Data Preprocessing Method for Cost Estimation of Building Projects

As an advanced process detection technology, electrical impedance tomography (EIT) has widely been paid attention to and studied in the industrial fields. But the EIT techniques are greatly limited to the low spatial resolutions. This problem may result from the incorrect preprocessing of measuring data and lack of general criterion to evaluate different preprocessing processes. In this paper, an EIT data preprocessing method is proposed by all rooting measured data and evaluated by two constructed indexes based on all rooted EIT measured data. By finding the optimums of the two indexes, the proposed method can be applied to improve the EIT imaging spatial resolutions. In terms of a theoretical model, the optimal rooting times of the two indexes range in [0.23, 0.33] and in [0.22, 0.35], respectively. Moreover, these factors that affect the correctness of the proposed method are generally analyzed. The measuring data preprocessing is necessary and helpful for any imaging process. Thus, the proposed method can be generally and widely used in any imaging process. Experimental results validate the two proposed indexes.

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A Data Preprocessing Method for Food Detection Data Warehouse

Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017) ◽

10.2991/icmmcce-17.2017.97 ◽

2017 ◽

Author(s):

Ying Han ◽

Chunlong Yao ◽

Qiuyan Xin ◽

Xiaoqiang Yu ◽

Xu Li

Keyword(s):

Data Warehouse ◽

Data Preprocessing ◽

Preprocessing Method ◽

Food Detection

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A novel data preprocessing method to estimate the air pollution (SO2): neighbor-based feature scaling (NBFS)

Neural Computing and Applications ◽

10.1007/s00521-011-0602-x ◽

2011 ◽

Vol 21 (8) ◽

pp. 1987-1994 ◽

Cited By ~ 8

Author(s):

Kemal Polat

Keyword(s):

Air Pollution ◽

Data Preprocessing ◽

Preprocessing Method ◽

Feature Scaling

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Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data

BMC Bioinformatics ◽

10.1186/s12859-020-03797-8 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Chunxiang Wang ◽

Xin Gao ◽

Juntao Liu

Keyword(s):

Single Cell ◽

Clustering Algorithm ◽

Clustering Algorithms ◽

Data Preprocessing ◽

Cell Types ◽

Rna Seq ◽

Cell Type ◽

Preprocessing Method ◽

Cell Clustering ◽

Cell Gene Expression

Abstract Background Advances in single-cell RNA-seq technology have led to great opportunities for the quantitative characterization of cell types, and many clustering algorithms have been developed based on single-cell gene expression. However, we found that different data preprocessing methods show quite different effects on clustering algorithms. Moreover, there is no specific preprocessing method that is applicable to all clustering algorithms, and even for the same clustering algorithm, the best preprocessing method depends on the input data. Results We designed a graph-based algorithm, SC3-e, specifically for discriminating the best data preprocessing method for SC3, which is currently the most widely used clustering algorithm for single cell clustering. When tested on eight frequently used single-cell RNA-seq data sets, SC3-e always accurately selects the best data preprocessing method for SC3 and therefore greatly enhances the clustering performance of SC3. Conclusion The SC3-e algorithm is practically powerful for discriminating the best data preprocessing method, and therefore largely enhances the performance of cell-type clustering of SC3. It is expected to play a crucial role in the related studies of single-cell clustering, such as the studies of human complex diseases and discoveries of new cell types.

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