Study of petrophysical properties of rocks in seafioor hydrothermal fields has great significance for inves- tigation of seafloor hydrothermal activities, especially for polymetallic sulfides prospecting. In the prese...Study of petrophysical properties of rocks in seafioor hydrothermal fields has great significance for inves- tigation of seafloor hydrothermal activities, especially for polymetallic sulfides prospecting. In the present study, based on the current experimental conditions, we conducted systematic experiments to measure the magnetic susceptibility, electrical resistivity, porosity, density, as well as acoustic wave velocity of seafloor rocks and sulfides. Subsequently, we measured the physical characteristics of hydrothermal sulfides, basalts and peridotites which were collected from newly discovered seafloor hydrothermal fields at 49.6°E, 50.5°E, 5 1°E, 63.5°E, and 63.9°E of the Southwest Indian Ridge （SWIR）. Previously available and newly collected data were combined to characterize the physical differences between polymetallic sulfides and rocks. We also discussed the impact of hydrothermal alteration on the bedrock and demonstrated how these petrophysical properties of rocks can help in geophysical prospecting of seafloor hydrothermal fields as indicators.展开更多
The growth characteristics of Aspergillus parasitic us incubated on two culture media were ex-amined using shortwave infrared(SWIR,1000-2500 nm)hyperspectral imaging(HSI)in this work.HSI images of the A.parasiticus co...The growth characteristics of Aspergillus parasitic us incubated on two culture media were ex-amined using shortwave infrared(SWIR,1000-2500 nm)hyperspectral imaging(HSI)in this work.HSI images of the A.parasiticus colonies growing on rose bengal medium(RBM)and maize agar medium(MAM)were recorded daily for 6 days.The growth phases of A.parasiticus were indicated through the pixel number and average spectra of colonies.On score plot of the first principal component(PC1)and PC2,four growth zones with varying mycelium densities were identified.Eight characteristic wavelengths(1095,1145,1195,1279,1442,1655,1834 and 1929 nm)were selected from PC1 loading,average spectra of each colony as well as each growth zone.F urthermore,support vector machine(S VM)classifier based on the eight wavelengths was built,and the classification accuracies for the four zones(from outer to inner zones)on the colonies on RBM were 99.77%,9935%,99.75%and 99.60%and 99.77%,9939%,99.31%and 98.22%for colonies on MAM.In addition,a new score plot of PC2 and PC3 was used to differ-entiate the colonies incubated on RBM and MAM for 6 days.Then characteristic wavelengths of 1067,1195,1279,1369,1459,1694,1834 and 1929 nm were selected from the loading of PC2 and PCg.Based on them,a new SVM model was developed to diferentiate colonies on RBM and MAM with accuracy of 100.00%and 9999%,respectively.In conclusion,SWIR hyperspectral image is a powerful tool for evaluation of growth characteristics of A.parasiticus incubated in diferent culture media.展开更多
无人机载和单兵的能力需求继续推动缩小尺寸、重量和功率(SWaP)和高灵敏度红外(IR)成像的应用,在以前,这些应用不切实际。现在,为了满足这些需求,Attollo工程公司开发了一款1280×1024、5mm像元间距的制冷中波红外(MWIR)传感器,在...无人机载和单兵的能力需求继续推动缩小尺寸、重量和功率(SWaP)和高灵敏度红外(IR)成像的应用,在以前,这些应用不切实际。现在,为了满足这些需求,Attollo工程公司开发了一款1280×1024、5mm像元间距的制冷中波红外(MWIR)传感器,在像元间距方面取得了突破,此外还开发了1280×1024、10mm像元的双波段传感器,在短波红外(SWIR)中具有更高的灵敏度,以利用SWIR现象,其中包括激光定位(laser see spot)功能。这两种传感器都提供MWIR传感功能,但也能够利用Attollo探测器设计的各个方面,实现不同程度的SWIR传感。这类制冷小像元、单波段和双波段红外传感器技术代表了传感器设计和开发的各个方面的进步,我们将讨论Attollo为实现这一能力所做的创新,包括基于III-V族化合物半导体的外延探测器设计、探测器阵列和焦平面阵列制造、低噪声设计、双波段CTIA/DI读出集成电路(ROIC)、真空杜瓦封装、电子和固件设计。在本文中,我们将介绍高清晰度小像元间距MWIR和双波段SWIR/MWIR成像技术在Attollo的现状,它涉及到上述传感器,包括设计和测量数据及成像。展开更多
