Optimization Algorithm was developed for the simula ti on of ceramic grain growth at atomistic scale. Based on the coordination informa tion of different atoms, a structure of trident tree was applied to save large q ...Optimization Algorithm was developed for the simula ti on of ceramic grain growth at atomistic scale. Based on the coordination informa tion of different atoms, a structure of trident tree was applied to save large q uantities data, so as to solve the problems of large data information and long r unning time. For every atom a binary tree was firstly formed according to the X coordination of atom. If the values of X coordination were the same, the middle sub-tree of first layer formed then a binary tree according to the Y coordinati on of atom. If the values of Y coordination were also the same, the middle sub- tree of second layer formed then a binary tree according to the Z coordination o f atom. In this way the speed of whole program is enhanced obviously. In order t o reduce memory, in this structure only need to store the exterior atoms’ infor mation, an integer is used to store the interior atoms’ information. If other a toms take up an atom’s all adjacent positions, this atom will be deleted in the data structure, for all the adjacent positions’ atoms, the integer’s relative bit will be set 1 to denote that there is an atom in this position but not be s tored in the trident tree. When an outside atom is deleted, for all the bits tha t are set 1,an atom will be added to the trident tree as an outside atom for the relative positions. And for this new added atom, the integer’s relative bi t of all the adjacent position’s atoms should be set 0 to denote that there is no interior atom in this position. In this way, if there are n 3 atoms, onl y need to store 6n 2 quantity’s atoms’ information. Large quantity of mem ory space can then be saved.展开更多
玉米育种过程中,灌浆期籽粒含水率检测时,通常需要脱粒,采集穗中间200粒为检测样本。为了保护亲本,避免破坏性检测,该研究提出一种基于近红外光谱的灌浆期玉米籽粒水分定量分析通用模型,用于灌浆期玉米籽粒水分的田间原位检测。首先构建...玉米育种过程中,灌浆期籽粒含水率检测时,通常需要脱粒,采集穗中间200粒为检测样本。为了保护亲本,避免破坏性检测,该研究提出一种基于近红外光谱的灌浆期玉米籽粒水分定量分析通用模型,用于灌浆期玉米籽粒水分的田间原位检测。首先构建GA-IRIV-DS光谱数据处理策略。利用遗传算法(genetic algorithm,GA)和迭代保留信息变量(iterative retention of information variables,IRIV)二次波长筛选方法,提取光谱数据中有效的水分变量信息,减小特征空间维度的同时提高模型预测精度;再结合直接校正算法(direct standardization,DS),降低预测样本与建模样本的差异性,将玉米灌浆期穗尖部籽粒光谱数据校正为中间200籽粒的光谱,使水分定量分析模型能够具备中间200籽粒和穗尖部籽粒2种检测样本的通用性。在GA-IRIV-DS光谱数据处理策略的基础上,构建基于偏最小二乘法(partial lpeast squares regression,PLSR)的水分定量分析通用模型。经过验证,GA-IRIV-DS光谱数据处理策略校正后的光谱差异性降低了59.4%。为了进一步验证GA-IRIV-DS光谱数据处理策略的有效性,分析了GA+IRIVN组合波长筛选提取光谱特征,并分别与全光谱、多种典型波长筛选方法结合DS方法构建基于偏最小二乘法(PLSR)的水分定量分析模型结果相比较。试验结果表明,两种样本预测集GA-IRIVN-DS-PLSR模型效果均优于全光谱和其他模型,中间籽粒样本和穗尖部籽粒样本的预测决定系数(R^(2))达到了0.9715和0.9012,均方根误差(RMSEP)较全光谱下降了80.10%和64.60%。证明基于GA-IRIVN-DS光谱数据处理策略建立的近红外光谱水分定量分析模型具有一定泛化能力,可以为玉米育种过程中,减少检测过程中的样本破坏和提高检测效率提供可行的参考方法。展开更多
基于近红外偏最小二乘法(near-infrared partial least squares,NIR-PLS)开发了一种近红外光谱快速检测模型,用于快速、无损检测清蒸高粱糊化度。同时分析了白酒酿造生产关键指标相关性,以清蒸高粱糊化度为基础,结合清蒸高粱感官评价、...基于近红外偏最小二乘法(near-infrared partial least squares,NIR-PLS)开发了一种近红外光谱快速检测模型,用于快速、无损检测清蒸高粱糊化度。同时分析了白酒酿造生产关键指标相关性,以清蒸高粱糊化度为基础,结合清蒸高粱感官评价、出入窖糟醅理化、出酒率、基础酒理化及评分,利用自适应权重算法(adaptive weights,AW)建立了一种清蒸高粱蒸粮效果评价方法。该检测模型在预测清蒸高粱糊化度方面具有较高的相关性,模型预测值与建模参考值之间的决定系数R^(2)=0.9771。此外,蒸粮效果评价方法验证结果与经验丰富的酿酒师的感官评定相吻合。当75≤糊化度检测值≤80时,清蒸高粱糊化效果评价为优级;65≤糊化度检测值<75时,评价为一级;55<糊化度检测值<65或80<糊化度检测值<90时,评价为二级;糊化度检测值≥90或糊化度检测值≤55时,评价为不合格。该评价方法量化了白酒生产中清蒸高粱蒸粮效果的评定,将依赖经验的感官评估方式转化为标准化、数字化的手段,有助于加强生产过程和产品质量的可控性,减少粮食损耗。展开更多
