Employing box plots to build high-dimensional manufacturing models for new products in TFT-LCD plants

Li, Der-Chiang; Huang, Wenting; Chen, Chien-Chih; Chang, Che-Jung

doi:10.1016/j.neucom.2014.03.043

Cited by 15 publications

(5 citation statements)

References 28 publications

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“…The R 2 scores of ten-fold cross-validation is visualized using box and whisker plot [32], which is convenient for quickly summarizing the results. A box and whisker plot for a symmetrically distributed data has the mean close to the median line [33].…”

Section: Resultsmentioning

confidence: 92%

Automatic Music Mood Detection Using Transfer Learning and Multilayer Perceptron

Bhattarai

Lee

2019

IJFIS

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This paper proposes an automatic mood detection of music with a composition of transfer learning and multilayer. The five layered convolutional neural network pre-trained on Million Song dataset is used to extract the features from EmoMusic dataset. We obtain a set of features from the different five layers, which is fed into multilayer perceptron (MLP)-based regression. Through the regression network we estimate the mood of music on Thayer's two-dimensional emotion space, which consists of the axes corresponding to arousal and valence. Because the EmoMusic dataset does not provide enough number of data for training, we augment the data by time stretching to make it tripled. We perform the experiment with the augmented data as well as the original EmoMusic dataset. Box and whisker plot along with the mean of 10-fold cross-validation has been used for evaluating the proposed mood detection. In terms of the percentage of R 2 score for measure of accuracy, the proposed MLP shows state-of-the-art estimates for the augmented EmoMusic dataset.

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Section: Resultsmentioning

confidence: 92%

Automatic Music Mood Detection Using Transfer Learning and Multilayer Perceptron

Bhattarai

Lee

2019

IJFIS

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“…Therefore, the missing and abnormal values of the original data need to be processed before the model is established. The classical methods of identifying outliers are principle and z-score method, the premise of using these two methods is that the data obey the normal distribution, but the actual sintering process data does not necessarily conform to this assumption, and the box plot method is an effective outlier detection method [17], which does not need to assume that the data obey a specific form of distribution in advance. Therefore, this paper uses the box plot to identify the outliers of the original data.…”

Section: Methodsmentioning

confidence: 99%

Prediction interval estimation of sinter drum index based on light gradient boosting machine and kernel density estimation

Xia

Liu

et al. 2023

Ironmaking & Steelmaking

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Owing to the uncertainty operation in the sintering process, it is easy to produce uncertain prediction errors in the single drum index prediction model, which makes the prediction results lack certain reliability. Accurate and reliable prediction of the drum index can help improve the drum index. In this paper, a prediction interval estimation method of drum index based on a light gradient boosting machine (LightGBM) and kernel density estimation (KDE) is proposed. LightGBM can obtain accurate points prediction of drum index, and then use the KDE method to obtain the estimated prediction interval of drum index. The comparison results of different methods show that LightGBM has high prediction performance, and KDE can well quantify the prediction error of drum index, which verifies the effectiveness of the prediction interval estimation method combined with LightGBM and KDE, and provides more reliable decision-making information for the optimisation of sintering process parameters.

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“…Some useful information is retained, and the adjacent data can be used to calculate the average value instead of this value. 6 Due to the different dimensions of each characteristic parameter, if the box plot is drawn directly, it will affect the data analysis results, so the data needs to be standardised to make it stable in a specific interval. This operation does not affect the relative position of the data in the box plot.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Some useful information is retained, and the adjacent data can be used to calculate the average value instead of this value. 6…”

Section: Data Analysis and Processingmentioning

confidence: 99%

Interval prediction of sinter screening index based on gate recurrent unit and kernel density estimation algorithm

Kang,

Wu,

Chen

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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Since there are many uncertain factors in the actual production process, reliable information cannot be obtained from point prediction results. Therefore, this paper proposes an interval prediction method to evaluate the screening index. Firstly, feature engineering algorithms are used to select the most suitable data and variables for modeling. Secondly, based on the sequential characteristics of the sintering process, the gated cycle unit (GRU) model is used to make point predictions on the indicators. then the kernel density estimation (KDE) algorithm is used to quantify the error of the index to obtain the interval prediction results, and the RandomizedSearchCV algorithm is used to optimize the parameters of the model. Finally, comparison shows that the GRU model has higher point prediction accuracy, and the KDE algorithm can better quantify the prediction error, and then obtain reliable interval prediction results. This method has guiding significance for the high-quality production of sinter.

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Employing box plots to build high-dimensional manufacturing models for new products in TFT-LCD plants

Cited by 15 publications

References 28 publications

Automatic Music Mood Detection Using Transfer Learning and Multilayer Perceptron

Automatic Music Mood Detection Using Transfer Learning and Multilayer Perceptron

Prediction interval estimation of sinter drum index based on light gradient boosting machine and kernel density estimation

Interval prediction of sinter screening index based on gate recurrent unit and kernel density estimation algorithm

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