Linrui Dai, Jinxiu Liang, Chu Zhou, Mae Yamaguchi, Ryuya Takahashi, Imari Sato
Extremely sparse-view computed tomography (xSV-CT) is critical when scanning time, motion, or radiation tolerance limits each scan to only a few projections. In many such scenarios, the same acquisition limits also constrain dataset construction, making pre-reconstructed 3D volumes from dense scans unavailable. This exposes a gap in current sparse-view approaches: Optimization-based methods, which use the sparse measurements directly but lack data-driven priors to suppress artifacts, and learning-based methods, which use strong priors but usually require densely scanned training volumes acquired outside the xSV-CT regime. We study this train-sparse / infer-sparse setting by learning a pose-aware diffusion prior from one 2D projection per training sample, then guiding diffusion sampling with measured-view fidelity, shared-volume reprojection, and projection-physics consistency for tomographic reconstruction. On two public CT datasets across 1-, 4-, and 8-view settings, the proposed method achieves the best novel-view synthesis scores and best or competitive reconstruction quality compared with baselines that use dense-volume supervision.
Mohammadhossein Ghahramani, Yan Qiao, Mengchu Zhou
Task allocation in smart manufacturing systems must operate under decentralized decision-making, dynamic workloads, and shared-resource constraints. In circular manufacturing settings, these challenges are further intensified because tasks compete for reusable, capacity-constrained assets, and machine selection also might affect processing energy. Although learning-based approaches have been explored for task allocation, improvements in predictive modeling do not necessarily translate into better allocation outcomes under decentralized negotiation. This work proposes an Edge-AI-driven decentralized task-allocation framework. We develop lightweight decision intelligence deployed at the machine level. It is developed progressively: first, a resource-aware heuristic establishes the decentralized bidding structure; a regression-based Edge-AI formulation then examines learned local bid approximation, and a compact autoencoder-regularized pairwise ranking model finally provides a learned correction to the analytical bid ordering. Each machine evaluates incoming tasks by using its processing capability, queue state, energy characteristics, and a compact signal representing contention over the reusable shared production asset. The framework is assessed using discrete-event simulation in scenarios characterized by high load and dependence on shared resources. Compared to the heuristic, the proposed ranking method increases completed tasks, reduces average tardiness, and lowers the deadline-miss rate, with statistically significant paired differences. Mean energy per completed task is also reduced. The results indicate that effective learning-assisted allocation depends not only on approximating local decision quantities, but also on shaping the relative preferences that determine negotiation outcomes.