Short-term traffic-flow forecasting predicts road-network states, yet spatio-temporal predictors can leave structured, context-dependent residuals. They are seldom reused at inference, while similarity-based transfer can introduce corrections that increase error. We propose Memory-Based Utility-Aligned Residual Evidenc...
Strong noise can obscure the transient and modulation signatures used for bearing fault diagnosis, while envelope-based methods remain sensitive to the frequency band selected before demodulation. This study proposes a learnable band-selective envelope fusion WDCNN (LBEF-WDCNN). A raw-waveform branch extracts temporal...
Shi-Qian Wu, Wei-Min Zhang, Yu-Rui Sang et al.· Machines· 0 citations
Accurate recognition and instance segmentation of chili flowers are fundamental to the development of intelligent breeding robots and the improvement of operational efficiency in protected agriculture. However, in unstructured field environments, chili flowers are typically small-scale targets and are frequently affect...
Min-Qiu Kuang, Xiao-Jian Li, Zhi-Zhen Hu et al.· Frontiers in Plant Science· 0 citations
Deep neural networks predict neural responses across the visual hierarchy, yet published alignment scores do not reveal whether this correspondence reflects learned representations, architectural inductive biases, low-level image statistics, or categorical structure. We decompose DNN-brain alignment into four sources...
Low-light image enhancement (LLIE) requires a practical balance among visibility recovery, reconstruction fidelity, chromatic stability, exposure safety, and computational efficiency. Aggressive enhancement can reveal dark content but may introduce highlight saturation and color distortion, whereas conservative enhance...
Tao Jiang, Su-Hang Yang, Yu-Chen Lu et al.· Journal of King Saud Univers...· 0 citations
DeCyReM converts reusable historical error experience into selective forecast corrections, providing an error-centric approach to forecasting abrupt and irregular traffic states.
Qian-Xin Xie, Jin-Feng Xu, Yu-Chen Lu et al.· Mathematics· 0 citations
RIFT-STGNN, a Robust Interleaved Frequency–Trend Spatio-Temporal Graph Neural Network for multi-step traffic flow forecasting, follows a coordinated information flow and shows competitive numerical performance relative to selected literature-reported baselines under the 12-step setting.
Qian-Xin Xie, Jin-Feng Xu, Yuchen Lu et al.· Mathematics· 1 citation
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