HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
Education and Research Center for Mathematical and Data Science >
Peer-reviewed Journal Articles, etc >

Rubber Material Property Prediction Using Electron Microscope Images of Internal Structures Taken under Multiple Conditions

Files in This Item:

The file(s) associated with this item can be obtained from the following URL:

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
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)

Export metadata:

OAI-PMH ( junii2 , jpcoar_1.0 )

MathJax is now OFF:


 - Hokkaido University