A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism

In railway electrification systems, the harmonic impedance of the traction network is of great value for avoiding harmonic resonance and electrical matching of impedance parameters between trains and traction networks. Therefore, harmonic impedance identification is beneficial to suppress harmonics and improve the power quality of the traction network. As a result of the coupling characteristics of the traction power supply system, the identification results of harmonic impedance may be inaccurate and controversial. In this context, an identification method based on a data evolution mechanism is proposed. At first, a harmonic impedance model is established and the equivalent circuit of the traction network is established. According to the harmonic impedance model, the proposed method eliminates the outliers of the measured data from trains by the Grubbs criterion and calculates the harmonic impedance by partial least squares regression. Then, the data evolution mechanism based on the sample coefficient of determination is introduced to estimate the reliability of the identification results and to divide results into several reliability levels. Furthermore, in the data evolution mechanism through adding new harmonic data, the low-reliability results can be replaced by the new results with high reliability and, finally, the high-reliability results can cover all frequencies. Moreover, the identification results based on the simulation data show the higher reliability results are more accurate than the lower reliability results. The measured data verify that the the data evolution mechanism can improve accuracy and reliability, and their results prove the feasibility and validation of the proposed method.

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Satellite-Derived Protein Concentration of Phytoplankton in the Southwestern East/Japan Sea

Journal of Marine Science and Engineering ◽

10.3390/jmse9020189 ◽

2021 ◽

Vol 9 (2) ◽

pp. 189

Author(s):

Hyeonji Bae ◽

Dabin Lee ◽

Jae Joong Kang ◽

Jae Hyung Lee ◽

Naeun Jo ◽

...

Keyword(s):

Protein Concentration ◽

Japan Sea ◽

Biological Properties ◽

Measured Data ◽

Trophic Levels ◽

Coefficient Of Determination ◽

P Value ◽

Chl A ◽

Major Factors

The cellular macromolecular contents and energy value of phytoplankton as primary food source determine the growth of higher trophic levels, affecting the balance and sustainability of oceanic food webs. Especially, proteins are more directly linked with basic functions of phytoplankton biosynthesis and cell division and transferred through the food chains. In recent years, the East/Japan Sea (EJS) has been changed dramatically in environmental conditions, such as physical and chemical characteristics, as well as biological properties. Therefore, developing an algorithm to estimate the protein concentration of phytoplankton and monitor their spatiotemporal variations on a broad scale would be invaluable. To derive the protein concentration of phytoplankton in EJS, the new regional algorithm was developed by using multiple linear regression analyses based on field-measured data which were obtained from 2012 to 2018 in the southwestern EJS. The major factors for the protein concentration were identified as chlorophyll-a (Chl-a) and sea surface nitrate (SSN) in the southwestern EJS. The coefficient of determination (r2) between field-measured and algorithm-derived protein concentrations was 0.55, which is rather low but reliable. The satellite-derived estimation generally follows the 1:1 line with the field-measured data, with Pearson’s correlation coefficient, which was 0.40 (p-value < 0.01, n = 135). No remarkable trend in the long-term annual protein concentration of phytoplankton was found in the study area during our observation period. However, some seasonal difference was observed in winter protein concentration between the 2003–2005 and 2017–2019 periods. The algorithm is developed for the regional East/Japan Sea (EJS) and could contribute to long-term monitoring for climate-associated ecosystem changes. For a better understanding of spatiotemporal variation in the protein concentration of phytoplankton in the EJS, this algorithm should be further improved with continuous field surveys.

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Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽

10.1186/s13007-021-00715-8 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Jordi Ortuño ◽

Sokratis Stergiadis ◽

Anastasios Koidis ◽

Jo Smith ◽

Chris Humphrey ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Least Squares ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Multivariate Calibration ◽

Plant Ecology ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

Mid Infrared ◽

Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

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ANALYSIS OF THE ERROR IN DETERMINING THE EQUIVALENT SIZE OF A DEFECT BY THE STANDARDLESS METHOD DURING ULTRASONIC TESTING

Kontrol Diagnostika ◽

10.14489/td.2021.04.pp.050-057 ◽

2021 ◽

pp. 50-57

Author(s):

A. N. Kireev ◽

M. A. Kireeva

Keyword(s):

