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1 – 10 of 915
Article
Publication date: 5 January 2021

Artwell Kadungure, Garrett Wallace Brown, Rene Loewenson and Gwati Gwati

This study examines key adaptations that occurred in the Zimbabwean Results-Based Financing (RBF) programme between 2010 and 2017, locating the endogenous and exogenous factors…

Abstract

Purpose

This study examines key adaptations that occurred in the Zimbabwean Results-Based Financing (RBF) programme between 2010 and 2017, locating the endogenous and exogenous factors that required adaptive response and the processes from which changes were made.

Design/methodology/approach

The study is based on a desk review and thematic analysis of 64 policy and academic literatures supplemented with 28 multi-stakeholder interviews.

Findings

The programme experienced substantive adaption between 2010 and 2017, demonstrating a significant level of responsiveness towards increasing efficiency as well as to respond to unforeseen factors that undermined RBF mechanisms. The programme was adaptive due to its phased design, which allowed revision competencies and responsive adaptation, which provide useful insights for other low-and-middle income countries (LMICs) settings where graduated scale-up might better meet contextualised needs. However, exogenous factors were often not systematically examined or reported in RBF evaluations, demonstrating that adaptation could have been better anticipated, planned, reported and communicated, especially if RBF is to be a more effective health system reform tool.

Originality/value

RBF is an increasingly popular health system reform tool in LMICs. However, there are questions about how exogenous factors affect RBF performance and acknowledgement that unforeseen endogenous programme design and implementation factors also greatly affect the performance of RBF. As a result, a better understanding of how RBF operates and adapts to programme level (endogenous) and exogenous (external) factors in LMICs is necessary.

Details

Journal of Health Organization and Management, vol. 35 no. 3
Type: Research Article
ISSN: 1477-7266

Keywords

Article
Publication date: 31 July 2021

Niu Zijie, Zhang Peng, Yongjie Cui and Zhang Jun

Omnidirectional mobile platforms are still plagued by the problem of heading deviation. In four-Mecanum-wheel systems, this problem arises from the phenomena of dynamic imbalance…

Abstract

Purpose

Omnidirectional mobile platforms are still plagued by the problem of heading deviation. In four-Mecanum-wheel systems, this problem arises from the phenomena of dynamic imbalance and slip of the Mecanum wheels while driving. The purpose of this paper is to analyze the mechanism of omnidirectional motion using Mecanum wheels, with the aim of enhancing the heading precision. A proportional-integral-derivative (PID) setting control algorithm based on a radial basis function (RBF) neural network model is introduced.

Design/methodology/approach

In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1.

Findings

The network RBF NN1 calculates the deviations ?Kp, ?Ki and ?Kd to regulate the three coefficients Kp, Ki and Kd of the heading angle PID controller. This corrects the driving heading in real time, resolving the problems of low heading precision and unstable driving. The experimental data indicate that, for a externally imposed deviation in the heading angle of between 34º and ∼38°, the correction time for an omnidirectional mobile platform applying the algorithm during longitudinal driving is reduced by 1.4 s compared with the traditional PID control algorithm, while the overshoot angle is reduced by 7.4°; for lateral driving, the correction time is reduced by 1.4 s and the overshoot angle is reduced by 4.2°.

Originality/value

In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. The method is innovative.

Details

Industrial Robot: the international journal of robotics research and application, vol. 49 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 17 October 2008

Minfen Shen, Jialiang Chen and Bin Li

The purpose of this paper is to present a novel algorithm for image inpainting, which has been widely used for removing unwanted objects from images or reconstructing damaged…

Abstract

Purpose

The purpose of this paper is to present a novel algorithm for image inpainting, which has been widely used for removing unwanted objects from images or reconstructing damaged photographs.

Design/methodology/approach

An image piecewise inpainting technique based on radial basis function (RBF) is used to transform the 2D image inpainting problem into 3D implicit surface reconstruction problem. And a RBF center reduction method is proposed. By RBF resampling, the algorithm can nicely fix the damaged image or remove specific objects.

Findings

Experimental results show that the proposed algorithms can prevent the edge blur caused by the isotropic character of RBF, and effectively reduce the RBF centers without a loss in accuracy.

Originality/value

The proposed inpainting approach is interesting for its combination of RBF method and region segmentation that can handle the restoring of high‐variation areas.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 1 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 3 October 2016

Marco Evangelos Biancolini, Emiliano Costa, Ubaldo Cella, Corrado Groth, Gregor Veble and Matej Andrejašič

The present paper aims to address the description of a numerical optimization procedure, based on mesh morphing, and its application for the improvement of the aerodynamic…

Abstract

Purpose

The present paper aims to address the description of a numerical optimization procedure, based on mesh morphing, and its application for the improvement of the aerodynamic performance of an industrial glider which suffers of a large separation occurring in the wing–fuselage junction region at high incidence angles.

