An Optimizing Dynamic Spectrum Differential Extraction Method for Noninvasive Blood Component Analysis

2019 ◽  
Vol 74 (1) ◽  
pp. 23-33 ◽  
Author(s):  
Wei Tang ◽  
Qiang Chen ◽  
Wenjuan Yan ◽  
Guoquan He ◽  
Gang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Correlation Coefficient ◽  
Extraction Method ◽  
Rbf Neural Network ◽  
Extraction Methods ◽  
Dynamic Spectrum ◽  
The Other ◽  
Single Trial ◽  
Differential Extraction ◽  
Better Than

Dynamic spectra (DS) can greatly reduce the influence of individual differences and the measurement environment by extracting the absorbance of pulsating blood at multiple wavelengths, and it is expected to achieve noninvasive detection of blood components. Extracting high-quality DS is the prerequisite for improving detection accuracy. This paper proposed an optimizing differential extraction method in view of the deficiency of existing extraction methods. In the proposed method, the sub-dynamic spectrum (sDS) is composed by sequentially extracting the absolute differences of two sample points corresponding to the height of the half peak on the two sides of the lowest point in each period of the logarithm photoplethysmography signal. The study was based on clinical trial data from 231 volunteers. Single-trial extraction method, original differential extraction method, and optimizing differential extraction method were used to extract DS from the volunteers’ experimental data. Partial least squares regression (PLSR) and radial basis function (RBF) neural network were used for modeling. According to the effect of PLSR modeling, by extracting DS using the proposed method, the correlation coefficient of prediction set ( Rp) has been improved by 17.33% and the root mean square error of prediction set has been reduced by 7.10% compared with the original differential extraction method. Compared with the single-trial extraction method, the correlation coefficient of calibration set ( Rc) has increased from 0.747659 to 0.8244, with an increase of 10.26%, while the correlation coefficient of prediction set ( Rp) decreased slightly by 3.22%, much lower than the increase of correction set. The result of the RBF neural network modeling also shows that the accuracy of the optimizing differential method is better than the other two methods both in calibration set and prediction set. In general, the optimizing differential extraction method improves the data utilization and credibility compared with the existing extraction methods, and the modeling effect is better than the other two methods.

The Effects of Solvents and Extraction Methods on the Antioxidant Activity of P. niruri

2014 ◽  
Vol 68 (5) ◽  
Author(s):  
Hafizah Mohd Hadzri ◽  
Mohd Azizi Che Yunus ◽  
Salman Zhari ◽  
Fahim Rithwan
Keyword(s):  
Antioxidant Activity ◽  
Extraction Method ◽  
Radical Scavenging Activity ◽  
Radical Scavenging ◽  
Soxhlet Extraction ◽  
Solvent Polarity ◽  
Extraction Methods ◽  
The Other ◽  
Natural Antioxidant ◽  
Total Phenolic

The effects of different types of solvents and extraction method were investigated to determine the presence of antioxidant contents and activity from the P. niruri plant. The aim of this study is to determine which extraction method will give higher natural antioxidant contents and antioxidant activity. The content of natural antioxidant and antioxidant activity were analysed by total phenolic content (TPC), total flavonoid content (TFC) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging activity assay. The results showed that extracts from a supercritical fluid extraction (SFE) method without the addition of modifier showed the highest content of total phenolic (187.66 mg GAE/ g) and flavonoid (1100.93 mg QE/g) in P. niruri compared to the other methods of extraction with different type of solvents. The extract of P. niruri from different extraction methods showed antioxidant activity on DPPH radical scavenging assay. The soxhlet extraction method by methanol showed the lowest IC50 compared to the other methods of extraction. The results revealed that P. niruri extracts had different content of antioxidant and antioxidant activity. The solvent polarity and different methods of extraction play significant roles in determining the most suitable method for production of antioxidant contents and antioxidant activity from P. niruri extracts.