Relative Error ◽

High Reliability ◽

Ultrasonic Method ◽

Experimental Studies ◽

Standard Samples ◽

Defect Identification ◽

Identification Method ◽

Maximum Value ◽

Software Product ◽

The Relationship

The article provides a review and analysis of the defect identification method for determining the size of discontinuities when diagnosing various machine parts and units by the manual ultrasonic method. This method makes it possible to determine the equivalent size of discontinuities of various types without using standard samples of an enterprise: point planar and volumetric; extended planar and volumetric. The method is based on the use of the relationship between the amplitude and time characteristics of the echo signal from the discontinuity and the backside signal in the object being diagnosed and the equivalent size of the discontinuity. The article presents the mathematical apparatus for the implementation of this method. Also presented is a software product that allows you to automate calculations when using this defect identification method. The article contains experimental studies of the method for determining the equivalent dimensions of discontinuities of various types, which have shown its high reliability. The maximum value of the relative error in determining the equivalent size of a point planar discontinuity was 2.867 %. The maximum value of the relative error in determining the equivalent size of a point volumetric discontinuity was 1.986 %. The maximum value of the relative error in determining the transverse equivalent size of an extended planar discontinuity was 0.667 %. The maximum value of the relative error in determining the transverse equivalent size of an extended volumetric discontinuity was 1.95 %.

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Convective Drying of Sewage Sludge Layer in Through-flow

Strojniški vestnik – Journal of Mechanical Engineering ◽

10.5545/sv-jme.2020.6717 ◽

2020 ◽

Vol 66 (9) ◽

pp. 481-493

Author(s):

Andraž Lipolt ◽

Brane Širok ◽

Marko Hočevar ◽

Lovrenc Novak

Keyword(s):

Sewage Sludge ◽

Drag Force ◽

Layer Structure ◽

Measured Data ◽

Matter Content ◽

Convective Drying ◽

Coefficient Of Determination ◽

Dry Matter Content ◽

Drying Time ◽

Drying Rates

Drying of the sewage sludge layer was investigated in a convective laboratory dryer at air temperatures of 65 °C and 80 °C and air speeds of 0.53 m/s and 0.83 m/s. The sludge layer was formed by loading cylindrical extrudates on a grate of 0.5 m × 0.5 m size. The drying air was directed through the layer, as typically encountered in industrial belt dryers. Under such setup, the sludge layer structure and porosity significantly affect the air flow conditions and thus the drying rates. Shrinkage and cracking of the material during drying caused changes in the layer’s porous structure, that affected the pressure drop and the drag force due to passing of air through the layer. The decreasing of drag force over time was modeled by a simple function that showed excellent agreement to the selected measured data. The sludge layer drying kinetics was determined by fitting the measured data to the most common drying models. Two models, the modified Nadhari and the Wang Singh model, were determined as most suitable for modeling of drying curves. The total drying time per kilogram of sludge was modeled as a function of drying air temperature, drying air velocity and initial sludge dry matter content. The coefficient of determination (R2) of the model is 0.944. Total drying times between 43 minutes per kilogram and 76 minutes per kilogram of sludge were obtained for the investigated range of drying air conditions.

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Investigations into the Performance of a Novel Pocket-Sized Near-Infrared Spectrometer for Cheese Analysis

Molecules ◽

10.3390/molecules24030428 ◽

2019 ◽

Vol 24 (3) ◽

pp. 428 ◽

Cited By ~ 12

Author(s):

Verena Wiedemair ◽

Dominik Langore ◽

Roman Garsleitner ◽

Klaus Dillinger ◽

Christian Huck

Keyword(s):

Data Analysis ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

Analysis Software ◽

Infrared Spectrometer ◽

Automated Tools ◽

Near Infrared Spectrometer ◽

Data Analysis Software

The performance of a newly developed pocket-sized near-infrared (NIR) spectrometer was investigated by analysing 46 cheese samples for their water and fat content, and comparing results with a benchtop NIR device. Additionally, the automated data analysis of the pocket-sized spectrometer and its cloud-based data analysis software, designed for laypeople, was put to the test by comparing performances to a highly sophisticated multivariate data analysis software. All developed partial least squares regression (PLS-R) models yield a coefficient of determination (R2) of over 0.9, indicating high correlation between spectra and reference data for both spectrometers and all data analysis routes taken. In general, the analysis of grated cheese yields better results than whole pieces of cheese. Additionally, the ratios of performance to deviation (RPDs) and standard errors of prediction (SEPs) suggest that the performance of the pocket-sized spectrometer is comparable to the benchtop device. Small improvements are observable, when using sophisticated data analysis software, instead of automated tools.

