Comparison of Techniques for Prediction of Mechanical Strength of Firefighters’ Protective Clothing Using Near-Infrared Spectral Data

2021 ◽  
Author(s):  
Henry U. Ohalele ◽  
Mackenzie Fulton ◽  
David A. Torvi ◽  
Scott D. Noble ◽  
Jane C. Batcheller
Keyword(s):  
Mechanical Strength ◽  
Spectral Data ◽  
Protective Clothing ◽  
Near Infrared ◽  
Infrared Spectral ◽  
Comparison Of Techniques

scholarly journals Biomechanical, biochemical, and near infrared spectral data of bovine knee ligaments and patellar tendon

Data in Brief ◽  
2021 ◽  
Vol 36 ◽  
pp. 106976
Author(s):  
Aapo Ristaniemi ◽  
Jari Torniainen ◽  
Tommi Paakkonen ◽  
Lauri Stenroth ◽  
Mikko A.J. Finnilä ◽  
...  
Keyword(s):  
Spectral Data ◽  
Patellar Tendon ◽  
Near Infrared ◽  
Knee Ligaments ◽  
Infrared Spectral

Wavelet Analysis of Near Infrared Spectral Data in the Application of Denoising

2011 ◽  
Vol 48-49 ◽  
pp. 1358-1362
Author(s):  
Xiao Mei Lin ◽  
Juan Wang ◽  
Qing Hua Yao
Keyword(s):  
Wavelet Analysis ◽  
Infrared Spectrum ◽  
Spectral Data ◽  
Signal Analysis ◽  
Near Infrared ◽  
Fourier Method ◽  
Signal Denoising ◽  
Data Pretreatment ◽  
Infrared Spectral ◽  
Near Infrared Spectrum

Spectrum signal may contain many peaks or mutations and noise also is not smooth white noise, to this kind of signal analysis, must do signal pretreatment, remove part of signal and extract useful part of signal.Based on the data of blood glucose near-infrared spectrum as the research object to explore the application of wavelet transform in the near infrared spectrum signal denoising, and through the simulation results show that using wavelet analysis of near infrared spectral data pretreatment than the traditional Fourier method can be higher precision of prediction.

scholarly journals Kernel partial diagnostic robust potential to handle high-dimensional and irregular data space on near infrared spectral data

Heliyon ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. e03176
Author(s):  
Divo Dharma Silalahi ◽  
Habshah Midi ◽  
Jayanthi Arasan ◽  
Mohd Shafie Mustafa ◽  
Jean-Pierre Caliman
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
High Dimensional ◽  
Data Space ◽  
Infrared Spectral ◽  
Irregular Data

scholarly journals Application of Permutation Entropy in Feature Extraction for Near-Infrared Spectroscopy Noninvasive Blood Glucose Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaoli Li ◽  
Chengwei Li
Keyword(s):  
Feature Extraction ◽  
Blood Glucose ◽  
Spectral Data ◽  
Glucose Concentration ◽  
Near Infrared ◽  
Partial Least Square ◽  
Permutation Entropy ◽  
Glucose Detection ◽  
Characteristic Wave ◽  
Infrared Spectral

Diabetes has been one of the four major diseases threatening human life. Accurate blood glucose detection became an important part in controlling the state of diabetes patients. Excellent linear correlation existed between blood glucose concentration and near-infrared spectral absorption. A new feature extraction method based on permutation entropy is proposed to solve the noise and information redundancy in near-infrared spectral noninvasive blood glucose measurement, which affects the accuracy of the calibration model. With the near-infrared spectral data of glucose solution as the research object, the concepts of approximate entropy, sample entropy, fuzzy entropy, and permutation entropy are introduced. The spectra are then segmented, and the characteristic wave bands with abundant glucose information are selected in terms of permutation entropy, fractal dimension, and mutual information. Finally, the support vector regression and partial least square regression are used to establish the mathematical model between the characteristic spectral data and glucose concentration, and the results are compared with conventional feature extraction methods. Results show that the proposed new method can extract useful information from near-infrared spectra, effectively solve the problem of characteristic wave band extraction, and improve the analytical accuracy of spectral and model stability.

A Preliminary Study on the near Infrared Spectral Characteristics of Purchased Flue-Cured Tobacco

2011 ◽  
Vol 396-398 ◽  
pp. 2027-2032
Author(s):  
Ying Zhang ◽  
Li Yuan He
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Wave Length ◽  
Spectral Characteristics ◽  
Spectral Difference ◽  
Tobacco Leaf ◽  
Positive Group ◽  
Grade Classification ◽  
Infrared Spectral ◽  
Insignificant Difference

In order to explore the application of near-infrared spectrum technology in the grading of purchased flue-cured tobacco, positive group of flue-cured tobacco with different grades was gathered; the near-infrared spectral data across different grades, in the same grade, and different test zones of the same tobacco leaf were determined and acquired to analyze their near-infrared spectral characteristics and their representativeness. The findings indicated that the positive group of flue-cured tobacco was highly homogeneous; there was identical near-infrared spectral characteristics in any zone of the same tobacco leaf; the peaks and troughs of wave length of the near-infrared spectral characteristics of different samples with identical grade were also consistent, with extremely insignificant difference in reflectivity; the near-infrared spectral difference between the tobacco leaves with different grades was much more significant than that in different test zones of the same tobacco leaf. It was highly probable to take the reflectance ratio of the peaks and troughs of wave length as the standard for grade classification. It is feasible to appraise the quality of purchased flue-cured tobacco using near-infrared spectral data.

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