scholarly journals A novel quality prediction method based on feature selection considering high dimensional product quality data

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Junying Hu ◽  
Xiaofei Qian ◽  
Jun Pei ◽  
Changchun Tan ◽  
Panos M. Pardalos ◽  
...  
Keyword(s):  
Feature Selection ◽  
Product Quality ◽  
Prediction Method ◽  
Quality Data ◽  
High Dimensional ◽  
Quality Prediction

scholarly journals High-Dimensional, Small-Sample Product Quality Prediction Method Based on MIC-Stacking Ensemble Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 23
Author(s):  
Jiahao Yu ◽  
Rongshun Pan ◽  
Yongman Zhao
Keyword(s):  
Product Quality ◽  
Ensemble Learning ◽  
Measurement Model ◽  
Learning Model ◽  
Ordinary Least Squares ◽  
Small Sample ◽  
Small Samples ◽  
High Dimensional ◽  
Quality Prediction ◽  
Maximal Information Coefficient

Accurate quality prediction can find and eliminate quality hazards. It is difficult to construct an accurate quality mathematical model for the production of small samples with high dimensionality due to the influence of quality characteristics and the complex mechanism of action. In addition, overfitting scenarios are prone to occur in high-dimensional, small-sample industrial product quality prediction. This paper proposes an ensemble learning and measurement model based on stacking and selects eight algorithms as the base learning model. The maximal information coefficient (MIC) is used to obtain the correlation between the base learning models. Models with low correlation and strong predictive power were chosen to build stacking ensemble models, which effectively avoids overfitting and obtains better predictive performance. To improve the prediction performance as the optimization goal, in the data preprocessing stage, boxplots, ordinary least squares (OLS), and multivariate imputation by chained equations (MICE) are used to detect and replace outliers. The CatBoost algorithm is used to construct combined features. Strong combination features were selected to construct a new feature set. Concrete slump data from the University of California Irvine (UCI) machine learning library were used to conduct comprehensive verification experiments. The experimental results show that, compared with the optimal single model, the minimum correlation stacking ensemble learning model has higher precision and stronger robustness, and a new method is provided to guarantee the accuracy of final product quality prediction.

scholarly journals FM-GRU: A Time Series Prediction Method for Water Quality Based on seq2seq Framework

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1031
Author(s):  
Jianlong Xu ◽  
Kun Wang ◽  
Che Lin ◽  
Lianghong Xiao ◽  
Xingshan Huang ◽  
...  
Keyword(s):  
Water Quality ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Quality Data ◽  
Enterprise Management ◽  
Feature Interaction ◽  
Quality Prediction ◽  
Water Quality Data ◽  
Water Quality Prediction

Water quality prediction plays a crucial role in both enterprise management and government environmental management. However, due to the variety in water quality data, inconsistent frequency of data acquisition, inconsistency in data organization, and volatility and sparsity of data, predicting water quality accurately and efficiently has become a key problem. This paper presents a recurrent neural network water quality prediction method based on a sequence-to-sequence (seq2seq) framework. The gate recurrent unit (GRU) model is used as an encoder and decoder, and a factorization machine (FM) is integrated into the model to solve the problem of high sparsity and high dimensional feature interaction in the data, which was not addressed by the water quality prediction models in prior research. Moreover, due to the long period and timespan of water quality data, we add a dual attention mechanism to the seq2seq framework to address memory failures in deep learning. We conducted a series of experiments, and the results show that our proposed method is more accurate than several typical water quality prediction methods.

A LASSO-based batch process modeling and end-product quality prediction method

2014 ◽  
Vol 47 (3) ◽  
pp. 6704-6709 ◽  
Author(s):  
Zhengbing Yan ◽  
Chih-Chiun Chiu ◽  
Weiwei Dong ◽  
Yuan Yao
Keyword(s):  
Product Quality ◽  
Process Modeling ◽  
Prediction Method ◽  
Batch Process ◽  
Quality Prediction

Feature selection and domain adaptation for cross-machine product quality prediction

2021 ◽  
Author(s):  
Yu Wang ◽  
Wei Cui ◽  
Nhu Khue Vuong ◽  
Zhenghua Chen ◽  
Yu Zhou ◽  
...  
Keyword(s):  
Feature Selection ◽  
Product Quality ◽  
Domain Adaptation ◽  
Quality Prediction

scholarly journals Production rescheduling through product quality prediction

2021 ◽  
Vol 54 ◽  
pp. 142-147
Author(s):  
Maik Frye ◽  
Dávid Gyulai ◽  
Júlia Bergmann ◽  
Robert H. Schmitt
Keyword(s):  
Product Quality ◽  
Quality Prediction
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