首页|Findings on Machine Learning Reported by Investigators at Northeastern Universit y (Rapid Detection of Iron Ore Grades Based On Fractional-order Derivative Spect roscopy and Machine Learning)
Findings on Machine Learning Reported by Investigators at Northeastern Universit y (Rapid Detection of Iron Ore Grades Based On Fractional-order Derivative Spect roscopy and Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating from Shenyang, People's Republic of China, by NewsRx correspondents, research stated, "The time-consumin g nature of chemical testing techniques makes them lag behind mineral processing . Therefore, this article combines visible-infrared reflectance spectroscopy wit h machine learning (ML) algorithms to achieve rapid detection of iron ore grades and meet the requirements of mining production." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from Northeastern University , "First, the standard normal variate (SNV) and de-trending (DT) are used to eli minate noise and baseline drift in the original spectral data. Then, extraneous signals are removed using direct orthogonal signal correction (DOSC). In additio n, fractional-order derivative (FOD) is performed on the DOSC spectrum to furthe r amplify the spectral details. To extract spectral features and reduce the spec tral dimension, a multilayer incremental extreme learning machine autoencoder (M IELM-AE) is proposed in this article. MIELM-AE can automatically match the optim al number of network nodes and network layers to minimize the reconstruction err or. The experimental results show that the Pearson correlation coefficient ( R-2 ) of the extreme learning machine (ELM) built using MIELM-AE improves from 0.71 5 to 0.821, compared with the ELM built without the dimensionality reduction met hod. To increase the measurement accuracy, this article uses Tikhonov regulariza tion and truncated singular value decomposition (TSVD) to alleviate the ill-cond itioned matrix of the hidden layer of the ELM and uses the incremental method to match the optimal network nodes. Finally, double-regularization incremental ELM (DRIELM) is proposed in this article."
ShenyangPeople's Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningNortheastern University
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