Institution profile

Korea Aerospace University

Academic institutionasia · kr
Official website
Research library17linked papers
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Selected work

Representative Papers

Can Vision Models Read the Radar Display? On the Feasibility of Radar Imagery for Air Traffic Complexity Estimation

Aug 12, 2026

This study investigates whether radar imagery can serve as an effective input modality for deep vision models to estimate air traffic complexity. The traffic situation is encoded into a five-channel image incorporating positional, heading, speed, and altitude information. For the first time, a Vision Transformer is employed to perform regression on four intrinsic complexity components derived from geometric relationships among aircraft. Experimental results demonstrate that the proposed approach achieves R² values exceeding 0.96 across all complexity components. Perturbation analysis further reveals the model’s sensitivity to key aircraft, with its responses closely aligning with those aircraft’s actual contributions to overall complexity, thereby validating the efficacy of sparse, self-similar radar images for modeling air traffic complexity.

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TESLA: Taylor Expansion of Sinusoidal Learnable Activations

Aug 12, 2026

This work addresses the challenge of learning parity functions—a canonical problem hindered by linear non-separability and global dependencies—by introducing TESLA, a novel activation function that explicitly controls polynomial order within the activation layer. Inspired by Fourier series, TESLA employs a learnable combination of sine and cosine components to selectively amplify high-frequency signals, thereby guiding the network to capture global structural patterns. Theoretical guarantees are provided through Lipschitz continuity and Rademacher complexity analyses. Empirically, TESLA achieves strong generalization on 32-dimensional parity tasks using only 100,000 samples—merely 0.002% of the input space—and demonstrates robustness to 30% label noise. It also significantly outperforms baseline methods on Forrelation and ImageNet-100 benchmarks.

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Recent publications

Latest Papers

Can Vision Models Read the Radar Display? On the Feasibility of Radar Imagery for Air Traffic Complexity Estimation

Aug 12, 2026

This study investigates whether radar imagery can serve as an effective input modality for deep vision models to estimate air traffic complexity. The traffic situation is encoded into a five-channel image incorporating positional, heading, speed, and altitude information. For the first time, a Vision Transformer is employed to perform regression on four intrinsic complexity components derived from geometric relationships among aircraft. Experimental results demonstrate that the proposed approach achieves R² values exceeding 0.96 across all complexity components. Perturbation analysis further reveals the model’s sensitivity to key aircraft, with its responses closely aligning with those aircraft’s actual contributions to overall complexity, thereby validating the efficacy of sparse, self-similar radar images for modeling air traffic complexity.

0 citationsRead paper

TESLA: Taylor Expansion of Sinusoidal Learnable Activations

Aug 12, 2026

This work addresses the challenge of learning parity functions—a canonical problem hindered by linear non-separability and global dependencies—by introducing TESLA, a novel activation function that explicitly controls polynomial order within the activation layer. Inspired by Fourier series, TESLA employs a learnable combination of sine and cosine components to selectively amplify high-frequency signals, thereby guiding the network to capture global structural patterns. Theoretical guarantees are provided through Lipschitz continuity and Rademacher complexity analyses. Empirically, TESLA achieves strong generalization on 32-dimensional parity tasks using only 100,000 samples—merely 0.002% of the input space—and demonstrates robustness to 30% label noise. It also significantly outperforms baseline methods on Forrelation and ImageNet-100 benchmarks.

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