Performance study of gradient-enhanced Kriging

The use of surrogate models for approximating computationally expensive simulations has been on the rise for the last two decades. Kriging-based surrogate models are popular for approximating deterministic computer models. In this work, the performance of Kriging is investigated when gradient information is introduced for the approximation of computationally expensive black-box simulations. This approach, known as gradient-enhanced Kriging, is applied to various benchmark functions of varying dimensionality (2D-20D). As expected, results from the benchmark problems show that additional gradient information can significantly enhance the accuracy of Kriging. Gradient-enhanced Kriging provides a better approximation even when gradient information is only partially available. Further comparison between gradient-enhanced Kriging and an alternative formulation of gradient-enhanced Kriging, called indirect gradient-enhanced Kriging, highlights various advantages of directly employing gradient information, such as improved surrogate model accuracy, better conditioning of the correlation matrix, etc. Finally, gradient-enhanced Kriging is used to model 6- and 10-variable fluid–structure interaction problems from bio-mechanics to identify the arterial wall’s stiffness.     

An optimal ensemble of predictors in convex correcting procedures

In this work the properties of the correcting procedures over a collection of forecasting algorithms, which calculate the integral solution in the form of a convex linear combination of forecasts obtained by some algorithms of the collection, are investigated.     

Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors

The paper presents the application of wavelet transformation and neural network ensemble to the accurate forecasting of the daily average concentration of particulate matter of diameter up to 10 μm (PM₁₀). Few neural predictors are applied: the multilayer perceptron, radial basis function, Elman network and support vector machine as well as one linear ARX model. They are used for prediction in combination with wavelet decomposition, forming many individual prediction results that will be combined in an ensemble. The important role in presented approach fulfills the wavelet transformation and the integration of this ensemble. We have proposed solution applying the additional neural network responsible for the final forecast (integration of all particular prediction results). The numerical experiments for prediction of the daily concentration of the PM₁₀ pollution in Warsaw are presented. They have shown good overall accuracy of prediction in terms of all investigated measures of quality.     

基于微分方程的程序性能分析

基于连续Petri网模型,用一组常微分方程来描述程序,通过研究微分方程的解来研究程序的性能。每个微分方程描述程序状态的变化,每个状态可由介于0和1之间的数来度量,显示程序到达状态的程度。该方法的好处在于在做程序分析时,可避开状态爆炸问题。     

Performance cost functions for a reaction-jet-controlled system during an on-off limit cycle

In this paper, analytical expressions are obtained for performance cost functions which describe the behavior of a second-order nonlinear control system during an on-off limit cycle. The cost functions are 1) fuel consumption per second (and the associated percent on-time and control pulse width); 2) average error; 3) peak-to-peak, limit-cycle amplitude; and 4) limit-cycle period. Each of these functions is expressed in terms of the amplitudes of the limit-cycle switch statestheta_{delta}andtheta_{delta}The results, which are given for a stable and an unstable plant, do not depend upon a specified control law.     

Performance prediction of roadheaders using ensemble machine learning techniques

Mechanical excavators are widely used in mining, tunneling and civil engineering projects. There are several types of mechanical excavators, such as a roadheader, tunnel boring machine and impact hammer. This is because these tools can bring productivity to the project quickly, accurately and safely. Among these, roadheaders have some advantages like selective mining, mobility, less over excavation, minimal ground disturbances, elimination of blast vibration, reduced ventilation requirements and initial investment cost. A critical issue in successful roadheader application is the ability to evaluate and predict the machine performance named instantaneous (net) cutting rate. Although there are several prediction methods in the literature, for the prediction of roadheader performance, only a few of them have been developed via artificial neural network techniques. In this study, for this purpose, 333 data sets including uniaxial compressive strength and power on cutting boom, 103 data set including RQD, and 125 data sets including machine weight are accumulated from the literature. This paper focuses on roadheader performance prediction using six different machine learning algorithms and a combination of various machine learning algorithms via ensemble techniques. Algorithms are ZeroR, random forest (RF), Gaussian process, linear regression, logistic regression and multi-layer perceptron (MLP). As a result, MLP and RF give better results than the other algorithms also the best solution achieved was bagging technique on RF and principle component analysis (PCA). The best success rate obtained in this study is 90.2% successful prediction, and it is relatively better than contemporary research.

     

Stories,journeys and smart maps: an approach to universal access

Universal Access in the Information Society - This paper proposes a new approach to universal access based on the premise that humans have the universal capacity to engage emotionally with a story,...     

General distortion of an ensemble of biparametric surfaces

When the shape of an object has been numerically defined, it is sometimes necessary to distort it to improve either its technical performance or its aesthetic appearance.After briefly recalling the major properties of space curves and surfaces defined by Bernstein polynomials, it is shown how the result can be automatically obtained by distorting an auxiliary triparametric set of references.The principle of an approximate method for high-order curves and surfaces is explained.     

Phase transition and hysteresis in an ensemble of stochastic spiking neurons

An ensemble of stochastic nonleaky integrate-and-fire neurons with global, delayed, and excitatory coupling and a small refractory period is analyzed. Simulations with adiabatic changes of the coupling strength indicate the presence of a phase transition accompanied by a hysteresis around a critical coupling strength. Below the critical coupling production of spikes in the ensemble is governed by the stochastic dynamics, whereas for coupling greater than the critical value, the stochastic dynamics loses its influence and the units organize into several clusters with self-sustained activity. All units within one cluster spike in unison, and the clusters themselves are phase-locked. Theoretical analysis leads to upper and lower bounds for the average interspike interval of the ensemble valid for all possible coupling strengths. The bounds allow calculating the limit behavior for large ensembles and characterize the phase transition analytically. These results may be extensible to pulse-coupled oscillators.     

