)
Home; Journals; Sadhana; Volume 35; Issue 5. Kernel based collaborative recommender system for -purchasing ... Recommender system a new marketing strategy plays an important role particularly in an electronic commerce environment. Among the various recommender systems, collaborative recommender system ...
Homotopy deform method for reproducing kernel space for ...
Indian Academy of Sciences (India)
2016-09-23
Sep 23, 2016 ... Nonlinear differential equations; the homotopy deform method; the simplified reproducing kernel ... an equivalent integro differential equation. ... an algorithm for solving nonlinear multipoint BVPs by combining homotopy perturbation and variational iteration methods. Most recently, Duan and Rach [12].
Quantitative trait locus (QTL) mapping for 100-kernel weight of ...
African Journals Online (AJOL)
hope&shola
2010-12-06
Zea mays L.), related to yield. To realize its ... Key words: Maize (Zea mays L.), 100-kernel weight, quantitative trait locus (QTL), recombinant inbred line. (RIL), nitrogen ... cient approach to realize genetic basis of trait, some.
Homotopy deform method for reproducing kernel space for ...
Indian Academy of Sciences (India)
2016-09-23
s12043-016-1269-8. Homotopy deform method for reproducing kernel space for nonlinear boundary value problems. MIN-QIANG XU. ∗ and YING-ZHEN LIN. School of Science, Zhuhai Campus, Beijing Institute of Technology, ...
MARS EXPLORATION ROVER 2 SPICE KERNELS V1.0
National Aeronautics and Space Administration — This data set includes the complete set of Mars Exploration Rover 2 SPICE data files (kernel files'), which can be accessed using SPICE software. The SPICE data...
ROSETTA ORBITER/LANDER SPICE KERNELS V1.0
National Aeronautics and Space Administration — This data set includes the complete set of Rosetta mission SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contains...
National Aeronautics and Space Administration — This data set includes the complete set of Mars Global Surveyor SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data...
MARS EXPLORATION ROVER 1 SPICE KERNELS V1.0
National Aeronautics and Space Administration — This data set includes the complete set of Mars Exploration Rover 1 SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data...
Linear and kernel methods for multi- and hypervariate change detection
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Canty, Morton J.
2010-01-01
The iteratively re-weighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsuper- vised change detection in multi- and hyperspectral remote sensing imagery as well as for automatic radiometric normalization of multi- or hypervariate multitemporal image sequences...... code exists which allows for fast data exploration and experimentation with smaller datasets. Computationally demanding kernelization of test data with training data and kernel image projections have been programmed to run on massively parallel CUDA-enabled graphics processors, when available, giving....... 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...
DEEP SPACE 1 SPICE KERNELS V1.0
National Aeronautics and Space Administration — This data set includes the complete set of Deep Space 1 (DS1) SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contains...
CLEMENTINE MOON SPICE KERNELS V1.0
National Aeronautics and Space Administration — This data set includes the complete set of Clementine SPICE data files (kernel files''), which can be accessed using SPICE software. The SPICE data contains...
ejIP: A TCP/IP Stack for Embedded Java
DEFF Research Database (Denmark)
Schoeberl, Martin
2011-01-01
To enable Java on resource constraint embedded devices, the whole system should be implemented in a single programming language to avoid overheads on language boundaries. However, most of the functionality that is dedicated to the operating system layer is usually written in C. In this paper we...... present the design and implementation of a network stack written entirely in Java. This implementation serves as an example how to implement system functions in a safe language and gives evidence that Java can be used for operating system related functionality. The described TCP/IP stack ejIP has already...... been successfully used in industrial projects on pure Java embedded systems....
Synthesis and characterization of straight and stacked-sheet AlN nanowires with high purity
International Nuclear Information System (INIS)
Lei, M.; Yang, H.; Li, P.G.; Tang, W.H.
2008-01-01
Large-scale AlN nanowires with hexagonal crystal structure were synthesized by the direct nitridation method at high temperatures. The experimental results indicate that these single-crystalline AlN nanowires have high purity and consist of straight and stacked-sheet nanowires. It is found that straight AlN nanowire grows along [1, 1, -2, 0] direction, whereas the stacked-sheet nanowire with hexagonal cross section is along [0 0 0 1] direction. It is thought that vapor-solid (VS) mechanism should be responsible for the growth of AlN nanowires
Identification and validation of genomic regions influencing kernel zinc and iron in maize.