Aflatoxin B1(AFB1)is a toxic fungal metabolite that contaminates almonds from cultivation to harvesting.It leads to chronic health problems and significant economic loss to the producers.Therefore,a fast and non-invas...Aflatoxin B1(AFB1)is a toxic fungal metabolite that contaminates almonds from cultivation to harvesting.It leads to chronic health problems and significant economic loss to the producers.Therefore,a fast and non-invasive detection technique is crucial for safeguarding food safety by swiftly identifying and eliminating contaminated almonds from the supply chain.Hyperspectral imaging has been explored as a potential non-destructive technology for detecting AFB1.However,the diverse geometries of almonds present a significant challenge on acquired images,thereby impacting the accuracy of the developed prediction and classification models.This study investigates the effectiveness of short-wave infrared(SwIR)hyperspectral imaging combined with deep learning for detecting AFB1 in almonds of varying geometries.Initially,partial least squares regression(PLSR)and support vector machine(SvM)regression models were evaluated for quantification,while SVM and quadratic discriminant analysis(QDA)classifiers were applied for classification.The results indicated that spectral responses varied with almond thickness,making quantification models unreliable for industrial applications.The Competitive Adaptive Reweighted Sampling(CARS)algorithm was employed to identify key spectral features for developing multi-spectral AFB1 classification models to evaluate the feasibility of high-speed,accurate in-line detection.The deep learning approach significantly outperformed traditional machine learning models,with the pre-trained Inception V3 network achieving a cross-validation accuracy of 84.82%,an F1-score of 0.8522,and an area under curve of 0.893.These findings highlight the superiority of deep learning-based hyperspectral imaging for accurate and reliable AFB1 detection in almonds with diverse shapes and thicknesses.展开更多
中国新疆维吾尔自治区、宁夏回族自治区、内蒙古自治区等煤炭资源丰富的地区分布着数百个煤田火区。煤火自燃排放大量温室气体,其无组织性致检测与量化工作面临困难。然而,煤火甲烷在全球温室气体排放中的贡献是不可忽视的。鉴于卫星分...中国新疆维吾尔自治区、宁夏回族自治区、内蒙古自治区等煤炭资源丰富的地区分布着数百个煤田火区。煤火自燃排放大量温室气体,其无组织性致检测与量化工作面临困难。然而,煤火甲烷在全球温室气体排放中的贡献是不可忽视的。鉴于卫星分辨率的限制,本研究采用地基遥感手段开展煤火源甲烷检测,利用2023年6月在新疆阜康煤田火区采集的宽幅全景地基高光谱影像集,结合甲烷在短波红外的光学敏感特征和高光谱混合像元分解等方法,提出了一套适用于不同地貌特征高温煤火源甲烷逃逸的检测算法,并针对各算法的检测效果进行对比验证和效果评价。结果表明:(1)与已有的甲烷反演指数CH_(4)I(CH_(4)Index)相比,本研究提出的修正的最小二乘图像增强算法MLSIE(Modified Least Squares Image Enhancement)、比值导数光谱解混算法RCH_(4)I(Ratio-based Derivative Spectral Unmixing for CH_(4)Index)和去阴影甲烷比值指数DSRCH_(4)I(Deshadowed Spectral Ratio CH_(4)Index)在煤火源甲烷检测中表现更佳;(2)2DSRCH_(4)I3、MLSIE(2.3μm)和RCH_(4)I1算法对于地貌复杂的煤火区检测效果较好,其中,2DSRCH_(4)I和MLSIE(2.3μm)算法也适用于地貌相对单一的山地煤火区,而RCH_(4)I1算法更适用于泄露量大(燃烧剧烈)的活跃煤火区;(3)MLSIE(2.3μm)算法具有较强的普适性,2DSRCH_(4)I3算法有效抑制伪影/假阳性,检测效果最佳;(4)本文提出算法检测出的煤火区甲烷羽流以与燃烧的火焰共存和以自由扩散形式逸出共两种形式存在。本研究可为检测煤火甲烷提供一种利用地基短波红外成像光谱仪的新方法,也从甲烷逃逸角度为开展煤火自燃的早期识别与预警提供了新思路。展开更多
基金The National Basic Research Program of China (973 Program) under contract No.2012CB417305COMRA Major Project under contract No.DY125-11-R-01-05the National Natural Science Foundation of China under contract Nos 49906004 and 41104073
