현재 위치
Home
Context Inference Including Cause Reasoning and Prediction
-
- 발행기관
- 보안공학연구지원센터(IJCA) 바로가기
-
- 간행물
- International Journal of Control and Automation SCOPUS 바로가기
-
- 통권
- Vol.6 No.5 (2013.10)바로가기
-
- 페이지
- pp.31-44
-
- 저자
-
- 언어
- 영어(ENG)
-
- URL
- https://www.earticle.net/Article/A205346
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- It is not enough to recognize the situations which currently occur simply. The current situations have the causes that they get to occur. The causes can just be generated and they can make the situations like the current states while the ones which occurred in the past have continued. Furthermore, if the causes which made the current situations don't disappear, they can continue to stay the same, get worse, or be changed to another situation. Therefore, limiting the range of context awareness to the situations which currently occur can be insufficient as the system which recognizes situations of the everyday world. Therefore, this study aims at problem-solving of two things. First, it recognizes situations without advance information. Second, it infers causes of situations and predicts how the situations will turn out in the future. To solve these problems, this study uses multiple sensor data fusion together using Dempster-Shafer Evidence Theory (DST) and Kalman Filter (KF). It recognizes situations under the conditions without any advance information through DST, infers causes of the current situations, and predicts how the current situations will turn out in the future. At this moment, BPA is important to recognize situations through DST and infer causes and state transition equation plays an important role in predicting arrangement through KF. The study carries out context inference and cause inference using DST. It describes the plan which infers causes of situations without advance information. It calculates required state transition equation to predict the progress of the research and infers how the causes revealed through DST by using it will arrange the current situations in the future by using KF.
목차
Abstract
1. Introduction
2. Related Works
2.1 Data Fusion and Context Inference
2.2 Context Progression Prediction
3. Cause Inference and Progression Prediction
4. Evaluation
5. Conclusion and Further Study
Acknowledgements
References
키워드
Context inference protection wall Kalman filter저자
간행물 정보
발행기관
-
- 발행기관명
- 보안공학연구지원센터(IJCA) [Science & Engineering Research Support Center, Republic of Korea(IJCA)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
-
- 간행물명
- International Journal of Control and Automation
- 간기
- 월간
- pISSN
- 2005-4297
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Control and Automation Vol.6 No.5
피인용수 : 0건 (자료제공 : 네이버학술정보)
함께 이용한 논문 이 논문을 다운로드한 분들이 이용한 다른 논문입니다.
출처 : 네이버학술정보
0개의 논문이 장바구니에 담겼습니다.