Design/methodology/approach

Shape variations were applied to the baseline configuration through a mesh morphing technique founded on the mathematical framework of radial basis functions (RBF). The aerodynamic solutions were obtained coupling an RANS code with the mesh morphing tool RBF Morph™. Two shape modifiers were set up to generate a parametric numerical model. An optimization procedure, based on a design of experiment sampling, was set up implementing the fully automated workflow within a high performance computing (HPC) environment. The optimal candidates maximizing the aerodynamic efficiency were identified by means of a cubic RBF response surface approach.

Findings

The separation was significantly reduced, modifying the local geometry of fuselage and fairing and maintaining the wing aerofoil unchanged. A relevant aerodynamic efficiency improvement was finally gained.

Practical implications

The developed procedure proved to be a very powerful and efficient tool in facing aerodynamic design problems. However, it might be computationally very expensive if a large number of design variables are adopted and, in those cases, the method can be suitably used only within the HPC environment.

Originality/value

Such an optimization study is part of an explorative set of analyses that focused on better addressing the numerical strategies to be used in the development of the EU FP7 Project RBF4AERO.

Details

Aircraft Engineering and Aerospace Technology, vol. 88 no. 6
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 2 October 2019

Yue Li, Xiaoquan Chu, Zetian Fu, Jianying Feng and Weisong Mu

The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis…

Abstract

Purpose

The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis function (RBF) neural network to achieve more accurate prediction than the current shelf life (SL) prediction methods.

Design/methodology/approach

First, the final indicators (storage temperature, relative humidity, sensory average score, peel hardness, soluble solids content, weight loss rate, rotting rate, fragmentation rate and color difference) affecting SL were determined by the correlation and significance analysis. Then using the analytic hierarchy process (AHP) to calculate the weight of each indicator and determine the end of SL under different storage conditions. Subsequently, the structure of the RBF network redesigned was 9-11-1. Ultimately, the membership degree of Fuzzy clustering (fuzzy c-means) was adopted to optimize the center and width of the RBF network by using the training data.

Findings

The results show that this method has the highest prediction accuracy compared to the current the kinetic–Arrhenius model, back propagation (BP) network and RBF network. The maximum absolute error is 1.877, the maximum relative error (RE) is 0.184, and the adjusted R2 is 0.911. The prediction accuracy of the kinetic–Arrhenius model is the worst. The RBF network has a better prediction accuracy than the BP network. For robustness, the adjusted R2 are 0.853 and 0.886 of Italian grape and Red Globe grape, respectively, and the fitting degree are the highest among all methods, which proves that the optimized method is applicable for accurate SL prediction of different table grape varieties.

Originality/value

This study not only provides a new way for the prediction of SL of different grape varieties, but also provides a reference for the quality and safety management of table grape during storage. Maybe it has a further research significance for the application of RBF neural network in the SL prediction of other fresh foods.

Details

British Food Journal, vol. 121 no. 11
Type: Research Article
ISSN: 0007-070X

Keywords

Article
Publication date: 20 July 2010

Francisco Bernal and Manuel Kindelan

The Motz problem can be considered as a benchmark problem for testing the performance of numerical methods in the solution of elliptic problems with boundary singularities. The…

Abstract

Purpose

The Motz problem can be considered as a benchmark problem for testing the performance of numerical methods in the solution of elliptic problems with boundary singularities. The purpose of this paper is to address the solution of the Motz problem using the radial basis function (RBF) method, which is a truly meshfree scheme.

Design/methodology/approach

Both the global RBF collocation method (also known as Kansa's method) and the recently proposed local RBF‐based differential quadrature (LRBFDQ) method are considered. In both cases, it is shown that the accuracy of the solution can be significantly increased by using special functions which capture the behavior of the singularity. In the case of global collocation, the functional space spanned by the RBF is enlarged by adding singular functions which capture the behavior of the local singular solution. In the case of local collocation, the problem is modified appropriately in order to eliminate the singularities from the formulation.

Findings

The paper shows that the exponential convergence both with increasing resolution and increasing shape parameter, which is typical of the RBF method, is lost in problems containing singularities. The accuracy of the solution can be increased by collocation of the partial differential equation (PDE) at boundary nodes. However, in order to restore the exponential convergence of the RBF method, it is necessary to use special functions which capture the behavior of the solution near the discontinuity.

Practical implications

The paper uses Motz's problem as a prototype for problems described by elliptic partial differential equations with boundary singularities. However, the results obtained in the paper are applicable to a wide range of problems containing boundaries with conditions which change from Dirichlet to Neumann, thus leading to singularities in the first derivatives.

Originality/value

The paper shows that both the global RBF collocation method and the LRBFDQ method, are truly meshless methods which can be very useful for the solution of elliptic problems with boundary singularities. In particular, when complemented with special functions that capture the behavior of the solution near the discontinuity, the method exhibits exponential convergence both with resolution and with shape parameter.

