Spectrum analysis of insect-damaged wheat BPE signal based on CEEMD
In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Qiao, Lihong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment - Cheng, Cheng ELSEVIER, 2020, international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy, München |
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| Übergeordnetes Werk: |
volume:150 ; year:2017 ; pages:62-70 ; extent:9 |
| Links: |
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| DOI / URN: |
10.1016/j.ijleo.2017.09.084 |
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ELV040640884 |
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| 520 | |a In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. | ||
| 520 | |a In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. | ||
| 650 | 7 | |a BPE |2 Elsevier | |
| 650 | 7 | |a Spectrum analysis |2 Elsevier | |
| 650 | 7 | |a CEEMD |2 Elsevier | |
| 650 | 7 | |a Hilbert marginal spectrum |2 Elsevier | |
| 700 | 1 | |a Jia, Manman |4 oth | |
| 700 | 1 | |a Liang, Yitao |4 oth | |
| 700 | 1 | |a Yang, Tiejun |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Cheng, Cheng ELSEVIER |t Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment |d 2020 |d international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy |g München |w (DE-627)ELV004102533 |
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10.1016/j.ijleo.2017.09.084 doi GBV00000000000006.pica (DE-627)ELV040640884 (ELSEVIER)S0030-4026(17)31154-3 DE-627 ger DE-627 rakwb eng 620 620 DE-600 333.7 VZ 43.00 bkl Qiao, Lihong verfasserin aut Spectrum analysis of insect-damaged wheat BPE signal based on CEEMD 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. BPE Elsevier Spectrum analysis Elsevier CEEMD Elsevier Hilbert marginal spectrum Elsevier Jia, Manman oth Liang, Yitao oth Yang, Tiejun oth Enthalten in Elsevier Cheng, Cheng ELSEVIER Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment 2020 international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy München (DE-627)ELV004102533 volume:150 year:2017 pages:62-70 extent:9 https://doi.org/10.1016/j.ijleo.2017.09.084 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 150 2017 62-70 9 045F 620 |
| spelling |
10.1016/j.ijleo.2017.09.084 doi GBV00000000000006.pica (DE-627)ELV040640884 (ELSEVIER)S0030-4026(17)31154-3 DE-627 ger DE-627 rakwb eng 620 620 DE-600 333.7 VZ 43.00 bkl Qiao, Lihong verfasserin aut Spectrum analysis of insect-damaged wheat BPE signal based on CEEMD 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. BPE Elsevier Spectrum analysis Elsevier CEEMD Elsevier Hilbert marginal spectrum Elsevier Jia, Manman oth Liang, Yitao oth Yang, Tiejun oth Enthalten in Elsevier Cheng, Cheng ELSEVIER Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment 2020 international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy München (DE-627)ELV004102533 volume:150 year:2017 pages:62-70 extent:9 https://doi.org/10.1016/j.ijleo.2017.09.084 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 150 2017 62-70 9 045F 620 |
| allfields_unstemmed |
10.1016/j.ijleo.2017.09.084 doi GBV00000000000006.pica (DE-627)ELV040640884 (ELSEVIER)S0030-4026(17)31154-3 DE-627 ger DE-627 rakwb eng 620 620 DE-600 333.7 VZ 43.00 bkl Qiao, Lihong verfasserin aut Spectrum analysis of insect-damaged wheat BPE signal based on CEEMD 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. BPE Elsevier Spectrum analysis Elsevier CEEMD Elsevier Hilbert marginal spectrum Elsevier Jia, Manman oth Liang, Yitao oth Yang, Tiejun oth Enthalten in Elsevier Cheng, Cheng ELSEVIER Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment 2020 international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy München (DE-627)ELV004102533 volume:150 year:2017 pages:62-70 extent:9 https://doi.org/10.1016/j.ijleo.2017.09.084 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 150 2017 62-70 9 045F 620 |
| allfieldsGer |
10.1016/j.ijleo.2017.09.084 doi GBV00000000000006.pica (DE-627)ELV040640884 (ELSEVIER)S0030-4026(17)31154-3 DE-627 ger DE-627 rakwb eng 620 620 DE-600 333.7 VZ 43.00 bkl Qiao, Lihong verfasserin aut Spectrum analysis of insect-damaged wheat BPE signal based on CEEMD 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. BPE Elsevier Spectrum analysis Elsevier CEEMD Elsevier Hilbert marginal spectrum Elsevier Jia, Manman oth Liang, Yitao oth Yang, Tiejun oth Enthalten in Elsevier Cheng, Cheng ELSEVIER Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment 2020 international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy München (DE-627)ELV004102533 volume:150 year:2017 pages:62-70 extent:9 https://doi.org/10.1016/j.ijleo.2017.09.084 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 150 2017 62-70 9 045F 620 |
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10.1016/j.ijleo.2017.09.084 doi GBV00000000000006.pica (DE-627)ELV040640884 (ELSEVIER)S0030-4026(17)31154-3 DE-627 ger DE-627 rakwb eng 620 620 DE-600 333.7 VZ 43.00 bkl Qiao, Lihong verfasserin aut Spectrum analysis of insect-damaged wheat BPE signal based on CEEMD 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. BPE Elsevier Spectrum analysis Elsevier CEEMD Elsevier Hilbert marginal spectrum Elsevier Jia, Manman oth Liang, Yitao oth Yang, Tiejun oth Enthalten in Elsevier Cheng, Cheng ELSEVIER Tracking variation of fluorescent dissolved organic matter during full-scale printing and dyeing wastewater treatment 2020 international journal for light and electron optics : official journal of the German Society of Applied Optics and the German Society of Electron Microscopy München (DE-627)ELV004102533 volume:150 year:2017 pages:62-70 extent:9 https://doi.org/10.1016/j.ijleo.2017.09.084 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 150 2017 62-70 9 045F 620 |
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This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. 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spectrum analysis of insect-damaged wheat bpe signal based on ceemd |
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Spectrum analysis of insect-damaged wheat BPE signal based on CEEMD |
| abstract |
In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. |
| abstractGer |
In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. |
| abstract_unstemmed |
In order to effectively carry out the work of reducing losses in grain storage, it is urgently necessary to explore a fast and safe detection method for insect-damaged wheat. This work adopts a new method for extracting features. The frequency characteristics of biophoton emission (BPE) signals of insect-damaged wheat and normal wheat are obtained through Complementary Ensemble Empirical Mode Decomposition (CEEMD). Specifically, CEEMD is used to decompose the BPE signal into a series of Intrinsic Mode Functions (IMF). A Hilbert transform is then performed on the IMFs to obtain a Hilbert spectrum and Hilbert marginal spectrum. The spectral gravity frequency (SGF), spectral edge frequency (SEF), spectral amplitude proportions of different frequency bands, and energy proportion of each IMF of the BPE signals of insect-damaged wheat and normal wheat are calculated. Experiments show that the intensity of the BPE signal is relatively weak and that the BPE signal is a low frequency signal (less than 0.1Hz). The parameters of the BPE signals mentioned above give a detail spectrum analysis and show differences between insect-damaged wheat and normal wheat. We further use BP neural network to classify the wheat. The results show that this method is feasible for detecting insect-damaged wheat. |
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Spectrum analysis of insect-damaged wheat BPE signal based on CEEMD |
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