Search results

1 – 10 of over 289000
Article
Publication date: 28 September 2022

Xvdong Ren, Xiuke Yan, Chen Xu, YanLi Zhang and Dexin Xie

The purpose of this study is to develop a reliable finite element algorithm based on the transmission line method (TLM) to solve the nonlinear problem in electromagnetic…

Abstract

Purpose

The purpose of this study is to develop a reliable finite element algorithm based on the transmission line method (TLM) to solve the nonlinear problem in electromagnetic field calculation.

Design/methodology/approach

In this paper, the TLM has been researched and applied to solve nonlinear iteration in FEM. LU decomposition method and the Jacobi preconditioned conjugate gradient method have been investigated to solve the equations in transmission line finite element method (FEM-TLM). The algorithms have been developed in C++ language. The algorithm is applied to analyze the magnetic field of a long straight current-carrying wire and a single-phase transformer.

Findings

FEM-TLM is more effective than traditional FEM in nonlinear iteration. The results of FEM-TLM have been compared and analyzed under different calculation scales. It is found that the LU decomposition method is more suitable for FEM-TLM because there is no need to repeatedly assemble the global coefficient matrix in the iterative solution process and it is not affected by the disturbance of the right-hand vector.

Originality/value

An effective algorithm is provided for solving nonlinear problems in the electromagnetic field, which can save a lot of computing costs. The efficiency of LU decomposition and CG method in FEM-TLM nonlinear iteration is investigated, which also makes a preliminary exploration for the research of FEM-TLM parallel algorithms.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0332-1649

Keywords

Book part
Publication date: 16 August 2011

Avinash Arya

This note presents a method of teaching accounting problems involving the use of the effective interest method such as bonds, notes, capital leases, and installment sales…

Abstract

This note presents a method of teaching accounting problems involving the use of the effective interest method such as bonds, notes, capital leases, and installment sales. The method is conceptually sound and simpler than the traditional method found in current textbooks and stimulates student interest by focusing on the economics of the transaction and relating it to real-life examples.

To assess its pedagogical efficacy, the method was tested in the introductory and intermediate accounting classes. In both courses, the results indicate that students' test scores are significantly higher under the new method than the traditional method. It is hoped that this evidence of the superiority of the new method in a classroom environment will spur its adoption by instructors and textbook writers.

Details

Advances in Accounting Education: Teaching and Curriculum Innovations
Type: Book
ISBN: 978-1-78052-223-4

Article
Publication date: 29 November 2022

Vahide Bulut

Surface curvature is needed to analyze the range data of real objects and is widely applied in object recognition and segmentation, robotics, and computer vision…

Abstract

Purpose

Surface curvature is needed to analyze the range data of real objects and is widely applied in object recognition and segmentation, robotics, and computer vision. Therefore, it is not easy to estimate the curvature of the scanned data. In recent years, machine learning classification methods have gained importance in various fields such as finance, health, engineering, etc. The purpose of this study is to classify surface points based on principal curvatures to find the best method for determining surface point types.

Design/methodology/approach

A feature selection method is presented to find the best feature vector that achieves the highest accuracy. For this reason, ten different feature selections are used and six sample datasets of different sizes are classified using these feature vectors.

Findings

The author examined the surface examples based on the feature vector using the machine learning classification methods. Also, the author compared the results for each experiment.

Originality/value

To the best of the author's knowledge, this is the first study to examine surface points according to principal curvatures using machine learning classification methods.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

Keywords

Open Access
Article
Publication date: 22 November 2022

Kedong Yin, Yun Cao, Shiwei Zhou and Xinman Lv

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index…

Abstract

Purpose

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Design/methodology/approach

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Findings

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

Originality/value

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

Details

Marine Economics and Management, vol. 5 no. 2
Type: Research Article
ISSN: 2516-158X

Keywords

Article
Publication date: 2 December 2022

Yi-Chung Hu

Forecasting tourism demand accurately can help private and public sector formulate strategic planning. Combining forecasting is feasible to improving the forecasting…

Abstract

Purpose

Forecasting tourism demand accurately can help private and public sector formulate strategic planning. Combining forecasting is feasible to improving the forecasting accuracy. This paper aims to apply multiple attribute decision-making (MADM) methods to develop new combination forecasting methods.

Design/methodology/approach

Grey relational analysis (GRA) is applied to assess weights for individual constituents, and the Choquet fuzzy integral is employed to nonlinearly synthesize individual forecasts from single grey models, which are not required to follow any statistical property, into a composite forecast.

Findings

The empirical results indicate that the proposed method shows the superiority in mean accuracy over the other combination methods considered.