Energy Demand Forecast of City Based on Cellular Genetic Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2122-2125
Author(s):  
Yu Gui Cheng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Energy Demand ◽  
The Other ◽  
Demand Forecast ◽  
Minimum Error ◽  
Search Optimization ◽  
Cellular Genetic Algorithm ◽  
Better Than

As a branch of genetic algorithm (GA), cellular genetic algorithm (CGA) has been used in search optimization of the population in recent years. Compared with traditional genetic algorithm and the algorithm combined with traditional genetic algorithm and BP neural network, energy demand forecast of city by the method of combining cellular genetic algorithm and BP neural network had the characteristic of the minimum training times, the shortest consumption time and the minimum error. Meanwhile, it was better than the other two algorithms from the point of fitting effect.

Design of RBF Neural Networks Based on Adjustable Radius

2010 ◽  
Vol 439-440 ◽  
pp. 605-610
Author(s):  
Xiao Yong Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Function Approximation ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Gaussian Function ◽  
Mean Square ◽  
Rbf Neural Networks ◽  
Data Points ◽  
Better Than

In this paper, a new RBF neural network (RBFNN) algorithm, called ar-RBFNN, is presented. In traditional RBFNNs based on clustering algorithm, called oRBFNN in this paper, the width of the basis function-Gaussian function, or called radius, ignored the effect of numbers in different clusters, or density of data points. New algorithm considers radius is effect to performance of algorithms in problem of function approximation. Mean Square Error is used to evaluate performances of two algorithms, oRBFNN and ar-RBFNN algorithms. Several experiments in function approximation show ar-RBFNN is better than oRBFNN.

scholarly journals The Analysis of China’s Grain Output Fluctuation Based on EMD

2017 ◽  
Vol 9 (11) ◽  
pp. 64
Author(s):  
Qiting Chen ◽  
Meng Wang
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Driving Forces ◽  
Unit Area ◽  
Rbf Neural Network ◽  
The Other ◽  
Cyclical Fluctuation ◽  
Output Fluctuations ◽  
Mode Decomposition ◽  
Emd Method

Food is one of the most important resources for staying alive. This paper analyzes grain output fluctuations and their driving forces in China from 1978 to 2014, based on Empirical Mode Decomposition (EMD) method. These results show that there are two type cycles of cyclical fluctuation, one is 3-yearterm, and another is 8-year term. These results show that the 8-year cyclical fluctuation is the major term. Grain production’s cyclical fluctuation in 3 years was mainly influenced by yield of grain per unit area from 1978-2004 and 2007-2014, and by the area sown from 2004 to 2007. On the other hand, the longer cyclical fluctuation of 8 years is mainly affected by the yield of grain per unit area. The grain output is predicted for the next three years through the RBF neural network optimized by PSO. These results show that China’s annul grain output in the next three years will be stabilized at about 600 million tons, which may grow slowly though.

A NEURAL BASED HUMAN FACE RECOGNITION SYSTEM USING AN EFFICIENT FEATURE EXTRACTION METHOD WITH PSEUDO ZERNIKE MOMENT

2002 ◽  
Vol 11 (03) ◽  
pp. 283-304 ◽  
Author(s):  
JAVAD HADDADNIA ◽  
KARIM FAEZ ◽  
MAJID AHMADI
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Extraction Method ◽  
Rbf Neural Network ◽  
Zernike Moment ◽  
Human Face ◽  
Shape Information ◽  
Feature Extraction Method ◽  
Face Features ◽  
Face Localization

This paper introduces an efficient method for the recognition of human faces in 2D digital images using a feature extraction technique that combines the global and local information in frontal view of facial images. The proposed feature extraction includes human face localization derived from the shape information. Efficient parameters are defined to eliminate irrelevant data while Pseudo Zernike Moments (PZM) with a new moment orders selection method is introduced as face features. The proposed method while yields better recognition rate, also reduces the classifier complexity. This paper also examines application of various feature domains as face features using the face localization method. These include Principle Component Analysis (PCA) and Discrete Cosine Transform (DCT). The Radial Basis Function (RBF) neural network has been used as the classifier and we have shown that the proposed feature extraction method requires an RBF neural network classifier with a simpler structure and faster training phase that is less sensitive to select training and testing images. Simulation results on the Olivetti Research Laboratory (ORL) database and comparison with other techniques indicate the effectiveness of the proposed method.

