Re-KISSME: A robust resampling scheme for distance metric learning in the presence of label noise

Zeng, Fanxia; Zhang, Wensheng; Zhang, Siheng; Zheng, Nan

doi:10.1016/j.neucom.2018.11.009

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…Since the computed score is greater than the statistical value, the null hypothesis is rejected. Furthermore, by applying the Iman-Davenport test [36], the null hypothesis is also rejected as the calculated value is greater than the critical value at the same significance level. These statistical results denote that there are significant differences between the effectiveness of the presented methods.…”

Section: Convergence Ratesmentioning

confidence: 99%

Modeling and Performance Improvement of Direct Power Control of Doubly-Fed Induction Generator Based Wind Turbine through Second-Order Sliding Mode Control Approach

Alhato

Rezk

2020

Mathematics

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A second-order sliding mode (SOSM)-based direct power control (DPC) of a doubly-fed induction generator (DFIG) is introduced in this research paper. Firstly, the DFIG output powers are regulated with the developed SOSM controller-based DPC scheme. The Super Twisting Algorithm (STA) has been used to reduce the chattering phenomenon. The proposed strategy is a combination of the Lyapunov theory and metaheuristics algorithms, which has been considered to identify the optimal gains of the STA-SOSM controllers. The Lyapunov function method is employed to define the stability regions of the controller parameters. On the other hand, the metaheuristics algorithms are mainly employed to select the fine controllers’ parameters from the predefined ranges. A Thermal Exchange Optimization (TEO) method is used to compute the optimal gain parameters. To prove the superiority of the proposed TEO, its obtained results have been compared with those obtained by other algorithms, including particle swarm optimization, genetic algorithm, water cycle algorithm, grasshopper optimization algorithm and harmony search algorithm. Moreover, the results of the introduced TEO-based SOSM controller have been also compared with the Proportional-Integral (PI)-based vector control and the conventional sliding mode control-based DPC. Moreover, an empirical comparison is carried out to investigate the indication of every metaheuristics method by employing Friedman’s rank and Bonferroni tests. The main findings indicate the effectiveness of STA-SOSM control for system stability and power quality improvement. The ripples in the active and reactive powers are minimized and the harmonics’ distortions of stator and rotor currents are improved. Besides, the STA-SOSM controller shows a superior performance of control in terms of chattering phenomenon elimination.

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Section: Convergence Ratesmentioning

confidence: 99%

Modeling and Performance Improvement of Direct Power Control of Doubly-Fed Induction Generator Based Wind Turbine through Second-Order Sliding Mode Control Approach

Alhato

Rezk

2020

Mathematics

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“…In this part, the mean execution values related to the different optimization criteria are sorted to assess the best operating one according to its average objective function performance. Moreover, a statistical comparison based on the nonparametric Friedman and Bonferroni-Dunn is carried out by using these mean performances [30,31]. The average ranks for all the proposed methods based on the four performances indices are provided in Table 6.…”

Section: Statistical Analysis and Comparisonmentioning

confidence: 99%

“…Since the above computed score is greater than this statistical value, the null hypothesis is declined. Moreover, for the Iman-Davenport test [30,31], the statistic is distributed with m − 1 = 5 and (m − 1) × (l − 1) = 15 degrees of freedom. For this test, the null hypothesis is also rejected as the calculated value F F = 4.64 is greater than the critical value F 5,15,0.05 = 2.9 at the same significant level of confidence.…”

Section: Statistical Analysis and Comparisonmentioning

confidence: 99%

Direct Power Control Optimization for Doubly Fed Induction Generator Based Wind Turbine Systems

Alhato

2019

MCA

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This study presents an intelligent metaheuristics-based design procedure for the Proportional-Integral (PI) controllers tuning in the direct power control scheme for 1.5 MW Doubly Fed Induction Generator (DFIG) based Wind Turbine (WT) systems. The PI controllers’ gains tuning is formulated as a constrained optimization problem under nonlinear and non-smooth operational constraints. Such a formulated tuning problem is efficiently solved by means of the proposed Thermal Exchange Optimization (TEO) algorithm. To evaluate the effectiveness of the introduced TEO metaheuristic, an empirical comparison study with the homologous particle swarm optimization, genetic algorithm, harmony search algorithm, water cycle algorithm, and grasshopper optimization algorithm is achieved. The proposed TEO algorithm is ensured to perform several desired operational characteristics of DFIG for the active/reactive power and DC-link voltage simultaneously. This is performed by solving a multi‐objective function optimization problem through a weighted‐sum approach. The proposed control strategy is investigated in MATLAB/environment and the results proved the capabilities of the proposed control system in tracking and control under different scenarios. Moreover, a statistical analysis using non-parametric Friedman and Bonferroni–Dunn’s tests demonstrates that the TEO algorithm gives very competitive results in solving global optimization problems in comparison to the other reported metaheuristic algorithms.

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“…Kissmetric is a data analysis model that can be used for customer engagement. The data analysis of this platform can help users understand customer engagement, analyze product performance, and determine whether the customized marketing plan is effective [6][7]. However, Kissmetric contains a large amount of data, and users may not be able to quickly obtain effective information from it.…”

Section: Introductionmentioning

confidence: 99%

Business Data Analysis Based on Kissmetric in the Context of Big Data

Wang¹

2023

IJACSA

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The kissmetric data analysis model can be used for the analysis and research of business data, and the focused research method in this model is cluster analysis. To realize the effective application of Kissmetric data analysis model, the focused method is improved in the experiment. An improved hierarchical clustering algorithm generated by splitting stage and merging stage is proposed in the experiment, and then the algorithm is combined with density clustering method while considering noise point processing to achieve automatic determination of clustering centers and improvement of clustering effect. In different dimensions, the highest F-measure index and ARI values of the hybrid clustering method are 0.997 and 0.998, respectively. In different numbers of classes of the dataset, the highest F-measure index and ARI values of the hybrid clustering method are 1.000 and 0.999, respectively. The mean accuracy and mean-variance were 95.94% vs. 5.89%, 94.72% vs. 0.57%, 89.72% vs. 4.97%, 87.45% vs. 5.53%, 93.83% vs. 5.76%, and 88.43% vs. 5.40 %, respectively. The mean and mean squared deviation of hybrid clustering method's accuracy was 89.71% vs. 6.17% and 88.85% vs. 0.33% when dealing with the real datasets 7 and 8, respectively. The quality and stability of the clustering results of the hybrid clustering method are better. Compared with other clustering methods, the accuracy and stability of this method are higher and have certain superiority.

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Re-KISSME: A robust resampling scheme for distance metric learning in the presence of label noise

Cited by 6 publications

References 15 publications

Modeling and Performance Improvement of Direct Power Control of Doubly-Fed Induction Generator Based Wind Turbine through Second-Order Sliding Mode Control Approach

Modeling and Performance Improvement of Direct Power Control of Doubly-Fed Induction Generator Based Wind Turbine through Second-Order Sliding Mode Control Approach

Direct Power Control Optimization for Doubly Fed Induction Generator Based Wind Turbine Systems

Business Data Analysis Based on Kissmetric in the Context of Big Data

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