scholarly journals Quantitative prediction of aging state of oil-paper insulation based on Raman spectroscopy

AIP Advances ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 035001
Author(s):  
Xingang Chen ◽  
Shuting Chen ◽  
Dingkun Yang ◽  
Hao Luo ◽  
Ping Yang ◽  
...  
2021 ◽  
Vol 127 (6) ◽  
Author(s):  
Shinsaku Tsuyama ◽  
Akinori Taketani ◽  
Takeharu Murakami ◽  
Michio Sakashita ◽  
Saki Miyajima ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ragini Kothari ◽  
Veronica Jones ◽  
Dominique Mena ◽  
Viviana Bermúdez Reyes ◽  
Youkang Shon ◽  
...  

AbstractThis study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy (LRS), can distinguish healthy and tumor tissue. We now report that combining LRS with two machine learning algorithms, unsupervised k-means and stochastic nonlinear neural networks (NN), provides rapid, quantitative, probabilistic tumor assessment with real-time error analysis. NNs were first trained on Raman spectra using human expert histopathology diagnostics as gold standard (74 spectra, 5 patients). K-means predictions using spectral data when compared to histopathology produced clustering models with 93.2–94.6% accuracy, 89.8–91.8% sensitivity, and 100% specificity. NNs trained on k-means predictions generated probabilities of correctness for the autonomous classification. Finally, the autonomous system characterized an extended dataset (203 spectra, 8 patients). Our results show that an increase in DNA|RNA signal intensity in the fingerprint region (600–1800 cm−1) and global loss of high wavenumber signal (2800–3200 cm−1) are particularly sensitive LRS warning signs of tumor. The stochastic nature of NNs made it possible to rapidly generate multiple models of target tissue classification and calculate the inherent error in the probabilistic estimates for each target.


Metals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1549
Author(s):  
Francis Gyakwaa ◽  
Tuomas Alatarvas ◽  
Qifeng Shu ◽  
Matti Aula ◽  
Timo Fabritius

Steel quality and properties can be affected by the formation of complex inclusions, including Ti-based inclusions such as TiN and Ti2O3 and oxides like Al2O3 and MgO·Al2O3 (MA). This study assessed the prospective use of Raman spectroscopy to characterize synthetic binary inclusion samples of TiN–Al2O3, TiN–MA, Ti2O3–MA, and Ti2O3–Al2O3 with varying phase fractions. The relative intensities of the Raman peaks were used for qualitative evaluation and linear regression calibration models were used for the quantitative prediction of individual phases. The model performance was evaluated with root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP). For the raw Raman spectra data, R2 values were between 0.48–0.98, the RMSECV values varied between 3.26–14.60 wt%, and the RMSEP ranged between 2.98–15.01 wt% for estimating the phases. The SNV Raman spectra data had estimated R2 values within 0.94–0.99 and RMSECV and RMSEP values ranged between 2.50–3.26 wt% and 2.80–9.01 wt%, respectively, showing improved model performance. The study shows that the specific phases of TiN, Al2O3, MA, and Ti2O3 in synthetic inclusion mixtures of TiN–(Al2O3 or MA) and Ti2O3–(Al2O3 or MA) could be characterized by the Raman spectroscopy.


2021 ◽  
Vol 28 (6) ◽  
pp. 1892-1900
Author(s):  
Zewei Wang ◽  
Weigen Chen ◽  
Weiran Zhou ◽  
Ruyue Zhang ◽  
Ruimin Song ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document