Relating the Friedman test adjusted for ties, the Cochran–Mantel–Haenszel mean score test and the ANOVA F test

Rayner, J. C. W.; Livingston, Glen

doi:10.1080/03610926.2021.1994606

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The Friedman test is included because it shows how the linked groups varied by comparing the mean rankings between them. A well-known nonparametric test statistical test, the Friedman test [44], [45], is additionally used to rank the performance of each algorithm's experimental results on all tested functions. Its final ranking is shown at the end of the statistical table of experimental results, helping to clearly illustrate the differences in algorithm performance.…”

Section: Evaluation Of the Unimodal Functionsmentioning

confidence: 99%

Synthetic on-demand charging optimization with ADCMA for energy conservation in WRSN

Mukase,

Xia,

Owoola

2024

Preprint

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Recently, the research community has shown a growing interest in using mobile chargers to recharge the energy supply of sensor nodes in wireless rechargeable sensor network (WRSN). Mobile energy chargers offer a more dependable energy source compared to devices that harvest dynamic energy from the surrounding environment. This research introduces a synthetic on-demand charging optimization approach using Adaptive Crossover Mayfly Algorithm (ADCMA) to enhance the charging performance in WRSN. The genetic operator of roulette wheel selection is employed to choose possibly valuable solutions for crossover and the objective is to reduce the overall energy consumption of the system and the mobile charger's total distance traveled, while simultaneously enhancing its vacation time ratio. Unlike prior systems that required the mobile charger to visit and charge all nodes in every cycle, this strategy simply requires the mobile charger to visit a subset of nodes. The proposed ADCMA is also tested on 46 benchmark functions and applied to the on-demand charging model by comparing it with other well-known seven algorithms. The mathematical results demonstrate that the ADCMA effectively decreases the overall energy consumption and distance traveled by the mobile charging device, while still maintaining performance. This algorithm is particularly advantageous for real-world networks as it allows for the network to remain operational with reduced complexity compared to other approaches.

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Section: Evaluation Of the Unimodal Functionsmentioning

confidence: 99%

Synthetic on-demand charging optimization with ADCMA for energy conservation in WRSN

Mukase,

Xia,

Owoola

2024

Preprint

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“…Analyzing the seasonality-based time divisions of the week in the frequency of an Air Handling Unit (AHU) induction motor dataset involves applying statistical tests to assess potential significant differences in motor frequency across different time divisions: morning, noon, and afternoon. One appropriate statistical test for this purpose is the Friedman test, a non-parametric method designed to detect differences in treatments across multiple groups [23].…”

Section: System Modelmentioning

confidence: 99%

Optimizing Air Handling Unit Induction Motor Frequency Predictions: Evaluating and Advancing Forecasting Techniques with a Modified Chen's Fuzzy Time Series Model

2024

IJIES

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The purpose of this study is to introduce a novel approach to predict induction motor frequency adjustments in Air Handling Units (AHU). This is essential as traditional methods have frequently been unable to effectively address the complex seasonal and stochastic fluctuations that are inherent in these environments. To overcome this challenge, the research focuses on utilizing experimental Chen's Fuzzy Time Series model, specifically designed to incorporate temporal and seasonal patterns into the predictive analysis. Various predictive models, including Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters Exponential Smoothing (HWES), neural network (NN) based ensemble model, hybrid of artificial neural network (ANN) and SARIMA, and Seasonal Autoregressive Integrated Moving Average with Exogenous factors (SARIMAX), are compared to determine the most effective model in optimizing AHU induction motor frequency. Results indicate that the modified Chen's Fuzzy Time Series model demonstrated high efficacy with an R-squared value of 0.9945 in a one-hour time interval in the seasonal pattern, showing an almost perfect fit between predicted outcomes and actual data compared to other prediction models. Furthermore, the modified Chen's model achieved a Mean Absolute Percentage Error (MAPE) of 2.41% and a Root Mean Square Error (RMSE) of 0.72, significantly outperforming other models in predictive accuracy and reliability. The modified Chen's model showed an efficiency improvement of 86% in MAPE and 87% in RMSE compared to other prediction models.

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Online learning from capricious data streams via shared and new feature spaces

Zhou,

Zhang,

et al. 2024

Appl Intell

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Relating the Friedman test adjusted for ties, the Cochran–Mantel–Haenszel mean score test and the ANOVA F test

Cited by 3 publications

References 6 publications

Synthetic on-demand charging optimization with ADCMA for energy conservation in WRSN

Synthetic on-demand charging optimization with ADCMA for energy conservation in WRSN

Optimizing Air Handling Unit Induction Motor Frequency Predictions: Evaluating and Advancing Forecasting Techniques with a Modified Chen's Fuzzy Time Series Model

Online learning from capricious data streams via shared and new feature spaces

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