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针对传统的可重构智能表面（reconfigurable intelligent surface，RIS，也称智能超表面）在无人机辅助安全通信中存在的灵活性不足和系统安全保障能力不强的问题，以及当前无人机半双工通信系统普遍面临的频谱利用率低和通信效率低的挑战，提出了一种空中可重构智能表面（aerial reconfigurable intelligent surface，ARIS）与全双工无人机的双机协同解决方案，旨在提升面对地面窃听者时的通信安全性和效率。联合优化用户通信调度、传输功率、无人机发送的干扰功率及其飞行轨迹，以及ARIS的相移和轨迹，设计了一个旨在最大化最小平均可达安全速率的优化问题，并提出基于交替优化（alternating optimization，AO）、半定松弛（semi-definite relaxation，SDR）技术和连续凸近似（successive convex approximation，SCA）的迭代算法，以获得高质量的次优解。仿真结果验证了ARIS与全双工无人机结合的双机协作方案在提升通信质量和安全性能方面的有效性，展示了其在复杂安全通信环境下的优越性。

面向未来海上分布式作战需求，以对抗环境下分布式作战场景中智能导弹齐射突防水面舰艇为背景，首先，分析了导弹突防策略规划问题；其次，设计了基于奖励塑造强化学习的智能导弹突防策略规划方法；然后，在墨子联合作战推演系统上构建导弹突防舰艇的作战想定，实验结果表明本文方法的智能导弹突防打击成功率为79%，验证了基于奖励塑造强化学习方法的有效性；最后，经过复盘发现，奖励塑造实验涌现出智能导弹的4类对舰打击突防策略：集中迂回攻击、分散突防多向攻击、分组延时攻击、巡弋探测指引攻击。

精确掌握材料高温下的性能参数,对结构抗火设计的准确性具有十分重要的意义。以S30408奥氏体不锈钢为研究对象,在现有S30408不锈钢高温材料性能试验的基础上,研究发现S30408不锈钢在同种条件下,相比碳钢具有较低的升温速率,且S30408不锈钢在600℃时仍能保持50%以上的强度与刚度。但不锈钢具有更大的热膨胀率,高温给结构的稳定性带来的威胁更大。其次,对比分析了国内外学者所提出的常温及高温下不锈钢的本构模型,根据S30408不锈钢高温下性能参数的变化规律,欧洲规范给出的建议值偏不安全,建议在不锈钢结构设计中采用Rasmussen模型,在结构抗火的数值模拟分析中采用Chen和Young的模型。

为研究低屈服点钢材LY100在反复拉力作用下的力学性能,设计LY100钢与Q345钢或Q235钢焊接连接的材性试件,共进行4组18个材性试件的单调和反复受拉加载试验研究。获得6种不同加载制度下试件的荷载-位移曲线、应力-应变曲线、滞回性能、超低周疲劳破坏机理以及延性特征等。研究结果表明,焊接接头对试件的超低周疲劳性能有不利影响,焊接接头使LY100钢材力学性能的稳定性和延性变差;LY100钢与Q345钢或Q235钢焊接后,材性试件的强度提高但延性下降;反复拉力作用下,带有焊接接头的材性试件呈现强度硬化、延性下降的受力特征,而未带焊接接头的材性试件会产生强度下降、延性增强的现象。

对基于40 Gbit/s归零码-差分相移键控（RZ-DPSK）的密集波分复用（DWDM）系统,为抑制包括自相位调制（SPM）和交叉相位调制（XPM）效应在内的克尔非线性引起的光脉冲相位抖动,提出了4种色散管理方案:前置、后置、中间和平衡补偿方案。分析了这4种补偿方案在抑制克尔非线性效应方面的优劣。通过选择合适的方案,可使RZ-DPSK系统在抑制SPM和XPM效应上平均有2 dB的性能提高。

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The breakthrough and progress of intelligent gaming technology with deep reinforcement learning as the core in the field of games provide a method reference for the research of agents in sea-air wargames. The architecture design of the agent is the primary core key problem that needs to be solved, and a good architecture can reduce the complexity and difficulty of training and accelerate the convergence of policies. A stochastic game model of sea-air cross-domain cooperative decisionmaking has been proposed, and its corresponding equilibrium solution concepts have been analyzed. Based on the analysis of typical agent frameworks, aiming at the decision-making gaming process of sea-air wargames, and then an agent bi-level architecture based on multi-Agent hierarchical reinforcement learning is proposed, which can effectively solve the problems of collaboration and dimensional disaster. The key technologies are analyzed from four aspects; force coordination, agent network design, adversary modeling and training mechanism. Hoping to provide architectural guidance for the subsequent design and implementation of sea-air wargaming agents. [ABSTRACT FROM AUTHOR]

