scholarly journals Research on TFe Content of Hematite Based on LU-TELM-SOA and Selection of Band

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanhua Fu ◽  
Lushan Wan ◽  
Yachun Mao ◽  
Tao Ren ◽  
Dong Xiao
Keyword(s):  
Infrared Spectrum ◽  
Extreme Learning Machine ◽  
Iron Ore ◽  
Near Infrared ◽  
Lu Decomposition ◽  
Pruning Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Selection Of ◽  
Near Infrared Spectrum

Iron ore is an important raw material for the steel industry, so it is of great economic significance to determine the grade of the iron ore quickly and accurately. And the TFe content is the main indicator that determines the grade of the iron ore and whether the iron ore can be smelted directly. Unlike manual methods and methods for chemical analysis, the paper uses the selection of band for the near-infrared spectrum based on the pruning method and the two-hidden-layer extreme learning machine based on LU decomposition and seagull optimization algorithm (LU-TELM-SOA) to identify the TFe content. First of all, the paper proposes the selection of band based on the pruning method to retain the sensitive band of the near-infrared spectrum. Aiming at the problems of poor stability and low accuracy of a single LU-TELM (the two-hidden-layer extreme learning machine based on LU decomposition) model, the paper proposes LU-TELM-SOA. The experimental results show that LU-TELM-SOA has the advantages of high accuracy and strong stability.

scholarly journals Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Gao ◽  
Jiangang Lv
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Feature Space ◽  
Single Stage ◽  
Training Data ◽  
Learning Machine ◽  
Hidden Layer ◽  
Signal Processing Methods ◽  
Hidden Nodes ◽  
Selection Of

Single-Stage Extreme Learning Machine (SS-ELM) is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM) has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.

scholarly journals Nanopatterned metallic transparent electrodes for the near-infrared spectrum

AIP Advances ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 045005
Author(s):  
Aliaksandr Hubarevich ◽  
Mikita Marus ◽  
Yauhen Mukha ◽  
Aliaksandr Smirnov ◽  
Xiao Wei Sun
Keyword(s):  
Infrared Spectrum ◽  
Near Infrared ◽  
Transparent Electrodes ◽  
Near Infrared Spectrum

An electrically driven whispering gallery polariton microlaser

Nanoscale ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 5448-5459
Author(s):  
Mingming Jiang ◽  
Peng Wan ◽  
Kai Tang ◽  
Maosheng Liu ◽  
Caixia Kan
Keyword(s):  
Infrared Spectrum ◽  
Near Infrared ◽  
Whispering Gallery ◽  
Near Infrared Spectrum

An electrically driven whispering gallery polariton microlaser composed of a ZnO:Ga microwire and a p-GaAs template was fabricated. Its working characteristics of polariton lasing in the near-infrared spectrum were demonstrated.

Negative Correlation Hidden Layer for the Extreme Learning Machine

2021 ◽  
pp. 107482
Author(s):  
Carlos Perales-González ◽  
Francisco Fernández-Navarro ◽  
Javier Pérez-Rodríguez ◽  
Mariano Carbonero-Ruz
Keyword(s):  
Extreme Learning Machine ◽  
Negative Correlation ◽  
Learning Machine ◽  
Hidden Layer

scholarly journals Theoretical Simulation of Near-Infrared Spectrum of Piperine: Insight into Band Origins and the Features of Regression Models

2021 ◽  
pp. 000370282110279
Author(s):  
Justyna Grabska ◽  
Krzysztof B. Beć ◽  
Sophia Mayr ◽  
Christian W. Huck
Keyword(s):  
Infrared Spectrum ◽  
Regression Models ◽  
Near Infrared ◽  
Computing Time ◽  
Black Pepper ◽  
Partial Least Square ◽  
Least Square ◽  
Complex Nature ◽  
Chemical Information ◽  
Near Infrared Spectrum

We investigated the near-infrared spectrum of piperine using quantum mechanical calculations. We evaluated two efficient approaches, DVPT2//PM6 and DVPT2//ONIOM [PM6:B3LYP/6-311++G(2df, 2pd)] that yielded a simulated spectrum with varying accuracy versus computing time factor. We performed vibrational assignments and unveiled complex nature of the near-infrared spectrum of piperine, resulting from a high level of band convolution. The most meaningful contribution to the near-infrared absorption of piperine results from binary combination bands. With the available detailed near-infrared assignment of piperine, we interpreted the properties of partial least square regression models constructed in our earlier study to describe the piperine content in black pepper samples. Two models were compared with spectral data sets obtained with a benchtop and a miniaturized spectrometer. The two spectrometers implement distinct technology which leads to a profound instrumental difference and discrepancy in the predictive performance when analyzing piperine content. We concluded that the sensitivity of the two instruments to certain types of piperine vibrations is different and that the benchtop spectrometer unveiled higher selectivity. Such difference in obtaining chemical information from a sample can be one of the reasons why the benchtop spectrometer performs better in analyzing the piperine content of black pepper. This evidenced direct correspondence between the features critical for applied near-infrared spectroscopic routine and the underlying vibrational properties of the analyzed constituent in a complex sample.

Functional Classification of Feed Items in Pampa Grassland, Based on Their Near-Infrared Spectrum

2020 ◽  
Vol 73 (3) ◽  
pp. 358-367
Author(s):  
Júlio Cezar Rebés Azambuja Filho ◽  
Paulo Cesar de Faccio Carvalho ◽  
Olivier Jean François Bonnet ◽  
Denis Bastianelli ◽  
Magali Jouven
Keyword(s):  
Infrared Spectrum ◽  
Near Infrared ◽  
Functional Classification ◽  
Near Infrared Spectrum
Sign in / Sign up

Export Citation Format

Share Document