https://scholar.dgist.ac.kr/handle/20.500.11750/10133 2026-10-18T14:58:10Z https://scholar.dgist.ac.kr/handle/20.500.11750/60966 Title: Interpretable In-Cabin Occupancy Detection Using FHRS-Based Residual Features from Fixed-ROI UWB Radar Signals Author(s): Cho, Hui-Sup; Park, Young-Jin Abstract: Vehicle in-cabin occupancy detection supports restraint-system control, child presence detection, and intelligent cabin sensing, with child presence detection linked to safety assessment frameworks such as the European New Car Assessment Program. High-dimensional radar representations show strong potential, but their physical basis can be difficult to interpret. Engineered-feature approaches improve interpretability, yet few explicitly separate the linear-delay component from the residual structure of the complex reflection response. This study proposes an interpretable representation derived from the complex reflection ratio within a fixed time-domain region of interest (ROI). The method extracts direct-delay cues, frequency-domain Hilbert residual signature (FHRS) scalar features, and continuous wavelet transform (CWT)-based residual-delay and log-magnitude-derivative shape features. In the original experiment, the residual-centered condition achieved 97.72% accuracy and a 97.73% macro F1-score, whereas the direct-delay-only condition achieved 100.00%. A separately acquired 20-trial challenge experiment used Empty Seat, Person, and Human-like Object-loaded Seat conditions, with a human-shaped test object used for the latter. In this challenge, the direct-delay-only, residual-centered, and all-feature conditions achieved accuracies of 79.44%, 96.92%, and 97.39%, respectively. These results demonstrate that residual features provide nonredundant discriminative information when direct-delay cues alone are insufficient. Participant-independent, cross-vehicle, and cross-installation generalization remain to be verified. 2026-07-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60921 Title: 에르고스테롤 검출용 프로브 및 이의 제조방법 Author(s): 김정희; 김은주; 최은숙 https://scholar.dgist.ac.kr/handle/20.500.11750/60914 Title: 시간 은행 기반 시골 빈집 공유 플랫폼 시스템 및 그 방법 Author(s): 조효비; 윤진효 Abstract: 본 발명은 시간 은행 기반 시골 빈집 공유 플랫폼 시스템 및 그 방법에 관한 것이다. 본 발명에 따르면, 집주인 단말로부터 시골 빈집 공유 의사를 포함한 시골 빈집 정보를 등록받아 각 지역 별 빈집 리스트를 구축하는 빈집 데이터 구축부; 빈집 이용 희망자 측의 희망자 단말에게 상기 지역 별 빈집 리스트를 제공하고 빈집 리스트 내에서 희망하는 빈집을 신청받고 해당 빈집에 설정된 기본 수리비를 안내하는 빈집 거주 신청부; 상기 희망자의 기본 수리비의 입금이 확인되면 상기 기본 수리비에 대응하는 보상 시간을 상기 희망자의 시간은행 계좌로 적립하는 시간 은행 관리부; 및 적어도 하나의 시간 기부자 측의 기부자 단말에게 빈집 별 지급된 보상 시간 정보를 포함한 빈집 리스트를 제공하고, 시간 기부를 희망하는 빈집과 기부 가능 시간 정보를 선택받아 희망자와 시간 기부자 간을 매칭하는 제어부를 포함하는 시간 은행 기반 시골 빈집 공유 플랫폼 시스템을 제공한다. 이와 같은 본 발명에 따르면, 시골 살이에 충분한 경험이 없는 도시인들이 시골 주택을 구매하지 않고 최소한의 비용으로 시골살이에 필요한 노하우와 노동력을 제공받을 수 있도록 한다. https://scholar.dgist.ac.kr/handle/20.500.11750/60856 Title: End-to-End 자율주행 AI의 안전성 확보를 위한 기술 동향과 국제 안전 표준 관점의 분석 Author(s): Son, Joonwoo; Park, Myoungouk; Jeong, Seok Chan; Kim, Sung-Hee Abstract: End-to-end (E2E) learning-based autonomous driving has emerged as a promising paradigm that directly maps sensor inputs to vehicle control commands using data-driven models. Compared to conventional modular architectures, E2E approaches offer advantages in terms of architectural simplification and holistic learning of complex driving contexts from large-scale data. However, since E2E systems rely on probabilistic decision-making, exhibit limited explainability, and remain vulnerable to distribution shifts, edge cases, and long-tail scenarios, those benefits actually introduce fundamental challenges in safety assurance. This paper reviews recent technological developments in E2E autonomous driving and systematically analyzes key AI safety issues from a system-level perspective. Core challenges including rare operational scenarios, uncertainty under out-of-distribution conditions, and limitations in traceability and accountability are examined in relation to existing automotive safety frameworks. In particular, the paper investigates how ISO 21448 (Safety of the Intended Functionality, SOTIF) and UL 4600 can be reinterpreted and applied to learning-based autonomous driving systems to complement traditional failure-based functional safety standards. To address the structural mismatch between E2E architectures and existing safety standards, this paper discusses rule-based safety shields and scenario-based validation as practical and standard-compatible mechanisms for mitigating non-failure-based risks and constructing evidence-driven safety arguments. The analysis demonstrates that instead of E2E autonomous driving invalidating existing safety frameworks, it actually necessitates their complementary and systematic integration to achieve robust safety assurance in learning-based autonomous driving systems. 2026-03-31T15:00:00Z