Reliability and controllability of selective removal of multiple paint layers from the surface of aircraft skin depend on effective online monitoring technology. An analysis was performed on the multi-pulse laser-induced breakdown spectroscopy (LIBS) on the surface of the aluminum alloy substrate, primer, and topcoat. Based on that, an exploration was conducted on the changes of the characteristic peaks corresponding to the characteristic elements that are contained in the topcoat, primer, and substrate with different layers of a laser action, in combination with analysis of microscopic morphology, composition, and depth of laser multi-pulse pits. The results show that the appearance and increase of the characteristic peak intensity of the Ca I at the wavelength of 422.7 nm can be regarded as the basis for the complete removal of the topcoat; the decrease or disappearance of the characteristic peak intensity can be regarded as the basis for the complete removal of the primer. Al I spectrum at the wavelength of 394.5 nm and 396.2 nm can be adopted to characterize the degree of damage to the aluminum alloy substrate. The feasibility and accuracy of the LIBS technology for the laser selective paint removal process and effect monitoring of aircraft skin were verified. Demonstrating that under the premise of not damaging the substrate, laser-based layered controlled paint removal (LLCPR) from aircraft skin can be achieved by monitoring the spectrum and composition change law of specified wavelength position corresponding tothe characteristic elements that are contained in the specific paint layer.
{"title":"LIBS Monitoring and Analysis of Laser-Based Layered Controlled Paint Removal from Aircraft Skin","authors":"Wenfeng Yang, Ziran Qian, Yu Cao, Yongchao Wei, Chanyuan Fu, TianQuan Li, Dehui Lin, Shaolong Li","doi":"10.1155/2021/4614388","DOIUrl":"https://doi.org/10.1155/2021/4614388","url":null,"abstract":"Reliability and controllability of selective removal of multiple paint layers from the surface of aircraft skin depend on effective online monitoring technology. An analysis was performed on the multi-pulse laser-induced breakdown spectroscopy (LIBS) on the surface of the aluminum alloy substrate, primer, and topcoat. Based on that, an exploration was conducted on the changes of the characteristic peaks corresponding to the characteristic elements that are contained in the topcoat, primer, and substrate with different layers of a laser action, in combination with analysis of microscopic morphology, composition, and depth of laser multi-pulse pits. The results show that the appearance and increase of the characteristic peak intensity of the Ca I at the wavelength of 422.7 nm can be regarded as the basis for the complete removal of the topcoat; the decrease or disappearance of the characteristic peak intensity can be regarded as the basis for the complete removal of the primer. Al I spectrum at the wavelength of 394.5 nm and 396.2 nm can be adopted to characterize the degree of damage to the aluminum alloy substrate. The feasibility and accuracy of the LIBS technology for the laser selective paint removal process and effect monitoring of aircraft skin were verified. Demonstrating that under the premise of not damaging the substrate, laser-based layered controlled paint removal (LLCPR) from aircraft skin can be achieved by monitoring the spectrum and composition change law of specified wavelength position corresponding tothe characteristic elements that are contained in the specific paint layer.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"32 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86591000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover.
{"title":"Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy","authors":"Xueying Li, Pingping Fan, Zongmin Li, Guangyuan Chen, Huimin Qiu, G. Hou","doi":"10.1155/2021/1508267","DOIUrl":"https://doi.org/10.1155/2021/1508267","url":null,"abstract":"Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"351 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89265438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiuyu Liu, Zhen Zhang, Tao Jiang, Xuehua Li, Yanyi Li
Total suspended matter (TSM) is a core parameter in the quantitative retrieval of ocean color remote sensing and an important indicator for evaluating the quality of the aquatic environment. This study selects part of Nansi Lake in North China as the study area. Researchers used Hyperion remote sensing data and field-measured TSM concentration as data sources. Firstly, the characteristic variables with high correlation were selected based on spectral analysis. Then, seven methods such as linear regression, BP neural network (BP), KNN, random forest (RF), and random forest based on genetic algorithm optimization (GA_RF) are used to construct the inversion model of TSM concentration. The retrieval accuracy of each model shows that the machine learning models are much more accurate than the linear model. Among them, the GA_RF model retrieves the suspended solids concentration with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.98, a root mean square error (RMSE) of 1.715 mg/L, and an average relative error (ARE) of 6.83%. Additionally, the spatial distribution of TSM concentration was inversed by Hyperion remote sensing image. The results showed that the concentration of TSM was lower in the northwest and higher in the southeast, and the concentration distribution was uneven, showing the characteristics of a typical shallow macrophytic lake. This study provides an effective method for monitoring TSM concentration and other water quality parameters in the shallow macrophytic lake and further proves the advantages of machine learning in ocean color inversion. All in all, this research provides some useful methods and suggestions for quantitative inversion of TSM concentration in shallow macrophytic lakes.
