• Restorative Dentistry and Endodontics
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• v.32 no.1
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• pp.19-27
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• 2007

The purpose of this study is to develope new dental color-space system. Twelve kinds of dental composites and one kind of dental porcelain were used in this study. Disk samples (15 mm in diameter, 4 mm in thickness) of used materials were made and sample's CIE $L^*a^*b^*$ value was measured by Spectrocolorimeter (MiniScan XE plus, Model 4000S, diffuse/$8^{\circ}$ viewing mode, 14.3 mm Port diameters, Hunter Lab USA) The range of measured color distribution was analyzed. All the data were applied in the form of T### which is expression unit in CNU Cons Dental Color Chart. The value of $L^*$ lies between 80.40 and 52.70. The value of $a^*$ are between 10.60 and 3.60 and $b^*$ are between 28.40 and 2.21. The average value of $L^*$ is 67.40, and median value is 67.30. The value of $a^*$ are 2.89 and 2.91 respectively. And for the $b^*$, 14.30 and 13.90 were obtained. The data were converted to T### that is the unit count system in CNU-Cons Dental Color Chart. The value of $L^*$ is converted in the first digit of the numbering system. Each unit is 2.0 measured values. The second digit is the value of $a^*$ and is converted new number by 1.0 measured value. For the third digit $b^*$ is replaced and it is 2.0 measured unit apart. T555 was set to the value of $L^*$ ranging from 66.0 to 68.0, value of $a^*$ ranging from 3 to 4 and $b^*$ value ranging from 14 to 16.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.