In the Industrial Internet of Things (IIoT), multi-label classification is challenging due to limited labeled data, class imbalance, and the necessity to consider temporal and spatial dependencies. We propose BiConED, a bi-contrast encoder–decoder self-supervised model integrating two contrasting methods: RAC employs an encoder–decoder with augmented data to capture temporal dependencies and boost information entropy, enhancing generalization under label scarcity. QuadC captures spatial dependencies across channels through convolutions on hidden vectors. Evaluated on the real-world industrial benchmark SKAB, BiConED improves feature extraction for underrepresented classes, achieving a 26% increase in F1 score, a 67.72% reduction in False Alarm Rate (FAR), and a 57.25% decrease in Missed Alarm Rate (MAR) compared to models without the proposed contrasts. Even with limited labeled data, BiConED maintains a FAR below 1% and recovers up to 85% of the F1 score without resampling, demonstrating its robustness in imbalanced IIoT environments.

Turing's model has been widely used to explain how simple, uniform structures can give rise to complex, patterned structures during the development of organisms. However, it is very hard to establish rigorous theoretical results for the dynamic evolution behavior of Turing's model since it is described by nonlinear partial differential equations. We focus on controllability of Turing's model by linearization and spatial discretization. This linearized model is a networked system whose agents are second order linear systems and these agents interact with each other by Laplacian dynamics on a graph. A control signal can be added to agents of choice. Under mild conditions on the parameters of the linearized Turing's model, we prove the equivalence between controllability of the linearized Turing's model and controllability of a Laplace dynamic system with agents of first order dynamics. When the graph is a grid graph or a cylinder grid graph, we then give precisely the minimal number of control nodes and a corresponding control node set such that the Laplace dynamic systems on these graphs with agents of first order dynamics are controllable.

This paper presents decentralized solutions for addressing pursuit-evasion problems involving high-order in-tegrators with intracoalition cooperation and intercoalition confrontation. To ensure that the control strategies indepen-dent of the relative velocities, accelerations and higher order information of neighbors, we introduce distinct error vari-ables and hyper-variables. Consequently, this approach only requires agents to exchange position information or measure the relative positions of neighbors. The distributed strategies reflect the goals of intracoalition cooperation or intercoalition confrontation of the players. Additionally, we present the conditions for capture and formation control with exponential convergence for three cases: one-pursuer-one-evader, multiple-pursuer-one-evader, and multiple- pursuer- multiple-evader. The results show that the conditions depend on the structure of the communication graph, the weights in the control law, and the expected formation configuration. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation results.

This paper presents decentralized solutions for pursuit-evasion problems involving high-order integrators with intracoalition cooperation and intercoalition confrontation. Distinct error variables and hyper-variables are introduced to ensure the control strategies to be independent of the relative velocities, accelerations and higher order information of neighbors. Consequently, our approach only requires agents to exchange position information or to measure the relative positions of the neighbors. The distributed strategies take into consideration the goals of intracoalition cooperation or intercoalition confrontation of the players. Furthermore, after establishing a sufficient and necessary condition for a class of high-order integrators, we present conditions for capture and formation control with exponential convergence for three scenarios: one-pursuer-one-evader, multiple-pursuer-one-evader, and multiple-pursuer-multiple-evader. It is shown that the conditions depend on the structure of the communication graph, the weights in the control law, and the expected formation configuration. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation results.

Core-Periphery structure, as a critical mesoscale structure, is commonly found in diverse real-world networks, in which nodes are endogenously categorized as core or peripheral nodes by the underlying interconnection patterns. It plays an important role in sustaining the intrinsic order and functional behavior of networked systems. However, despite the study on inherent core–periphery vulnerabilities to node removals, little is known on the core–periphery robustness when networks are suffering from the attacks that happen on links between nodes, especially for the more practical situations where attackers have limited ability to obtain precise network information. In this paper, a novel index is proposed for measuring the capacity of the core–periphery structure to resist link-based attacks. By introducing an attack precision parameter, we establish a unified evaluation framework for link-based attacks with imprecise information, which divides attack behaviors into localized attacks and non-localized attacks, and treats the usual random attacks and targeted attacks as the two special situations of our non-localized case. Several enhancing algorithms guided by our index with local search strategy are exquisitely devised, and experimental results are provided to demonstrate the efficacy of our framework and algorithms that remarkably improve the core–periphery robustness against link-based attacks with imprecise information.