Eye pupil localization with an ensemble of randomized trees

We describe a method for eye pupil localization based on an ensemble of randomized regression trees and use several publicly available datasets for its quantitative and qualitative evaluation. The method compares well with reported state-of-the-art and runs in real-time on hardware with limited processing power, such as mobile devices.     

Regular and almost universal hashing: an efficient implementation

Random hashing can provide guarantees regarding the performance of data structures such as hash tables – even in an adversarial setting. Many existing families of hash functions are universal: given two data objects, the probability that they have the same hash value is low given that we pick hash functions at random. However, universality fails to ensure that all hash functions are well behaved. We might further require regularity: when picking data objects at random they should have a low probability of having the same hash value, for any fixed hash function. We present the efficient implementation of a family of non‐cryptographic hash functions (PM+) offering good running times, good memory usage, and distinguishing theoretical guarantees: almost universality and component‐wise regularity. On a variety of platforms, our implementations are comparable with the state of the art in performance. On recent Intel processors, PM+ achieves a speed of 4.7 bytes per cycle for 32‐bit outputs and 3.3 bytes per cycle for 64‐bit outputs. We review vectorization through Single Instruction on Multiple Data instructions (e.g., AVX2) and optimizations for superscalar execution. Copyright © 2016 John Wiley & Sons, Ltd.     

Kriging, cokriging, radial basis functions and the role of positive definiteness

There are at least three developments for interpolators that lead to the same functional form for the interpolator; the thin plate spline, radial basis functions and the regression method known as kriging. The key to the interrelationship lies in the positive definiteness of the kernel function. Micchelli has known that a weak form of positive definiteness is sufficient to ensure a unique solution to the system of equations determining the coefficients in the interpolator. Both the positive definiteness and the interpolator can be extended to vector valued functions via the kriging approach which is also independent of the dimension of the underlying space. The kriging approach leads naturally to various methods for simulation as well.     

Toward an optimal ensemble of kernel-based approximations with engineering applications

This paper presents a general approach toward the optimal selection and ensemble (weighted average) of kernel-based approximations to address the issue of model selection. That is, depending on the problem under consideration and loss function, a particular modeling scheme may outperform the others, and, in general, it is not known a priori which one should be selected. The surrogates for the ensemble are chosen based on their performance, favoring non-dominated models, while the weights are adaptive and inversely proportional to estimates of the local prediction variance of the individual surrogates. Using both well-known analytical test functions and, in the surrogate-based modeling of a field scale alkali-surfactant-polymer enhanced oil recovery process, the ensemble of surrogates, in general, outperformed the best individual surrogate and provided among the best predictions throughout the domains of interest. This work was supported in part by the Fondo Nacional de Ciencia, Tecnología e Innovación (FONACIT), Venezuela under Grant F-2005000210. N. Q. Author also acknowledges that this material is based upon work supported by National Science Foundation under Grant DDM-423280.     

An efficient image retrieval based on an integration of HSV,RLBP, and CENTRIST features using ensemble classifier learning

Multimedia Tools and Applications - Recently, we have been witnessing a tremendous rise in digital image quantities, which in return calls for an adjustment and management system to fulfill...     

On the importance of having an identity or, is consensus really universal?

We show that Naming - the existence of distinct IDs known to all - is a hidden, but necessary, assumption of Herlihy's universality result for Consensus. We then show in a very precise sense that Naming is harder than Consensus and bring to the surface some relevant differences existing between popular shared memory models. Received: 24 January 2002, Revised: 15 October 2002, Accepted: 19 November 2004, Published online: 10 March 2005     

Addressing the complexity of personalized,context-aware and health-aware food recommendations: an ensemble topic modelling based approach

Journal of Intelligent Information Systems - Food Recommender Systems (FRS) have the potential to support informed and satisfying food choices. However, to realize their full potential, FRS must...     

基于Kriging模型的航空发动机管道系统有限元模型修正

为获得精确可靠的航空发动机外部管道结构动力学模型,采用将Kriging模型与多目标遗传算法(MOGA)相结合的模型修正方法进行有限元模型修正.首先进行管道模型的模态试验和有限元建模,分别获得模态参数的试验值和有限元分析值；然后在合理的参数选取和试验设计(DOE)的基础上,拟合得到Kriging模型；最后基于Kriging模型采用多目标遗传算法进行有限元模型修正,并对比了不同修正方法的精度和修正效果.结果表明：采用Kriging模型进行有限元模型修正可以有效提升修正效果,获得更为准确的有限元模型；对于航空发动机管道系统,基于Kriging模型的模型修正方法相较于基于灵敏度分析的模型修正方法具有更高的修正效率和修正精度.     

Associated legendre polynomials,ordinary and modified spherical harmonics

Title of program : LEGENDRE Catalogue : ABME Program obtainable from : CPC Program Library, Queen's University of Belfast, N. Ireland (see application form in this issue). Computer : CDC 6600; Installation : University of Texas, Austin, Texas, USA Operating system : CDC Scope Programming language used : FORTRAN IV High speed storage required : 4928 words. No. of bits in a word : 60 Is the program overlaid? No No. of magnetic tapes required : None What other peripherals are used? Card reader, line printer No. of cards in combined program and test deck : 160 Card punching code : CDC