{"title":"Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China","authors":"Xiuyu Liu, Zhen Zhang, Tao Jiang, Xuehua Li, Yanyi Li","doi":"10.1155/2021/5957376","DOIUrl":"https://doi.org/10.1155/2021/5957376","url":null,"abstract":"Total suspended matter (TSM) is a core parameter in the quantitative retrieval of ocean color remote sensing and an important indicator for evaluating the quality of the aquatic environment. This study selects part of Nansi Lake in North China as the study area. Researchers used Hyperion remote sensing data and field-measured TSM concentration as data sources. Firstly, the characteristic variables with high correlation were selected based on spectral analysis. Then, seven methods such as linear regression, BP neural network (BP), KNN, random forest (RF), and random forest based on genetic algorithm optimization (GA_RF) are used to construct the inversion model of TSM concentration. The retrieval accuracy of each model shows that the machine learning models are much more accurate than the linear model. Among them, the GA_RF model retrieves the suspended solids concentration with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.98, a root mean square error (RMSE) of 1.715 mg/L, and an average relative error (ARE) of 6.83%. Additionally, the spatial distribution of TSM concentration was inversed by Hyperion remote sensing image. The results showed that the concentration of TSM was lower in the northwest and higher in the southeast, and the concentration distribution was uneven, showing the characteristics of a typical shallow macrophytic lake. This study provides an effective method for monitoring TSM concentration and other water quality parameters in the shallow macrophytic lake and further proves the advantages of machine learning in ocean color inversion. All in all, this research provides some useful methods and suggestions for quantitative inversion of TSM concentration in shallow macrophytic lakes.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"45 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77228579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A novel wood species spectral classification scheme is proposed based on a fuzzy rule classifier. The visible/near-infrared (VIS/NIR) spectral reflectance curve of a wood sample’s cross section was captured using a USB 2000-VIS-NIR spectrometer and a FLAME-NIR spectrometer. First, the wood spectral curve—with spectral bands of 376.64–779.84 nm and 950–1650 nm—was processed using the principal component analysis (PCA) dimension reduction algorithm. The wood spectral data were divided into two datasets, namely, training and testing sets. The training set was used to generate the membership functions and the initial fuzzy rule set, with the fuzzy rule being adjusted to supplement and refine the classification rules to form a perfect fuzzy rule set. Second, a fuzzy classifier was applied to the VIS and NIR bands. An improved decision-level fusion scheme based on the Dempster–Shafer (D-S) evidential theory was proposed to further improve the accuracy of wood species recognition. The test results using the testing set indicated that the overall recognition accuracy (ORA) of our scheme reached 94.76% for 50 wood species, which is superior to that of conventional classification algorithms and recent state-of-the-art wood species classification schemes. This method can rapidly achieve good recognition results, especially using small datasets, owing to its low computational time and space complexity.