In this paper, several inverse Kalman filtering problems are addressed, where unknown parameters and/or inputs in a filtering model are reconstructed from observations of the posterior estimates that can be noisy or incomplete. In particular, duality in inverse filtering and inverse optimal control is studied. It is shown that identifiability and solvability of the inverse Kalman filtering is closely related to that of an inverse linear quadratic regulator (LQR). Covariance matrices of model uncertainties are estimated by solving a well-posed inverse LQR problem. Identifiability of the considered inverse filtering models is established and least squares estimators are designed to be statistically consistent. In addition, algorithms are proposed to reconstruct the unknown sensor parameters as well as raw sensor measurements. Effectiveness and efficiency of the proposed methods are illustrated by numerical simulations.

In this paper, we consider the problem of placing a minimal number of controls to achieve controllability for a class of networked control systems that are based on the original Turing reaction-diffusion model, which is governed by a set of ordinary differential equations with interactions defined by a ring graph. Turing model considers two morphogens reacting and diffusing over the spatial domain and has been widely accepted as one of the most fundamental models to explain pattern formation in a developing embryo. It is of great importance to understand the mechanism behind the various reaction kinetics that generate such a wide range of patterns. As a first step towards this goal, in this paper we study controllability of Turing model for the case of cells connected as a square grid in which controls can be applied to the boundary cells. We first investigate the minimal control placement problem for the diffusion only system. The eigenvalues of the diffusion matrix are classified by their geometric multiplicity, and the properties of the corresponding eigenspaces are studied. The symmetric control sets are designed to categorize control candidates by symmetry of the network topology. Then the necessary and sufficient condition is provided for placing the minimal control to guarantee controllability for the diffusion system. Furthermore, we show that the necessary condition can be extended to Turing model by a natural expansion of the symmetric control sets. Under certain circumstances, we prove that it is also sufficient to ensure controllability of Turing model.

In this paper, a non-visual robotic formation control method based on a streaming communication architecture and a leader-follower control model is studied. The proposed stream-based communication architecture is inspired by the flocking behavior of fish. We analogize it into a form resembling an N-ary tree for communication purposes. Communication proceeds to the next layer only when all nodes in the upper layer have completed the follower selection. We also introduce a fault-tolerance mechanism and a termination filtering mechanism to prevent multiple leaders from choosing the same follower, avoiding a scenario where the robots in the last layer enter an endless loop of follower selection. The proposed stream-based communication architecture, built upon serial and parallel tracking, can achieve more complex formations, such as rectangular formations, closely resembling real-world scenarios, significantly enhancing formation efficiency. Simulation experiments on the e-puck platform validate the effectiveness and robustness of this architecture.

This paper investigates the problem of placing a given number of controls to optimize energy efficiency for a family of linear dynamical systems, whose structure is induced by the Laplacian of a square-grid network. To quantify the performance of control combinations, several metrics have been proposed based on the spectrum of the controllability Gramian. But commonly used algorithms to compute the spectrum are usually time-consuming. In this paper, we first classify five anchor symmetries of the network systems. Then motivated by various advantages of symmetric control combinations, we provide a method to compute the eigenvalues and eigenvectors of their controllability Gramians more efficiently. Specifically, we show that they can be expressed by those of two lower-dimensional matrices. Furthermore, our method can be applied for non-symmetric cases to provide upper and lower bounds for the spectrum of the controllability Gramians. Finally, by employing the sum of eigenvalues, i.e., the trace of controllability Gramian, as the objective function, we provide a closed-form algorithm to the spectrum optimization problem with a given number of controls subject to system controllability.

Combining safety objectives with stability objectives is crucial for safety-critical systems. Existing studies generally unified these two objectives by constructing Lyapunov-type barrier functions. However, insufficient analysis of key set relationships within the system may render the proposed safety and stability conditions conservative, and these studies also did not provide how to use such conditions to design safety-stability control strategies. This paper proposed a feasible and constructive design to achieve stabilization of safety-critical systems by a relaxed converse Lyapunov-barrier approach. By analyzing the relationships between a series of sets associated with the safety-critical system, the stability and safety conditions can be appropriately relaxed. Then, with the help of relaxed converse control Lyapunov-barrier functions (RCCLBFs), a theoretical result was obtained for the stability of affine nonlinear systems with safety constraints. Subsequently, a constructive method was developed for a second-order strict-feedback system to transform the process of solving RCCLBFs into a Lyapunov-like stabilization problem. Finally, the proposed safety-stability control method is exerted on a robotic system and demonstrated by simulations.