Iron ore image classification is essential for achieving high production efficiency and classification precision in mineral processing. However, real industrial environments face classification challenges due to small samples, inter-class similarity, and on-site noise. Existing methods are limited by single-view approaches that provide insufficient representation, difficulty in achieving adaptive balance between performance and complexity through manual or fixed feature selection and fusion, and susceptibility to overfitting with poor robustness under small sample conditions. To address these issues, this paper proposes the evolutionary deep fusion framework EDF-NSDE. The framework introduces multi-view feature extraction that combines lightweight and classical convolutional neural networks to obtain complementary features. Additionally, it was utilized to design evolutionary fusion that utilizes NSGA-II and differential evolution for multi-objective search to adaptively balance accuracy and model complexity while reducing overfitting and enhancing robustness through a generalization penalty and adaptive mutation. Furthermore, to overcome data limitations, we constructed a six-class dataset including hematite, magnetite, ilmenite, limonite, pyrite, and rock based on real production scenarios. The experimental results show that on our self-built dataset, EDF-NSDE achieves 84.86%/88.38% on original/augmented test sets, respectively, comprehensively outperforming other models. On a public seven-class mineral dataset, it achieves 92.51%, validating its generalization capability across different mineral types and imaging conditions. In summary, EDF-NSDE provides an automated feature fusion solution that achieves automated upgrading of the mineral classification process, contributing to the development of intelligent manufacturing technology and the industrial internet ecosystem.

This paper investigates the problems of remote state estimation and instruction adjustment for multi-agent systems (MASs) under simultaneous sensor and instruction attacks, as well as external disturbances. Unlike existing research that primarily focuses on single-channel attacks, this paper investigates the more challenging scenario of dual-channel attacks. To decouple the detrimental effects of instruction attacks on state estimation, an augmented descriptor system is constructed, treating sensor attacks as part of an extended state. Then, a distributed unknown input observer is designed to estimate the system states. Theoretical analysis demonstrates that state estimation error is asymptotically convergent and independent of instruction estimation. Furthermore, to counteract instruction attacks, a distributed cooperative estimation strategy based on an adaptive saturation mechanism is proposed, enabling all agents to achieve bounded estimation of the true reference instruction within a finite number of iterations. Finally, the effectiveness and robustness of the proposed approach under dual-channel attacks are verified by a simulation of an unmanned vehicle platoon.