{"title":"Wood Species Recognition Based on Visible and Near-Infrared Spectral Analysis Using Fuzzy Reasoning and Decision-Level Fusion","authors":"Peng Zhao, Zhen-Yu Li, Cheng-Kun Wang","doi":"10.1155/2021/6088435","DOIUrl":"https://doi.org/10.1155/2021/6088435","url":null,"abstract":"A novel wood species spectral classification scheme is proposed based on a fuzzy rule classifier. The visible/near-infrared (VIS/NIR) spectral reflectance curve of a wood sample’s cross section was captured using a USB 2000-VIS-NIR spectrometer and a FLAME-NIR spectrometer. First, the wood spectral curve—with spectral bands of 376.64–779.84 nm and 950–1650 nm—was processed using the principal component analysis (PCA) dimension reduction algorithm. The wood spectral data were divided into two datasets, namely, training and testing sets. The training set was used to generate the membership functions and the initial fuzzy rule set, with the fuzzy rule being adjusted to supplement and refine the classification rules to form a perfect fuzzy rule set. Second, a fuzzy classifier was applied to the VIS and NIR bands. An improved decision-level fusion scheme based on the Dempster–Shafer (D-S) evidential theory was proposed to further improve the accuracy of wood species recognition. The test results using the testing set indicated that the overall recognition accuracy (ORA) of our scheme reached 94.76% for 50 wood species, which is superior to that of conventional classification algorithms and recent state-of-the-art wood species classification schemes. This method can rapidly achieve good recognition results, especially using small datasets, owing to its low computational time and space complexity.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"1 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2021-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87268098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Imtiaz Ahmad, S. H. Serbaya, A. Rizwan, M. Mehmood
Introduction of modern technologies and methods and quality analysis for the gemstone industry are the main strategic initiatives of the Small and Medium Development Authority (SMEDA) of Pakistan. In this regard, four natural gemstones Quartz, Pyrope-Almandine Garnet, Black tourmaline, and Amethyst brought from Hunza valley Pakistan were analyzed by state-of-the-art spectroscopic techniques including EDX, UV-VIS, and FTIR spectroscopy. EDX revealed the traces of Fe, Mg, and Ca in Pyrope-Almandine garnet, Mg and Fe in Black tourmaline, Au and Ca in Amethyst. UV-VIS data revealed the values of Urbach energies 520, 210, 460, and 430 meV, and the values of direct bandgap energies 5.14, 6.12, 5.54, 5.74 eV, respectively. The higher structural disorder due to the presence of Fe and other impurities in stones except Quartz was attributed to the higher values of Urbach energies and decrease in band gaps: FTIR data Fe-O and Si-O stretching vibration in Pyrope-Almandine garnet, Si-O bending vibrations and O-H stretching vibration in Quartz, Si-O-Si bending and stretching vibrations and C=O stretching vibrations in Black tourmaline, Ca-O stretching vibrations and Si-OH weak-vibrations in Amethyst. Photoluminescence results also showed useful information in investigating the properties of gemstones.
{"title":"Spectroscopic Analysis for Harnessing the Quality and Potential of Gemstones for Small and Medium-Sized Enterprises (SMEs)","authors":"Imtiaz Ahmad, S. H. Serbaya, A. Rizwan, M. Mehmood","doi":"10.1155/2021/6629640","DOIUrl":"https://doi.org/10.1155/2021/6629640","url":null,"abstract":"Introduction of modern technologies and methods and quality analysis for the gemstone industry are the main strategic initiatives of the Small and Medium Development Authority (SMEDA) of Pakistan. In this regard, four natural gemstones Quartz, Pyrope-Almandine Garnet, Black tourmaline, and Amethyst brought from Hunza valley Pakistan were analyzed by state-of-the-art spectroscopic techniques including EDX, UV-VIS, and FTIR spectroscopy. EDX revealed the traces of Fe, Mg, and Ca in Pyrope-Almandine garnet, Mg and Fe in Black tourmaline, Au and Ca in Amethyst. UV-VIS data revealed the values of Urbach energies 520, 210, 460, and 430 meV, and the values of direct bandgap energies 5.14, 6.12, 5.54, 5.74 eV, respectively. The higher structural disorder due to the presence of Fe and other impurities in stones except Quartz was attributed to the higher values of Urbach energies and decrease in band gaps: FTIR data Fe-O and Si-O stretching vibration in Pyrope-Almandine garnet, Si-O bending vibrations and O-H stretching vibration in Quartz, Si-O-Si bending and stretching vibrations and C=O stretching vibrations in Black tourmaline, Ca-O stretching vibrations and Si-OH weak-vibrations in Amethyst. Photoluminescence results also showed useful information in investigating the properties of gemstones.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"142 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73472520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fengxiao Li, Bin Tang, Mingfu Zhao, Xinyu Hu, Sheng-hui Shi, Mi Zhou
