This study presents a quasi T-S fuzzy model to represent networked control systems (NCS), which takes the performance of network with time-delay and data packet dropouts into the investigations. Representing the probability of the time-delay and the data packet dropouts in networks as the degree of membership, then according to the data packet dropouts in different node of system, this study constructs the corresponding system model, in which the system's stability is proved with linear matrix inequality (LMI). As a bench test, a double inverted pendulum control system is selected to demonstrate the feasibility and efficiency through numerical analysis. This study provides a new method of analysing and designing networked control systems.

Fault diagnosis is critical to flight safety for multi-rotor unmanned aerial vehicles (UAVs). While Graph Neural Networks (GNNs) have shown validity in modeling complex structural data, conventional approaches relying on undirected graphs and local message passing struggle with graph heterophily and limited receptive fields. To overcome these limitations, this paper proposes DIGPGNN—a novel GNN architecture that integrates Directional Information and Global Perception Fusion for enhanced UAV fault diagnosis. Initially, a directed graph is constructed based on feature similarity to reduce graph heterophily. Subsequently, an Adaptive Bi-directional Aggregation GNN (ABAGNN) is employed to adaptively aggregate the feature information of bidirectional neighbor nodes, thereby enhancing the model’s focus on homophilic neighborhood nodes. Finally, the Efficient Graph Transformer (EGT) incorporates a global self-attention strategy to learn the optimal graph structure by connecting distant homophilic nodes. Experimental results validate the effectiveness of DIGPGNN, achieving a 4.01% and 1.88% improvement in classification accuracy on public and self-built UAV datasets, respectively.

Many industrial processes are operated in multiple modes due to different manufacturing strategies. Multimodality of process data is often accompanied with nonlinear and non‐Gaussian characteristics, which makes data‐driven monitoring more complicated. In this paper, statistics pattern analysis (SPA) is introduced to extract low‐ and high‐order statistics from raw process data. Support vector data description (SVDD), which can deal with nonlinear and non‐Gaussian problems, is applied to monitor multimode process in this paper. To improve detection performance of SVDD for training multimode data with outliers, modified local reachability density ratio (mLRDR) is proposed as a weight factor to be embedded in the weighted‐SVDD (wSVDD) model, in which the local neighbors in terms of both space and time are considered. Finally, the effectiveness and superiority of our proposed method are demonstrated by the Tennessee‐Eastman (TE) process and wastewater treatment process (WWTP).

With the rapid development of modern industry, actual production processes generally have a variety of complex characteristics including nonlinearity, multimodality, and contamination. Those characteristics, as well as the faults, bring great challenges to traditional process monitoring. To deal with all the above-mentioned three problems simultaneously, this paper develops a robust nonlinear multimode process monitoring scheme. First, the robust decomposition of kernel function (RDKF) algorithm is proposed to detect outliers. Then, a nonlinear mode identification method is presented by combining block diagonal kernel function matrix and spectral clustering. For the online sample, a mode indicator is derived from kernel function to judge whether it belongs to a fault or a certain mode. Finally, the effectiveness of the proposed method is validated by two cases in terms of both mode identification and fault detection.

提出一种新的有限长一维符号序列的复杂性度量——格子复杂性，建立在LempelZiv复杂性和一维迭代映射系统的符号动力学基础上.同时提出了符号序列的细粒化方法，可与格子复杂性以及LempelZiv复杂性结合.新度量在细粒化指数较小时与LempelZiv复杂性基本一致，在细粒化指数增大时显示出截然不同的特性.以Logistic映射为对象的计算实验表明，格子复杂性对混沌区的边缘最敏感.最后还讨论了上述复杂性度量的其他一些重要性质.