A reflective seawater salinity and temperature sensor based on surface plasmon resonance (SPR) effect with multimode single-mode (MS) fiber structure has been proposed and demonstrated. Firstly, the sensing mechanism of the optical fiber SPR sensor was analyzed. Next, the influence of the type of metal, the thickness of the metal film, and the length of sensing area upon the SPR spectrum were simulated and compared. Based on these measurements, a reflective and highly-sensitive refractive index sensor was fabricated and experimentally demonstrated. Finally, a dual-parameter SPR sensor for the simultaneous measurement of seawater salinity and temperature was proposed by coating polydimethylsiloxane (PDMS) on half of the sensor as a temperature sensitive material, whose sensitivities were demonstrated to be 0.31 nm/‰ and -2.02 nm/°C, respectively. The proposed sensor not only resolves the cross-sensitivity problem between salinity and temperature, but also provides a simple structure with low cost and high sensitivity.

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.

Algorithm of outlier removal for learning-based 3D point cloud registration is usually regarded as a classification problem. The core for this to be successful is to learn the discriminative inlier/outlier feature representations. This letter proposes a two-stage efficient network (MuSCLe-Reg) with multi-scale local feature fusion embedding. Specifically, we have designed a two-stage registration architecture. Firstly, we construct a graph topology feature consisting of correspondences and their feature neighborhoods. Then, the feature representation of correspondences was enhanced through multi-scale feature mapping and fusion (MSF). In addition, we propose a local correspondence rectification strategy (LCR) based on feature neighbors to evaluate initial candidates and generate higher-quality correspondences. The experimental results on various datasets show that compared with existing learning-based algorithms, this network has better accuracy and stronger generalization ability. Especially, in robustness testing across varying numbers of correspondences in the 3DLoMatch dataset, the algorithm demonstrated superior estimation performance compared to current state-of-the-art registration techniques.

Multiple linear and BP NN (back propagation neural network) models are used to approximate the complex nonlinear system, and different model reference adaptive controllers based on these models and different switching mechanisms are applied to a nonlinear system to trace a reference trajectory. From the simulation, it can be shown that the multiple model adaptive control method proposed in this paper can improve the control performance greatly compared with conventional adaptive neural network controller.