In the field of food safety testing,variety,brand,origin,and adulteration are four important factors.In this study,a novel food safety testing method based on infrared spectroscopy is proposed to investigate these fac...In the field of food safety testing,variety,brand,origin,and adulteration are four important factors.In this study,a novel food safety testing method based on infrared spectroscopy is proposed to investigate these factors.Fourier transform infrared spectroscopy data are analyzed using negentropy-sorted kernel independent component analysis(NS-kICA)as the feature optimization method.To rank the components,negentropy is performed to measure the non-Gaussian independent components.In our experiment,the proposed method was run on four datasets to comprehensively investigate the variety,brand,origin,and adulteration of agricultural products.The experimental results show that NS-kICA outperforms conventional feature selection methods.The support vector machine model outperforms the backpropagation artificial neural network and partial least squares models.The combination of NS-kICA and support vector machine(SVM)is the best method for achieving high,stable,and efficient recognition performance.These findings are of great importance for food safety testing.展开更多
A class of rapid algorithms for independent component analysis (ICA) is presented. This method utilizes multi-step past information with respect to an existing fixed-point style for increasing the non-Gaussianity. Thi...A class of rapid algorithms for independent component analysis (ICA) is presented. This method utilizes multi-step past information with respect to an existing fixed-point style for increasing the non-Gaussianity. This can be viewed as the addition of a variable-size momentum term. The use of past information comes from the idea of surrogate optimization. There is little additional cost for either software design or runtime execution when past information is included. The speed of the algorithm is evaluated on both simulated and real-world data. The real-world data includes color images and electroencephalograms (EEGs), which are an important source of data on human-computer interactions. From these experiments, it is found that the method we present here, the RapidICA, performs quickly, especially for the demixing of super-Gaussian signals.展开更多
A novel blind source separation （BSS） algorithm based on the combination of negentropy and signal noise ratio （SNR） is presented to solve the deficiency of the traditional independent component analysis （ICA） al...A novel blind source separation （BSS） algorithm based on the combination of negentropy and signal noise ratio （SNR） is presented to solve the deficiency of the traditional independent component analysis （ICA） algorithm after the introduction of the principle and algorithm of ICA. The main formulas in the novel algorithm are elaborated and the idiographic steps of the algorithm are given. Then the computer simulation is used to test the performance of this algorithm. Both the traditional FastlCA algorithm and the novel ICA algorithm are applied to separate mixed signal data. Experiment results show the novel method has a better performance in separating signals than the traditional FastlCA algorithm based on negentropy. The novel algorithm could estimate the source signals from the mixed signals more precisely.展开更多
Monitoring transmission towers is of great importance to prevent severe thefts on them and ensure the reliability and safety of the power grid operation.Independent component analysis(ICA) is a method for finding unde...Monitoring transmission towers is of great importance to prevent severe thefts on them and ensure the reliability and safety of the power grid operation.Independent component analysis(ICA) is a method for finding underlying factors or components from multivariate statistical data based on dimension reduction methods,and it is applicable to extract the non-stationary signals.FastICA based on negentropy is presented to effectively extract and separate the vibration signals caused by human activity in this paper.A new method combined empirical mode decomposition(EMD) technique with the adaptive threshold method is applied to extract the vibration pulses,and suppress the interference signals.The practical tests demonstrate that the method proposed in the paper is effective in separating and extracting the vibration signals.展开更多
基金sponsored by the National Natural Science Foundation of China(31872849)a subproject of major innovation projects in Shandong Province,China(2021TZXD003-003,2021LZGC026-09)+1 种基金Shandong University Youth Entrepreneurship plan team project(2020KJF004)Qingdao Agricultural University High-level Talents Research Fund,China(1119005).
文摘In the field of food safety testing,variety,brand,origin,and adulteration are four important factors.In this study,a novel food safety testing method based on infrared spectroscopy is proposed to investigate these factors.Fourier transform infrared spectroscopy data are analyzed using negentropy-sorted kernel independent component analysis(NS-kICA)as the feature optimization method.To rank the components,negentropy is performed to measure the non-Gaussian independent components.In our experiment,the proposed method was run on four datasets to comprehensively investigate the variety,brand,origin,and adulteration of agricultural products.The experimental results show that NS-kICA outperforms conventional feature selection methods.The support vector machine model outperforms the backpropagation artificial neural network and partial least squares models.The combination of NS-kICA and support vector machine(SVM)is the best method for achieving high,stable,and efficient recognition performance.These findings are of great importance for food safety testing.
文摘A class of rapid algorithms for independent component analysis (ICA) is presented. This method utilizes multi-step past information with respect to an existing fixed-point style for increasing the non-Gaussianity. This can be viewed as the addition of a variable-size momentum term. The use of past information comes from the idea of surrogate optimization. There is little additional cost for either software design or runtime execution when past information is included. The speed of the algorithm is evaluated on both simulated and real-world data. The real-world data includes color images and electroencephalograms (EEGs), which are an important source of data on human-computer interactions. From these experiments, it is found that the method we present here, the RapidICA, performs quickly, especially for the demixing of super-Gaussian signals.
文摘A novel blind source separation （BSS） algorithm based on the combination of negentropy and signal noise ratio （SNR） is presented to solve the deficiency of the traditional independent component analysis （ICA） algorithm after the introduction of the principle and algorithm of ICA. The main formulas in the novel algorithm are elaborated and the idiographic steps of the algorithm are given. Then the computer simulation is used to test the performance of this algorithm. Both the traditional FastlCA algorithm and the novel ICA algorithm are applied to separate mixed signal data. Experiment results show the novel method has a better performance in separating signals than the traditional FastlCA algorithm based on negentropy. The novel algorithm could estimate the source signals from the mixed signals more precisely.
文摘Monitoring transmission towers is of great importance to prevent severe thefts on them and ensure the reliability and safety of the power grid operation.Independent component analysis(ICA) is a method for finding underlying factors or components from multivariate statistical data based on dimension reduction methods,and it is applicable to extract the non-stationary signals.FastICA based on negentropy is presented to effectively extract and separate the vibration signals caused by human activity in this paper.A new method combined empirical mode decomposition(EMD) technique with the adaptive threshold method is applied to extract the vibration pulses,and suppress the interference signals.The practical tests demonstrate that the method proposed in the paper is effective in separating and extracting the vibration signals.
