Slope entropy (SlEn) is a time series complexity indicator proposed in recent years, which has shown excellent performance in the fields of medical and hydroacoustics. In order to improve the ability of SlEn to distinguish different types of signals and solve the problem of two threshold parameters selection, a new time series complexity indicator on the basis of SlEn is proposed by introducing fractional calculus and combining particle swarm optimization (PSO), named PSO fractional SlEn (PSO-FrSlEn). Then we apply PSO-FrSlEn to the field of fault diagnosis and propose a single feature extraction method and a double feature extraction method for rolling bearing fault based on PSO-FrSlEn. The experimental results illustrated that only PSO-FrSlEn can classify 10 kinds of bearing signals with 100% classification accuracy by using double features, which is at least 4% higher than the classification accuracies of the other four fractional entropies.

Fire detection using aerial platform is an important technology for forest surveillance. But the real-time detection capability is still a challenging problem. In this paper, an edge computing-based real-time forest fire detection strategy is designed using the unmanned aerial vehicle (UAV). The objective is to improve the timely response capability and the detection accuracy for early-stage small fires. The thermal and color images obtained from the onboard cameras are registered to the same scale and merged with appropriate proportions. These pre-processed dual-modal images become the input for training the fire detection network model. To deploy this model on the resource-constrained UAV edge computing device, it is compressed and accelerated to reduce size and enhance efficiency. Experiments based on self-made UAV dual-modal images of simulated fire scenarios and public datasets derived from real forest environments are conducted to validate the accuracy and speed of the proposed method. Experimental results show that, on the self-made dataset, the mAP is 93.76%, and the inference speed reaches 34.6FPS on the ground computer. On the public dataset, the mAP is 97.53%, and the inference speed reaches 16FPS on the edge computing device iCrest 2-s. Compared to several state-of-the-art methods, our proposed method achieves a good trade-off between accuracy and speed

This paper presents a mission allocation and path planning solution for the pesticide spraying mission of dense trees in hilly terrains using cooperative multicopters. The problem is formulated as a single-depot single-end multiple traveling salesman problem (mTSP). Three different algorithms, namely classical mTSP algorithm, Grouping-TSP combined algorithm, and Grouping-TSP decoupled algorithm, are developed to solve the proposed mTSP. Simulation results indicate that the classical mTSP algorithm provides an evenly distributed task allocation, while the Grouping-TSP combined algorithm delivers the optimal solution. Additionally, the Grouping-TSP decoupled algorithm minimizes computational complexity. Both Grouping-TSP algorithms integrate a sub-regions segmentation process to guarantee collision avoidance between the multicopters.

Autonomous landing is one of the key technologies for unmanned aerial vehicles (UAVs) which can improve task flexibility in various fields. In this paper, a vision-based autonomous landing strategy is proposed for a quadrotor micro-UAV based on a novel camera view angle conversion method, fast landing marker detection, and an autonomous guidance approach. The front-view camera of the micro-UAV video is first modified by a new strategy to obtain a top-down view. By this means, the landing marker can be captured by the onboard camera of the micro-UAV and is then detected by the YOLOv5 algorithm in real time. The central coordinate of the landing marker is estimated and used to generate the guidance commands for the flight controller. After that, the guidance commands are sent by the ground station to perform the landing task of the UAV. Finally, the flight experiments using DJI Tello UAV are conducted outdoors and indoors, respectively. The original UAV platform is modified using the proposed camera view angle-changing strategy so that the top-down view can be achieved for performing the landing mission. The experimental results show that the proposed landing marker detection algorithm and landing guidance strategy can complete the autonomous landing task of the micro-UAV efficiently.

Classification of coronary artery stenosis is essential in assisting physicians in diagnosing cardiovascular diseases. However, due to the complexity of medical diagnosis and the confidentiality of medical images, it is difficult to obtain many coronary artery stenosis image samples for scientific research in general. In addition, the degree, location, and morphology of coronary artery stenosis in different patients, as well as the noise in computed tomography angiography (CTA) images, make it challenging to extract typing features effectively. To address the above problems, first, a joint segmentation method is proposed based on maximum between-class variance and region growing to extract key regions from CTA images to facilitate further feature extraction. Then, a coronary artery stenosis classification model based on convolutional block attention module (CBAM) and transfer learning is constructed, which can effectively improve the model training effect under the insufficient samples. Finally, a dataset of CTA images from actual patients is applied for experimental verification. The experimental results show that the accuracy (Acc) of the proposed method is up to 98.99%, which is greatly improved compared with several machine learning algorithms and neural network methods. It can be concluded that the accuracy of coronary stenosis classification can be considerably improved, and a reasonable scientific basis is provided for clinical medical diagnosis.

This article focuses on the 3-D guidance strategy for a reusable launch vehicle (RLV) during terminal area energy management (TAEM) phase. Based on sliding-mode and shrinking-horizon techniques, the proposed scheme consists of trajectory generation and correction mechanisms, which can enhance the guidance precision and robustness against disturbances. The RLV guidance model, in the form of a set of highly nonlinear differential equations in the time domain, is recast as an altitude-domain model. By this means, the main characteristics of TAEM gliding motion are extracted. The altitude-domain model is thereby used for trajectory generation. A sliding surface and a guidance law are proposed. Hybrid TAEM constraints can be fully satisfied when the proposed guidance law drives the altitude-domain vehicle model to the designated altitude. Using the proposed guidance law as the input of the altitude-domain model, a constrained TAEM trajectory is generated, leading to TAEM guidance commands simultaneously. The commands are utilized to drive the time-domain model to the terminal target. In an attempt to compensate for model uncertainties and initial deviations, the guidance commands are modified periodically by the shrinking-horizon correction mechanism according to current states. Simulations on different scenarios are provided to demonstrate the performance of the proposed guidance strategy.