文摘Study of petrophysical properties of rocks in seafioor hydrothermal fields has great significance for inves- tigation of seafloor hydrothermal activities, especially for polymetallic sulfides prospecting. In the present study, based on the current experimental conditions, we conducted systematic experiments to measure the magnetic susceptibility, electrical resistivity, porosity, density, as well as acoustic wave velocity of seafloor rocks and sulfides. Subsequently, we measured the physical characteristics of hydrothermal sulfides, basalts and peridotites which were collected from newly discovered seafloor hydrothermal fields at 49.6°E, 50.5°E, 5 1°E, 63.5°E, and 63.9°E of the Southwest Indian Ridge （SWIR）. Previously available and newly collected data were combined to characterize the physical differences between polymetallic sulfides and rocks. We also discussed the impact of hydrothermal alteration on the bedrock and demonstrated how these petrophysical properties of rocks can help in geophysical prospecting of seafloor hydrothermal fields as indicators.
基金the National Natural Science Foundation of China(No.31772062)Gannan Camellia Industry Development and Innovative Center Open Fund(Grant No.YK201610).
文摘The growth characteristics of Aspergillus parasitic us incubated on two culture media were ex-amined using shortwave infrared(SWIR,1000-2500 nm)hyperspectral imaging(HSI)in this work.HSI images of the A.parasiticus colonies growing on rose bengal medium(RBM)and maize agar medium(MAM)were recorded daily for 6 days.The growth phases of A.parasiticus were indicated through the pixel number and average spectra of colonies.On score plot of the first principal component(PC1)and PC2,four growth zones with varying mycelium densities were identified.Eight characteristic wavelengths(1095,1145,1195,1279,1442,1655,1834 and 1929 nm)were selected from PC1 loading,average spectra of each colony as well as each growth zone.F urthermore,support vector machine(S VM)classifier based on the eight wavelengths was built,and the classification accuracies for the four zones(from outer to inner zones)on the colonies on RBM were 99.77%,9935%,99.75%and 99.60%and 99.77%,9939%,99.31%and 98.22%for colonies on MAM.In addition,a new score plot of PC2 and PC3 was used to differ-entiate the colonies incubated on RBM and MAM for 6 days.Then characteristic wavelengths of 1067,1195,1279,1369,1459,1694,1834 and 1929 nm were selected from the loading of PC2 and PCg.Based on them,a new SVM model was developed to diferentiate colonies on RBM and MAM with accuracy of 100.00%and 9999%,respectively.In conclusion,SWIR hyperspectral image is a powerful tool for evaluation of growth characteristics of A.parasiticus incubated in diferent culture media.
文摘无人机载和单兵的能力需求继续推动缩小尺寸、重量和功率(SWaP)和高灵敏度红外(IR)成像的应用,在以前,这些应用不切实际。现在,为了满足这些需求,Attollo工程公司开发了一款1280×1024、5mm像元间距的制冷中波红外(MWIR)传感器,在像元间距方面取得了突破,此外还开发了1280×1024、10mm像元的双波段传感器,在短波红外(SWIR)中具有更高的灵敏度,以利用SWIR现象,其中包括激光定位(laser see spot)功能。这两种传感器都提供MWIR传感功能,但也能够利用Attollo探测器设计的各个方面,实现不同程度的SWIR传感。这类制冷小像元、单波段和双波段红外传感器技术代表了传感器设计和开发的各个方面的进步,我们将讨论Attollo为实现这一能力所做的创新,包括基于III-V族化合物半导体的外延探测器设计、探测器阵列和焦平面阵列制造、低噪声设计、双波段CTIA/DI读出集成电路(ROIC)、真空杜瓦封装、电子和固件设计。在本文中,我们将介绍高清晰度小像元间距MWIR和双波段SWIR/MWIR成像技术在Attollo的现状,它涉及到上述传感器,包括设计和测量数据及成像。
基金the Research Training Program International(RTPi)scholarship from Commonwealth Australiathe top-up scholarship provided by SureNut Ltd.SureNut Ltd.for supplying all the almonds used in this study.