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A simple approach to estimate coastal soil salinity using digital camera images

Soil Research ◽

10.1071/sr20009 ◽

2020 ◽

Vol 58 (8) ◽

pp. 737

Author(s):

Lu Xu ◽

Raphael A. Viscarra Rossel ◽

Juhwan Lee ◽

Zhichun Wang ◽

Hongyuan Ma

Keyword(s):

Soil Salinity ◽

Precision Agriculture ◽

Mean Squared Error ◽

Rapid Development ◽

Salt Content ◽

Digital Camera ◽

Soil Surface ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

High Concentration

Soil salinisation is a global problem that hinders the sustainable development of ecosystems and agricultural production. Remote and proximal sensing technologies have been used to effectively evaluate soil salinity over large scales, but research on digital camera images is still lacking. In this study, we propose to relate the pixel brightness of soil surface digital images to the soil salinity information. We photographed the surface of 93 soils in the field at different times and weather conditions, and sampled the corresponding soils for laboratory analyses of soil salinity information. Results showed that the pixel digital numbers were related to soil salinity, especially at the intermediate and higher brightness levels. Based on this relationship, we employed random forest (RF) and partial least-squares regression (PLSR) to model soil salt content and ion concentrations, and applied root mean squared error, coefficient of determination and Lin’s concordance correlation coefficient to evaluate the accuracy of models. We found that ions with high concentration were estimated more accurately than ions with low concentrations, and RF models performed overall better than PLSR models. However, the method is only suitable for bare land of coastal soil, and verification is needed for other conditions. In conclusion, a new approach of using digital camera images has good potential to predict and manage soil salinity in the context of precision agriculture with the rapid development of unmanned aerial vehicles.

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OTIMIZAÇÃO DE CALIBRAÇÕES BASEADAS EM ESPECTROSCOPIA NO INFRAVERMELHO PRÓXIMO PARA ESTIMATIVA DE PROPRIEDADES DA MADEIRA DE Eucalyptus

FLORESTA ◽

10.5380/rf.v40i3.18923 ◽

2010 ◽

Vol 40 (3) ◽

Cited By ~ 2

Author(s):

Paulo Ricardo Gherardi Hein ◽

José Tarcísio Lima ◽

Gilles Chaix Gilles Chaix

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Laboratory Data ◽

Wood Properties ◽

Coefficient Of Determination ◽

Mathematical Treatment ◽

Least Squares Regression ◽

Eucalyptus Wood ◽

Prediction Density

A espectroscopia no infravermelho próximo (NIRS) é uma técnica não-destrutiva, rápida e utilizada para avaliação, caracterização e classificação de materiais, sobretudo de origem biológica. A obtenção de informações contida nos espectros NIR é complexa e requer a utilização de métodos quimiométricos. Assim, por meio de regressão multivariada, os espectros de absorbância podem ser associados às propriedades da madeira, tornando possível a sua predição em amostras desconhecidas. Existem algumas ferramentas quimiométricas que melhoram o ajuste dos modelos preditivos. Assim, o objetivo deste trabalho foi simular regressões dos mínimos quadrados parciais baseados nas informações espectrais e de laboratório e estudar a influência da aplicação de tratamentos matemáticos, do descarte de amostras anômalas e da seleção de comprimentos de onda no ajuste dos modelos para estimativa da densidade básica e do módulo de elasticidade em ensaio de compressão paralela às fibras da madeira de Eucalyptus. A aplicação da primeira e segunda derivada nos espectros, o descarte de amostras anômalas e a seleção de algumas das variáveis espectrais melhorou significativamente o ajuste do modelo, reduzindo o erro padrão e aumentando o coeficiente de determinação e a relação de desempenho do desvio.Palavras-chave: Espectroscopia no infravermelho próximo; predição; densidade básica; MOE; madeira; Eucalyptus. AbstractOptimization of calibrations based on near infrared spectroscopy for estimation of Eucalyptus wood properties. Near infrared spectroscopy (NIRS) is a non-destructive technique used for rapid evaluation, characterization and classification of biological materials. The extraction of the information contained in the NIR spectrum is complex and requires the use of chemo metric methods. Thus, by means of multivariate regression, the absorbance spectra are correlated to wood properties, making possible the prediction in unknown samples. There are some chemo metric tools that can improve the adjustment of the predictive models. The aim of this work was to simulate partial least squares regression based on NIR spectra and laboratory data and to study the influence of the application of mathematical treatment, the removal of outliers and the wavelengths selection in the adjustment of models to estimate the density and modulus of elasticity in Eucalyptus wood. The use of the first and second derivative spectra, the disposal of outliers, and the variables selection improved significantly the model fit, reducing the standard error and increasing the coefficient of determination and the ratio of performance to deviation.Keywords: Near infrared; spectroscopy; prediction; density; MOE; wood; Eucalyptus.

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Rapid Quantitative Detection of Deltamethrin in Corydalis yanhusuo by SERS Coupled with Multi-Walled Carbon Nanotubes

Molecules ◽

10.3390/molecules25184081 ◽

2020 ◽

Vol 25 (18) ◽

pp. 4081

Author(s):

Hui Zhang ◽

Pengcheng Nie ◽

Zhengyan Xia ◽

Xuping Feng ◽

Xiaoxi Liu ◽

...

