Deep learning technologies have demonstrated their effectiveness in removing cloud cover from optical remote-sensing images. Convolutional Neural Networks (CNNs) exert dominance in the cloud removal tasks. However, constrained by the inherent limitations of convolutional operations, CNNs can address only a modest fraction of cloud occlusion. In recent years, diffusion models have achieved state-of-the-art (SOTA) proficiency in image generation and reconstruction due to their formidable generative capabilities. Inspired by the rapid development of diffusion models, we first present an iterative diffusion process for cloud removal (IDF-CR), which exhibits a strong generative capabilities to achieve component divide-and-conquer cloud removal. IDF-CR consists of a pixel space cloud removal module (Pixel-CR) and a latent space iterative noise diffusion network (IND). Specifically, IDF-CR is divided into two-stage models that address pixel space and latent space. The two-stage model facilitates a strategic transition from preliminary cloud reduction to meticulous detail refinement. In the pixel space stage, Pixel-CR initiates the processing of cloudy images, yielding a suboptimal cloud removal prior to providing the diffusion model with prior cloud removal knowledge. In the latent space stage, the diffusion model transforms low-quality cloud removal into high-quality clean output. We refine the Stable Diffusion by implementing ControlNet. In addition, an unsupervised iterative noise refinement (INR) module is introduced for diffusion model to optimize the distribution of the predicted noise, thereby enhancing advanced detail recovery. Our model performs best with other SOTA methods, including image reconstruction and optical remote-sensing cloud removal on the optical remote-sensing datasets.

Traditionally, production scheduling is optimised using objective functions based on punctuality criteria such as earliness and tardiness. However, synchronisation in complicated manufacturing process is always neglected. Due to the lack of synchronisation, severe problems happen frequently such as long holding time and unreasonable setups. Motivated by the practical challenges, the authors are among the first to present the IoT-enabled real-time synchronisation approaches for an automobile standard part factory with the following objectives. First, the synchronisation paradigm is proposed in terms of two dimensions, namely the Horizontal Synchronisation (HSync) for the consistency of batch production progresses within one customer order, and the Vertical Synchronisation (VSync) for the coordination between adjacent processing stages. Second, the IoT-enabled synchronisation mechanisms using rule-based heuristics and real-time production data is presented. Third, an advanced planning and scheduling (APS) system framework is developed to automatically implement the synchronisation mechanisms. Finally, the effectiveness of the synchronisation mechanisms is verified through a case study.

A stick-slip/inchworm hybrid rotary piezomotor based on a symmetric triangular driving mechanism, which can simultaneously achieve the benefits of both stick-slip and inchworm motors, was reported in this letter. It is based on the principle of stick-slip motors, and, inspired by the clamping-releasing actions from inchworm motors, it employs a symmetric triangular driving mechanism to generate a clamping action during the stick phase and a releasing action during the slip phase. Compared with stick-slip motors, it involves a clamping action during the stick phase and a releasing action during the slip phase, thus resulting in a larger driving force. Compared with inchworm motors, which require active control and coordination of clamping/releasing modules with feeding modules, it involves the control and operation of only one feeding piezoactuator without any actively controlled clamping/releasing module. Therefore, the control is easier, and a much larger operation frequency and driving speed can be achieved. Under the sawtooth waveform voltage of 90 V at 2600 Hz with a self-holding torque of 4 N m, the prototype achieved a no-load speed higher than 0.6 rad/s, a load torque capacity larger than 1.8 N m, and a weight carrying capacity more than 100 kg for both clockwise and anticlockwise directions. Compared with load torque capacity and weight carrying capacity in the reported stick-slip and inchworm rotary piezomotors, the current levels in terms of the same driving speed have been improved over 60 times and 12 times, respectively, in the proposed hybrid motor.

To satisfy the needs of the individualized manufacturing of products, the smart manufacturing system (SMS) is frequently reconfigured. To quickly verify the reliability and adaptability of industrial software in reconfiguring the SMS for new or upgraded product orders, a semi-physical simulation method for testing and evaluation of industrial software is proposed based on digital-twins-driven technology. By establishing a semi-physical simulation model of SMS, the reliability and robustness of the software system are quickly verified by running industrial software in various manufacturing scenarios. In this paper, the key technologies to carry out semi-physical simulation testing and evaluation of industrial software for SMSs are expounded in detail, including how to synchronize cyber and physical systems, how to conduct semi-physical accelerated simulation testing, and how to identify defects quickly in industrial software used in actual production environments. By establishing a semi-physical simulation production line model for stepper motors, the effectiveness and practicality of the proposed approach are verified, and the testing verification time of industrial software is significantly reduced. Finally, the robustness of the industrial software for SMS is further verified by conducting fault injection testing, so as to provide implications for fault prognostics or fault-prevention research.