This paper presents a computational pipeline for window-level semantic enrichment of texture-mapped building models used in urban digital twins (UDTs). The pipeline combines SAM-based candidate generation, super-resolution-based input matching (SRIM), a fine-tuned ResNet-50 window/non-window classifier, and texture-to-...
Ah-Yun Lee, Sungpil Woo, Siyeon Park et al.· Applied Sciences· 0 citations
Efficient adaptive inference reduces computational demands, but selecting among policies on the same calibration data without controlling errors simultaneously can compromise safety conclusions. Practical deployment also requires explicit reporting of infeasibility, conditional efficiency analysis, and certification ac...
Sooyoung Jang, Siyeon Park, Sungpil Woo et al.· IEEE Access· 0 citations
GPU LDPC latency comparisons across implementations are challenging due to variations in decoder schedules, batch granularity, and timing boundaries. Batch completion was characterized for FP32-layered and flooding decoders on a GPU, considering block acquisition, matched outcomes, and three timing boundaries. The sche...
The results support DACP as a decoder-aligned objective for cumulative anytime decoding when class evidence is temporally distributed, while identifying fast-saturating event-image tasks as boundary cases rather than domains of universal DACP dominance.
