In order to improve the quality of 3D printed raspberry preserves after post-processing,microwave ovens combining infrared and microwave methods were utilized.The effects of infrared heating temperature,infrared heati...In order to improve the quality of 3D printed raspberry preserves after post-processing,microwave ovens combining infrared and microwave methods were utilized.The effects of infrared heating temperature,infrared heating time,microwave power,microwave heating time on the center temperature,moisture content,the chroma(C*),the total color difference(ΔE*),shape fidelity,hardness,and the total anthocyanin content of 3D printed raspberry preserves were analyzed by response surface method(RSM).The results showed that under combining with the two methods,infrared heating improved the fidelity and quality degradation of printed products,while microwave heating enhanced the efficiency of infrared heating.Infrared-microwave combination cooking could maintain relatively stable color appearance and shape of 3D printed raspberry preserves.The AHP–CRITIC hybrid weighting method combined with the response surface test to determine the comprehensive weights of the evaluation indicators optimized the process parameters,and the optimal process parameters were obtained:infrared heating temperature of 190℃,infrared heating time of 10 min and 30 s,microwave power of 300 W,and microwave heating time of 2 min and 6 s.The 3D printed raspberry cooking methods obtained under the optimal conditions seldom had color variation,porous structure,uniform texture,and high shape fidelity,which retained the characteristics of personalized manufacturing by 3D printing.This study could provide a reference for the postprocessing and quality control of 3D cooking methods.展开更多
In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detectio...In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detection algorithm of infrared small and dim target is proposed in this paper.Firstly,the original infrared images are changed into a new infrared patch tensor mode through data reconstruction.Then,the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics,and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness.Finally,the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image,and the final small target is worked out by a simple partitioning algorithm.The test results in various spacebased downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate.It is a kind of infrared small and dim target detection method with good performance.展开更多
为应对红外气体泄漏成像过程中因设备转动等因素导致的图像不稳定及泄漏气体检测效果不佳的问题,提出1种结合图像帧子块匹配法和改进快速鲁棒模糊C均值算法(fast and robust fuzzy c-means,FRFCM)的红外图像细节增强方法。该方法利用图...为应对红外气体泄漏成像过程中因设备转动等因素导致的图像不稳定及泄漏气体检测效果不佳的问题,提出1种结合图像帧子块匹配法和改进快速鲁棒模糊C均值算法(fast and robust fuzzy c-means,FRFCM)的红外图像细节增强方法。该方法利用图像帧子块匹配法配准图像帧,同时引入背景建模和差分方法从背景中分离动态气体目标,并在FRFCM基础上增加自适应调整模糊因子以优化图像帧的羽流强化特征效果。研究结果表明:该方法能够有效去除冗余信息,使图像帧匹配误差降低约75%,对比度增强值提高4.7%,羽流分割的平均交并比达到0.68,在保持较高分割准确度的同时显著提升检测速度,适用于油气田、集输站及氢气站等气体安全检测系统。研究结果可为气体泄漏监测技术的优化与应用提供参考。展开更多
精准评估土壤质量是保障育种质量先决条件之一,对评估种子品质和精准施肥具有指导意义。土壤成分含量是土壤质量评估的重要指标,光谱技术已经被证实可以快速、绿色地进行土壤成分检测。然而单一模态光谱技术难以满足种田多种土壤成分含...精准评估土壤质量是保障育种质量先决条件之一,对评估种子品质和精准施肥具有指导意义。土壤成分含量是土壤质量评估的重要指标,光谱技术已经被证实可以快速、绿色地进行土壤成分检测。然而单一模态光谱技术难以满足种田多种土壤成分含量检测的需求。故运用原子激光诱导击穿光谱(LIBS)和分子可见-近红外光谱(VIS-NIR)技术结合化学计量学方法,对宁夏润丰种业育种玉米田采集的288份土壤样本进行分析,建立金属元素和土壤有机质(SOM)含量的预测模型,并实现金属元素和SOM含量空间可视化分布。首先,利用共线双脉冲LIBS系统采集土壤样本的LIBS数据,利用air-PLS对光谱数据进行基线矫正以减少试验误差。选择的金属元素特征谱线查找于美国国家标准与技术研究院(National Institute of Standards and Technology,NIST)的标准原子光谱数据库。基于国家标准土样的LIBS光谱与其金属元素含量真实值,建立4种金属元素(Na、K、Mg、Mn)的偏最小二乘回归模型(PLSR),其中Mn含量的预测效果最好,R_(p)^(2)达到0.813,RMSEP为0.155 g·kg^(-1)。另一方面,采集可见-近红外光谱数据后,利用SG卷积平滑(SGCS)、一阶导数变换、多元散射矫正(MSC)对光谱数据进行预处理,并分别建立SOM含量的PLSR预测模型对三种预处理方法进行评价,经MSC预处理后所建立的PLSR模型效果最好;随后利用蒙特卡洛交叉验证法(MCCV)剔除SOM含量异常样本。利用竞争自适应重加权采样法(CARS)和连续投影算法(SPA)选择特征波长,分别建立SOM含量的PLSR预测模型对两种算法进行评价;得出利用CARS算法选择的特征波长建立的预测模型性能有所提高。用CARS算法选择的特征波长与SOM含量真实值,分别建立PLSR和反向传播人工神经网络(BPNN)预测模型,其中PLSR模型的效果最好,R_(p)^(2)达到0.864,RMSEP为0.612 g·kg^(-1),RPD_(v)为2.733。最后,利用国家标准土样所建立的PLSR模型预测玉米种田四种金属元素含量,建立PLSR模型预测值和BPNN模型预测值的空间分布图。研究结果表明,LIBS技术和可见-近红外光谱定量分析技术可以对种田土壤金属元素和SOM含量检测,为土壤成分含量的检测和空间可视化分布提供了参考价值并对土壤科学合理地施肥具有指导意义。展开更多