Hindu, Vemuri; Palacios-Rojas, Natalia; Babu, Raman; Suwarno, Willy B; Rashid, Zerka; Usha, Rayalcheruvu; Saykhedkar, Gajanan R; Nair, Sudha K
2018-03-24
Genome-wide association study (GWAS) on 923 maize lines and validation in bi-parental populations identified significant genomic regions for kernel-Zinc and-Iron in maize. Bio-fortification of maize with elevated Zinc (Zn) and Iron (Fe) holds considerable promise for alleviating under-nutrition among the world's poor. Bio-fortification through molecular breeding could be an economical strategy for developing nutritious maize, and hence in this study, we adopted GWAS to identify markers associated with high kernel-Zn and Fe in maize and subsequently validated marker-trait associations in independent bi-parental populations. For GWAS, we evaluated a diverse maize association mapping panel of 923 inbred lines across three environments and detected trait associations using high-density Single nucleotide polymorphism (SNPs) obtained through genotyping-by-sequencing. Phenotyping trials of the GWAS panel showed high heritability and moderate correlation between kernel-Zn and Fe concentrations. GWAS revealed a total of 46 SNPs (Zn-20 and Fe-26) significantly associated (P ≤ 5.03 × 10 -05 ) with kernel-Zn and Fe concentrations with some of these associated SNPs located within previously reported QTL intervals for these traits. Three double-haploid (DH) populations were developed using lines identified from the panel that were contrasting for these micronutrients. The DH populations were phenotyped at two environments and were used for validating significant SNPs (P ≤ 1 × 10 -03 ) based on single marker QTL analysis. Based on this analysis, 11 (Zn) and 11 (Fe) SNPs were found to have significant effect on the trait variance (P ≤ 0.01, R 2 ≥ 0.05) in at least one bi-parental population. These findings are being pursued in the kernel-Zn and Fe breeding program, and could hold great value in functional analysis and possible cloning of high-value genes for these traits in maize.
7 CFR 981.401 - Adjusted kernel weight.
2010-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in processing 1 1.00 0 4. Less excess moisture of edible kernels (excess moisture×line 2) 1.06 1.68 5. Net... Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing...
Palm kernel shell as aggregate for light weight concrete | Idah ...
African Journals Online (AJOL)
The palm kernel, cement, sand and gravel were mixed and cast in steel or cast iron moulds of 150mm2 cubes. The results show that the PK1 with ratio of 1 :2:3: 1· of cement, sand, gravel. and palm kernel shells respectively gave the highest compressive strength of 8.03N/mm2 after 28 days of curing. Comparing the results ...
Mathematical Modelling of Thin Layer Dried Cashew Kernels | Asiru ...
African Journals Online (AJOL)
In this paper mathematical models describing thin layer drying of cashew kernels in a batch dryer were presented. The range of drying air temperature was 70 – 110°C. The initial moisture content of the cashew kernels was 9.29% (d.b.) and the final moisture content was in the range of 3.5 to 4.6% dry-basis. Seven different ...
Assessing Gamma kernels and BSS/LSS processes
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole E.
This paper reviews the roles of gamma type kernels in the theory and modelling for Brownian and Lévy semistationary processes. Applications to financial econometrics and the physics of turbulence are pointed out.......This paper reviews the roles of gamma type kernels in the theory and modelling for Brownian and Lévy semistationary processes. Applications to financial econometrics and the physics of turbulence are pointed out....