文摘Optimization Algorithm was developed for the simula ti on of ceramic grain growth at atomistic scale. Based on the coordination informa tion of different atoms, a structure of trident tree was applied to save large q uantities data, so as to solve the problems of large data information and long r unning time. For every atom a binary tree was firstly formed according to the X coordination of atom. If the values of X coordination were the same, the middle sub-tree of first layer formed then a binary tree according to the Y coordinati on of atom. If the values of Y coordination were also the same, the middle sub- tree of second layer formed then a binary tree according to the Z coordination o f atom. In this way the speed of whole program is enhanced obviously. In order t o reduce memory, in this structure only need to store the exterior atoms’ infor mation, an integer is used to store the interior atoms’ information. If other a toms take up an atom’s all adjacent positions, this atom will be deleted in the data structure, for all the adjacent positions’ atoms, the integer’s relative bit will be set 1 to denote that there is an atom in this position but not be s tored in the trident tree. When an outside atom is deleted, for all the bits tha t are set 1,an atom will be added to the trident tree as an outside atom for the relative positions. And for this new added atom, the integer’s relative bi t of all the adjacent position’s atoms should be set 0 to denote that there is no interior atom in this position. In this way, if there are n 3 atoms, onl y need to store 6n 2 quantity’s atoms’ information. Large quantity of mem ory space can then be saved.
文摘玉米育种过程中,灌浆期籽粒含水率检测时,通常需要脱粒,采集穗中间200粒为检测样本。为了保护亲本,避免破坏性检测,该研究提出一种基于近红外光谱的灌浆期玉米籽粒水分定量分析通用模型,用于灌浆期玉米籽粒水分的田间原位检测。首先构建GA-IRIV-DS光谱数据处理策略。利用遗传算法(genetic algorithm,GA)和迭代保留信息变量(iterative retention of information variables,IRIV)二次波长筛选方法,提取光谱数据中有效的水分变量信息,减小特征空间维度的同时提高模型预测精度;再结合直接校正算法(direct standardization,DS),降低预测样本与建模样本的差异性,将玉米灌浆期穗尖部籽粒光谱数据校正为中间200籽粒的光谱,使水分定量分析模型能够具备中间200籽粒和穗尖部籽粒2种检测样本的通用性。在GA-IRIV-DS光谱数据处理策略的基础上,构建基于偏最小二乘法(partial lpeast squares regression,PLSR)的水分定量分析通用模型。经过验证,GA-IRIV-DS光谱数据处理策略校正后的光谱差异性降低了59.4%。为了进一步验证GA-IRIV-DS光谱数据处理策略的有效性,分析了GA+IRIVN组合波长筛选提取光谱特征,并分别与全光谱、多种典型波长筛选方法结合DS方法构建基于偏最小二乘法(PLSR)的水分定量分析模型结果相比较。试验结果表明,两种样本预测集GA-IRIVN-DS-PLSR模型效果均优于全光谱和其他模型,中间籽粒样本和穗尖部籽粒样本的预测决定系数(R^(2))达到了0.9715和0.9012,均方根误差(RMSEP)较全光谱下降了80.10%和64.60%。证明基于GA-IRIVN-DS光谱数据处理策略建立的近红外光谱水分定量分析模型具有一定泛化能力,可以为玉米育种过程中,减少检测过程中的样本破坏和提高检测效率提供可行的参考方法。
文摘基于近红外偏最小二乘法(near-infrared partial least squares,NIR-PLS)开发了一种近红外光谱快速检测模型,用于快速、无损检测清蒸高粱糊化度。同时分析了白酒酿造生产关键指标相关性,以清蒸高粱糊化度为基础,结合清蒸高粱感官评价、出入窖糟醅理化、出酒率、基础酒理化及评分,利用自适应权重算法(adaptive weights,AW)建立了一种清蒸高粱蒸粮效果评价方法。该检测模型在预测清蒸高粱糊化度方面具有较高的相关性,模型预测值与建模参考值之间的决定系数R^(2)=0.9771。此外,蒸粮效果评价方法验证结果与经验丰富的酿酒师的感官评定相吻合。当75≤糊化度检测值≤80时,清蒸高粱糊化效果评价为优级;65≤糊化度检测值<75时,评价为一级;55<糊化度检测值<65或80<糊化度检测值<90时,评价为二级;糊化度检测值≥90或糊化度检测值≤55时,评价为不合格。该评价方法量化了白酒生产中清蒸高粱蒸粮效果的评定,将依赖经验的感官评估方式转化为标准化、数字化的手段,有助于加强生产过程和产品质量的可控性,减少粮食损耗。
