UAV Cluster Mission Planning Strategy for Area Coverage Tasks.

In the context of area coverage tasks in three-dimensional space, unmanned aerial vehicle (UAV) clusters face challenges such as uneven task assignment, low task efficiency, and high energy consumption. This paper proposes an efficient mission planning strategy for UAV clusters in area coverage tasks. First, the area coverage search task is analyzed, and the coverage scheme of the task area is determined. Based on this, the cluster task area is divided into subareas. Then, for the UAV cluster task allocation problem, a step-by-step solution is proposed. Afterward, an improved fuzzy C-clustering algorithm is used to determine the UAV task area. Furthermore, an optimized particle swarm hybrid ant colony (PSOHAC) algorithm is proposed to plan the UAV cluster task path. Finally, the feasibility and superiority of the proposed scheme and improved algorithm are verified by simulation experiments. The simulation results show that the proposed method achieves full coverage of the task area and efficiently completes the task allocation of the UAV cluster. Compared with related comparison algorithms, the method proposed in this paper can achieve a maximum improvement of 21.9% in balanced energy consumption efficiency for UAV cluster task search planning, and the energy efficiency of the UAV cluster can be improved by up to 7.9%.

IRMSwin-T: A lightweight shifted windows transformer based on inverted residual structure and residual multi-layer perceptron for rolling bearing fault diagnosis.

The data-driven fault diagnosis method has achieved many good results. However, classical convolutional and recurrent neural networks have problems with large parameters and poor anti-noise performance. To solve these problems, we propose a lightweight shifted windows transformer based on inverted residual structure and residual multi-layer perceptron (IRMSwin-T) for fault diagnosis of rolling bearings. First, the original data are expanded by using overlapping sampling technology. Then, the collected one-dimensional vibration signals are vector serialized by using the patch embedding strategy. Finally, the IRMSwin-T network is developed to extract features of vector sequences and classify faults. The experimental results showed that compared with mainstream lightweight models, the IRMSwin-T model in this paper has fewer parameters and higher diagnostic accuracy.

An improved re-parameterized visual geometry group network for rolling bearing fault diagnosis.

The spectrum of data-driven fault diagnosis models is greatly expanded by deep learning. However, classical convolution and multiple branching structures have their faults in computational complexity and feature extraction. To address these issues, we propose an improved re-parameterized visual geometry group (VGG) network (RepVGG) for rolling bearing fault diagnosis. In order to meet the requirements of neural networks for the amount of data, data augmentation is performed to increase the amount of original data. Then, the original one-dimensional vibration signal is processed into a single-channel time-frequency image using the short-time Fourier transform and converted into a three-channel color time-frequency image using pseudo-color processing technology. Finally, the RepVGG model with an embedded convolutional block attention mechanism structure is developed to extract defect features from three-channel time-frequency images and perform defect classification. Two datasets of vibration data from rolling bearings are used to demonstrate the strong adaptability of this method compared with other methods.

Research on Configuration Constraints of Airborne Bistatic SARs.

Based on the analysis of the airborne bistatic synthetic aperture radar (SAR) imaging geometric mode, an extended nonlinear chirp scaling algorithm is employed to simulate and verify the imaging effect of the bistatic SARs. A gradient theory-based two-dimensional resolution bistatic SAR model is proposed, and the constraints of the multi-platform flight trajectory parameters meeting the imaging accuracy of the bistatic SAR are analyzed. Finally, through the bistatic SAR imaging simulation of cooperative flight trajectories under various situations, the spatial configuration constraint envelope between the flight vehicles to achieve the optimal resolution is revealed. The results of this paper will provide a theoretical reference for the SAR application in formation flight control.

Signal Source Positioning Based on Angle-Only Measurements in Passive Sensor Networks.

Some passive sensors can measure only directions of arrival of signals, but the real positions of signal sources are often desirable, which can be estimated by combining distributed passive sensors as a network. However, passive observations should be correctly associated first. This paper studies the multi-target data association and signal localization problem in distributed passive sensor networks. With angle-only measurements from distributed passive sensors, multiple lines in a 3-dimensional (3D) scenario can be built and then those that will intersect in a small volume in 3D are classified into the same source. The center of the small volume is taken as an estimate of the signal source position, whose statistical distributions are formulated. If the minimum distance is less than an association threshold, then two lines are considered to be from the same signal source. In numerical results, the impacts of angle measurement accuracy and platform self-positioning accuracy are analyzed, indicating that this method can achieve a prescribed data association rate and a high positioning performance with a low computation cost.

