Case-based planning can take advantage of former problem-solving experiences by storing in a plan library previously generated plans that can be reused to solve similar planning problems in the future. Although compar...
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Case-based planning can take advantage of former problem-solving experiences by storing in a plan library previously generated plans that can be reused to solve similar planning problems in the future. Although comparative worst-case complexity analyses of plan generation and reuse techniques reveal that it is not possible to achieve provable efficiency gain of reuse over generation, we show that the case-based planning approach can be an effective alternative to plan generation when similar reuse candidates can be chosen. In this paper we describe an innovative case-based planning system, called OAKPLAN, which can efficiently retrieve planning cases from plan libraries containing more than ten thousand cases, choose heuristically a suitable candidate and adapt it to provide a good quality solution plan which is similar to the one retrieved from the case library. Given a planning problem we encode it as a compact graph structure, that we call Planning Encoding Graph, which gives us a detailed description of the topology of the planning problem. By using this graph representation, we examine an approximate retrieval procedure based on kernel functions that effectively match planning instances, achieving extremely good performance in standard benchmark domains. The experimental results point out the effect of the case base size and the importance of accurate matching functions for global system performance. Overall, we show that OAKPLAN is competitive with state-of-the-art plan generation systems in terms of number of problems solved, CPU time, plan difference values and plan quality when cases similar to the current planning problem are available in the plan library. (C) 2010 Elsevier B.V. All rights reserved.
Water pollution is a challenging problem encountered in total environmental development. Near-infrared (NIR) spectroscopy is a well-refined technology for rapid water pollution detection. Calibration models are establ...
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Water pollution is a challenging problem encountered in total environmental development. Near-infrared (NIR) spectroscopy is a well-refined technology for rapid water pollution detection. Calibration models are established and optimized to search for chemometric algorithms with considerably improved prediction effects. Machine learning improves the prediction capability of NIR spectroscopy for the accurate assessment of water pollution. Least squares support vector machine (LSSVM) algorithm fits parameters to target problems in a data-driven manner. The modeling capability of this algorithm mainly depends on its kernel functions. In this study, the LSSVM method was used to establish NIR calibration models for the quantitative determination of chemical oxygen demand, which is a critical indicator of water pollution level. The effects of different kernels embedded in LSSVM were investigated. A novel kernel was proposed by using a logistic-based neural network. In contrast to common kernels, this novel kernel can utilize a deep learning approach for parameter optimization. The proposed kernel also strengthens model resistance to over-fitting such that cross-validation can be reasonably utilized. The proposed novel kernel is applicable for the quantitative determination of water pollution and is a prospective solution to other problems in the field of water resource management. (C) 2020 Elsevier B.V. All rights reserved.
In this work, we solve distributed order diffusion equations (DODEs) by applying the theory on reproducing kernel functions (RKFs). The classical numerical quadrature formulae is used to approximate the DODE to a mult...
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In this work, we solve distributed order diffusion equations (DODEs) by applying the theory on reproducing kernel functions (RKFs). The classical numerical quadrature formulae is used to approximate the DODE to a multi-term Caputo fractional order diffusion equation (FDE). The Mittag-Leffler RKF is introduced to estimate fractional derivatives of Caputo. And a space-time RKFs collocation scheme is derived for the multi-term Caputo time FDEs. The accuracy of the present numerical technique is indicated by employing several experiments.
We introduce a family of positive definite kernels specifically designed for problems described by categorical information. The kernels are based on the comparison of the probability mass function of the variables and...
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ISBN:
(纸本)9781614993209;9781614993193
We introduce a family of positive definite kernels specifically designed for problems described by categorical information. The kernels are based on the comparison of the probability mass function of the variables and have a clear interpretation in terms of similarity computations between the modalities. We report experimental results on two different problems in the life sciences indicating that the proposed approach may markedly outperform standard kernels, so it can be used as a good alternative to other common kernel functions (at least for SVM classification) in order to obtain better accuracy.
By employing the kernel functions with the form of piecewise polynomials in the Sobolev reproducing kernel Hilbert spaces (RKHSs), a globally superconvergent numerical technique is proposed to solve the second kind li...
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By employing the kernel functions with the form of piecewise polynomials in the Sobolev reproducing kernel Hilbert spaces (RKHSs), a globally superconvergent numerical technique is proposed to solve the second kind linear integral equations of Fredholm type. This method has an order of global convergence 0 (h(4)) and 0 (h(6)) based on the kernel functions in the Sobolev RKHSs H-1 and H-2, respectively. Three linear Fredholm integral equations, one Volterra-Fredholm integral equation and one nonlinear Fredholm integral equation are numerically solved by the present approach to verify the superconvergence and effectiveness. (C) 2021 IMACS. Published by Elsevier B.V. All rights reserved.
This paper focus on the drought monitoring and forecasting for semi-arid region based on the various machine learning models and SPI index. Drought phenomena are crucial role in the agriculture and drinking purposes i...
