인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Accurate prediction of battery-swapping demand is crucial for minimizing operational costs and maintaining service quality in electric motorcycle Battery-as-a-Service (BaaS) deployment. However, early-stage BaaS providers often face severe data scarcity and bias, especially in newly expanded regions. In these areas, newly deployed battery swapping stations (BSS) encounter cold-start problems due to limited historical labels, and existing regional models degrade under domain shift caused by distributional changes in contextual covariates. Although conventional data augmentation techniques like SMOTE are often employed to mitigate these challenges, they perform poorly in multi-class settings by exacerbating inter-class overlap near complex decision boundaries. To address this limitation, we introduce TriMa (Tri-strategy Margin-filtered Pseudo-labeling), an advanced pseudo-labeling framework designed to enhance predictive performance under sparse and imbalanced multi-class data distributions. TriMa constructs augmented samples exclusively from high-confidence pseudo-labels, which are derived through three complementary labeling strategies combined with margin-based filtering. Experiments on UCI datasets under simulated sparsity and bias, as well as on real-world operational data from a BaaS provider in Seoul, demonstrate the efficacy of our proposed method. Results show that TriMa consistently improves the F1 score and reduces performance variability relative to existing techniques, while remaining robust to label noise.
본문·목차
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UCI(KEPA) : I410-151-26-02-096548626