The turbidity interference caused by suspended particles in water seriously affects the accuracy of ultraviolet-visible spectroscopy in detecting water quality chemical oxygen demand. Based on this, the application of ultraviolet-visible spectroscopy to detect water quality chemical oxygen demand usually requires physical and mathematical methods to correct the spectral baseline interference caused by turbidity. Because of the slow response speed and unstable compensation effect of traditional correction methods, this paper proposes to use a compressed sensing algorithm to perform baseline correction and achieve good results. In the experiment, we selected formazin turbidity solution and sodium oxalate standard solution and carried out the research on the algorithm of turbidity correction for detecting chemical oxygen demand of water quality by ultraviolet-visible spectroscopy. The experiment obtains the absorption spectra of different concentrations of formazine turbidity solutions and the same concentration of sodium oxalate with different turbidity standard solutions at 210∼845 nm and analyzes the nonlinear effect of absorbance on turbidity. This article uses standard solution experiments to explore the compressed sensing theory for turbidity correction, and through the correction of the absorption spectrum of the actual water sample, it verifies the feasibility of the compression theory for turbidity correction. The method effectively corrects the baseline shift or drift of the water quality ultraviolet-visible absorption spectrum caused by suspended particles, while retaining the absorption characteristics of the ultraviolet spectrum, and it can effectively improve the accuracy and accuracy of the ultraviolet-visible spectroscopy water quality chemical oxygen demand detection.
{"title":"Research on Correction Method of Water Quality Ultraviolet-Visible Spectrum Data Based on Compressed Sensing","authors":"Fengxiao Li, Bin Tang, Mingfu Zhao, Xinyu Hu, Sheng-hui Shi, Mi Zhou","doi":"10.1155/2021/6650630","DOIUrl":"https://doi.org/10.1155/2021/6650630","url":null,"abstract":"The turbidity interference caused by suspended particles in water seriously affects the accuracy of ultraviolet-visible spectroscopy in detecting water quality chemical oxygen demand. Based on this, the application of ultraviolet-visible spectroscopy to detect water quality chemical oxygen demand usually requires physical and mathematical methods to correct the spectral baseline interference caused by turbidity. Because of the slow response speed and unstable compensation effect of traditional correction methods, this paper proposes to use a compressed sensing algorithm to perform baseline correction and achieve good results. In the experiment, we selected formazin turbidity solution and sodium oxalate standard solution and carried out the research on the algorithm of turbidity correction for detecting chemical oxygen demand of water quality by ultraviolet-visible spectroscopy. The experiment obtains the absorption spectra of different concentrations of formazine turbidity solutions and the same concentration of sodium oxalate with different turbidity standard solutions at 210∼845 nm and analyzes the nonlinear effect of absorbance on turbidity. This article uses standard solution experiments to explore the compressed sensing theory for turbidity correction, and through the correction of the absorption spectrum of the actual water sample, it verifies the feasibility of the compression theory for turbidity correction. The method effectively corrects the baseline shift or drift of the water quality ultraviolet-visible absorption spectrum caused by suspended particles, while retaining the absorption characteristics of the ultraviolet spectrum, and it can effectively improve the accuracy and accuracy of the ultraviolet-visible spectroscopy water quality chemical oxygen demand detection.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"4 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2021-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86949569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dan Peng, Yali Liu, Jiasheng Yang, Yanlan Bi, Jingnan Chen
The rapid and accurate detection of the moisture content is of great significance to the quality evaluation and oil extraction process of walnut kernel. Near-infrared (NIR) spectroscopy is an ideal method for measuring the moisture content in walnut kernel. In this study, a regression model for moisture content in walnut kernel was developed based on NIR diffuse reflectance spectroscopy using chemometric methods. The different spectral pretreatment methods were adopted to preprocess the original spectral data. The whole spectra band was divided into 5 subbands, 10 subbands, 15 subbands, and 20 subbands to screen specific wavelengths relevant to the walnut kernel moisture content. PLS (partial least square regression), MLR (multivariate linear regression), PCR (principle component regression), and SVR (support vector regression) were used to establish the relationship model between the spectral data and measurement values of the moisture content. In comparison, the optimized modeling conditions were determined as follows: detection wavelength 1349–1490 nm, SNV-FD (standard normal variate transformation and first derivative) preprocessing method, and PLS algorithm. Under these conditions, the square correlation coefficient (R2) and root mean square error of prediction (RMSEP) of the prediction model were 0.9865 and 0.0017, respectively. The results of this study provided a feasible method for the rapid detection of moisture content in walnut kernel. To improve the performance and applicability of the model, it is necessary to continuously expand the size of the sample set.
