Aug 2026· Frontiers of Mechanical Engineering· Vol 12· 0 citations· 34 references
TL;DR
The results indicate that the proposed GAESA-iFormer model achieves improved prediction accuracy on the current compressor dataset under limited experimental data.
Abstract
Accurate determination of aircraft engine compressor characteristics is vital for stability and performance optimization. However, experimental data acquisition is often hindered by high costs, resulting in a strong reliance on limited test data.
To address this challenge, this paper proposes the Graph Attention Enhanced Scale-Aware Inverted Transformer (GAESA-iFormer), a novel surrogate modeling framework integrating a Graph Attention Network (GAT) encoder, a Multi-scale Feature Enhancement (MSFE) module, and a cross-attention decoder based on the inverted Transformer architecture. The GAT encoder explicitly models relational dependencies among neighboring operating points along each speed line, capturing local continuity while reducing computational complexity. The MSFE module employs parallel convolutional kernels of sizes 1, 3, and 5 to extract multi-granularity features, effectively broadening the receptive field. Furthermore, the cross-attention decoder enables explicit knowledge transfer across different speed lines, leveraging structural similarities between well-sampled and sparsely sampled speeds—a critical advantage when data is scarce.
Comprehensive evaluations demonstrate that GAESA-iFormer achieves optimal performance with a feature dimension of 64 and four encoder layers. When trained on transformed secondary data, the proposed model significantly outperforms state-of-the-art baselines, reducing the RMSE by 27.03% and MAE by 31.78%.
The results indicate that the proposed GAESA-iFormer model achieves improved prediction accuracy on the current compressor dataset under limited experimental data.
Real-time human action recognition on Internet-of-Things (IoT) edge devices requires models that capture rich spatio-temporal cues within strict latency, memory, and power envelopes. Current 3D CNNs, video transformers, and shift-based ViT deliver high accuracy but come at computational costs that preclude edge IoT deployment. This paper proposes CoDAT, a Collaborative Dual-Attention Transformer that replaces conventional multi-head attention with a lightweight dual-branch module: Spatial Convolutional Attention (SCA) for local aggregation and Strided Single-Head Attention (SSHA) for global context. SSHA jointly compresses the spatial resolution and channel dimensions of the query, key, and value tensors via stride-based sparse projection, then fuses the resulting global and local features at a markedly reduced cost. To enable temporal communication across frames, a parameter-free TShift module is embedded in each block. Extensive experiments on Jetson AGX Orin and Raspberry Pi 5 demonstrate that CoDAT achieves an energy-accuracy balance in both image and action recognition. On ImageNet-1K, CoDAT-M runs 2x faster than EfficientViT384 and FastViT-S12 at comparable accuracy, and CoDAT-L matches ViT-S with 3x fewer parameters at 2x higher throughput. On Kinetics-400 and MA-52, CoDAT achieves competitive Top-1 accuracy against state-of-the-art CNN, transformer, and hybrid baselines while running up to 2.9x faster than VSwin-T, 2x faster than ViT-Temporal-Shift variants, and 5x faster than UniFormer-B. On UCF-101, CoDAT-S384 matches TokShift and LAPS while being 6x faster and requiring up to 13x fewer FLOPs, establishing an efficiency-accuracy balance for real-time action recognition in edge IoT perception systems. Code is available at https://github.com/novendrastywn/CoDAT .
Novendra Setyawan, Chi-Chia Sun, Mao-Hsiu Hsu et al.· IEEE Internet of Things Jour...· 0 citations
UDT is proposed, a U-Net diffusion transformer that combines the representation power of DiTs with the encoding-decoding benefits of U-Nets, through data-adaptive token merging for downsampling and upsampling, while preserving the DiT token dimension.
Junno Yun, Y. Alçalar, Mehmet Akçakaya· 0 citations
The core finding empirically identifies a data-efficiency frontier: for domain-proximal scenes with>= 200 samples, Swin-Graft surpasses YOLOv11x and CNNs retain advantage for domain-distant scenes.
Window-based attention mechanisms have been introduced to alleviate the excessive computational cost inherent in global attention mechanisms. In this paper, we propose a novel architecture named FwNet-ECA, which integrates the Fourier transform with learnable weight matrices to enhance spectral features of images. By performing filter enhancement after window-based attention, our method establishes a global receptive field, thereby overcoming the limited receptive field typically associated with windowed attention. Furthermore, we incorporate the existing Efficient Channel Attention module to improve interchannel information exchange. Unlike approaches that rely on physical window shifting, our method leverages frequency-domain enhancement to implicitly connect spatial regions. We evaluate our model on the iCartoonFace dataset and demonstrate competitive performance on fine-grained classification benchmarks. Experimental results show that, compared to shift-based window methods, our model achieves comparable accuracy with fewer parameters and lower computational overhead. Moreover, visualization analyses clearly indicate that the filter enhancement technique is particularly effective in the shallow layers of the network, where feature maps are relatively large. This work presents an effective solution to the limited receptive field problem in window-based attention mechanisms. The code is publicly available at
https://github.com/qingxiaoli/FwNet-ECA
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Xiaoqing Li, Ya Wang, Nannan Gu et al.· The European Journal on Arti...· 0 citations
