Various real-world applications with interval uncertainty, such as the path planning of mobile robot, layout of radio frequency identification readers and solar desalination, can be formulated as an interval multi-objective optimization problem (IMOOP), which is usually transformed into one or a series of certain problems to solve by using evolutionary algorithms. However, a definite characteristic among them is that only a single optimization task can be catched up at a time. Inspired by the multifactorial evolutionary algorithm (MFEA), a novel interval MFEA (IMFEA) is proposed to solve IMOOPs simultaneously using a single population of evolving individuals. In the proposed method, the potential interdependency across related problems can be explored in the unified genotype space, and multitasks of multi-objective interval optimization problems are solved at once by promoting knowledge transfer for greater synergistic search to improve the convergence speed and the quality of the optimal solution set. Specifically, an interval crowding distance based on shape evaluation is calculated to evaluate the interval solutions more comprehensively. In addition, an interval dominance relationship based on the evolutionary state of the population is designed to obtain the interval confidence level, which considers the difference of average convergence levels and the relative size of the potential possibility between individuals. Correspondingly, the strict transitivity proof of the presented dominance relationship is given. The efficacy of the associated evolutionary algorithm is validated on a series of benchmark test functions, as well as a real-world case of robot path planning with many terrains that provides insight into the performance of the method in the face of IMOOPs.

Real-time object detection on unmanned aerial vehicles (UAVs) poses a challenging issue due to the limited computing resources of edge devices. To address this problem, we propose a novel lightweight object detection network named LWUAVDet for real-time UAV applications. The detector comprises three core components: E-FPN, PixED Head, and Aux Head. Firstly, we develop an extended and refined topology in the Neck layer, called E-FPN, to enhance the multi-scale representation of each stage and alleviate the aliasing effect caused by the repetitive feature fusion of the Neck. Secondly, we propose a pixel encoder and decoder for dimension exchange between space and channel to achieve flexible and effective feature extraction in the Head layer, named PixED Head. Furthermore, Aux Head for the auxiliary task merely using the Head layer is presented for online distillation to enhance feature representation. Specially, in Aux Head, we introduce the weighted sum of Focal Loss and complete intersection over union loss for the cost matrix of the sample assigner to alleviate category imbalance and aspect ratio imbalance of the UAV data. The performance of our LWUAVDet is validated experimentally on the NVIDIA Jetson Xavier NX and Jetson Nano GPU devices. Extensive experiments demonstrate that the LWUAVDet models achieve a better trade-off between accuracy and latency on VisDrone, UAVDT, and VOC2012 datasets compared to state-of-the-art lightweight models.

Fault detection and diagnosis in the pumping unit is a challenging industrial problem for the system that exhibits nonlinearity, coupled parameters and time-varying noise. This paper proposes a novel unscented Kalman filter - radical basis function (UKF-RBF) method based on an adaptive noise factor (NAUKF-RBF) for fault diagnosis in the pumping unit. First, to avoid the curse of dimensionality, the Fourier descriptor method based on an approximate polygon is introduced to extract the features of the indicator diagram curve. Then, an RBF neural network is applied to establish the fault diagnosis model, and UKF is used to optimize the weighting, center, and width of the RBF neural network due to its strong nonlinear tracking performance. In particular, the adaptive noise factor method is presented to address the unknown and time-varying noise statistics in the actual production process. The proposed method is applied to the pumping unit system, and experimental results show the effectiveness and favorable recognition rate in classifying multiple faults.

Monitoring marine debris has long been a challenging issue owing to the complex and changeable underwater environment. To fast and accurately detect marine debris, in this paper, a novel object detection network termed as YOLOTrashCan is proposed for detecting underwater marine debris. The YOLOTrashCan model consists of feature enhancement and feature fusion. In the feature enhancement part, the ECA_DO-Conv_CSPDarknet53 backbone, which combines efficient channel attention (ECA) module and depthwise over-parameterized convolutional (DO-Conv), is proposed to extract the depth semantic features of marine debris. In the feature fusion part, the DPMs_PixelShuffle_PANET module is presented to improve the detection ability for marine debris, where dilated parallel modules (DPMs) with multi-scale dilated rate are designed as enhanced feature modules for the different scale objects of marine debris. Notably, the size of the network is only 214MB by using the DPMs method. Extensive experiments and thorough analysis are validated on the TrashCan 1.0 dataset. Experimental results show that the proposed algorithm not only improves the detection accuracy of underwater marine debris, but also reduces the size of the network model.

The main challenge of scene classification is to understand the semantic context information of high-resolution remote sensing images. Although vision transformer (ViT)-based methods have been explored to boost the long-range dependencies of high-resolution remote sensing images, the connectivity between neighboring windows is still limited. Meanwhile, ViT-based methods commonly contain a large number of parameters, resulting in a huge computational consumption. In this paper, a novel lightweight dual-branch swin transformer (LDBST) method for remote sensing scene classification is proposed, and the discriminative ability of scene features is increased through combining a ViT branch and convolutional neural network (CNN) branch. First, based on the hierarchical swin transformer model, LDBST divides the input features of each stage into two parts, which are then separately fed into the two branches. For the ViT branch, a dual multilayer perceptron structure with a depthwise convolutional layer, termed Conv-MLP, is integrated into the branch to boost the connections with neighboring windows. Then, a simple-structured CNN branch with maximum pooling preserves the strong features of the scene feature map. Specifically, the CNN branch lightens the LDBST, by avoiding complex multi-head attention and multilayer perceptron computations. To obtain better feature representation, LDBST was pretrained on the large-scale remote scene classification images of the MLRSN and RSD46-WHU datasets. These two pretrained weights were fine-tuned on target scene classification datasets. The experimental results showed that the proposed LDBST method was more effective than some other advanced remote sensing scene classification methods.

Improvements in the production and energy consumption of the aluminum electrolysis process (AEP) directly depend on the operating parameters of the electrolytic cell. To balance the conflicting goals of efficiency and productivity with reduced energy consumption and emissions, AEP operating parameter optimization is formulated as a constrained multiobjective optimization problem with competing objectives of current efficiency and cell voltage. Then, the improved multiobjective quantum-behaved particle swarm optimization (IMQPSO) algorithm is proposed. The application of an adaptive opposition-based learning strategy and a piecewise Gauss mutation operator can increase the diversity of the population and enhance the global search ability of the IMQPSO. To expand the creativity of the particles, two iterative methods of the mean best position with weighting and the attractor position are redesigned. Experimental analyses are conducted for the benchmark problems and a real case to verify the effectiveness of the proposed method.