{"title":"Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy","authors":"Dan Peng, Yali Liu, Jiasheng Yang, Yanlan Bi, Jingnan Chen","doi":"10.1155/2021/9986940","DOIUrl":"https://doi.org/10.1155/2021/9986940","url":null,"abstract":"The rapid and accurate detection of the moisture content is of great significance to the quality evaluation and oil extraction process of walnut kernel. Near-infrared (NIR) spectroscopy is an ideal method for measuring the moisture content in walnut kernel. In this study, a regression model for moisture content in walnut kernel was developed based on NIR diffuse reflectance spectroscopy using chemometric methods. The different spectral pretreatment methods were adopted to preprocess the original spectral data. The whole spectra band was divided into 5 subbands, 10 subbands, 15 subbands, and 20 subbands to screen specific wavelengths relevant to the walnut kernel moisture content. PLS (partial least square regression), MLR (multivariate linear regression), PCR (principle component regression), and SVR (support vector regression) were used to establish the relationship model between the spectral data and measurement values of the moisture content. In comparison, the optimized modeling conditions were determined as follows: detection wavelength 1349–1490 nm, SNV-FD (standard normal variate transformation and first derivative) preprocessing method, and PLS algorithm. Under these conditions, the square correlation coefficient (R2) and root mean square error of prediction (RMSEP) of the prediction model were 0.9865 and 0.0017, respectively. The results of this study provided a feasible method for the rapid detection of moisture content in walnut kernel. To improve the performance and applicability of the model, it is necessary to continuously expand the size of the sample set.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"44 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2021-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86708793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The tobacco in plateau mountains has the characteristics of fragmented planting, uneven growth, and mixed/interplanting of crops. It is difficult to extract effective features using an object-oriented image analysis method to accurately extract tobacco planting areas. To this end, the advantage of deep learning features self-learning is relied on in this paper. An accurate extraction method of tobacco planting areas based on a deep semantic segmentation model from the unmanned aerial vehicle (UAV) remote sensing images in plateau mountains is proposed in this paper. Firstly, the tobacco semantic segmentation dataset is established using Labelme. Four deep semantic segmentation models of DeeplabV3+, PSPNet, SegNet, and U-Net are used to train the sample data in the dataset. Among them, in order to reduce the model training time, the MobileNet series of lightweight networks are used to replace the original backbone networks of the four network models. Finally, the predictive images are semantically segmented by trained networks, and the mean Intersection over Union (mIoU) is used to evaluate the accuracy. The experimental results show that, using DeeplabV3+, PSPNet, SegNet, and U-Net to perform semantic segmentation on 71 scene prediction images, the mIoU obtained is 0.9436, 0.9118, 0.9392, and 0.9473, respectively, and the accuracy of semantic segmentation is high. The feasibility of the deep semantic segmentation method for extracting tobacco planting surface from UAV remote sensing images has been verified, and the research method can provide a reference for subsequent automatic extraction of tobacco planting areas.