Practical implications

For tourism practitioners who have no experience of using grey prediction, the proposed methods can help them avoid the risk of forecasting failure arising from wrong selection of one single grey model. The experimental results demonstrated the high applicability of the proposed nonadditive combination method for tourism demand forecasting.

Originality/value

By treating both weight assessment and forecast combination as MADM problems in the tourism context, this research investigates the incorporation of MADM methods into combination forecasting by developing weighting schemes with GRA and nonadditive forecast combination with the fuzzy integral.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 16 November 2022

Sanaz Faridi, Mahdi Madanchi Zaj, Amir Daneshvar, Shadi Shahverdiani and Fereydoon Rahnamay Roodposhti

This paper presents a combined method of ensemble learning and genetics to rebalance the corporate portfolio. The primary purpose of this paper is to determine the amount…

Abstract

Purpose

This paper presents a combined method of ensemble learning and genetics to rebalance the corporate portfolio. The primary purpose of this paper is to determine the amount of investment in each of the shares of the listed company and the time of purchase, holding or sale of shares to maximize total return and reduce investment risk.

Design/methodology/approach

To achieve the goals of the problem, a two-level combined intelligent method, such as a support vector machine, decision tree, network Bayesian, k-nearest neighbors and multilayer perceptron neural network as heterogeneous basic models of ensemble learning in the first level, was applied. Then, the majority vote method (weighted average) in the second stage as the final model of learning was collectively used. Therefore, the data collected from 208 listed companies active in the Tehran stock exchange (http://tsetmc.com) from 2011 to 2015 have been used to teach the data. For testing and analysis, the data of the same companies between 2016 and 2020 have been used.

Findings

The results showed that the method of combined ensemble learning and genetics has the highest total stock portfolio yield of 114.12%, with a risk of 0.905%. Also, by examining the rate of return on capital, it was observed that the proposed method has the highest average rate of return on investment of 110.64%. As a result, the proposed method leads to higher returns with lower risk than the purchase and maintenance method for fund managers and companies and predicts market trends.

Research limitations/implications

In the forthcoming research, there were no limitations to obtain research data were easily extracted from the site of Tehran Stock Exchange Technology Management Company and Rahvard Novin software, and simulation was performed in MATLAB software.

Practical implications

In this paper, using combined machine learning methods, companies’ stock prices are predicted and stock portfolio optimization is optimized. As companies and private organizations are trying to increase their rate of return, so they need a way to predict stock prices based on specific indicators. It turned out that this algorithm has the highest stock portfolio return with reasonable investment risk, and therefore, investors, portfolio managers and market timers can be used this method to optimize the stock portfolio.

Social implications

The homogeneous and heterogeneous two-level hybrid model presented in the research can be used to predict market trends by market timers and fund managers. Also, adjusting the portfolio with this method has a much higher return than the return on buying and holding, and with controlled risk, it increases the security of investors’ capital, and investors invest their capital in the funds more safely. And will achieve their expected returns. As a result, the psychological security gained from using this method for portfolio arrangement will eventually lead to the growth of the capital market.

Originality/value

This paper tries to present the best combination of stock portfolios of active companies of the Tehran Stock Exchange by using the two-level combined intelligent method and genetic algorithm.

Details

Journal of Financial Reporting and Accounting, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1985-2517

Keywords

Article
Publication date: 14 June 2022

Rie Isshiki, Ryota Kawamata, Shinji Wakao and Noboru Murata

The density method is one of the powerful topology optimization methods of magnetic devices. The density method has the advantage that it has a high degree of freedom of…

Abstract

Purpose

The density method is one of the powerful topology optimization methods of magnetic devices. The density method has the advantage that it has a high degree of freedom of shape expression which results in a high-performance design. On the other hand, it has also the drawback that unsuitable shapes for actually manufacturing are likely to be generated, e.g. checkerboards or grayscale. The purpose of this paper is to develop a method that enables topology optimization suitable for fabrication while taking advantage of the density method.

Design/methodology/approach

This study proposes a novel topology optimization method that combines convolutional neural network (CNN) as an effective smoothing filter with the density method and apply the method to the shield design with magnetic nonlinearity.

Findings

This study demonstrated some numerical examples verifying that the proposed method enables to efficiently obtain a smooth and easy-to-manufacture shield shape with high shielding ability. A network architecture suitable as smoothing filter was also exemplified.