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This paper focus on the drought monitoring and forecasting for semi-arid region based on the various machine learning models and SPI index. Drought phenomena are crucial role in the agriculture and drinking purposes in the area. In this study, Standardized Precipitation Index (SPI) was used to predicted the future drought in the upper Godavari River basin, India. We have selected the ten input combinations of ML model were used to prediction of drought for three SPI timescales (i.e., SPI -3, SPI-6, and SPI-12). The historical data of SPI from 2000 to 2019 was used for creation of ML models SPI prediction, these datasets was divided into training (75% of the data) and testing (25% of the data) models. The best subset regression method and sensitivity analysis were applied to estimate the most effective input variables for estimation of SPI 3, 6, and 12. The improved support vector machine model using sequential minimal optimization (SVM-SMO) with various kernel functions i.e., SMO-SVM poly kernel, SMO-SVM Normalized poly kernel, SMO-SVM PUK (Pearson Universal kernel) and SMO-SVM RBF (radial basis function) kernel was developed to forecasting of the SPI-3,6 and 12 months. The ML models accuracy were compared with various statistical indicators i.e., root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), and correlation coefficient (r). The results of study area have been showed that the SMO-SVM poly kernel model precisely predicted the SPI-3 (R-2 = 0.819) and SPI-12 (R-2 = 0.968) values at Paithan station;the SPI-3 (R-2 = 0.736) and SPI-6 (R-2 = 0.841) values at Silload station, respectively. The SMO-SVM PUK kernel is found that the best ML model for the prediction of SPI-6 (R-2 = 0.846) at Paithan station and SPI-12 (R-2 = 0.975) at the Silload station. The compared with SVM-SMO poly kernel and SVM-SMO PUK kernel was observed, these models are best forecasting of drought (i.e. SPI-6 and SPI-12), wh
This paper is based on CO2 and CH4 semi-hourly mole fraction measurements obtained at the Low Atmosphere Research Centre (CIB) between 2010 and 2016 using a Picarro G1301 analyser. The main aims of the study were to e...
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This paper is based on CO2 and CH4 semi-hourly mole fraction measurements obtained at the Low Atmosphere Research Centre (CIB) between 2010 and 2016 using a Picarro G1301 analyser. The main aims of the study were to examine the temporal variation of CO2 and CH4 by using six different kernel functions, and to study the suitability of these functions to the dataset. The method used for the current study was based on experimental contour plots of R-2 values in order to simultaneously determine the bandwidths of kernel functions for the long-term and short-term. An Epanechnikov, a Gaussian, a biweight, a triangular, a tricubic and a rectangular kernel function were applied to extract the salient features of both the long-term (trend) and the short-term (seasonality). The average linear increase growth rates found were mainly attributed to the terrestrial biosphere cycle and changes in the atmospheric circulation regime. The seasonal cycle exhibited a cyclical variation, revealing summer minima for both gases, which may be explained by a biological minimum. kernel analysis showed two nocturnal CO2 maxima, in spring and autumn, linked to an increase in rainfall. For CO2 daytime records, only the spring peak was detected. As regards CH4, the maximum was located in winter. The best fit for the trend was obtained by the biweight kernel. In contrast, the best adjustment for seasonality was achieved from the Gaussian and the triangular kernel. To sum up, optimal bandwidth selection is important when kernel regression functions are employed. Since no important differences were found between the kernels employed, those which involve least computational effort are recommended.
In this paper, a novel approach is proposed to detect crack damage in nonlinear beam based on the Volterra kernel functions analysis. Volterra kernel functions are the extension of impulse response function for linear...
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In this paper, a novel approach is proposed to detect crack damage in nonlinear beam based on the Volterra kernel functions analysis. Volterra kernel functions are the extension of impulse response function for linear system to nonlinear system. The key issue involved in crack detection using Volterra kernel functions based analysis is the accurate identification of its kernel functions. To improve the identification accuracy of Volterra kernel functions, a wavelet balance method based approach is proposed to identify the Volterra kernel functions from observations of the in- and outgoing signals. A Volterra kernel function-based index is defined in the present study for crack detection. The new crack detection approach mainly includes three steps. First, the Volterra kernel functions are identified from the input output data. Then, the Volterra kernel functions-based indexes are calculated. Finally, crack detection is conducted by comparing the values of the Volterra kernel functions-based indexes of the inspected beam with the values of the indexes for an uncracked beam. The numerical simulation results show that the crack detection method is sensitive to the appearance of crack in the beam, and can therefore be used as crack detection indicator to indicate the existence and the size of crack. (C) 2014 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Some new reproducing kernel functions on time scales are presented. Reproducing kernel functions have not been found on time scales till now. These functions are very important on time scales and they will be very use...
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Some new reproducing kernel functions on time scales are presented. Reproducing kernel functions have not been found on time scales till now. These functions are very important on time scales and they will be very useful for researchers. We need these functions to solve dynamic equations on time scales with the reproducing kernel method.
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