Adaptively Re-weighting Multi-Loss Untrained Transformer for Sparse-View Cone-Beam CT Reconstruction

Cone-Beam Computed Tomography (CBCT) has been proven useful in diagnosis, but how to shorten scanning time with lower radiation dosage and how to efficiently reconstruct 3D image remain as the main issues for clinical practice. The recent development of tomographic image reconstruction on sparse-view measurements employs deep neural networks in a supervised way to tackle such issues, whereas the success of model training requires quantity and quality of the given paired measurements/images. We propose a novel untrained Transformer to fit the CBCT inverse solver without training data. It is mainly comprised of an untrained 3D Transformer of billions of network weights and a multi-level loss function with variable weights. Unlike conventional deep neural networks (DNNs), there is no requirement of training steps in our approach. Upon observing the hardship of optimising Transformer, the variable weights within the loss function are designed to automatically update together with the iteration process, ultimately stabilising its optimisation. We evaluate the proposed approach on two publicly available datasets: SPARE and Walnut. The results show a significant performance improvement on image quality metrics with streak artefact reduction in the visualisation. We also provide a clinical report by an experienced radiologist to assess our reconstructed images in a diagnosis point of view. The source code and the optimised models are available from the corresponding author on request at the moment.

How human-robot collaboration impacts construction productivity: an agent-based multi-fidelity modeling approach

Though construction robots have drawn attention in research and practice for decades, human-robot collaboration (HRC) remains important to conduct complex construction tasks. Considering its complexity and uniqueness, it is still unclear how HRC process will impact construction productivity. To this end, an agent-based (AB) multi-fidelity modeling approach is introduced to simulate and evaluate how HRC influences construction productivity. A high-fidelity model is first proposed for a scenario with one robot. Then, a low-fidelity model is established to extract key parameters that capture the inner relationship among scenarios. The multi-fidelity models work together to simulate complex scenarios. Simulation and experiements show that: 1) the proposed approach is feasible and flexible for simulation of complex HRC processes, and can cover multiple collaboration and interaction modes; 2) the influence of the supplement strategy is simple when there is only one robot, where lower Check Interval (CI) and higher Supplement Limit (SL) will improve productivity. But the influence becomes much more complicated when there are more robots due to the internal competition among robots for the limited time of workers; 3) the productivity per robot improves when there are more robots and workers, even if the human-robot ratio remains the same; 4) introducing proactive interaction between robots and workers could improve productivity significantly, up to 22% in our experiments, which further depends on the supplement strategy and the human-robot ratio. Overall, this research contributes an integrated approach to simulate and evaluate HRC's impacts on productivity as well as valuable insights on how to optimize HRC for better performance.

Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI

In an aerial hybrid massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) system, how to design a spectral-efficient broadband multi-user hybrid beamforming with a limited pilot and feedback overhead is challenging. To this end, by modeling the key transmission modules as an end-to-end (E2E) neural network, this paper proposes a data-driven deep learning (DL)-based unified hybrid beamforming framework for both the time division duplex (TDD) and frequency division duplex (FDD) systems with implicit channel state information (CSI). For TDD systems, the proposed DL-based approach jointly models the uplink pilot combining and downlink hybrid beamforming modules as an E2E neural network. While for FDD systems, we jointly model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid beamforming modules as an E2E neural network. Different from conventional approaches separately processing different modules, the proposed solution simultaneously optimizes all modules with the sum rate as the optimization object. Therefore, by perceiving the inherent property of air-to-ground massive MIMO-OFDM channel samples, the DL-based E2E neural network can establish the mapping function from the channel to the beamformer, so that the explicit channel reconstruction can be avoided with reduced pilot and feedback overhead. Besides, practical low-resolution phase shifters (PSs) introduce the quantization constraint, leading to the intractable gradient backpropagation when training the neural network. To mitigate the performance loss caused by the phase quantization error, we adopt the transfer learning strategy to further fine-tune the E2E neural network based on a pre-trained network that assumes the ideal infinite-resolution PSs. Numerical results show that our DL-based schemes have considerable advantages over state-of-the-art schemes.