基金Supported by the National Natural Science Foundation of China(32072352)。
文摘In order to improve the quality of 3D printed raspberry preserves after post-processing,microwave ovens combining infrared and microwave methods were utilized.The effects of infrared heating temperature,infrared heating time,microwave power,microwave heating time on the center temperature,moisture content,the chroma(C*),the total color difference(ΔE*),shape fidelity,hardness,and the total anthocyanin content of 3D printed raspberry preserves were analyzed by response surface method(RSM).The results showed that under combining with the two methods,infrared heating improved the fidelity and quality degradation of printed products,while microwave heating enhanced the efficiency of infrared heating.Infrared-microwave combination cooking could maintain relatively stable color appearance and shape of 3D printed raspberry preserves.The AHP–CRITIC hybrid weighting method combined with the response surface test to determine the comprehensive weights of the evaluation indicators optimized the process parameters,and the optimal process parameters were obtained:infrared heating temperature of 190℃,infrared heating time of 10 min and 30 s,microwave power of 300 W,and microwave heating time of 2 min and 6 s.The 3D printed raspberry cooking methods obtained under the optimal conditions seldom had color variation,porous structure,uniform texture,and high shape fidelity,which retained the characteristics of personalized manufacturing by 3D printing.This study could provide a reference for the postprocessing and quality control of 3D cooking methods.
文摘In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detection algorithm of infrared small and dim target is proposed in this paper.Firstly,the original infrared images are changed into a new infrared patch tensor mode through data reconstruction.Then,the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics,and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness.Finally,the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image,and the final small target is worked out by a simple partitioning algorithm.The test results in various spacebased downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate.It is a kind of infrared small and dim target detection method with good performance.
文摘为应对红外气体泄漏成像过程中因设备转动等因素导致的图像不稳定及泄漏气体检测效果不佳的问题,提出1种结合图像帧子块匹配法和改进快速鲁棒模糊C均值算法(fast and robust fuzzy c-means,FRFCM)的红外图像细节增强方法。该方法利用图像帧子块匹配法配准图像帧,同时引入背景建模和差分方法从背景中分离动态气体目标,并在FRFCM基础上增加自适应调整模糊因子以优化图像帧的羽流强化特征效果。研究结果表明:该方法能够有效去除冗余信息,使图像帧匹配误差降低约75%,对比度增强值提高4.7%,羽流分割的平均交并比达到0.68,在保持较高分割准确度的同时显著提升检测速度,适用于油气田、集输站及氢气站等气体安全检测系统。研究结果可为气体泄漏监测技术的优化与应用提供参考。
文摘精准评估土壤质量是保障育种质量先决条件之一,对评估种子品质和精准施肥具有指导意义。土壤成分含量是土壤质量评估的重要指标,光谱技术已经被证实可以快速、绿色地进行土壤成分检测。然而单一模态光谱技术难以满足种田多种土壤成分含量检测的需求。故运用原子激光诱导击穿光谱(LIBS)和分子可见-近红外光谱(VIS-NIR)技术结合化学计量学方法,对宁夏润丰种业育种玉米田采集的288份土壤样本进行分析,建立金属元素和土壤有机质(SOM)含量的预测模型,并实现金属元素和SOM含量空间可视化分布。首先,利用共线双脉冲LIBS系统采集土壤样本的LIBS数据,利用air-PLS对光谱数据进行基线矫正以减少试验误差。选择的金属元素特征谱线查找于美国国家标准与技术研究院(National Institute of Standards and Technology,NIST)的标准原子光谱数据库。基于国家标准土样的LIBS光谱与其金属元素含量真实值,建立4种金属元素(Na、K、Mg、Mn)的偏最小二乘回归模型(PLSR),其中Mn含量的预测效果最好,R_(p)^(2)达到0.813,RMSEP为0.155 g·kg^(-1)。另一方面,采集可见-近红外光谱数据后,利用SG卷积平滑(SGCS)、一阶导数变换、多元散射矫正(MSC)对光谱数据进行预处理,并分别建立SOM含量的PLSR预测模型对三种预处理方法进行评价,经MSC预处理后所建立的PLSR模型效果最好;随后利用蒙特卡洛交叉验证法(MCCV)剔除SOM含量异常样本。利用竞争自适应重加权采样法(CARS)和连续投影算法(SPA)选择特征波长,分别建立SOM含量的PLSR预测模型对两种算法进行评价;得出利用CARS算法选择的特征波长建立的预测模型性能有所提高。用CARS算法选择的特征波长与SOM含量真实值,分别建立PLSR和反向传播人工神经网络(BPNN)预测模型,其中PLSR模型的效果最好,R_(p)^(2)达到0.864,RMSEP为0.612 g·kg^(-1),RPD_(v)为2.733。最后,利用国家标准土样所建立的PLSR模型预测玉米种田四种金属元素含量,建立PLSR模型预测值和BPNN模型预测值的空间分布图。研究结果表明,LIBS技术和可见-近红外光谱定量分析技术可以对种田土壤金属元素和SOM含量检测,为土壤成分含量的检测和空间可视化分布提供了参考价值并对土壤科学合理地施肥具有指导意义。
