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1 – 10 of over 3000Dhanasekar R, Ganesh Kumar Srinivasan and Marco Rivera
The purpose of this study is to stabilize the rotating speed of the permanent magnet direct current (PMDC) motor driven by a DC-DC boost converter under mismatched disturbances…
Abstract
Purpose
The purpose of this study is to stabilize the rotating speed of the permanent magnet direct current (PMDC) motor driven by a DC-DC boost converter under mismatched disturbances (i.e.) under varying load circumstances like constant, frictional, fan type, propeller and undefined torques.
Design/methodology/approach
This manuscript proposes a higher order sliding mode control to elevate the dynamic behavior of the speed controller and the robustness of the PMDC motor. A second order classical sliding surface and proportional-integral-derivative sliding surface (PIDSS) are designed and compared.
Findings
For the boost converter with PMDC motor, both simulation and experimentation are exploited. The prototype is built for an 18 W PMDC motor with field programmable gate arrays. The suggested sliding mode with second order improves the robustness of the arrangement under disturbances with a wide range of control. Both the simulation and experimental setup shows satisfactory results.
Originality/value
According to software-generated mathematical design and experimental findings, PIDSS exhibits excellent performance with respect to settling speed, steady-state error and peak overshoot.
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Shubham Garg, Sangeeta Mittal and Aman Garg
This study aims to investigate the determinants of GSTefficiency of the Indian states to assist the policymakers, government and GST council to devise their policies and…
Abstract
Purpose
This study aims to investigate the determinants of GSTefficiency of the Indian states to assist the policymakers, government and GST council to devise their policies and strategies to boost the GSTefficiency of the Indian states.
Design/methodology/approach
The analysis has used the panel data set of 27 Indian states and 3 UTs with a time span of 2017–18 to 2022–23. The study has used the Generalized Method of Moment regression for exploring the determinants of GSTefficiency of the state governments in India.
Findings
The findings depict that sectoral composition, inflation rate, financial development, state’s self-reliance, per capita income and gross fiscal deficit have a significant effect on GSTefficiency of the state governments. The findings support the Tanzi effect 1977 and claim that the rise in the inflation level erodes GSTefficiency of the state governments. The rise in the self-reliance of the state government will make the Indian states self-dependent and will reduce their reliance on central transfers.
Practical implications
The government should make efforts to make the Indian states self-reliant by increasing the share of OTR (Own Tax Revenue) instead of increasing their revenue efficiency in short-run through devolution and central transfers. Moreover, the Indian government should devise their macro-economic policies to curb the inflation level and gross fiscal deficit of the state governments in the country.
Originality/value
To the best of the authors’ knowledge, this may be the first study to explore the determinants of GSTefficiency of the state governments in India.
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Nader Asadi Ejgerdi and Mehrdad Kazerooni
With the growth of organizations and businesses, customer acquisition and retention processes have become more complex in the long run. That is why customer lifetime value (CLV…
Abstract
Purpose
With the growth of organizations and businesses, customer acquisition and retention processes have become more complex in the long run. That is why customer lifetime value (CLV) has become crucial to sales managers. Predicting the CLV is a strategic weapon and competitive advantage in increasing profitability and identifying customers with more splendid profitability and is one of the essential key performance indicators (KPI) used in customer segmentation. Thus, this paper proposes a stacked ensemble learning method, a combination of multiple machine learning methods, for CLV prediction.
Design/methodology/approach
In order to utilize customers’ behavioral features for predicting the value of each customer’s CLV, the data of a textile sales company was used as a case study. The proposed stacked ensemble learning method is compared with several popular predictive methods named deep neural networks, bagging support vector regression, light gradient boosting machine, random forest and extreme gradient boosting.
Findings
Empirical results indicate that the regression performance of the stacked ensemble learning method outperformed other methods in terms of normalized rooted mean squared error, normalized mean absolute error and coefficient of determination, at 0.248, 0.364 and 0.848, respectively. In addition, the prediction capability of the proposed method improved significantly after optimizing its hyperparameters.
Originality/value
This paper proposes a stacked ensemble learning method as a new method for accurate CLV prediction. The results and comparisons support the robustness and efficiency of the proposed method for CLV prediction.
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Christian Nnaemeka Egwim, Hafiz Alaka, Oluwapelumi Oluwaseun Egunjobi, Alvaro Gomes and Iosif Mporas
This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.
Abstract
Purpose
This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.
Design/methodology/approach
This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics.
Findings
Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting.
Research limitations/implications
While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK.
Practical implications
This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system.
Originality/value
This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.
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INT/US: Basic income may boost global entrepreneurship
Details
DOI: 10.1108/OXAN-ES288490
ISSN: 2633-304X
Keywords
Geographic
Topical
BOLIVIA: Coup bid may boost Arce but problems endure
SENEGAL: Parliament dissolution will likely boost Faye
Details
DOI: 10.1108/OXAN-ES289641
ISSN: 2633-304X
Keywords
Geographic
Topical
NIGERIA: State election win may boost ruling party
Details
DOI: 10.1108/OXAN-ES289836
ISSN: 2633-304X
Keywords
Geographic
Topical
EGYPT: Ties with Turkey are set for a further boost
Details
DOI: 10.1108/OXAN-ES289455
ISSN: 2633-304X
Keywords
Geographic
Topical
US: Global AI Summit to boost global cooperation