MULTITASKER, Multitasking Kernel for C and FORTRAN Under UNIX
International Nuclear Information System (INIS)
Brooks, E.D. III
1988-01-01
1 - Description of program or function: MULTITASKER implements a multitasking kernel for the C and FORTRAN programming languages that runs under UNIX. The kernel provides a multitasking environment which serves two purposes. The first is to provide an efficient portable environment for the development, debugging, and execution of production multiprocessor programs. The second is to provide a means of evaluating the performance of a multitasking program on model multiprocessor hardware. The performance evaluation features require no changes in the application program source and are implemented as a set of compile- and run-time options in the kernel. 2 - Method of solution: The FORTRAN interface to the kernel is identical in function to the CRI multitasking package provided for the Cray XMP. This provides a migration path to high speed (but small N) multiprocessors once the application has been coded and debugged. With use of the UNIX m4 macro preprocessor, source compatibility can be achieved between the UNIX code development system and the target Cray multiprocessor. The kernel also provides a means of evaluating a program's performance on model multiprocessors. Execution traces may be obtained which allow the user to determine kernel overhead, memory conflicts between various tasks, and the average concurrency being exploited. The kernel may also be made to switch tasks every cpu instruction with a random execution ordering. This allows the user to look for unprotected critical regions in the program. These features, implemented as a set of compile- and run-time options, cause extra execution overhead which is not present in the standard production version of the kernel
Commutators of integral operators with variable kernels on Hardy ...
Indian Academy of Sciences (India)
(1.2). Then T ,0 is the singular integral with variable kernel, and we simply write it as T . The. Lp-boundedness of the singular integral operator with variable kernel appears in [1] (see also [3,7]). It turns out that such kind of operators are much more closely related to the elliptic partial differential equations of second order with ...
Resummed memory kernels in generalized system-bath master equations
Mavros, Michael G.; Van Voorhis, Troy
2014-08-01
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.
Local Kernel for Brains Classification in Schizophrenia
Castellani, U.; Rossato, E.; Murino, V.; Bellani, M.; Rambaldelli, G.; Tansella, M.; Brambilla, P.
In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.
Kernel spectral clustering with memory effect
Langone, Rocco; Alzate, Carlos; Suykens, Johan A. K.
2013-05-01
Evolving graphs describe many natural phenomena changing over time, such as social relationships, trade markets, metabolic networks etc. In this framework, performing community detection and analyzing the cluster evolution represents a critical task. Here we propose a new model for this purpose, where the smoothness of the clustering results over time can be considered as a valid prior knowledge. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness. The latter allows the model to cluster the current data well and to be consistent with the recent history. We also propose new model selection criteria in order to carefully choose the hyper-parameters of our model, which is a crucial issue to achieve good performances. We successfully test the model on four toy problems and on a real world network. We also compare our model with Evolutionary Spectral Clustering, which is a state-of-the-art algorithm for community detection of evolving networks, illustrating that the kernel spectral clustering with memory effect can achieve better or equal performances.
KERNEL MAD ALGORITHM FOR RELATIVE RADIOMETRIC NORMALIZATION
Directory of Open Access Journals (Sweden)
Y. Bai
2016-06-01
Full Text Available The multivariate alteration detection (MAD algorithm is commonly used in relative radiometric normalization. This algorithm is based on linear canonical correlation analysis (CCA which can analyze only linear relationships among bands. Therefore, we first introduce a new version of MAD in this study based on the established method known as kernel canonical correlation analysis (KCCA. The proposed method effectively extracts the non-linear and complex relationships among variables. We then conduct relative radiometric normalization experiments on both the linear CCA and KCCA version of the MAD algorithm with the use of Landsat-8 data of Beijing, China, and Gaofen-1(GF-1 data derived from South China. Finally, we analyze the difference between the two methods. Results show that the KCCA-based MAD can be satisfactorily applied to relative radiometric normalization, this algorithm can well describe the nonlinear relationship between multi-temporal images. This work is the first attempt to apply a KCCA-based MAD algorithm to relative radiometric normalization.
Calpain cleavage prediction using multiple kernel learning.
Directory of Open Access Journals (Sweden)
David A DuVerle
Full Text Available Calpain, an intracellular Ca²⁺-dependent cysteine protease, is known to play a role in a wide range of metabolic pathways through limited proteolysis of its substrates. However, only a limited number of these substrates are currently known, with the exact mechanism of substrate recognition and cleavage by calpain still largely unknown. While previous research has successfully applied standard machine-learning algorithms to accurately predict substrate cleavage by other similar types of proteases, their approach does not extend well to calpain, possibly due to its particular mode of proteolytic action and limited amount of experimental data. Through the use of Multiple Kernel Learning, a recent extension to the classic Support Vector Machine framework, we were able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality (6% over highest AUC score produced by state-of-the-art methods. In addition to producing a stronger machine-learning model for the prediction of calpain cleavage, we were able to highlight the importance and role of each feature of substrate sequences in defining specificity: primary sequence, secondary structure and solvent accessibility. Most notably, we showed there existed significant specificity differences across calpain sub-types, despite previous assumption to the contrary. Prediction accuracy was further successfully validated using, as an unbiased test set, mutated sequences of calpastatin (endogenous inhibitor of calpain modified to no longer block calpain's proteolytic action. An online implementation of our prediction tool is available at http://calpain.org.