Originality/value

In the field of magnetic field analysis, very few studies have verified the usefulness of smoothing by using CNN in the topology optimization of magnetic devices. This paper develops a novel topology optimization method that skillfully combines CNN with the nonlinear magnetic field analysis and also clarifies a suitable network architecture that makes it possible to obtain a target device shape that is easy to manufacture while minimizing the objective function value.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 41 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 25 October 2022

Siwen Wang and Qiyou Cheng

Computational fluid dynamics (CFD)/computational structural dynamics (CSD) coupling analysis is an important method in the research of helicopter aeroelasticity due to its…

Abstract

Purpose

Computational fluid dynamics (CFD)/computational structural dynamics (CSD) coupling analysis is an important method in the research of helicopter aeroelasticity due to its high precision. However, this method still suffers from some problems, such as wake dissipation and large computational cost. In this study, a new coupling method and a new air load correction method that combine the free wake model with the CFD/CSD method are proposed to maintain computational efficiency whilst solving the wake dissipation problem of the prior coupling methods.

Design/methodology/approach

A new coupling method and a new air load correction method that combine the free wake model with the CFD/CSD method are proposed. With the introduction of the free wake model, the CFD solver can adopt two-order accuracy schemes and fewer aerodynamic grids, thus maintaining computational efficiency whilst solving the wake dissipation problem of the prior coupling methods.

Findings

Compared with the predictions of the prior methods and flight test data, those of the proposed method are more accurate and closer to the test data. The difference between the two methods in high-speed forward flight is minimal.

Research limitations/implications

Because of the chosen research approach, the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed method further.

Originality/value

In this paper, a CFD/CSD/free wake coupling method is proposed to improve the computational accuracy of the traditional CFD/CSD coupled method and ensure the computational efficiency.

Details

Aircraft Engineering and Aerospace Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1748-8842

Keywords

Open Access
Article
Publication date: 20 October 2022

Chongjun Wu, Dengdeng Shu, Hu Zhou and Zuchao Fu

In order to improve the robustness to noise in point cloud plane fitting, a combined model of improved Cook’s distance (ICOOK) and WTLS is proposed by setting a modified…

Abstract

Purpose

In order to improve the robustness to noise in point cloud plane fitting, a combined model of improved Cook’s distance (ICOOK) and WTLS is proposed by setting a modified Cook’s increment, which could help adaptively remove the noise points that exceeds the threshold.

Design/methodology/approach

This paper proposes a robust point cloud plane fitting method based on ICOOK and WTLS to improve the robustness to noise in point cloud fitting. The ICOOK to denoise the initial point cloud was set and verified with experiments. In the meanwhile, weighted total least squares method (WTLS) was adopted to perform plane fitting on the denoised point cloud set to obtain the plane equation.

Findings

(a) A threshold-adaptive Cook’s distance method is designed, which can automatically match a suitable threshold. (b) The ICOOK is fused with the WTLS method, and the simulation experiments and the actual fitting of the surface of the DD motor are carried out to verify the actual application. (c) The results shows that the plane fitting accuracy and unit weight variance of the algorithm in this paper are substantially enhanced.

Originality/value

The existing point cloud plane fitting methods are not robust to noise, so a robust point cloud plane fitting method based on a combined model of ICOOK and WTLS is proposed. The existing point cloud plane fitting methods are not robust to noise, so a robust point cloud plane fitting method based on a combined model of ICOOK and WTLS is proposed.

Details

Journal of Intelligent Manufacturing and Special Equipment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2633-6596

Keywords

Article
Publication date: 25 October 2022

David Tae and Kumar K. Tamma

The purpose of this paper is to describe a novel implementation of a multispatial method, multitime-scheme subdomain differential algebraic equation (DAE) framework…

Abstract

Purpose

The purpose of this paper is to describe a novel implementation of a multispatial method, multitime-scheme subdomain differential algebraic equation (DAE) framework allowing a mix of different space discretization methods and different time schemes by a robust generalized single step single solve (GS4) family of linear multistep (LMS) algorithms on a single body analysis for the first-order nonlinear transient systems.

Design/methodology/approach

This proposed method allows the coupling of different numerical methods, such as the finite element method and particle methods, and different implicit and/or explicit algorithms in each subdomain into a single analysis with the GS4 framework. The DAE, which constrains both space and time in multi-subdomain analysis, combined with the GS4 framework ensures the second-order time accuracy in all primary variables and Lagrange multiplier. With the appropriate GS4 parameters, the algorithmic temperature rate variable shift can be matched for all time steps using the DAE. The proposed method is used to solve various combinations of spatial methods and time schemes between subdomains in a single analysis of nonlinear first-order system problems.

Findings

The proposed method is capable of coupling different spatial methods for multiple subdomains and different implicit/explicit time integration schemes in the GS4 framework while sustaining second-order time accuracy.

Originality/value

Traditional approaches do not permit such robust and flexible coupling features. The proposed framework encompasses most of the LMS methods that are second-order time accurate and unconditionally stable.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0961-5539

Keywords

1 – 10 of over 289000