Liquid phase in-situ synthesis of LiF coated boron powder composite and performance study

Among practical metal additives, boron (B) has a high volumetric heating value, making it a promising choice as a fuel additive. Although B can theoretically yield a large amount of energy upon complete combustion, its combustion is retarded by the initial presence of B oxide, which coats the surfaces of B particle. To improve the ignition and combustion properties of B powder, LiOH and NH₄F were used as precursors to synthesize uniformly LiF-coated B composites (LiF-B) in situ. The LiF-B mixture was also prepared for comparison using a physical method. X-ray diffraction (XRD), Fourier-transform infrared (FTIR), scanning electron microscope (SEM), and energy-dispersive X-ray spectroscopy (EDS) were used to characterize the morphologies and compositions of the products. The thermal and combustion properties of the samples were characterized by thermal gravity-differential thermal gravity (TG-DTG), differential scanning calorimetry (DSC) and closed bomb experiment. The XRD, FTIR, SEM and EDS results demonstrated the successful preparation of the coated LiF-B sample. The TG-DTG and closed bomb experiment results indicated that the addition of LiF decreased the ignition temperature of B powder, and increasing its reaction efficiency. DSC results show that when LiF-B was added, the released heat of underwater explosive increased by 6727.2, 7280.4 and 3109.6 J/g at heating rates of 5, 10, and 15 °C/min, respectively. Moreover, LiF-B decreased the activation energy of secondary combustion reaction of explosive system as calculated through Kissinger's method by 28.9%, which indicated an excellent catalytic effect for the thermal decomposition of underwater explosive. The results reveal that LiF can improve the combustion efficiency of B powder, thereby increasing the total energy of explosives. The mechanical sensitivity increased slightly after adding LiF-B to the underwater explosive. Compared to the underwater explosive with added B, the mechanical sensitivity of the explosive with added LiF-B was significantly lower.     

作战使命分解与任务建模方法

为解决联合作战计划的多样性、复杂性与不确定性,在计划的基础工作上,定义了基本概念,包括作战想定、作战使命、子任务与元任务,提出了任务描述方法、使命分解原则与分解方法,实现了不同分解方法在结果上的同一性,为作战计划的标准化、程序化与智能化奠定了基础.     

飞机发动机关键系统智能故障诊断方法

飞机发动机是一个非常复杂的大系统,由于其结构复杂,工作环境恶劣,对其关键系统的故障进行准确诊断始终是困扰业界的技术瓶颈之一。提出了采用EMD小波阈值降噪与主元分析相结合的方法,对飞机发动机气路系统故障诊断进行了深入研究。针对某型真实飞机发动机进行测试试验采集的气路多参量数据,首先采用经验模态分解(Empirical Mode Decomposition,EMD)对气路系统各参量信号进行EMD分解,然后采用软阈值函数对其进行降噪,并进行信号重构,从而可得到飞机发动机气路工作状态有效数据。在此基础上,设计了飞机发动机气路系统主元分析故障诊断模型,并结合预处理得到的飞机发动机气路有效数据,运用所设计的主元分析故障诊断模型对飞机发动机进行故障诊断技术研究。研究结果表明,所提出的方法能够很好地诊断出飞机发动机气路系统实际运行时所出现的故障,具有重要的实际应用价值,并有广泛的应用前景。     

基于提升小波包的漏磁信号去噪研究

为了更好地解决对原始漏磁信号的噪声抑制问题,在讨论提升小波包变换的基本原理及特点的基础上,提出应用这种方法对漏磁信号进行分解和重构。与传统小波包变换相比较,该方法具有更高的分辨率和近似优化能力,研究结果表明,它能在保留原始漏磁信号有效成分的同时,更好地去除噪声。     

An eikonal equation based path planning method using polygon decomposition and curve evolution

Path planning is a key technique of autonomous navigation for robots, and the velocity field is an important part. Constructing velocity field in a complex workspace is still challenging. In this paper, an inner normal guided segmentation algorithm in a complex polygon is proposed to decompose the complex workspace in this paper. The artificial potential field model based on probability theory is then used to calculate the potential field of the decomposed workspace, and the velocity field is obtained by utilizing the potential field of this workspace. Path optimization is implemented by curve evolution, during which the internal force generated in the smoothing process of the initial path by a mean filter and the external force is obtained from the gradient of the workspace potential field. The parameter selection principle is deduced by analyzing the influence of several parameters on the path length and smoothness. Simulation results show that the designed polygon decomposition algorithm can effectively segment complex workspace and that the path optimization algorithm can shorten and smoothen paths.     

