An Improved MSR-Based Data-Driven Detection Method Using Smoothing Pre-Processing

Yan, Yujia; Wu, Guangshun; Dong, Yuan; Bai, Yechao

doi:10.1109/lsp.2021.3058008

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Combining the self-organizing map clustering mechanism, Tang et al [15] put forward a novel data pre-processing approach with high feasibility and effectiveness in the research of intelligent root selection of hanger and support. To improve the recognition rate of the speaker identification systems, two pre-processing approaches, adaptive noise canceller and Savitzky-Golay filter, were applied in [16] to reduce the influence of noise in a complex environment. Yan et al [17] presented a smoothing pre-processing approach to effectively enhance the identification performance of mean spectral radius-based detectors.…”

Section: Related Work 21 Data Pre-processingmentioning

confidence: 99%

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

Han,

Wang,

Chen

et al. 2024

J. of Syst. Eng. Electron.

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With the development of information technology, a large number of product quality data in the entire manufacturing process is accumulated, but it is not explored and used effectively. The traditional product quality prediction models have many disadvantages, such as high complexity and low accuracy. To overcome the above problems, we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model: radial basis function model optimized by the firefly algorithm with Levy flight mechanism (RBFFALM). First, the new data equalization method is introduced to pre-process the dataset, which reduces the dimension of the data, removes redundant features, and improves the data distribution. Then the RBFFALFM is used to predict product quality. Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

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Section: Related Work 21 Data Pre-processingmentioning

confidence: 99%