Details

Engineering Computations, vol. 27 no. 5
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 29 October 2021

Ran Feng and Xiaoe Qu

To identify and analyze the occurrence of Internet financial market risk, data mining technology is combined with deep learning to process and analyze. The market risk management…

Abstract

Purpose

To identify and analyze the occurrence of Internet financial market risk, data mining technology is combined with deep learning to process and analyze. The market risk management of the Internet is to improve the management level of Internet financial risk, improve the policy of Internet financial supervision and promote the healthy development of Internet finance.

Design/methodology/approach

In this exploration, data mining technology is combined with deep learning to mine the Internet financial data, warn the potential risks in the market and provide targeted risk management measures. Therefore, in this article, to improve the application ability of data mining in dealing with Internet financial risk management, the radial basis function (RBF) neural network algorithm optimized by ant colony optimization (ACO) is proposed.

Findings

The results show that the actual error of the ACO optimized RBF neural network is 0.249, which is 0.149 different from the target error, indicating that the optimized algorithm can make the calculation results more accurate. The fitting results of the RBF neural network and ACO optimized RBF neural network for nonlinear function are compared. Compared with the performance of other algorithms, the error of ACO optimized RBF neural network is 0.249, the running time is 2.212 s, and the number of iterations is 36, which is far less than the actual results of the other two algorithms.

Originality/value

The optimized algorithm has a better spatial mapping and generalization ability and can get higher accuracy in short-term training. Therefore, the ACO optimized RBF neural network algorithm designed in this exploration has a high accuracy for the prediction of Internet financial market risk.

Details

Journal of Enterprise Information Management, vol. 35 no. 4/5
Type: Research Article
ISSN: 1741-0398

Keywords

Book part
Publication date: 6 September 2019

Vivian M. Evangelista and Rommel G. Regis

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector…

Abstract

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78754-290-7

Keywords

Article
Publication date: 26 February 2019

Corrado Groth, Emiliano Costa and Marco Evangelos Biancolini

Numerical simulation of icing has become a standard. Once the iced shape is known, however, the analyst needs to update the computational fluid dynamics (CFD) grid. This paper…

Abstract

Purpose

Numerical simulation of icing has become a standard. Once the iced shape is known, however, the analyst needs to update the computational fluid dynamics (CFD) grid. This paper aims to propose a method to update the numerical mesh with ice profiles.

Design/methodology/approach

The present paper concerns a novel and fast radial basis functions (RBF) mesh morphing technique to efficiently and accurately perform ice accretion simulations on industrial models in the aviation sector. This method can be linked to CFD analyses to dynamically reproduce the ice growth.

Findings

To verify the consistency of the proposed approach, one of the most challenging ice profile selected in the LEWICE manual was replicated and simulated through CFD. To showcase the effectiveness of this technique, predefined ice profiles were automatically applied on two-dimensional (2D) and three-dimensional (3D) cases using both commercial and open-source CFD solvers.

Practical implications

If ice accreted shapes are available, the meshless characteristic of the proposed approach enables its coupling with the CFD solvers currently supported by the RBF4AERO platform including OpenFOAM, SU2 and ANSYS Fluent. The advantages provided by the use of RBF are the high performance and reliability, due to the fast application of mesh smoothing and the accuracy in controlling surface mesh nodes.

Originality/value

As far as authors’ knowledge is concerned, this is the first time in scientific literature that RBF are proposed to handle icing simulations. Due to the meshless characteristic of the RBF mesh morphing, the proposed approach is cross solver and can be used for both 2D and 3D geometries.

Details

Aircraft Engineering and Aerospace Technology, vol. 91 no. 4
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 28 October 2014

Zhang Huaiqing, Nie Xin, Chen Yu and Fu Zhihong

The purpose of this paper is to solve the interface discontinuities in radial basis function (RBF) method for multi-medium boundary value problems (BVPs). The discontinuity of the…

Abstract

Purpose

The purpose of this paper is to solve the interface discontinuities in radial basis function (RBF) method for multi-medium boundary value problems (BVPs). The discontinuity of the solution derivatives is not easily handled with RBF method because of infinitely smoothness.

Design/methodology/approach

The essence of solving BVP is to construct the continuous potential function surfaces. Hence, from constructing surface aspect, this paper proposed and compared the global and subzone schemes for RBF method. Their implementation schemes and mathematic models can then be derived. Numerical experiments and comparison are carried out for electric and magnetic field calculation.

Findings

In the numerical experiments, the subzone scheme has shown its significant advantageous, it can approximate not only the potential function but also its derivative on interface boundary with high accuracy. So the physical characteristics of discontinuities on the interface can be revealed clearly. The overall precision is significantly improved.

Originality/value

This paper proposed an effective subzone scheme for RBF method in multi-medium BVP. It is an improvement for RBF method based on its domain decomposition idea. And it is also a candidate for solving complex BVP.

Details

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 33 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

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