高原山地烟叶具有散植、生长不均匀、混播/套种的特点。采用面向对象的图像分析方法准确提取烟草种植面积，难以提取有效特征。为此，本文依靠深度学习特征自学习的优势。提出了一种基于深度语义分割模型的高原山区无人机遥感影像烟草种植面积精确提取方法。首先，利用Labelme建立烟草语义分割数据集;采用DeeplabV3+、PSPNet、SegNet和U-Net四种深度语义分割模型对数据集中的样本数据进行训练。其中，为了减少模型训练时间，采用MobileNet系列轻量级网络替代原有四种网络模型的骨干网。最后，通过训练好的网络对预测图像进行语义分割，并使用平均交联(Intersection over Union, mIoU)来评估准确率。实验结果表明，使用DeeplabV3+、PSPNet、SegNet和U-Net对71幅场景预测图像进行语义分割，得到的mIoU分别为0.9436、0.9118、0.9392和0.9473，语义分割的准确率较高。验证了深度语义分割方法在无人机遥感影像中提取烟草种植地表的可行性，研究方法可为后续自动提取烟草种植面积提供参考。
{"title":"Depth Semantic Segmentation of Tobacco Planting Areas from Unmanned Aerial Vehicle Remote Sensing Images in Plateau Mountains","authors":"Liang Huang, Xuequn Wu, Qiuzhi Peng, Xueqin Yu","doi":"10.1155/2021/6687799","DOIUrl":"https://doi.org/10.1155/2021/6687799","url":null,"abstract":"The tobacco in plateau mountains has the characteristics of fragmented planting, uneven growth, and mixed/interplanting of crops. It is difficult to extract effective features using an object-oriented image analysis method to accurately extract tobacco planting areas. To this end, the advantage of deep learning features self-learning is relied on in this paper. An accurate extraction method of tobacco planting areas based on a deep semantic segmentation model from the unmanned aerial vehicle (UAV) remote sensing images in plateau mountains is proposed in this paper. Firstly, the tobacco semantic segmentation dataset is established using Labelme. Four deep semantic segmentation models of DeeplabV3+, PSPNet, SegNet, and U-Net are used to train the sample data in the dataset. Among them, in order to reduce the model training time, the MobileNet series of lightweight networks are used to replace the original backbone networks of the four network models. Finally, the predictive images are semantically segmented by trained networks, and the mean Intersection over Union (mIoU) is used to evaluate the accuracy. The experimental results show that, using DeeplabV3+, PSPNet, SegNet, and U-Net to perform semantic segmentation on 71 scene prediction images, the mIoU obtained is 0.9436, 0.9118, 0.9392, and 0.9473, respectively, and the accuracy of semantic segmentation is high. The feasibility of the deep semantic segmentation method for extracting tobacco planting surface from UAV remote sensing images has been verified, and the research method can provide a reference for subsequent automatic extraction of tobacco planting areas.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"17 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75222450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Atmospheric pressure plasma jet (APPJ) is a promising technique for the sterilization of pathogenic microorganisms in an ambient environment. In this work, a helium-APPJ was generated by double dielectric barrier discharge and applied to the sterilization of model microorganism in air and water. Discharge characteristics (including waveform and frequency of applied voltage), jet properties (such as feed gas flow rate, jet length, thermal effect, and optic emission spectra), and sterilization performance (in terms of clear/sterilized area, size of plaques, and sterilization efficiency) were investigated. Homogeneous helium plasma jet was generated in an energy-efficient way (18 kHz, 6 kV, 0.08 W) with a 19 mm jet and limited heating. The He-APPJ achieved good sterilization performances within very short treatment time (as short as 30 s). For surface sterilization, the area of clear zone and size of the plaque were 1809 mm2 and 48 mm, respectively, within 5 min treatment. For water sterilization, 99.8% sterilization efficiency was achieved within 5 min treatment. The optic emission spectra suggest that active species such as excited molecules, ions, and radicals were produced in the He-APPJ. The as-produced active species played important roles in the sterilization process.
