Boxplot fences in proficiency testing

Morales, Clara; Giraldo, Ramón; Torres, Marta E

doi:10.1007/s00769-021-01474-8

Cited by 4 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, the poor level of production conformity makes outlier points increase, which means that more cells will be judged as abnormal. Therefore, the threshold of the local outlier factor is determined by the box-plot method [43]. As shown in Figure 5, if the local outlier factor of a data point is beyond the upper boundary, this data point is judged as an outlier point.…”

Section: Performance Analysis Of K-value Dynamic Detection Methodsmentioning

confidence: 99%

A K-Value Dynamic Detection Method Based on Machine Learning for Lithium-Ion Battery Manufacturing

Zhang¹,

Kong

Yuan

et al. 2023

Batteries

View full text Add to dashboard Cite

During the manufacturing process of the lithium-ion battery, metal foreign matter is likely to be mixed into the battery, which seriously influences the safety performance of the battery. In order to reduce the outflow of such foreign matter defect cells, the production line universally adopted the K-value test process. In the traditional K-value test, the detection threshold is determined empirically, which has poor dynamic characteristics and probably leads to missing or false detection. Based on comparing the screening effect of different machine learning algorithms for the production data of lithium-ion cells, this paper proposes a K-value dynamic screening algorithm for the cell production line based on the local outlier factor algorithm. The analysis results indicate that the proposed method can adaptively adjust the detection threshold. Furthermore, we validated its effectiveness through the metal foreign matter implantation experiment conducted in the pilot manufacturing line. Experiment results show that the proposed method’s detection rate is improved significantly. The increase in the detection rate of foreign matter defects is beneficial to improving battery quality and safety.

show abstract

Section: Performance Analysis Of K-value Dynamic Detection Methodsmentioning

confidence: 99%

A K-Value Dynamic Detection Method Based on Machine Learning for Lithium-Ion Battery Manufacturing

Zhang¹,

Kong

Yuan

et al. 2023

Batteries

View full text Add to dashboard Cite

show abstract

“…In addition, the boxplot can also be used as supporting evidence to identify the existence of heterogeneity within the drying parameters. This is because the boxplot is useful for examining symmetry and variability as well as for identifying potential outliers [30]. Therefore, the variability of the five single seaweed drying parameters can be shown by the boxplot in Figure 2.…”

Section: Identification Of Heterogeneity Parametersmentioning

confidence: 99%

Identifying heterogeneity for increasing the prediction accuracy of machine learning models

Ravi Kumar,

Majahar Ali,

Ibidoja

2024

J. Nig. Soc. Phys. Sci.

View full text Add to dashboard Cite

In recent years, the significance of machine learning in agriculture has surged, particularly in post-harvest monitoring for sustainable aquaculture. Challenges like heterogeneity, irrelevant variables and multicollinearity hinder the implementation of smart monitoring systems. However, this study focuses on investigating heterogeneity among drying parameters that determine the moisture content removal during seaweed drying due to its limited attention, particularly within the field of agriculture. Additionally, a heterogeneity model within machine learning algorithms is proposed to enhance accuracy in predicting seaweed moisture content removal, both before and after the removal of heterogeneity parameters and also after the inclusion of single-eliminated heterogeneity parameters. The dataset consists of 1914 observations with 29 independent variables, but this study narrows down to five: Temperature (T1, T4, T7), Humidity (H5), and Solar Radiation (PY). These variables are interacted up to second-order interactions, resulting in 55 variables. Variance inflation factor and boxplots are employed to identify heterogeneity parameters. Two predictive machine learning models, namely random forest and elastic net are then utilized to identify the 15 and 20 highest important parameters for seaweed moisture content removal. Evaluation metrics (MSE, SSE, MAPE, and R-squared) are used to assess model performance. Results demonstrate that the random forest model outperforms the elastic net model in terms of higher accuracy and lower error, both before and after removing heterogeneity parameters, and even after reintroducing single-eliminated heterogeneity parameters. Notably, the random forest model exhibits higher accuracy before excluding heterogeneity parameters.

show abstract

Heat stress tolerance in wheat seedling: Clustering genotypes and identifying key traits using multivariate analysis

Hasan,

Mia,

Ahmed

et al. 2024

Heliyon

View full text Add to dashboard Cite

Boxplot fences in proficiency testing

Cited by 4 publications

References 15 publications

A K-Value Dynamic Detection Method Based on Machine Learning for Lithium-Ion Battery Manufacturing

A K-Value Dynamic Detection Method Based on Machine Learning for Lithium-Ion Battery Manufacturing

Identifying heterogeneity for increasing the prediction accuracy of machine learning models

Heat stress tolerance in wheat seedling: Clustering genotypes and identifying key traits using multivariate analysis

Contact Info

Product

Resources

About