RBF Neural Network Wind Power Prediction Based on Chaos Theory

2014 ◽  
Vol 1070-1072 ◽  
pp. 315-318
Author(s):  
Li Dong Zhang ◽  
Shan Shan Li ◽  
Xu Dong He
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Lyapunov Exponent ◽  
Wind Power ◽  
Chaos Theory ◽  
Rbf Neural Network ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Better Than

Using the C - C method to reconstruct the phase space of wind power time series, get the maximum wind power time series Lyapunov exponent, confirmed that the wind power time series have chaotic characteristics. Followed by the radial basis function (RBF) neural network model for wind power chaotic local multi-step prediction, results show that the prediction effect is better than that of the predicted effect of 48 hours for 24 hours.

Comparison of extraction methods for quantifying extracellular polymers in biofilms

1999 ◽  
Vol 39 (7) ◽  
pp. 211-218 ◽  
Author(s):  
Xiaoqi Zhang ◽  
Paul L. Bishop ◽  
Brian K. Kinkle
Keyword(s):  
Extraction Method ◽  
Extracellular Polymeric Substances ◽  
Cell Lysis ◽  
Extraction Methods ◽  
The Other ◽  
Protein Yield ◽  
Total Carbohydrate ◽  
Extracellular Polymers ◽  
Sulfate Reducing ◽  
Edta Extraction

Five commonly used extraction methods - regular centrifugation, EDTA extraction, ultracentrifugation, steaming extraction and regular centrifugation with formaldehyde (RCF) - were selected to study their effectiveness and repeatability in extracting extracellular polymeric substances (EPS) from aerobic/sulfate reducing and nitrifying/denitrifying biofilm samples. Biofilm EPS extraction yields were represented by carbohydrate and protein concentrations; the amount of cell lysis during the extractions was indicated by DNA concentration. The results showed that analyzing wash waters is essential in quantifying biofilm EPS; the contribution of this step varied from 8-50% of the total carbohydrate yield, depending on the extraction method. Among the extraction methods, the RCF extraction gave the greatest carbohydrate yield, the steaming extraction gave the greatest protein yield, and the other three extraction methods gave approximately equivalent amounts of carbohydrate and proteins for both types of biofilm. DNA in the EPS was 27 times smaller than in the pellets, indicating no significant cell lysis occurred during the extractions.

scholarly journals Partial Discharge Signal Extraction Method Based on EDSSV and Low Rank RBF Neural Network

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xiaoli Yang ◽  
Hongguang Huang ◽  
Qin Shu ◽  
Dakun Zhang ◽  
Bojian Chen
Keyword(s):  
Neural Network ◽  
Extraction Method ◽  
Rbf Neural Network ◽  
Partial Discharge ◽  
Low Rank ◽  
Signal Extraction

scholarly journals Octave-band Filtering for Convolutional Neural Network-based Diagnostics for Rotating Machinery

2020 ◽  
Vol 12 (1) ◽  
pp. 9
Author(s):  
Namkyoung Lee ◽  
Michael Azarian ◽  
Michael Pecht
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Speech Recognition ◽  
Convolutional Neural Network ◽  
Extraction Method ◽  
Extraction Methods ◽  
Rotating Machinery ◽  
Octave Band ◽  
Feature Extraction Method ◽  
Model Research

The performance of a machine learning model depends on the quality of the features used as input to the model. Research into feature extraction methods for convolutional neural network (CNN)-based diagnostics for rotating machinery remains in a developmental stage. In general, the input to CNN-based diagnostics consists of a spectrogram without significant pre-processing. This paper introduces octave-band filtering as a feature extraction method for preprocessing a spectrogram prior to use with CNN. This method is an adaptation of a feature extraction method originally developed for speech recognition. The method developed for diagnosis of machinery faults differs from filtering methods applied to speech recognition in its use of octave bands, to which weighting has been applied that is optimal for machinery diagnosis. Through a case study, the effectiveness of octave-band filtering is demonstrated. The method not only improves the accuracy of the CNN-based diagnostics but also reduces the size of the CNN.

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