Keyword(s):

Carbon Nanotubes ◽

Solid Phase ◽

Large Diameter ◽

Surface Enhanced Raman Spectroscopy ◽

Coefficient Of Determination ◽

Quantitative Detection ◽

Least Squares Regression ◽

Quechers Method ◽

Multi Walled Carbon Nanotubes ◽

Walled Carbon Nanotubes

With the increase in demand, artificially planting Chinese medicinal materials (CHMs) has also increased, and the ensuing pesticide residue problems have attracted more and more attention. An optimized quick, easy, cheap, effective, rugged and safe (QuEChERS) method with multi-walled carbon nanotubes as dispersive solid-phase extraction sorbents coupled with surface-enhanced Raman spectroscopy (SERS) was first proposed for the detection of deltamethrin in complex matrix Corydalis yanhusuo. Our results demonstrate that using the optimized QuEChERS method could effectively extract the analyte and reduce background interference from Corydalis. Facile synthesized gold nanoparticles with a large diameter of 75 nm had a strong SERS enhancement for deltamethrin determination. The best prediction model was established with partial least squares regression of the SERS spectra ranges of 545~573 cm−1 and 987~1011 cm−1 with a coefficient of determination (R2) of 0.9306, a detection limit of 0.484 mg/L and a residual predictive deviation of 3.046. In summary, this article provides a new rapid and effective method for the detection of pesticide residues in CHMs.

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Increase of energy efficiency of electric power distribution networks by adjustment of reactive energy consumption in a traction network

MATEC Web of Conferences ◽

10.1051/matecconf/201823901049 ◽

2018 ◽

Vol 239 ◽

pp. 01049 ◽

Cited By ~ 2

Author(s):

Natalia Shurova ◽

Valerii Li

Keyword(s):

Energy Efficiency ◽

Power Supply ◽

Power Supply System ◽

Supply System ◽

Traction Power Supply System ◽

Traction Network ◽

Traction Substations ◽

Compensating Devices ◽

Traction Power Supply ◽

Traction Power

In the past few years, there has been a trend towards an increase in the volume of transportation by railway. At the same time, the load on the railway infrastructure increases, in particular, on the traction power supply system. It is necessary to solve the problem of increasing the energy efficiency of the external electric power supply system in the conditions of growing freight turnover and taking into account the uncertainty of the initial data. The paper considers one of the methods of strengthening the traction power supply system. Based on the results of the study, an algorithm was developed for selecting the installation sites and power of compensating devices in a traction network in the conditions of increasing freight turnover and under the condition of increasing the energy efficiency of the external power supply system of traction substations due to unloading of supply lines by reactive power and leveling the load in phases. This methodology includes predicting power consumption, determining the installation sites and power of compensating devices in the traction network under condition of uncertainty of the initial data, and then assessing the energy efficiency of the decision made. A calculation was carried out for the proposed algorithm for a section of the Far Eastern Railway which includes nine traction substations.

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Rapid analysis of chemical composition in intact and milled rice cookies using near infrared spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033517726724 ◽

2017 ◽

Vol 25 (5) ◽

pp. 330-337 ◽

Cited By ~ 5

Author(s):

Latthika Wimonsiri ◽

Pitiporn Ritthiruangdej ◽

Sumaporn Kasemsumran ◽

Nantawan Therdthai ◽

Wasaporn Chanput ◽

...

Keyword(s):

Chemical Composition ◽

Near Infrared ◽

Wheat Gluten ◽

Nir Spectroscopy ◽

Rice Flour ◽

Coefficient Of Determination ◽

Gluten Free ◽

Least Squares Regression ◽

Milled Rice ◽

Control Programs

This study has investigated the potential of near infrared (NIR) spectroscopy to predict the content of moisture, protein, fat and gluten in rice cookies in different sample forms (intact and milled samples). Gluten-free (n = 48) and gluten (n = 48) rice cookies were formulated with brown and white rice flours in which butter was substituted with fat replacer at 0, 15, 30 and 45%. With regard to gluten cookies, rice flour was substituted with wheat gluten at 1, 3 and 5%. Partial least squares regression modeling produced models with coefficient of determination (R2) values greater than 0.88 from NIR spectra of intact samples and greater than 0.92 for milled samples. These models were able to predict the four components with a ratio of prediction to deviation greater than 2.7 and 3.8 in intact and milled samples, respectively. The results suggest that the models obtained from the intact samples can be successfully applied for chemical composition of rice cookies and are reliable enough use for potential quality control programs.

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