Preprocessing method for weighing data in electronic analytical balance

2011 ◽  
Vol 24 (12) ◽  
pp. 1107-1113
Author(s):  
Liangzhu Chen ◽  
Zhaosheng Teng ◽  
Min Yang ◽  
Wanli Shao
Keyword(s):  
Analytical Balance ◽  
Preprocessing Method

Determination of Pyridostigmine Bromide in Presence of its Related Impurities by Four Modified Classical Least Square Based Models: A Comparative Study

2020 ◽  
Vol 17 (1) ◽  
pp. 87-94
Author(s):  
Ibrahim A. Naguib ◽  
Fatma F. Abdallah ◽  
Aml A. Emam ◽  
Eglal A. Abdelaleem
Keyword(s):  
Comparative Study ◽  
Least Squares ◽  
Orthogonal Projection ◽  
Predictive Ability ◽  
Least Square ◽  
Projection To Latent Structures ◽  
Preprocessing Method ◽  
Spectral Residual ◽  
Latent Structures

: Quantitative determination of pyridostigmine bromide in the presence of its two related substances; impurity A and impurity B was considered as a case study to construct the comparison. Introduction: Novel manipulations of the well-known classical least squares multivariate calibration model were explained in detail as a comparative analytical study in this research work. In addition to the application of plain classical least squares model, two preprocessing steps were tried, where prior to modeling with classical least squares, first derivatization and orthogonal projection to latent structures were applied to produce two novel manipulations of the classical least square-based model. Moreover, spectral residual augmented classical least squares model is included in the present comparative study. Methods: 3 factor 4 level design was implemented constructing a training set of 16 mixtures with different concentrations of the studied components. To investigate the predictive ability of the studied models; a test set consisting of 9 mixtures was constructed. Results: The key performance indicator of this comparative study was the root mean square error of prediction for the independent test set mixtures, where it was found 1.367 when classical least squares applied with no preprocessing method, 1.352 when first derivative data was implemented, 0.2100 when orthogonal projection to latent structures preprocessing method was applied and 0.2747 when spectral residual augmented classical least squares was performed. Conclusion: Coupling of classical least squares model with orthogonal projection to latent structures preprocessing method produced significant improvement of the predictive ability of it.

The Analytical Balance: Its Care and Use

1951 ◽  
Vol 43 (5) ◽  
pp. 247-247
Author(s):  
M. L. Jackson
Keyword(s):  
Analytical Balance

scholarly journals Apical Extrusion of Debris Produced during Continuous Rotating and Reciprocating Motion

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Giselle Nevares ◽  
Felipe Xavier ◽  
Luciana Gominho ◽  
Flávia Cavalcanti ◽  
Marcely Cassimiro ◽  
...  
Keyword(s):  
Cross Section ◽  
Sodium Hypochlorite ◽  
Reciprocating Motion ◽  
Continuous Motion ◽  
Analytical Balance ◽  
Root Canals ◽  
Apical Extrusion ◽  
Final Weight ◽  
The Difference

This study aimed to analyse and compare apical extrusion of debris in canals instrumented with systems used in reciprocating and continuous motion. Sixty mandibular premolars were randomly divided into 3 groups (n=20): the Reciproc (REC), WaveOne (WO), and HyFlex CM (HYF) groups. One Eppendorf tube per tooth was weighed in advance on an analytical balance. The root canals were instrumented according to the manufacturer’s instructions, and standardised irrigation with 2.5% sodium hypochlorite was performed to a total volume of 9 mL. After instrumentation, the teeth were removed from the Eppendorf tubes and incubated at 37°C for 15 days to evaporate the liquid. The tubes were weighed again, and the difference between the initial and final weight was calculated to determine the weight of the debris. The data were statistically analysed using the Shapiro-Wilk, Wilcoxon, and Mann-Whitney tests (α=5%). All systems resulted in the apical extrusion of debris. Reciproc produced significantly more debris than WaveOne (p<0.05), and both systems produced a greater apical extrusion of debris than HyFlex CM (p<0.001). Cross section and motion influenced the results, despite tip standardization.

Research on SCADA data preprocessing method of Wind Turbine

2021 ◽  
Author(s):  
Fuyu Qiao ◽  
Yongguang Ma ◽  
Liangyu Ma ◽  
Sihan Chen ◽  
Hao Yang ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Data Preprocessing ◽  
Preprocessing Method

A Data Preprocessing Method for Deep Learning Based Device-Free Localization

2021 ◽  
pp. 1-1
Author(s):  
Wu Wei ◽  
Jun Yan ◽  
Xiaofu Wu ◽  
Chen Wang ◽  
Gengxin Zhang
Keyword(s):  
Deep Learning ◽  
Data Preprocessing ◽  
Preprocessing Method ◽  
Device Free

scholarly journals An Effective Measured Data Preprocessing Method in Electrical Impedance Tomography

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Chenglong Yu ◽  
Shihong Yue ◽  
Jianpei Wang ◽  
Huaxiang Wang
Keyword(s):  
Theoretical Model ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Data Preprocessing ◽  
Measured Data ◽  
General Criterion ◽  
Impedance Tomography ◽  
Preprocessing Method ◽  
Detection Technology ◽  
Process Detection

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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