针对食品安全领域对层次数据的对比和关联分析需求,提出了一种对比与关联可视分析方法(Visual Analysis Method for Comparison and Association),这个方法抛弃以往的通过直线连接来展现关联关系的可视化方法,通过交互的手段直接展示两...针对食品安全领域对层次数据的对比和关联分析需求,提出了一种对比与关联可视分析方法(Visual Analysis Method for Comparison and Association),这个方法抛弃以往的通过直线连接来展现关联关系的可视化方法,通过交互的手段直接展示两类层次数据的关联关系,比如农药与农产品的检出关系,从而避免了视觉杂乱。同样这个方法可以在同一个布局中对比分析两个相同结构的树的属性,比如不同地区或同一地区不同时间点的农产品中农药检出状况。经过用户体验和评价,该方法的表现证明它是有效而且有用的。展开更多
To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explo...To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.展开更多
现有的嵌套圆排列方法主要采用自顶向下的排列方式。排列过程中的多次缩放和平移将导致算法时间复杂度增高、各层节点大小比例不一致以及局部排列不够紧密等问题。为解决上述问题,在总结层次结构中同层兄弟节点圆外切排列算法的基础上,...现有的嵌套圆排列方法主要采用自顶向下的排列方式。排列过程中的多次缩放和平移将导致算法时间复杂度增高、各层节点大小比例不一致以及局部排列不够紧密等问题。为解决上述问题,在总结层次结构中同层兄弟节点圆外切排列算法的基础上,提出了自底向上父子节点的递归排列算法——圆形-矩形中心法CRCA(Circle and Rectangle Center Algorithm),并提出了一种评价父子节点排列紧密性的指标——面积比AR(Area Ratio)。将基于CRCA算法的嵌套圆排列方法应用于各国农药最大残留限量标准数据的可视化中。实验表明,该方法能够保持同层节点的大小比例和更紧密的排列效果,提高空间利用率,在数据展示方面取得良好效果。展开更多
文摘针对食品安全领域对层次数据的对比和关联分析需求,提出了一种对比与关联可视分析方法(Visual Analysis Method for Comparison and Association),这个方法抛弃以往的通过直线连接来展现关联关系的可视化方法,通过交互的手段直接展示两类层次数据的关联关系,比如农药与农产品的检出关系,从而避免了视觉杂乱。同样这个方法可以在同一个布局中对比分析两个相同结构的树的属性,比如不同地区或同一地区不同时间点的农产品中农药检出状况。经过用户体验和评价,该方法的表现证明它是有效而且有用的。
基金Supported by the National Key R&D Program of China(2023YFD2101001)National Natural Science Foundation of China(32202144,61807001)。
文摘To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.
文摘现有的嵌套圆排列方法主要采用自顶向下的排列方式。排列过程中的多次缩放和平移将导致算法时间复杂度增高、各层节点大小比例不一致以及局部排列不够紧密等问题。为解决上述问题,在总结层次结构中同层兄弟节点圆外切排列算法的基础上,提出了自底向上父子节点的递归排列算法——圆形-矩形中心法CRCA(Circle and Rectangle Center Algorithm),并提出了一种评价父子节点排列紧密性的指标——面积比AR(Area Ratio)。将基于CRCA算法的嵌套圆排列方法应用于各国农药最大残留限量标准数据的可视化中。实验表明,该方法能够保持同层节点的大小比例和更紧密的排列效果,提高空间利用率,在数据展示方面取得良好效果。
