为实现对制丝过程烘丝段出料水分的精准控制,科学、客观地识别并筛选影响水分的关键工艺参数。选取以某牌号卷烟的制丝过程全批次稳态数据为研究对象,在传统逐步回归方法的基础上,引入三种通过添加惩罚项来压缩变量系数的Lasso族方法,即...为实现对制丝过程烘丝段出料水分的精准控制,科学、客观地识别并筛选影响水分的关键工艺参数。选取以某牌号卷烟的制丝过程全批次稳态数据为研究对象,在传统逐步回归方法的基础上,引入三种通过添加惩罚项来压缩变量系数的Lasso族方法,即:Lasso方法、适应性Lasso方法和SCAD方法,分别构建以烘丝段出料水分为因变量的变量选择模型,并采用AIC、BIC和MSE三种评价指标对模型进行比较,最后依据最优模型进行关键工艺参数的筛选及重要性排序。结果表明:① Lasso族方法在模型拟合优度、预测精度和复杂度控制方面均显著优于传统逐步回归方法,其中SCAD方法的综合性能表现最优;② 基于SCAD方法确定了烘丝段的4个关键工艺参数,按其重要性排序依次为:II区筒壁温度、膨胀单元蒸汽体积流量、切叶丝含水率和工艺气速度。To achieve precise control over the moisture content of the discharge in the drying section of the tobacco primary processing, it is essential to scientifically and objectively identify and screen the key process parameters affecting moisture. This study selects steady state data from the entire batch of a specific brand of cigarette production process as the research object. Building on the traditional stepwise regression method, three Lasso family methods—Lasso, Adaptive Lasso, and SCAD are introduced, which compress variable coefficients by adding penalty terms. Variable selection models are constructed with the moisture content of the drying section discharge as the dependent variable. The models are compared using three evaluation metrics: AIC, BIC, and MSE. Finally, the optimal model is used to screen and rank the importance of key process parameters. The results show that: ① The Lasso-family methods significantly outperform the traditional stepwise regression method in terms of model goodness-of-fit, prediction accuracy, and complexity control, with the SCAD method demonstrating the best overall performance;② Based on the SCAD method, four key process parameters for the drying section are identified, ranked in order of importance as follows: Zone II wall temperature, expansion unit steam volumetric flow rate, cut tobacco moisture content, and process air velocity.展开更多
阵元失效下多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达虚拟阵列协方差矩阵出现大批整行整列元素缺失,破坏原有内在完整结构,导致波达方向(Direction of Arrival,DOA)估计性能下降。为此,提出一种联合核范数和SCAD(Smoothly...阵元失效下多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达虚拟阵列协方差矩阵出现大批整行整列元素缺失,破坏原有内在完整结构,导致波达方向(Direction of Arrival,DOA)估计性能下降。为此,提出一种联合核范数和SCAD(Smoothly Clipped Absolute Deviation)惩罚的完整协方差矩阵重构方法,以利于阵元失效下MIMO雷达DOA的有效估计。首先对待恢复的协方差矩阵建立核范数和SCAD惩罚双先验约束模型,并利用等正弦空间稀疏化方式划分粗网格空间,在可容忍的模型误差内能大大降低运算复杂度;然后利用ALM-ADMM(Augmented Lagrange Multipliers-Alternating Direction Method of Multipliers)算法对双先验约束模型进行求解,从而恢复协方差矩阵中大量整行整列的缺失数据;最后通过RD-ESPRIT(Reduced Dimensional ESPRIT)算法进行目标DOA估计。仿真结果验证该方法能快速恢复虚拟协方差矩阵中的缺失数据,从而有效提高阵元失效下MIMO雷达的DOA估计性能。展开更多
G-DINA(the generalizeddeterministic input,noisy and gate)模型限制条件少,应用范围广,满足大量心理与教育评估测验数据的要求。研究提出一种适用于G-DINA等模型的同时标定新题Q矩阵与项目参数的认知诊断计算机化自适应测验(CD-CAT)...G-DINA(the generalizeddeterministic input,noisy and gate)模型限制条件少,应用范围广,满足大量心理与教育评估测验数据的要求。研究提出一种适用于G-DINA等模型的同时标定新题Q矩阵与项目参数的认知诊断计算机化自适应测验(CD-CAT)在线标定新方法SCADOCM,以期促进CD-CAT在实践中的推广与应用。本研究分别基于模拟题库以及真实题库进行研究,结果表明:相比传统的SIE方法,SCADOCM在各实验条件下均具有较为理想的标定精度与标定效率,应用前景较好;SIE方法不适用于饱和的G-DINA等模型,其各实验条件下的Q矩阵标定精度均较低。展开更多
