Focused on the issue that multimodal emotion recognition in conversation (MERC) is difficult to effectively capture cross-modal semantic associations in conversation rounds and has limited discrimination ability for minority classes and semantically confusing classes of emotions, a new multimodal sentiment analysis model (FuseNet) is proposed. This model adopts the bidirectional attention dialogue encoder (BiDRN) to capture the context dependency of the dialogue, effectively integrates audio and visual cues from different speakers, and realizes dynamic multimodal fusion through the Hi-gated fusion module based on the hierarchical gated mechanism. Meanwhile, class-aware multimodal contrastive (CAMC) loss is introduced to enhance the inter-class discriminability and improve the discrimination ability of minority classes and semantically similar sentiment categories. Experimental results on the two benchmark ERC datasets of IEMOCAP and MELD show that compared with the current advanced model CORECT, the F1 score of the proposed framework has improved by 2.91% and 2.00%, respectively, which are better than the existing baseline model in terms of classification performance in most emotions, especially in identifying a few classes and semantic similar categories of emotions.

For coastal estuary water quality monitoring environments, traditional conductivity sensors suffer from issues such as bulky size and susceptibility to corrosion. This paper proposes a non-contact seawater conductivity measurement method based on single-coil sweep-frequency resonant impedance measurement. A coil equivalent circuit model in seawater environments was established, with in-depth analysis of the mechanism by which seawater eddy current losses affect system resonance characteristics. It elucidates the linear mapping relationship between resonant equivalent impedance and seawater conductivity under resonant conditions. Finite element simulation was employed to perform linear fitting on simulated data, validating the accuracy of theoretical derivations. Building on this, a sweep-frequency-based conductivity measurement system was constructed, achieving precise extraction of resonant point impedance. Experimental results demonstrate that in low-conductivity environments (saltwater intrusion), this method maintains consistent high measurement sensitivity, with a maximum fitting error of merely 0.0417 mS/cm. Compared to existing research, the proposed approach significantly enhances detection precision for subtle conductivity variations while improving anti-contamination capabilities. Furthermore, this method enables pre-calculation of fitting parameters via simulation software, thereby reducing human and material resources required for sensor calibration and optimizing sensor fabrication processes. It offers a novel solution for estuary water quality monitoring characterized by low-cost, high-reliability, and high-sensitivity.

This paper proposes a set of high-precision visual measurement methods to address the challenges of perspective distortion, thickness corner offset, and continuous tracking of multiple workpieces in the dynamic environment of intelligent manufacturing. In the preprocessing stage, the collected images are converted into approximately orthographic projection views through camera calibration and perspective correction. To obtain accurate edge images, this paper proposes an edge detection algorithm based on multi-scale edge fusion. By applying guided filtering to the collected images at different scales and then using dynamic Canny edge detection, the complete contour of the workpiece is obtained. To address the corner offset caused by the thickness of the workpiece, this paper proposes a high-precision corner extraction algorithm based on thickness interference elimination. By fusing sub-pixel corners and fitted corners, precise corner positioning is achieved. In addition, an object tracking algorithm is designed to match and identify the centroids of the workpieces, enabling automatic size recognition and measurement of multiple workpieces in consecutive frames. Experimental results show that this method can measure the sizes of multiple workpieces in arbitrary poses, with a mean error of 0.599 mm and a standard deviation of 0.172 mm, meeting the measurement requirements in industrial production.

Underwater litter detection is a crucial technology for maintaining the balance of underwater ecosystems. To address the challenge of significant variations in target scales encountered in underwater litter detection, we propose the YOLO11-MDA based on YOLO11 is proposed. Firstly, a multidomain feature extraction module MFEM is proposed, which is capable of extracting different scales of features from the input feature map by extracting the target features in both spatial and frequency domains, and enhances the ability of expression of the global features and local information. Second, the lightweight dynamic up-sampling DySample module is introduced to integrate contextual information and improve the quality and efficiency of up-sampling. Finally, the adaptive threshold focused classification loss ATFL is introduced to reduce the impact of the uneven distribution of multi-scale samples on the detection results and improve the detection accuracy of multi-scale targets. The experimental results show that compared with the baseline model, the mAP of YOLO11-MDA in TrashCan dataset and Trash_ICRA19 dataset reaches 91.4% and 97% respectively, which is an enhancement of 3.1% and 10.7%, and the FPS reaches the detection speed of 354.3 fps, which fully demonstrates that the overall performance of the improved model outperforms that of other algorithms, and it can provide an effective method for the automated monitoring of underwater environments.

To address the lack of real-time monitoring for wind turbine pitch control system power supplies and the cumbersome replacement procedures during failures, this paper proposes a hot-swappable dual-module switching power supply system based on microcontroller control. This solution enables faulted power supply replacement without system shutdown. This solution adopts a modular dual-power-supply redundant architecture. The power supply is designed based on a flyback circuit with modular components, featuring a wide input voltage range of 20~80 V and a stable output of up to 24 V/3 A. It integrates an STM32 microcontroller and TPS2491 hot-swap chip, enabling automatic switching during power failures and supporting rapid hot-swap replacement of faulty units. A monitoring platform developed using the Bootstrap5 framework was established to achieve intelligent power supply monitoring and management. Experimental results demonstrate that the system achieves short switching times (≤10 ms under full load conditions) and minimal voltage dips during power failures. Concurrently, the monitoring platform enables real-time power status monitoring, fault alerts and operational data analysis, thereby enhancing the power supply reliability of the variable pitch system.