The potential of the visible infrared(Vis–IR)(400–1100 nm)transmittance method to assess the internal quality(freshness)of intact chicken egg during storage at a temperature of 30±7C and 25±4%relative hum...The potential of the visible infrared(Vis–IR)(400–1100 nm)transmittance method to assess the internal quality(freshness)of intact chicken egg during storage at a temperature of 30±7C and 25±4%relative humidity was investigated.Two hundred chicken egg samples were used for measuring freshness and spectra collection during egg storage(up to 25 days).Two correlation models,firstly between Haugh unit(HU)and storage time,and secondly between the yolk coefficient(YC)and storage time,were developed and yielded correlation coefficients(R^2)of 0.86 and 0.96,respectively.These models spanned the period for which egg quality decreased dramatically and are statistically significant(P<0.05).In addition,to reduce the dimensionality of the spectra and extract effective wavelengths,two methods were developed based on principal component analysis(PCA)and a genetic algorithm(GA).The output of PCA and GA were also used comparatively to design an egg quality intelligent system.The result of the analyses indicated that identification ratio of GAwith fast Fourier transform(FFT)preprocessing was superior to other methods,and that the quality classification rates of this method for one-day-old eggs are 100%.This study shows that identification of an egg’s freshness using NIR spectroscopy with GA and artificial neural network(ANN)is reliable.展开更多
In the bridge technical condition assessment standards,the evaluation of bridge conditions primarily relies on the defects identified through manual inspections,which are determined using the comprehensive hierarchica...In the bridge technical condition assessment standards,the evaluation of bridge conditions primarily relies on the defects identified through manual inspections,which are determined using the comprehensive hierarchical analysis method.However,the relationship between the defects and the technical condition of the bridges warrants further exploration.To address this situation,this paper proposes a machine learning-based intelligent diagnosis model for the technical condition of highway bridges.Firstly,collect the inspection records of highway bridges in a certain region of China,then standardize the severity of diverse defects in accordance with relevant specifications.Secondly,in order to enhance the independence between the defects,the key defect indicators were screened using Principal Component Analysis(PCA)in combination with the weights of the building blocks.Based on this,an enhanced Naive Bayesian Classification(NBC)algorithm is established for the intelligent diagnosis of technical conditions of highway bridges,juxtaposed with four other algorithms for comparison.Finally,key defect variables that affect changes in bridge grades are discussed.The results showed that the technical condition level of the superstructure had the highest correlation with cracks;the PCA-NBC algorithm achieved an accuracy of 93.50%of the predicted values,which was the highest improvement of 19.43%over other methods.The purpose of this paper is to provide inspectors with a convenient and predictive information-rich method to intelligently diagnose the technical condition of bridges based on bridge defects.The results of this research can help bridge inspectors and even non-specialists to better understand the condition of bridge defects.展开更多
文摘The potential of the visible infrared(Vis–IR)(400–1100 nm)transmittance method to assess the internal quality(freshness)of intact chicken egg during storage at a temperature of 30±7C and 25±4%relative humidity was investigated.Two hundred chicken egg samples were used for measuring freshness and spectra collection during egg storage(up to 25 days).Two correlation models,firstly between Haugh unit(HU)and storage time,and secondly between the yolk coefficient(YC)and storage time,were developed and yielded correlation coefficients(R^2)of 0.86 and 0.96,respectively.These models spanned the period for which egg quality decreased dramatically and are statistically significant(P<0.05).In addition,to reduce the dimensionality of the spectra and extract effective wavelengths,two methods were developed based on principal component analysis(PCA)and a genetic algorithm(GA).The output of PCA and GA were also used comparatively to design an egg quality intelligent system.The result of the analyses indicated that identification ratio of GAwith fast Fourier transform(FFT)preprocessing was superior to other methods,and that the quality classification rates of this method for one-day-old eggs are 100%.This study shows that identification of an egg’s freshness using NIR spectroscopy with GA and artificial neural network(ANN)is reliable.
基金financially supported by the National Natural Science Foundation of China(No.51808301)the Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202248860)the National“111”Centre on Safety and Intelligent Operation of Sea Bridge(D21013).
文摘In the bridge technical condition assessment standards,the evaluation of bridge conditions primarily relies on the defects identified through manual inspections,which are determined using the comprehensive hierarchical analysis method.However,the relationship between the defects and the technical condition of the bridges warrants further exploration.To address this situation,this paper proposes a machine learning-based intelligent diagnosis model for the technical condition of highway bridges.Firstly,collect the inspection records of highway bridges in a certain region of China,then standardize the severity of diverse defects in accordance with relevant specifications.Secondly,in order to enhance the independence between the defects,the key defect indicators were screened using Principal Component Analysis(PCA)in combination with the weights of the building blocks.Based on this,an enhanced Naive Bayesian Classification(NBC)algorithm is established for the intelligent diagnosis of technical conditions of highway bridges,juxtaposed with four other algorithms for comparison.Finally,key defect variables that affect changes in bridge grades are discussed.The results showed that the technical condition level of the superstructure had the highest correlation with cracks;the PCA-NBC algorithm achieved an accuracy of 93.50%of the predicted values,which was the highest improvement of 19.43%over other methods.The purpose of this paper is to provide inspectors with a convenient and predictive information-rich method to intelligently diagnose the technical condition of bridges based on bridge defects.The results of this research can help bridge inspectors and even non-specialists to better understand the condition of bridge defects.