文摘Aflatoxin B1(AFB1)is a toxic fungal metabolite that contaminates almonds from cultivation to harvesting.It leads to chronic health problems and significant economic loss to the producers.Therefore,a fast and non-invasive detection technique is crucial for safeguarding food safety by swiftly identifying and eliminating contaminated almonds from the supply chain.Hyperspectral imaging has been explored as a potential non-destructive technology for detecting AFB1.However,the diverse geometries of almonds present a significant challenge on acquired images,thereby impacting the accuracy of the developed prediction and classification models.This study investigates the effectiveness of short-wave infrared(SwIR)hyperspectral imaging combined with deep learning for detecting AFB1 in almonds of varying geometries.Initially,partial least squares regression(PLSR)and support vector machine(SvM)regression models were evaluated for quantification,while SVM and quadratic discriminant analysis(QDA)classifiers were applied for classification.The results indicated that spectral responses varied with almond thickness,making quantification models unreliable for industrial applications.The Competitive Adaptive Reweighted Sampling(CARS)algorithm was employed to identify key spectral features for developing multi-spectral AFB1 classification models to evaluate the feasibility of high-speed,accurate in-line detection.The deep learning approach significantly outperformed traditional machine learning models,with the pre-trained Inception V3 network achieving a cross-validation accuracy of 84.82%,an F1-score of 0.8522,and an area under curve of 0.893.These findings highlight the superiority of deep learning-based hyperspectral imaging for accurate and reliable AFB1 detection in almonds with diverse shapes and thicknesses.
文摘中国新疆维吾尔自治区、宁夏回族自治区、内蒙古自治区等煤炭资源丰富的地区分布着数百个煤田火区。煤火自燃排放大量温室气体,其无组织性致检测与量化工作面临困难。然而,煤火甲烷在全球温室气体排放中的贡献是不可忽视的。鉴于卫星分辨率的限制,本研究采用地基遥感手段开展煤火源甲烷检测,利用2023年6月在新疆阜康煤田火区采集的宽幅全景地基高光谱影像集,结合甲烷在短波红外的光学敏感特征和高光谱混合像元分解等方法,提出了一套适用于不同地貌特征高温煤火源甲烷逃逸的检测算法,并针对各算法的检测效果进行对比验证和效果评价。结果表明:(1)与已有的甲烷反演指数CH_(4)I(CH_(4)Index)相比,本研究提出的修正的最小二乘图像增强算法MLSIE(Modified Least Squares Image Enhancement)、比值导数光谱解混算法RCH_(4)I(Ratio-based Derivative Spectral Unmixing for CH_(4)Index)和去阴影甲烷比值指数DSRCH_(4)I(Deshadowed Spectral Ratio CH_(4)Index)在煤火源甲烷检测中表现更佳;(2)2DSRCH_(4)I3、MLSIE(2.3μm)和RCH_(4)I1算法对于地貌复杂的煤火区检测效果较好,其中,2DSRCH_(4)I和MLSIE(2.3μm)算法也适用于地貌相对单一的山地煤火区,而RCH_(4)I1算法更适用于泄露量大(燃烧剧烈)的活跃煤火区;(3)MLSIE(2.3μm)算法具有较强的普适性,2DSRCH_(4)I3算法有效抑制伪影/假阳性,检测效果最佳;(4)本文提出算法检测出的煤火区甲烷羽流以与燃烧的火焰共存和以自由扩散形式逸出共两种形式存在。本研究可为检测煤火甲烷提供一种利用地基短波红外成像光谱仪的新方法,也从甲烷逃逸角度为开展煤火自燃的早期识别与预警提供了新思路。