Project W-420 Stack Monitoring system upgrades conceptual design report
Energy Technology Data Exchange (ETDEWEB)
TUCK, J.A.
1998-11-06
This document describes the scope, justification, conceptual design, and performance of Project W-420 stack monitoring system upgrades on six NESHAP-designated, Hanford Tank Farms ventilation exhaust stacks.
Heuristic Solution Approaches to the Double TSP with Multiple Stacks
DEFF Research Database (Denmark)
Petersen, Hanne Løhmann
This paper introduces the Double Travelling Salesman Problem with Multiple Stacks and presents a three different metaheuristic approaches to its solution. The Double Travelling Salesman Problem with Multiple Stacks is concerned with finding the shortest route performing pickups and deliveries...
Heuristic Solution Approaches to the Double TSP with Multiple Stacks
DEFF Research Database (Denmark)
Petersen, Hanne Løhmann
2006-01-01
This paper introduces the Double Travelling Salesman Problem with Multiple Stacks and presents a three different metaheuristic approaches to its solution. The Double Travelling Salesman Problem with Multiple Stacks is concerned with finding the shortest route performing pickups and deliveries...
Exploration of Shorea robusta (Sal seeds, kernels and its oil
Directory of Open Access Journals (Sweden)
Shashi Kumar C.
2016-12-01
Full Text Available Physical, mechanical, and chemical properties of Shorea robusta seed with wing, seed without wing, and kernel were investigated in the present work. The physico-chemical composition of sal oil was also analyzed. The physico-mechanical properties and proximate composition of seed with wing, seed without wing, and kernel at three moisture contents of 9.50% (w.b, 9.54% (w.b, and 12.14% (w.b, respectively, were studied. The results show that the moisture content of the kernel was highest as compared to seed with wing and seed without wing. The sphericity of the kernel was closer to that of a sphere as compared to seed with wing and seed without wing. The hardness of the seed with wing (32.32, N/mm and seed without wing (42.49, N/mm was lower than the kernels (72.14, N/mm. The proximate composition such as moisture, protein, carbohydrates, oil, crude fiber, and ash content were also determined. The kernel (30.20%, w/w contains higher oil percentage as compared to seed with wing and seed without wing. The scientific data from this work are important for designing of equipment and processes for post-harvest value addition of sal seeds.
Self-organization as an iterative kernel smoothing process.
Mulier, F; Cherkassky, V
1995-11-01
Kohonen's self-organizing map, when described in a batch processing mode, can be interpreted as a statistical kernel smoothing problem. The batch SOM algorithm consists of two steps. First, the training data are partitioned according to the Voronoi regions of the map unit locations. Second, the units are updated by taking weighted centroids of the data falling into the Voronoi regions, with the weighing function given by the neighborhood. Then, the neighborhood width is decreased and steps 1, 2 are repeated. The second step can be interpreted as a statistical kernel smoothing problem where the neighborhood function corresponds to the kernel and neighborhood width corresponds to kernel span. To determine the new unit locations, kernel smoothing is applied to the centroids of the Voronoi regions in the topological space. This interpretation leads to some new insights concerning the role of the neighborhood and dimensionality reduction. It also strengthens the algorithm's connection with the Principal Curve algorithm. A generalized self-organizing algorithm is proposed, where the kernel smoothing step is replaced with an arbitrary nonparametric regression method.
Sun, Yueping; Zhang, Yu; Li, Jiao
2017-01-01
Chemical-induced disease relations (CID) are crucial in various biomedical tasks. In the CID task of Biocreative V, no classifiers with multiple kernels have been developed. In this study, a multiple kernel learning-boosting (MKLB) method is proposed. Different kernel functions according to different types of features were constucted and boosted, each of which were learned with multiple kernels. Our multiple kernel learning-boosting (MKLB) method achieved a F1 score of 0.5068 without incorporating knowledge bases.