Down-regulation of MST1 in hippocampus protects against stress-induced depression-like behaviours and synaptic plasticity impairments.

Depression is a common mental disorder, and its main environmental risk factor is chronic stress. The activation of mammalian STE20-like kinase 1 (MST1), a key factor involved in the underlying pathophysiology of stress, can trigger synaptic plasticity impairment, neuronal dysfunction and neuroinflammation. However, it is unclear whether down-regulation of MST1 in the hippocampus protects against stress-induced behavioural dysfunctions. In this study, three mouse models were used to assess the role of MST1 in stress. Various behavioural tests, in vivo electrophysiological recordings, Western blotting, Golgi staining and immunofluorescence assay were used. The data showed that the level of phospho-MST1 (T183) was significantly increased in the hippocampus of mice subjected to chronic unpredictable mild stress (CUMS) and that mice with MST1 overexpression showed depression-like behaviours. Importantly, the impairment of cognitive functions and the hippocampal synaptic plasticity induced by CUMS were significantly improved by MST1 knockdown, suggesting that MST1 down-regulation effectively protected against stress-induced behavioural dysfunctions. Moreover, MST1 knockdown suppressed CUMS-induced microglial activation, reduced the abnormal expression of inflammatory cytokines and impeded the activation of p38, implying that the antidepressant-like effects of MST1 knockdown were associated with inhibiting the p38 pathway. These findings suggest that hippocampal MST1 is an essential regulator of stress, which can be an ideal target for the development of antidepressants in the future.

An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods.

Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers' first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.

Single Image Super-Resolution Based on Global Dense Feature Fusion Convolutional Network.

Deep neural networks (DNNs) have been widely adopted in single image super-resolution (SISR) recently with great success. As a network goes deeper, intermediate features become hierarchical. However, most SISR methods based on DNNs do not make full use of the hierarchical features. The features cannot be read directly by the subsequent layers, therefore, the previous hierarchical information has little influence on the subsequent layer output, and the performance is relatively poor. To address this issue, a novel global dense feature fusion convolutional network (DFFNet) is proposed, which can take full advantage of global intermediate features. Especially, a feature fusion block (FFblock) is introduced as the basic module. Each block can directly read raw global features from previous ones and then learns the feature spatial correlation and channel correlation between features in a holistic way, leading to a continuous global information memory mechanism. Experiments on the benchmark tests show that the proposed method DFFNet achieves favorable performance against the state-of-art methods.

Research on Detection and Modeling of Chlorothalonil Pesticide Residue in Typical Fruit Juice with Fluorescence Spectrometry.

Fluorescence spectrum of mixed solution between chlorothalonil and typical fruit juice (apple juice and peach juice) were obtained with fluorescence spectrophotometer. It was found that there was a characteristic peak in 352 nm of chlorothalonil. Regression analysis was applied in the modeling of relationship between fluorescence intensity and chlorothalonil concentration. Estimation function of chlorothalonil concentration was deduced through fluorescence spectrum and its derivative fluorescence spectrum. The correlation coefficients of the exponential prediction model under two kinds of spectral patterns were higher than 0.99, which was better than the linear function model. For the two kinds of fruit juice, the average recoveries of the exponential model function under the original spectral pattern were 101% and 100% while the average recoveries of the linear model were 110% and 118%, respectively; The average recoveries of the exponential model function under the derivative spectral mode were 101% and 102%, and the average recoveries of the linear model were 109% and 120% respectively. The analysis results showed that fluorescence spectrometry can be used to detect and predict the chlorothalonil residue in fruit juice, and the performance of established exponential function model was better than the linear function model. At the same time, derivative fluorescence spectrometry method was found to have no significant advantage in the concentration prediction model of chlorothalonil residue in fruit juice, so the original fluorescence spectrum can be directly applied in the modeling analysis.

[A new method for small displacement test and measurement based on the light reflection theory].

A new idea for small displacement test and measurement system based on light reflection is presented in this paper. Some theoretical researches using the method and experiments in practice were carried out. The results proved that the theory is feasible and efficient. Compared with the traditional small displacement test and measurement system, such as mechanical displacement magnifier; resistance strain test and measurement method; piezoelectric material strain test and measurement system and so on, this method has the following advantages: it creates little disturbance of the test and measurement system; the displacement magnification coefficient is high and is convenient for user to adjust; the test and measurement precision is high and is very easy for its realization; and the cost is low. It fits a lot of test and measurement situations.