원문정보
초록
영어
Based on World Health Organization (WHO) children and adults have a problem with their oral health, such as Dental cavities and periodontal disease. It is not easy to obtain the high convince level of result of the dental and periodontal diseases. Because each of them have different degrees of uncertainty and there have several discounting factors (error rates) in different of survey. To solve this problem we propose the Dezert-Smarandache Theory (DSmT) for efficient combination of uncertain, imprecise and highly conflicting sources of information. Moreover, we apply the SEFP as a context reasoning. Finally, we make the simulation by using 12 surveys and compare Propotional Conflict Redistribution 5 (PCR5) and Dempster-Shafer Theory (DST) to show the belief or probability for the low, a heavy, high and ultra-high risk situation.
목차
1. Introduction
2. Basics of Sensor Data Fusion Methods
2.1 Dempster-Shafter Theory (DST)
2.2 Combination Rules (Dempster’s and PCR5)
2.3 Classical and Generalized Pignistic Transformation: CPT and GPT
2.4 Sensor
3. Static Evidential Fusion Process (SEFP)
3.1 Evidential Operations with SENs
4. Dental Diagnosis Example
5. Conclusion
References