Stacking faults in a layered cobalt tellurium phosphate oxochloride
Zimmermann, Iwan; Johnsson, Mats
2015-02-01
The new compound Co2Te3(PO4)O6Cl was synthesized by chemical reactions in a sealed and evacuated silica tube. The crystal structure was solved from single crystal diffraction data and is made up by charge neutral layers. Within the layers two types of chains are made up by edge sharing [CoO6] and [CoO5Cl] polyhedra respectively. The chains are separated by tellurium oxide and phosphate building blocks. There are only weak Van der Waals interactions in between the layers and severe diffuse scattering is observed due to faulted stacking of the layers. Structure solutions in a P-1 triclinic cell and a larger monoclinic cell in P21/c are discussed and compared to a computer generated model. The reasons for the stacking faults may be due to that there are two positions available for each layer that results in similar connectivity to the next layer in addition to the relatively wide channels in between the layers that reduce the Van der Waals interactions in between them.
Wu, C. W.; Liu, B.; Wei, M. Y.; Liu, L. F.
2017-05-01
Proton exchange membrane fuel cell (PEMFC) stack usually undergoes various vibrations during packing, transportation and serving time, in particular for those used in the automobiles and portable equipment. Based on the Miner fatigue damage theory, the fatigue lives of the fuel cell components are first assessed. Then the component fatigue life contours of the stack are obtained under four working conditions, i.e. the three single-axial (in X-, Y- and Z-axis separately) and multi-axial random vibrations. Accordingly, the component damage under various vibrations is evaluated. The stress distribution on the gasket and PEM will greatly affect their fatigue lives. Finally, we compare the fatigue lives of 4-bolt- and 6-bolt-clamping stacks under the same total clamping force, and find that increasing the bolt number could improve the bolt fatigue lives.
AC loss in stacks of Bi-2223/Ag tapes modified with ferromagnetic covers at the edges
Energy Technology Data Exchange (ETDEWEB)
Safran, S; Goemoery, F [Institute of Electrical Engineering, Slovak Academy of Sciences, Dubravska cesta 9, 842 39 Bratislava (Slovakia); Gencer, Ali [Physics Department, Faculty of Sciences, Ankara University, Tandogan, 06100 Ankara (Turkey)
2010-10-15
We investigated the magnetization loss of stacked Bi-2223/Ag tapes with a ferromagnetic cover on the edges. Such modification has been found recently to reduce the AC loss of a single tape; however, the behavior in a coil winding could be different. With experiments and numerical calculations we show that a ferromagnetic cover on the edges of a superconducting tape could reduce its magnetization loss also when the tapes are arranged in a stack. The effect is weaker for larger numbers of tapes but nevertheless remained significant in a stack of four tapes, which was the maximum number studied here. The effects observed experimentally are nicely explained by the results of numerical calculations.
OPTIMIZATION OF PLY STACKING SEQUENCE OF COMPOSITE DRIVE SHAFT USING PARTICLE SWARM ALGORITHM
Directory of Open Access Journals (Sweden)
CHANNAKESHAVA K. R.
2011-06-01
Full Text Available In this paper an attempt has been made to optimize ply stacking sequence of single piece E-Glass/Epoxy and Boron /Epoxy composite drive shafts using Particle swarm algorithm (PSA. PSA is a population based evolutionary stochastic optimization technique which is a resent heuristic search method, where mechanics are inspired by swarming or collaborative behavior of biological population. PSA programme is developed to optimize the ply stacking sequence with an objective of weight minimization by considering design constraints as torque transmission capacity, fundamental natural frequency, lateral vibration and torsional buckling strength having number of laminates, ply thickness and stacking sequence as design variables. The weight savings of the E-Glass/epoxy and Boron /Epoxy shaft from PAS were 51% and 85 % of the steel shaft respectively. The optimum results of PSA obtained are compared with results of genetic algorithm (GA results and found that PSA yields better results than GA.
