This paper proposes a systematic approach to designing industrial device function models using AutomationML (AML) aligned with the Asset Administration Shell (AAS) framework. The methodology addresses the challenges of multi-domain data integration, cross-platform interoperability, and dynamic adaptability in Industry 4.0 environments. By leveraging AML’s XML-based engineering data exchange capabilities and AAS’s standardized digital twin architecture, the proposed model achieves seamless integration of mechanical, electrical, control, and semantic information. At the end, we design the use case by the AML-AAS tools to verify the function.

Advanced text-to-motion models should one by one generate all sub-actions in the complex motion following the given motion description. Existing text-to-motion models have performed well on simple motion descriptions but they usually fail when the motion description involves multiple actions, which indicates the lack of ability to generate sub-actions step-by-step. To solve this problem, we propose a "Coach-Athlete" text-to-motion pretraining framework. In the framework, we employ the large language model (LLM) as the Coach to create incremental text and sub-action sequence pairs from complex text-motion samples. With these data, the Coach guides the Athlete, a text-to-motion model, to predict sub-action sequences in complex motion from short to long. Our approach provides a new way to inject the LLM’s motion decomposition and synthesis capabilities into a small-scale text-to-motion model. Experimental results on the current largest text-to-motion dataset HumanML3D and representative dataset KIT-ML show that our pretraining framework can facilitate performances of several state-of-the-art text-to-motion models, especially for complex motions.

Graph convolution networks (GCNs) have achieved remarkable performance in skeleton-based action recognition. However, previous GCN-based methods rely on elaborate human priors excessively and construct complex feature aggregation mechanisms, which limits the generalizability and effectiveness of networks. To solve these problems, we propose a novel Spatial Topology Gating Unit (STGU), an MLP-based variant without extra priors, to capture the co-occurrence topology features that encode the spatial dependency across all joints. In STGU, to learn the point-wise topology features, a new gate-based feature interaction mechanism is introduced to activate the features point-to-point by the attention map generated from the input sample. Based on the STGU, we propose the first MLP-based model, SiT-MLP, for skeleton-based action recognition in this work. Compared with previous methods on three large-scale datasets, SiT-MLP achieves competitive performance. In addition, SiT-MLP reduces the parameters significantly with favorable results. The code will be available at https://github.com/BUPTSJZhang/SiT-MLP.

Human motion prediction is challenging due to the complex spatiotemporal feature modeling. Among all methods, graph convolution networks (GCNs) are extensively utilized because of their superiority in explicit connection modeling. Within a GCN, the graph correlation adjacency matrix drives feature aggregation, and thus, is the key to extracting predictive motion features. State-of-the-art methods decompose the spatiotemporal correlation into spatial correlations for each frame and temporal correlations for each joint. Directly parameterizing these correlations introduces redundant parameters to represent common relations shared by all frames and all joints. Besides, the spatiotemporal graph adjacency matrix is the same for different motion samples, and thus, cannot reflect samplewise correspondence variances. To overcome these two bottlenecks, we propose dynamic spatiotemporal decompose GC (DSTD-GC), which only takes 28.6% parameters of the state-of-the-art GC. The key of DSTD-GC is constrained dynamic correlation modeling, which explicitly parameterizes the common static constraints as a spatial/temporal vanilla adjacency matrix shared by all frames/joints and dynamically extracts correspondence variances for each frame/joint with an adjustment modeling function. For each sample, the common constrained adjacency matrices are fixed to represent generic motion patterns, while the extracted variances complete the matrices with specific pattern adjustments. Meanwhile, we mathematically reformulate GCs on spatiotemporal graphs into a unified form and find that DSTD-GC relaxes certain constraints of other GC, which contributes to a better representation capability. Moreover, by combining DSTD-GC with prior knowledge like body connection and temporal context, we propose a powerful spatiotemporal GCN called DSTD-GCN. On the Human3.6M, Carnegie Mellon University (CMU) Mocap, and 3D Poses in the Wild (3DPW) datasets, DSTD-GCN outperforms state-of-the-art methods by 3.9%-8.7% in prediction accuracy with 55.0%-96.9% fewer parameters. Codes are available at https://github.com/Jaakk0F/DSTD-GCN.

During islanded operation in microgrids, a clean and balanced sinusoidal output voltage is required at the point of common coupling (PCC). To reduce the harmonic content at the PCC, an optimal control strategy based on disturbance rejection is proposed in this paper. First, an augmented error dynamic system included the future information of the reference voltage, the history information of repetitive control, and the current information of the system state vector is built Second, a particular mathematical transformation method is employed to eliminate the augmented error dynamic system’s time delay, which makes it suitable for general optimal theory. Based on this, in combination with the fundamental equivalent input disturbance (EID) theory, the influence of disturbance at the PCC is eliminated, and the recovery time of repetitive control systems is shortened. Finally, the simulation and experiment results verify the effectiveness of the proposed control strategy.

Predicting crowd flows is crucial for urban planning, traffic management and public safety. However, predicting crowd flows is not trivial because of three challenges: 1) highly heterogeneous mobility data collected by various services; 2) complex spatio-temporal correlations of crowd flows, including multi-scale spatial correlations along with non-linear temporal correlations. 3) diversity in long-term temporal patterns. To tackle these challenges, we proposed an end-to-end architecture, called pyramid dilated spatial-temporal network (PDSTN), to effectively learn spatial-temporal representations of crowd flows with a novel attention mechanism. Specifically, PDSTN employs the ConvLSTM structure to identify complex features that capture spatial-temporal correlations simultaneously, and then stacks multiple ConvLSTM units for deeper feature extraction. For further improving the spatial learning ability, a pyramid dilated residual network is introduced by adopting several dilated residual ConvLSTM networks to extract multi-scale spatial information. In addition, a novel attention mechanism, which considers both long-term periodicity and the shift in periodicity, is designed to study diverse temporal patterns. Extensive experiments were conducted on three highly heterogeneous real-world mobility datasets to illustrate the effectiveness of PDSTN beyond the state-of-the-art methods. Moreover, PDSTN provides intuitive interpretation into the prediction.