건축공간 환경관리 지원을 위한 AI･IoT 기반 이상패턴 검출에 관한 연구

• 영문명: A Study on Detection of Abnormal Patterns Based on AI · IoT to Support Environmental Management of Architectural Spaces
• 발행기관: 한국BIM학회
• 저자명: 강태욱(Kang, Tae-Wook)
• 간행물 정보: 『KIBIM Magazine』13권 3호, 12~20쪽, 전체 9쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2023.09.30

영문 초록

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

목차

1. 서론

2. IoT 데이터수집 프로세스 및 아키텍처 디자인

3. 환경데이터 예측 및 이상패턴 검출 모델설계

4. 프로토타입 구현 및 성능 분석

5. 결론

감사의 글

References

키워드

IoT 인공지능 딥러닝 이상패턴 환경관리 건축공간 IoT AI Deep Learning Anomaly Detection Environment Management Architectural Space