Kanda, Hiroyuki; Uzum, Abdullah; Nishino, Hitoshi; Umeyama, Tomokazu; Imahori, Hiroshi; Ishikawa, Yasuaki; Uraoka, Yukiharu; Ito, Seigo
2016-12-14
Engineering of photonics for antireflection and electronics for extraction of the hole using 2.5 nm of a thin Au layer have been performed for two- and four-terminal tandem solar cells using CH 3 NH 3 PbI 3 perovskite (top cell) and p-type single crystal silicon (c-Si) (bottom cell) by mechanically stacking. Highly transparent connection multilayers of evaporated-Au and sputtered-ITO films were fabricated at the interface to be a point-contact tunneling junction between the rough perovskite and flat silicon solar cells. The mechanically stacked tandem solar cell with an optimized tunneling junction structure was ⟨perovskite for the top cell/Au (2.5 nm)/ITO (154 nm) stacked-on ITO (108 nm)/c-Si for the bottom cell⟩. It was confirmed the best efficiency of 13.7% and 14.4% as two- and four-terminal devices, respectively.
DEVS Models of Palletized Ground Stacking in Storeyed Grain Warehouse
Directory of Open Access Journals (Sweden)
Hou Shu-Yi
2016-01-01
Full Text Available Processed grain stored in storeyed warehouse is generally stacked on the ground without pallets. However, in order to improve the storing way, we developed a new stacking method, palletized ground stacking. Simulation should be used to present this new storing way. DEVS provides a formalized way to describe the system model. In this paper, DEVS models of palletized ground stacking in storeyed grain warehouse are given and a simulation model is developed by AutoMod.
Sport stacking motor intervention programme for children with ...
African Journals Online (AJOL)
The purpose of this study was to explore sport stacking as an alternative intervention approach with typically developing children and in addition to improve DCD. Sport stacking consists of participants stacking and unstacking 12 specially designed plastic cups in predetermined sequences in as little time as possible.
Notes on G-theory of Deligne-Mumford stacks
Toen, B.
1999-01-01
Based on the methods used by the author to prove the Riemann-Roch formula for algebraic stacks, this paper contains a description of the rationnal G-theory of Deligne-Mumford stacks over general bases. We will use these results to study equivariant K-theory, and also to define new filtrations on K-theory of algebraic stacks.
Learning algorithms for stack filter classifiers
Energy Technology Data Exchange (ETDEWEB)
Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory; Zimmer, Beate G [TEXAS A& M
2009-01-01
Stack Filters define a large class of increasing filter that is used widely in image and signal processing. The motivations for using an increasing filter instead of an unconstrained filter have been described as: (1) fast and efficient implementation, (2) the relationship to mathematical morphology and (3) more precise estimation with finite sample data. This last motivation is related to methods developed in machine learning and the relationship was explored in an earlier paper. In this paper we investigate this relationship by applying Stack Filters directly to classification problems. This provides a new perspective on how monotonicity constraints can help control estimation and approximation errors, and also suggests several new learning algorithms for Boolean function classifiers when they are applied to real-valued inputs.
Industrial stacks design; Diseno de chimeneas industriales
Energy Technology Data Exchange (ETDEWEB)
Cacheux, Luis [Instituto de Investigaciones Electricas, Cuernavaca (Mexico)
1986-12-31
The Instituto de Investigaciones Electricas (IIE) though its Civil Works Department, develops, under contract with CFE`s Gerencia de Proyectos Termoelectricos (Management of Fossil Power Plant Projects), a series of methods for the design of stacks, which pretends to solve the a present day problem: the stack design of the fossil power plants that will go into operation during the next coming years in the country. [Espanol] El Instituto de Investigaciones Electricas (IIE), a traves del Departamento de Ingenieria Civil, desarrolla, bajo contrato con la Gerencia de Proyectos Termoelectricos, de la Comision Federal de Electricidad (CFE), un conjunto de metodos para el diseno de chimeneas, con el que se pretende resolver un problema inmediato: el diseno de las chimeneas de las centrales termoelectricas que entraran en operacion durante los proximos anos, en el pais.
System for inspection of stacked cargo containers
Derenzo, Stephen [Pinole, CA
2011-08-16
The present invention relates to a system for inspection of stacked cargo containers. One embodiment of the invention generally comprises a plurality of stacked cargo containers arranged in rows or tiers, each container having a top, a bottom a first side, a second side, a front end, and a back end; a plurality of spacers arranged in rows or tiers; one or more mobile inspection devices for inspecting the cargo containers, wherein the one or more inspection devices are removeably disposed within the spacers, the inspection means configured to move through the spacers to detect radiation within the containers. The invented system can also be configured to inspect the cargo containers for a variety of other potentially hazardous materials including but not limited to explosive and chemical threats.
