Chaotic systems exhibit extreme sensitivity to initial conditions, where even slight differences on the initial conditions can cause trajectories to diverge over time. Since the 1990s, researchers have demonstrated that chaotic behavior can be controlled – a phenomenon known as chaos control. In this context, this work focuses on stabilizing chaotic dynamics in two-dimensional maps by steering them toward a periodic orbit of a specified period. Our approach builds on a previous method that applies pulses of intensity λ to the system variables every Δ n iterations, where λ and Δ n are method parameters. We formulate this problem as a complex, multimodal, multivariate, continuous nonlinear optimization task, tackled using a popular swarm intelligence technique called cuckoo search algorithm. Computational experiments on the Holmes map under various parameter settings demonstrate that our method performs effectively, representing a promising step toward fully automated chaos control in chaotic maps.
Currently, power equipment defect detection predominantly relies on single-modal models, which exhibit significant limitations in capturing comprehensive device features and coping with environmental variations and noise interference. To address these issues, we propose a defect detection model that combines the features of visible light images (VIS) and thermal infrared images (TIR). Specifically, we first initialize the Differential Evolution (DE) population using a chaos-enhanced opposition learning strategy and introduce Gaussian variation, then the enhanced DE is used to select features extracted from various layers of the backbone network, and search for suitable fusion operations to integrate the features from different modalities. By fully leveraging the complementary advantages of multi-modal information, our method demonstrates significant improvements in noise suppression and detection feature capture, thereby markedly enhancing the accuracy and robustness of power equipment defect detection. We validate the performance on a self-constructed multi-modal insulator string defect detection data set and compared it with other multi-modal models. Experimental results indicate that the proposed algorithm outperforms its counterparts.
This paper explores the quasi-consensus problem in time-delayed nonlinear heterogeneous multi-agent systems. It introduces a novel non-periodic intermittent saturation-based adaptive control protocol designed to optimize both internal coupling among followers and feedback communication from the leader. By employing matrix theory, inequality techniques, and the Lyapunov function approach, the study establishes critical conditions for achieving quasi-synchronization. A distinguishing feature of this protocol is the integration of a saturated adaptive control scheme, which halts updates to the adaptive rate once the quasi-consensus error falls below a predefined threshold. The theoretical findings are validated through numerical simulations, demonstrating the effectiveness of the proposed strategies.
Existing detection algorithms for underwater object in images of unmanned aerial vehicles present challenges such as suboptimal accuracy for small objects and enormous computational demands. This work proposes a lightweight detection network with an enhanced pyramid for small objects based on YOLOv11, named SOEP-YOLO. To tackle the small object detection challenge, a feature enhancement network is proposed, which improves the processing capability of small target features. Instead of the approach of adding a P2 detection layer, the P2 layer feature map is processed through space-to-depth convolution to obtain enriched features containing small target information, which reduces the number of the model parameters and inference time. To enhance detection efficiency and reduce model complexity, the cross-stage partial concept is adopt to optimize the omni-kernel module, resulting in a lightweight module for feature integration. This module is designed to effectively learn feature representations from global to local scales, ultimately improving the detection accuracy of small objects. The experimental results on the SeaDroneSee dataset indicate that SOEP-YOLO enhances the mean average precision by 3.03
The maintenance of cigarette formulations directly affects product flavor stability and continuous manufacturing. However, traditional manual maintenance, which relies heavily on experience, struggles to address the complex constraints arising from fluctuations in raw material inventories and quality degradation during storage. We formulate the inventory-constrained cigarette formulation maintenance problem as a mixed-integer program built on two quality metrics: the Formulation Reliability Index and the Formulation Maturation Index. To effectively solve this model, we propose a Mutual Information-based Adaptive Large Neighborhood Search algorithm that integrates the historical co-occurrence information of different formulations. We design multiple removal and insertion operators and incorporate a simulated annealing mechanism to enhance the global search capabilities of the algorithm. Multi-scenario industrial tests show that our approach significantly improves objective values under inventory limits while remaining computationally efficient.