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In view of the difficulties of wax formation during the production/transportation of crude oil and the huge economic losses caused by the wax plugging of oil wells・ Studying wax removal and prevention technology is of great significance to ensure the high efficient production of crude oiL The mechanism and the influence of different factors on the characteristic of wax precipitation are summarized・ The research progress and current situation of chemical wax removal solvent are reviewed, including the mechanism and research achievement of oil-soluble, water-soluble and emulsion removal solvent・ To solved the high carbon number wax deposition and asphaltene-wax co-position deplugging solvent is proposed as the development trend of wax removal solvent, hoping to provide reference for further research and development of wax removal and prevention. [ABSTRACT FROM AUTHOR]

Low-light image segmentation is always the difficulty of image segmentation. The low contrast and high fuzziness caused by low light make this kind of image segmentation much more difficult than general image segmentation. In order to improve the accuracy of semantic segmentation in low light environment, this paper proposed a semantic segmentation model of low light scene with filtering light style under noise guidance(SFIS) according to the characteristics of low-light image. The model comprehensively used signal-to-noise ratio as prior knowledge, and adopted different distance interaction for different noise regions in the image by guiding the self-attention operation in the long distance branch and the feature fusion of long/short distance branches. This paper also further designed an illumination filter, which was a module that further extracted the illumination style information from the overall style of the image. By alternately training the illumination filter and the semantic segmentation model, the lighting style gap between different lighting conditions was gradually reduced, so that the segmentation network could learn illumination invariant features. The proposed model outperforms the previous work on the dataset LLRGBD and achieves the best results. The mIoU on the real dataset LLRGBD-real reaches 66.8%, it shows that the proposed long and short distance branch module and the illumination filter module can effectively improve the semantic segmentation ability of the model in low light environment. [ABSTRACT FROM AUTHOR]

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Porous material structures have been widely used in civil engineering, mechanical engineering, aerospace engineering and other fields due to their high specific strength and specific stiffness. The stochastic response analysis of porous material structures under random excitations deserves more attention. The multiscale governing differential equations for porous material structures were derived based on the multiscale asymptotic-homogenization method (AHM), and the macroscale and microscale explicit time-domain expressions of structural responses were further established. On this basis, the statistical moments of dynamic responses of porous material structures under non-stationary random excitations were achieved with the explicit time-domain method (ETDM). The proposed method combines the advantages of the AHM for high-efficiency explicit formulation of macroscale and microscale dynamic responses of porous material structures and the benefits of the ETDM for fast analysis of non-stationary random vibration problems. A numerical example shows the computation accuracy and efficiency of the presented approach for non-stationary random vibration analysis of porous material structures. [ABSTRACT FROM AUTHOR]

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Geopolymer has superior properties such as lightweight, high strength, low thermal conductivity, fire resistance and corrosion resistance, which makes it an ideal candidate of inorganic binders in fireproof coating for steel structure. Tn this study, inorganic fireproof coating is obtained by using metakaolin-based geopolymer binder, and the influence of silica fume on the structures and properties of the coatings is researched. Results show that silica fume could further reduce the dry density and thermal conductivity of fireproof coating, however, mechanical properties and fire resistance are all improved. Tn addition, the addition of silica fume greatly improves the bonding strength of the fireproof coating, and avoids the surface cracking and peeling problems of metakaolin-based geopolymer thin fireproof coating. Fireproof coating prepared by metakaolin and silica fume with the mass ratio of 1:1 has optimal comprehensive performance, including 619 kg/m1' of dry density, ().1388 W/(m • K) of the thermal conductivity. 6.1 MPa of the compressive strength, ().4 MPa of the bonding strength and below 251 °C of the maximum backfire temperature within 1 h flame. Pore size formed by the stacking of submicron silica fume particles is much smaller, and silica fume particles could also fill large pores in geopolymer matrix. On the other hand, silica fume could participate in the geopolymerization reaction to increase the Si/Al ratio of geopolymer gel and the content of gel phase, thus improving the performance of metakaolin-based geopolymer fireproof coating. [ABSTRACT FROM AUTHOR]

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