Multistage Force Amplification of Piezoelectric Stacks
Xu, Tian-Bing (Inventor); Siochi, Emilie J. (Inventor); Zuo, Lei (Inventor); Jiang, Xiaoning (Inventor); Kang, Jin Ho (Inventor)
2015-01-01
Embodiments of the disclosure include an apparatus and methods for using a piezoelectric device, that includes an outer flextensional casing, a first cell and a last cell serially coupled to each other and coupled to the outer flextensional casing such that each cell having a flextensional cell structure and each cell receives an input force and provides an output force that is amplified based on the input force. The apparatus further includes a piezoelectric stack coupled to each cell such that the piezoelectric stack of each cell provides piezoelectric energy based on the output force for each cell. Further, the last cell receives an input force that is the output force from the first cell and the last cell provides an output apparatus force In addition, the piezoelectric energy harvested is based on the output apparatus force. Moreover, the apparatus provides displacement based on the output apparatus force.
Absorption spectra of AA-stacked graphite
International Nuclear Information System (INIS)
Chiu, C W; Lee, S H; Chen, S C; Lin, M F; Shyu, F L
2010-01-01
AA-stacked graphite shows strong anisotropy in geometric structures and velocity matrix elements. However, the absorption spectra are isotropic for the polarization vector on the graphene plane. The spectra exhibit one prominent plateau at middle energy and one shoulder structure at lower energy. These structures directly reflect the unique geometric and band structures and provide sufficient information for experimental fitting of the intralayer and interlayer atomic interactions. On the other hand, monolayer graphene shows a sharp absorption peak but no shoulder structure; AA-stacked bilayer graphene has two absorption peaks at middle energy and abruptly vanishes at lower energy. Furthermore, the isotropic features are expected to exist in other graphene-related systems. The calculated results and the predicted atomic interactions could be verified by optical measurements.
Development of on-site PAFC stacks
Energy Technology Data Exchange (ETDEWEB)
Hotta, K.; Matsumoto, Y. [Kansai Electric Power Co., Amagasaki (Japan); Horiuchi, H.; Ohtani, T. [Mitsubishi Electric Corp., Kobe (Japan)
1996-12-31
PAFC (Phosphoric Acid Fuel Cell) has been researched for commercial use and demonstration plants have been installed in various sites. However, PAFC don`t have a enough stability yet, so more research and development must be required in the future. Especially, cell stack needs a proper state of three phases (liquid, gas and solid) interface. It is very difficult technology to keep this condition for a long time. In the small size cell with the electrode area of 100 cm{sup 2}, gas flow and temperature distributions show uniformity. But in the large size cell with the electrode area of 4000 cm{sup 2}, the temperature distributions show non-uniformity. These distributions would cause to be shorten the cell life. Because these distributions make hot-spot and gas poverty in limited parts. So we inserted thermocouples in short-stack for measuring three-dimensional temperature distributions and observed effects of current density and gas utilization on temperature.
CAM and stack air sampler design guide
International Nuclear Information System (INIS)
Phillips, T.D.
1994-01-01
About 128 air samplers and CAMs presently in service to detect and document potential radioactive release from 'H' and 'F' area tank farm ventilation stacks are scheduled for replacement and/or upgrade by Projects S-5764, S-2081, S-3603, and S-4516. The seven CAMs scheduled to be upgraded by Project S-4516 during 1995 are expected to provide valuable experience for the three remaining projects. The attached document provides design guidance for the standardized High Level Waste air sampling system
Contemporary sample stacking in analytical electrophoresis
Czech Academy of Sciences Publication Activity Database
Malá, Zdeňka; Gebauer, Petr; Boček, Petr
2011-01-01
Roč. 32, č. 1 (2011), s. 116-126 ISSN 0173-0835 R&D Projects: GA ČR GA203/08/1536; GA ČR GAP206/10/1219; GA AV ČR IAA400310703 Institutional research plan: CEZ:AV0Z40310501 Keywords : biological samples * stacking * trace analysis * zone electrophoresis Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 3.303, year: 2011
Calculation of the time resolution of the J-PET tomograph using kernel density estimation
Raczyński, L.; Wiślicki, W.; Krzemień, W.; Kowalski, P.; Alfs, D.; Bednarski, T.; Białas, P.; Curceanu, C.; Czerwiński, E.; Dulski, K.; Gajos, A.; Głowacz, B.; Gorgol, M.; Hiesmayr, B.; Jasińska, B.; Kamińska, D.; Korcyl, G.; Kozik, T.; Krawczyk, N.; Kubicz, E.; Mohammed, M.; Pawlik-Niedźwiecka, M.; Niedźwiecki, S.; Pałka, M.; Rudy, Z.; Rundel, O.; Sharma, N. G.; Silarski, M.; Smyrski, J.; Strzelecki, A.; Wieczorek, A.; Zgardzińska, B.; Zieliński, M.; Moskal, P.