{"title":"He-Plasma Jet Generation and Its Application for E. coli Sterilization","authors":"Tiejian Liu, Yuxuan Zeng, Xin Xue, Yinyi Sui, Yingying Liang, Fushan Wang, Fada Feng","doi":"10.1155/2021/6671531","DOIUrl":"https://doi.org/10.1155/2021/6671531","url":null,"abstract":"Atmospheric pressure plasma jet (APPJ) is a promising technique for the sterilization of pathogenic microorganisms in an ambient environment. In this work, a helium-APPJ was generated by double dielectric barrier discharge and applied to the sterilization of model microorganism in air and water. Discharge characteristics (including waveform and frequency of applied voltage), jet properties (such as feed gas flow rate, jet length, thermal effect, and optic emission spectra), and sterilization performance (in terms of clear/sterilized area, size of plaques, and sterilization efficiency) were investigated. Homogeneous helium plasma jet was generated in an energy-efficient way (18 kHz, 6 kV, 0.08 W) with a 19 mm jet and limited heating. The He-APPJ achieved good sterilization performances within very short treatment time (as short as 30 s). For surface sterilization, the area of clear zone and size of the plaque were 1809 mm2 and 48 mm, respectively, within 5 min treatment. For water sterilization, 99.8% sterilization efficiency was achieved within 5 min treatment. The optic emission spectra suggest that active species such as excited molecules, ions, and radicals were produced in the He-APPJ. The as-produced active species played important roles in the sterilization process.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"14 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2021-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78787697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
W. Ye, Yiqian Chen, Chong Gao, T. Xie, Hongjun Jing, Yousheng Deng
To investigate the microstructure of paleosol and its expansion characteristics, the paleosol of the Zaosheng #3 tunnel of the Yinxi high-speed railway was studied. Based on X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), nuclear magnetic resonance (NMR), and scanning electron microscopy techniques (SEM), the microstructure of the paleosol was analyzed in terms of the mineral composition, formation elements, pore structure, and particle morphology. Five groups of undisturbed and remolded soils with different moisture contents were tested for the unloaded expansion rate and loaded expansion rate. The results show that the mineral components of the paleosol are mainly quartz, potash feldspar, calcite, and hematite, with the highest-content-component quartz accounting for 45.4% of the total content; the clay mineral composition of the paleosol has the highest content of montmorillonite at 12.3%. The elemental composition of the paleosol is dominated by Al, Si, Ca, and Fe, which form expansive mineral components such as quartz and montmorillonite, creating inherent conditions for expansibility of the paleosol. The T2 distribution curves of the undisturbed and remolded paleosol are composed of three peaks. The pore distribution of paleosol mainly includes medium pores, followed by large pores, and the contents of small pores and superlarge pores are very small. In terms of particle contact, the undisturbed soil is mostly in the form of “surface-surface” and “surface-edge” contact, and the remolded soil is mainly in the form of “point-surface” and “point-point” contact. The unloaded expansion rate of remolded soil is approximately twice that of undisturbed soil. The rate of loaded expansion of both soils decreases with increasing moisture content.
{"title":"Experimental Study on the Microstructure and Expansion Characteristics of Paleosol Based on Spectral Scanning","authors":"W. Ye, Yiqian Chen, Chong Gao, T. Xie, Hongjun Jing, Yousheng Deng","doi":"10.1155/2021/6689073","DOIUrl":"https://doi.org/10.1155/2021/6689073","url":null,"abstract":"To investigate the microstructure of paleosol and its expansion characteristics, the paleosol of the Zaosheng #3 tunnel of the Yinxi high-speed railway was studied. Based on X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), nuclear magnetic resonance (NMR), and scanning electron microscopy techniques (SEM), the microstructure of the paleosol was analyzed in terms of the mineral composition, formation elements, pore structure, and particle morphology. Five groups of undisturbed and remolded soils with different moisture contents were tested for the unloaded expansion rate and loaded expansion rate. The results show that the mineral components of the paleosol are mainly quartz, potash feldspar, calcite, and hematite, with the highest-content-component quartz accounting for 45.4% of the total content; the clay mineral composition of the paleosol has the highest content of montmorillonite at 12.3%. The elemental composition of the paleosol is dominated by Al, Si, Ca, and Fe, which form expansive mineral components such as quartz and montmorillonite, creating inherent conditions for expansibility of the paleosol. The T2 distribution curves of the undisturbed and remolded paleosol are composed of three peaks. The pore distribution of paleosol mainly includes medium pores, followed by large pores, and the contents of small pores and superlarge pores are very small. In terms of particle contact, the undisturbed soil is mostly in the form of “surface-surface” and “surface-edge” contact, and the remolded soil is mainly in the form of “point-surface” and “point-point” contact. The unloaded expansion rate of remolded soil is approximately twice that of undisturbed soil. The rate of loaded expansion of both soils decreases with increasing moisture content.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"41 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84123974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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