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Rubber Material Property Prediction Using Electron Microscope Images of Internal Structures Taken under Multiple Conditions
Title: | Rubber Material Property Prediction Using Electron Microscope Images of Internal Structures Taken under Multiple Conditions |
Authors: | Togo, Ren Browse this author | Saito, Naoki Browse this author | Maeda, Keisuke Browse this author | Ogawa, Takahiro Browse this author →KAKEN DB | Haseyama, Miki Browse this author →KAKEN DB |
Keywords: | rubber materials | property prediction | electron microscope images | Dempster–Shafer evidence theory | Shafer evidence theory |
Issue Date: | 16-Mar-2021 |
Publisher: | MDPI |
Journal Title: | Sensors |
Volume: | 21 |
Issue: | 6 |
Start Page: | 2088 |
Publisher DOI: | 10.3390/s21062088 |
Abstract: | A method for prediction of properties of rubber materials utilizing electron microscope images of internal structures taken under multiple conditions is presented in this paper. Electron microscope images of rubber materials are taken under several conditions, and effective conditions for the prediction of properties are different for each rubber material. Novel approaches for the selection and integration of reliable prediction results are used in the proposed method. The proposed method enables selection of reliable results based on prediction intervals that can be derived by the predictors that are each constructed from electron microscope images taken under each condition. By monitoring the relationship between prediction results and prediction intervals derived from the corresponding predictors, it can be determined whether the target prediction results are reliable. Furthermore, the proposed method integrates the selected reliable results based on Dempster-Shafer (DS) evidence theory, and this integration result is regarded as a final prediction result. The DS evidence theory enables integration of multiple prediction results, even if the results are obtained from different imaging conditions. This means that integration can even be realized if electron microscope images of each material are taken under different conditions and even if these conditions are different for target materials. This nonconventional approach is suitable for our application, i.e., property prediction. Experiments on rubber material data showed that the evaluation index mean absolute percent error (MAPE) was under 10% by the proposed method. The performance of the proposed method outperformed conventional comparative property estimation methods. Consequently, the proposed method can realize accurate prediction of the properties with consideration of the characteristic of electron microscope images described above. |
Type: | article |
URI: | http://hdl.handle.net/2115/81972 |
Appears in Collections: | 数理・データサイエンス教育研究センター (Education and Research Center for Mathematical and Data Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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