2017-06-01
In this paper we estimate the time resolution of the J-PET scanner built from plastic scintillators. We incorporate the method of signal processing using the Tikhonov regularization framework and the kernel density estimation method. We obtain simple, closed-form analytical formulae for time resolution. The proposed method is validated using signals registered by means of the single detection unit of the J-PET tomograph built from a 30 cm long plastic scintillator strip. It is shown that the experimental and theoretical results obtained for the J-PET scanner equipped with vacuum tube photomultipliers are consistent.
Directory of Open Access Journals (Sweden)
Xiong Luo
2017-07-01
Full Text Available Recently, inspired by correntropy, kernel risk-sensitive loss (KRSL has emerged as a novel nonlinear similarity measure defined in kernel space, which achieves a better computing performance. After applying the KRSL to adaptive filtering, the corresponding minimum kernel risk-sensitive loss (MKRSL algorithm has been developed accordingly. However, MKRSL as a traditional kernel adaptive filter (KAF method, generates a growing radial basis functional (RBF network. In response to that limitation, through the use of online vector quantization (VQ technique, this article proposes a novel KAF algorithm, named quantized MKRSL (QMKRSL to curb the growth of the RBF network structure. Compared with other quantized methods, e.g., quantized kernel least mean square (QKLMS and quantized kernel maximum correntropy (QKMC, the efficient performance surface makes QMKRSL converge faster and filter more accurately, while maintaining the robustness to outliers. Moreover, considering that QMKRSL using traditional gradient descent method may fail to make full use of the hidden information between the input and output spaces, we also propose an intensified QMKRSL using a bilateral gradient technique named QMKRSL_BG, in an effort to further improve filtering accuracy. Short-term chaotic time-series prediction experiments are conducted to demonstrate the satisfactory performance of our algorithms.
When is stacking confusing? The impact of confusion on stacking in deep H I galaxy surveys
Jones, Michael G.; Haynes, Martha P.; Giovanelli, Riccardo; Papastergis, Emmanouil
2016-01-01
We present an analytic model to predict the H I mass contributed by confused sources to a stacked spectrum in a generic H I survey. Based on the ALFALFA (Arecibo Legacy Fast ALFA) correlation function, this model is in agreement with the estimates of confusion present in stacked Parkes telescope data, and was used to predict how confusion will limit stacking in the deepest Square Kilometre Array precursor H I surveys. Stacking with LADUMA (Looking At the Distant Universe with MeerKAT) and DINGO UDEEP (Deep Investigation of Neutral Gas Origins - Ultra Deep) data will only be mildly impacted by confusion if their target synthesized beam size of 10 arcsec can be achieved. Any beam size significantly above this will result in stacks that contain a mass in confused sources that is comparable to (or greater than) that which is detectable via stacking, at all redshifts. CHILES (COSMOS H I Large Extragalactic Survey) 5 arcsec resolution is more than adequate to prevent confusion influencing stacking of its data, throughout its bandpass range. FAST (Five hundred metre Aperture Spherical Telescope) will be the most impeded by confusion, with H I surveys likely becoming heavily confused much beyond z = 0.1. The largest uncertainties in our model are the redshift evolution of the H I density of the Universe and the H I correlation function. However, we argue that the two idealized cases we adopt should bracket the true evolution, and the qualitative conclusions are unchanged regardless of the model choice. The profile shape of the signal due to confusion (in the absence of any detection) was also modelled, revealing that it can take the form of a double Gaussian with a narrow and wide component.