The control of complex industrial processes has been a forefront research topic. Biodiesel production, as a typical complex industrial reaction process, exhibits multivariable coupling, nonlinearity, and high latency, making it challenging for traditional control methods to achieve real-time control under varying operating conditions. To address this issue, this paper proposes a control method based on the twin delayed deep deterministic policy gradient (TD3) algorithm, which dynamically adjusts process parameters to achieve the adaptive optimization control of complex processes. A simplified biodiesel production process model was established to simulate the actual production process. Subsequently, a controller based on the TD3 algorithm was designed and implemented for this model. To evaluate the performance of the proposed controller, comparative experiments were conducted with traditional controllers and deep deterministic policy gradient (DDPG) controllers. The effects of different control strategies were analyzed through simulation experiments. The results demonstrate that the proposed controller achieves the objectives while exhibiting a lower overshoot and shorter settling time and fully validates the effectiveness of the proposed control strategy in terms of both the dynamic and steady-state performance in the production process.

Accurate microscopic images segmentation of activated sludge is essential for monitoring wastewater treatment processes. However, it is a challenging task due to poor contrast, artifacts, morphological similarities, and distribution imbalance. A novel image segmentation model (FafFormer) was developed in the work based on Transformer that incorporated pyramid pooling and flow alignment fusion. Pyramid Pooling Module was used to extract multi-scale features of flocs and filamentous bacteria with different morphology in the encoder. Multi-scale features were fused by flow alignment fusion module in the decoder. The module used generated semantic flow as auxiliary information to restore boundary details and facilitate fine-grained upsampling. The Focal–Lovász Loss was designed to handle class imbalance for filamentous bacteria and flocs. Image-segmentation experiments were conducted on an activated sludge dataset from a municipal wastewater treatment plant. FafFormer showed relative superiority in accuracy and reliability, especially for filamentous bacteria compared to existing models.

In order to solve the problem of small and dense targets in drone aerial images, a small target detection model based on dual branches and adaptive feature fusion is proposed. The model first constructs a small target detection framework with dual branches to improve the detection accuracy while reducing the number of parameters. Secondly, the model introduces semantic and detail injection (SDI) in the neck network and embeds bidirectional adaptive feature fusion in the detection head to innovate and optimize the feature fusion mechanism, achieve the full interaction of deep and shallow information, enhance the feature representation of small targets, and overcome the problem of scale inconsistency. Finally, in order to focus on the target area more accurately, we introduce the large separable kernel attention mechanism into the convolutional layer to provide it with a richer and more comprehensive feature representation, which significantly improves the detection accuracy of targets of different scales. The experimental results show that the model algorithm performs well in the VisDrone2019 dataset. Compared with the original model, the mAP50 of this model increases by 20.9%, the mAP50–95 increases by 23.7%, and the total number of parameters decreases by 61.3%, making it more suitable for drones.

The electromagnetic (EM) inversion refers to reconstructing the EM properties of unknown targets from measured scattered fields. In practical applications, various algorithms have been developed, especially those that rely on greedy pursuit (GP) within the compressive sensing framework. However, the GP-based algorithms still suffer from several intractable issues, such as poor priors of the pre-given sparsity index, insufficient robustness of generated single support, and possible failure when reconstructing non-sparse targets. A novel algorithm based on deep-first multipath matching pursuit (MMP-DF) assisted by a convolutional neural network (CNN) is proposed to overcome these issues. Specifically, the CNN is designed to predict the sparsity index, and MMP-DF is implemented to identify multiple promising supports for improving the likelihood of finding favorable support, and the Fourier basis is applied to transform the case of non-sparse targets into sparse ones. Theoretical analysis and numerical simulations show that, compared with the algorithm based on orthogonal matching pursuit (OMP), the proposed algorithm achieves improved and robust results in the case of non-sparse targets.