Semi-supervised learning for forest fire segmentation using UAV imagery

Unmanned aerial vehicles (UAVs) are an efficient tool for monitoring forest fire due to its advantages, e.g., cost-saving, lightweight, flexible, etc. Semantic segmentation can provide a model aircraft to rapidly and accurately determine the location of a forest fire. However, training a semantic segmentation model requires a large number of labeled images, which is labor-intensive and time-consuming to generate. To address the lack of labeled images, we propose, in this paper, a semi-supervised learning-based segmentation network, SemiFSNet. By taking into account the unique characteristics of UAV-acquired imagery of forest fire, the proposed method first uses occlusion-aware data augmentation for labeled data to increase the robustness of the trained model. In SemiFSNet, a dynamic encoder network replaces the ordinary convolution with dynamic convolution, thus enabling the learned feature to better represent the fire feature with varying size and shape. To mitigate the impact of complex scene background, we also propose a feature refinement module by integrating an attention mechanism to highlight the salient feature information, thus improving the performance of the segmentation network. Additionally, consistency regularization is introduced to exploit the rich information that unlabeled data contain, thus aiding the semi-supervised learning. To validate the effectiveness of the proposed method, extensive experiments were conducted on the Flame dataset and Corsican dataset. The experimental results show that the proposed model outperforms state-of-the-art methods and is competitive to its fully supervised learning counterpart

A multiscale point-supervised network for counting maize tassels in the wild

Accurate counting of maize tassels is essential for monitoring crop growth and estimating crop yield. Recently, deep-learning-based object detection methods have been used for this purpose, where plant counts are estimated from the number of bounding boxes detected. However, these methods suffer from 2 issues: (a) The scales of maize tassels vary because of image capture from varying distances and crop growth stage; and (b) tassel areas tend to be affected by occlusions or complex backgrounds, making the detection inefficient. In this paper, we propose a multiscale lite attention enhancement network (MLAENet) that uses only point-level annotations (i.e., objects labeled with points) to count maize tassels in the wild. Specifically, the proposed method includes a new multicolumn lite feature extraction module that generates a scale-dependent density map by exploiting multiple dilated convolutions with different rates, capturing rich contextual information at different scales more effectively. In addition, a multifeature enhancement module that integrates an attention strategy is proposed to enable the model to distinguish between tassel areas and their complex backgrounds. Finally, a new up-sampling module, UP-Block, is designed to improve the quality of the estimated density map by automatically suppressing the gridding effect during the up-sampling process. Extensive experiments on 2 publicly available tassel-counting datasets, maize tassels counting and maize tassels counting from unmanned aerial vehicle, demonstrate that the proposed MLAENet achieves marked advantages in counting accuracy and inference speed compared to state-of-the-art methods. The model is publicly available at https://github.com/ShiratsuyuShigure/MLAENet-pytorch/tree/main

State Control and the Effects of Foreign Relations on Bilateral Trade

Do states use trade to reward and punish partners? WTO rules and the pressures of globalization restrict states’ capacity to manipulate trade policies, but we argue that governments can link political goals with economic outcomes using less direct avenues of influence over firm behavior. Where governments intervene in markets, politicization of trade is likely to occur. In this paper, we examine one important form of government control: state ownership of firms. Taking China and India as examples, we use bilateral trade data by firm ownership type, as well as measures of bilateral political relations based on diplomatic events and UN voting to estimate the effect of political relations on import and export flows. Our results support the hypothesis that imports controlled by state-owned enterprises (SOEs) exhibit stronger responsiveness to political relations than imports controlled by private enterprises. A more nuanced picture emerges for exports; while India’s exports through SOEs are more responsive to political tensions than its flows through private entities, the opposite is true for China. This research holds broader implications for how we should think about the relationship between political and economic relations going forward, especially as a number of countries with partially state-controlled economies gain strength in the global economy

Path Planning of Laser Soldering System Based on Intelligent Algorithm

Laser soldering has been gradually applied to the soldering of electronic components due to the rapid development of microelectronics. However, it is inefficient to use a mechanical shaft to move a laser beam. Here, a laser soldering system is constructed using galvanometer scanning, and an intelligent algorithm is also introduced to optimize the soldering path. Firstly, a laser soldering system for scanning of galvanometers is established, and the functions of visual monitoring, motion planning and parameter integration are presented. Secondly, the position of the laser beam and the corresponding soldering spot are determined, and the coordinate information is provided to plan a route by camera calibration and coordinate system transformation. Finally, the problem of path planning in this system is decomposed into the generation of the soldering point full coverage processing frame, and the route optimization of processing platform and laser beam motion. Furthermore, an improved clustering algorithm, based on the characteristics of system structure, and a hybrid optimization algorithm are designed to deal with the generation of the soldering point full coverage processing frame, the route optimization of processing platform and laser beam motion. In addition, the simulations and experiments are verified by test board. These findings shown that the established system and designed optimization algorithm can promote the efficiency of laser soldering

Polarization tunability in multiferroic DyMn2O5: Influence of Y and Eu co-doping and 3d-4f exchange

Coupling effects among spin, charge, and lattice in a strongly correlated system are critical for next generation spintronic and data storage devices. However, the complex effects are elusive and difficult to distinguish their contributions to polarization modulation. Here we tailored the polarization by co-doping of non-magnetic Y and Eu at A-sites in DyMn2O5. The structure, specific heat, magnetism, and ferroelectricity of the polycrystalline Dy1-x(Eu0.24Y0.76)(x)Mn2O5 ceramics were comprehensively explored. Interestingly, the co-doping does not cause lattice distortion of DyMn2O5, and all the ceramics are orthorhombic structures, while the independent Dy3+ spin order and the Dy3+-Mn3+ coupling can be suppressed. With increasing the co-doping content x, the spins related properties associated with the Dy3+-Mn4+-Dy3+ sub-lattice are progressively inhibited, while they keep less disturbance in the Mn3+-Mn4+-Mn3+ block. Moreover, the spin coupling of Dy3+-Mn3+ ions is stronger again the magnetic field than that of Dy3+-Mn3+. Our results enhance the understanding of ferrielectricity in DyMn2O5, and provide a method for controlling the polarization in the multiferroic manganite coexisting 3d and 4f elements