Monitoring data contain the important status information of the monitored object, and are the basis for following data mining and analysis. However, the monitoring data usually suffer the pollution of the outliers, leading to negative effect on the subsequent data processing. To address the problem, this paper proposed an outlier detection method based on stacked autoencoder (SAE). SAE has a powerful capability of feature extraction and greatly preserves the original information of the data. The trained SAE by normal data can learn the characteristics of normal data. When a set of data with outliers are inputted to the trained network, there are larger reconstruction errors at the outliers between the original input data and the reconstructed data obtained by using the encoding parameters and the decoding parameter mapping, which provides a basis for locating outliers. Meanwhile, this paper introduced the Grubbs criterion and the PauTa criterion to identify the reconstruction errors corresponding to the outliers based on the traditional threshold method. The method can quickly isolate the abnormal data from the normal data according to the reconstruction error and the identification criterion. The effectiveness and superiority of the proposed method have been validated by experiment on real data and comparisons with traditional outlier detection algorithms.

Lightweight anchor-free detectors are currently gaining more and more popularity in the field of industrial defect detection. In general, it is difficult to achieve competitive performance compared to deep anchor-based detector. Knowledge distillation is an effective way to solve this problem. In this article, we propose a novel anchor-free method based on knowledge distillation, called D-CenterNet. In our method, we employ an optimization approach using an adaptive label encoding strategy to better regress the centroid information of extreme aspect ratio (EAR) defects and improve detection accuracy. Backbone is improved for industrial defects with large aspect ratios and complex backgrounds, and strip pooling (SP) module is introduced to extract image features more efficiently. By redefining the distilled knowledge as the interrelationship between the output information, we train the student model using the pseudo-label generated by the teacher model, and then improve the model using the real label data to further enhance the performance of the student model without increasing model complexity. The experimental results on Aliyun Tianchi fabric dataset demonstrate that D-CenterNet achieves 75.5% mean average precision (mAP) and increases the mAP of the student model (60.1%, before distillation) to 63.8% (after distillation) without increasing the complexity of the model. Extensive experimental results validate the superiority of our method and demonstrate that our D-CenterNet achieves state-of-the-art (SOTA) performance in both accuracy and real-time performance.

Detecting tiny objects in remote sensing images has always been a challenging and intensive research area. This problem has not been well solved due to the fact that object detection in remote sensing images is characterized by large scale variations and complex backgrounds. On this basis, we propose the EMS-Net constructed based on YOLOv8s for tiny object detection network in remote sensing images. First, a new module multi-branch context aggregation(MCA) is proposed to improve deep feature extraction and deep feature fusion of the model. In addition, we use our self-designed multi-scale feature communication module (MFCM) aimed at reducing the loss of semantic information of object and mitigating the obstruction of foreground object by complex background. Finally, Wise IoU-Normalized Wasserstein distance (WIoU-NWD) is used as the bounding box regression loss to adapt the model to different object scale while improving the ability to localize tiny object. Comprehensive experiments on three popular datasets demonstrate that our method outperforms existing detectors, particularly in detecting tiny objects. Specifically, our approach achieves the mean average precision (mAP) of 77.2% on the DIOR dataset, 96.7% on the RSOD dataset and 75.1% on the DOTA-v1.5 dataset.

The main goal of this work is to study an averaging principle for two-time-scales stochastic partial differential equations with jumps. The solutions of reduced equations with modified coefficients are derived to approximate the slow component of the original equation under suitable conditions. It is shown that the slow component can strongly converge to the solution of the corresponding reduced equation in the pth-mean. Our key and novel idea is how to cope with the changes caused by jumps and higher order moments.

The development of solar-powered unmanned aerial vehicles (UAVs) primarily focuses on enhancing the efficiency of the propulsion system to minimize energy consumption during the conversion of valuable solar energy. However, due to the unique nature of UAV propulsion systems, there is limited cross-reference ability among existing solar-powered UAV systems. This paper proposes an integrated design approach for the propulsion system and UAV structure. Based on this approach, an overall design is conducted for the solar-powered UAV, including initial design goals and performance parameters. Aerodynamic layout design and performance estimation are carried out, and a prototype is fabricated and assembled for flight testing validation. The results demonstrate the significant importance of this approach in improving the efficiency of the solar-powered UAV propulsion system.

Flutter is an extremely significant academic topic in both aerodynamics and aircraft design. Since flutter can cause multiple types of phenomena including bifurcation, period doubling, and chaos, it becomes one of the most unpredictable instability phenomena. The complexity of modeling aeroelasticity of high flexibility wings will be substantially simplified by investigating the prospect of system identification techniques to forecast flutter velocity. Therefore, a novel neural network (NN)-based method for aeroelastic system identification is proposed. The proposed NN-based approach constructs an NN framework of high flexibility wings flutter models with different materials and sizes, which can effectively predict the flutter velocity of flexible wings. The accuracy of the method is demonstrated by comparing with the simulation results.