文摘为实现对制丝过程烘丝段出料水分的精准控制,科学、客观地识别并筛选影响水分的关键工艺参数。选取以某牌号卷烟的制丝过程全批次稳态数据为研究对象,在传统逐步回归方法的基础上,引入三种通过添加惩罚项来压缩变量系数的Lasso族方法,即:Lasso方法、适应性Lasso方法和SCAD方法,分别构建以烘丝段出料水分为因变量的变量选择模型,并采用AIC、BIC和MSE三种评价指标对模型进行比较,最后依据最优模型进行关键工艺参数的筛选及重要性排序。结果表明:① Lasso族方法在模型拟合优度、预测精度和复杂度控制方面均显著优于传统逐步回归方法,其中SCAD方法的综合性能表现最优;② 基于SCAD方法确定了烘丝段的4个关键工艺参数,按其重要性排序依次为:II区筒壁温度、膨胀单元蒸汽体积流量、切叶丝含水率和工艺气速度。To achieve precise control over the moisture content of the discharge in the drying section of the tobacco primary processing, it is essential to scientifically and objectively identify and screen the key process parameters affecting moisture. This study selects steady state data from the entire batch of a specific brand of cigarette production process as the research object. Building on the traditional stepwise regression method, three Lasso family methods—Lasso, Adaptive Lasso, and SCAD are introduced, which compress variable coefficients by adding penalty terms. Variable selection models are constructed with the moisture content of the drying section discharge as the dependent variable. The models are compared using three evaluation metrics: AIC, BIC, and MSE. Finally, the optimal model is used to screen and rank the importance of key process parameters. The results show that: ① The Lasso-family methods significantly outperform the traditional stepwise regression method in terms of model goodness-of-fit, prediction accuracy, and complexity control, with the SCAD method demonstrating the best overall performance;② Based on the SCAD method, four key process parameters for the drying section are identified, ranked in order of importance as follows: Zone II wall temperature, expansion unit steam volumetric flow rate, cut tobacco moisture content, and process air velocity.
文摘阵元失效下多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达虚拟阵列协方差矩阵出现大批整行整列元素缺失,破坏原有内在完整结构,导致波达方向(Direction of Arrival,DOA)估计性能下降。为此,提出一种联合核范数和SCAD(Smoothly Clipped Absolute Deviation)惩罚的完整协方差矩阵重构方法,以利于阵元失效下MIMO雷达DOA的有效估计。首先对待恢复的协方差矩阵建立核范数和SCAD惩罚双先验约束模型,并利用等正弦空间稀疏化方式划分粗网格空间,在可容忍的模型误差内能大大降低运算复杂度;然后利用ALM-ADMM(Augmented Lagrange Multipliers-Alternating Direction Method of Multipliers)算法对双先验约束模型进行求解,从而恢复协方差矩阵中大量整行整列的缺失数据;最后通过RD-ESPRIT(Reduced Dimensional ESPRIT)算法进行目标DOA估计。仿真结果验证该方法能快速恢复虚拟协方差矩阵中的缺失数据,从而有效提高阵元失效下MIMO雷达的DOA估计性能。
文摘G-DINA(the generalizeddeterministic input,noisy and gate)模型限制条件少,应用范围广,满足大量心理与教育评估测验数据的要求。研究提出一种适用于G-DINA等模型的同时标定新题Q矩阵与项目参数的认知诊断计算机化自适应测验(CD-CAT)在线标定新方法SCADOCM,以期促进CD-CAT在实践中的推广与应用。本研究分别基于模拟题库以及真实题库进行研究,结果表明:相比传统的SIE方法,SCADOCM在各实验条件下均具有较为理想的标定精度与标定效率,应用前景较好;SIE方法不适用于饱和的G-DINA等模型,其各实验条件下的Q矩阵标定精度均较低。