基于不同能量值特征优选的水声目标识别

采用Bartlet平均周期图、小波变换和经验模态分解方法分别提取信号的频段能量值特征、IMF能量值特征，重点对IMF能量值特征进行特征优选。通过设计BP神经网络分类器，对实测的四类舰船目标的辐射噪声信号进行测试，取得了较好的识别效果。     

Balancing a one‐way corridor capacity and safety‐oriented reliability: A stochastic optimization approach for metro train timetabling

In urban rail transit systems of large cities, the headway and following distance of successive trains have been compressed as much as possible to enhance the corridor capacity to satisfy extremely high passenger demand during peak hours. To prevent train collisions and ensure the safety of trains, a safe following distance of trains must be maintained. However, this requirement is subject to a series of complex factors, such as the uncertain train braking performance, train communication delay, and driver reaction time. In this paper, we propose a unified mathematical framework to analyze the safety‐oriented reliability of metro train timetables with different corridor capacities, that is, the train traffic density, and determine the most reliable train timetable for metro lines in an uncertain environment. By employing a space‐time network representation in the formulations, the reliability‐based train timetabling problem is formulated as a nonlinear stochastic programming model, in which we use 0‐1 variables to denote the time‐dependent velocity and position of all involved trains. Several reformulation techniques are developed to obtain an equivalent mixed integer programming model with quadratic constraints (MIQCP) that can be solved to optimality by some commercial solvers. To improve the computational efficiency of the MIQCP model, we develop a dual decomposition solution framework that decomposes the primal problem into several sets of subproblems by dualizing the coupling constraints across different samples. An exact dynamic programming combined with search space reduction strategies is also developed to solve the exact optimal solutions of these subproblems. Two sets of numerical experiments, which involve a relatively small‐scale case and a real‐world instance based on the operation data of the Beijing subway Changping Line are implemented to verify the effectiveness of the proposed approaches.     

膨胀型阻燃聚丙烯的制备及热分解动力学研究

以五氧化二磷／季戊四醇／三聚氰胺为原料合成了聚合型膨胀阻燃剂，并制备了膨胀型阻燃聚丙烯。比较和讨论了热分解动力学方法，通过测定体积流速评价了流变性，用热重分析方法研究了膨胀型阻燃聚丙烯的热分解特点。     

关于右连左极函数的Gronwall不等式

给出了传统意义下的Gronwall不等式的推广形式,并且给出了在右连左极函数积分下的一种Gronwall不等式。     

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics. Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing. Owing to the complexity of marine environment and the particularity of underwater acoustic channel, noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing. In order to solve the dilemma, we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), minimum mean square variance criterion (MMSVC) and least mean square adaptive filter (LMSAF). This noise reduction technique, named CEEMDAN-MMSVC-LMSAF, has three main advantages: (i) as an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN can better suppress mode mixing, and can avoid selecting the number of decomposition in variational mode decomposition (VMD); (ii) MMSVC can identify noisy intrinsic mode function (IMF), and can avoid selecting thresholds of different permutation entropies; (iii) for noise reduction of noisy IMFs, LMSAF overcomes the selection of decomposition number and basis function for wavelet noise reduction. Firstly, CEEMDAN decomposes the original signal into IMFs, which can be divided into noisy IMFs and real IMFs. Then, MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs. Finally, both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained. Compared with other noise reduction techniques, the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals, which has the better noise reduction effect and has practical application value. CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection, feature extraction, classification and recognition of underwater acoustic signals.