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.     

装甲车辆计划预防工作时机及范围的确定

利用装甲车辆使用中的可靠性统计数据,确定装置(构件)计划预防工作时机的方法,论述了从单一装置(构件)的计划预防工作,向整车的计划预防工作转化时装甲车辆计划预防时机及范围的确定原则,对装甲车辆部署初期的保养间隔期和修理间隔期的确定或修订具有理论及实践上的指导意义;给出了根据装置(构件)技术状态检测结果,独立确定装置(构件)计划预防工作时机的方法、实例以及需要解决的关键技术问题。     

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。     

基于扩展HTN规划的作战任务分析方法

任务分析是一个认识和了解任务本质的过程,作为作战计划拟制过程中的一个核心环节,作战任务分析的结论是否准确决定着作战计划的好与坏,并影响战争的结局。为了解决目前任务分析没有统一的标准及合适方法的问题,分析了层级任务网络(HTN)在任务分析中的不足之处,给出了OOR框架进行任务细化的方法和步骤,将其作为领域知识引入HTN规划,为任务分析提供了一个有效的方法,并设计了图元素用于表示任务树中任务之间的层级关系以及时序逻辑关系。最后以空间信息支援作战为例,验证了方法的可行性和有效性。     

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.     

Optimizing fishbone aisles for dual‐command operations in a warehouse

Unit‐load warehouses store and retrieve unit‐loads, typically pallets. When storage and retrieval operations are not coordinated, travel is from a pickup and deposit (P&D) point to a pallet location and back again. In some facilities, workers interleave storage and retrieval operations to form a dual‐command cycle. Two new aisle designs proposed by Gue and Meller (“Improving the unit‐load warehouse.” In Progress in Material Handling Research: 2006. Material Handling Industry of America, Charlotte, NC, 2006) use diagonal aisles to reduce the travel distance to a single pallet location by approximately 10 and 20[percnt] for the two designs, respectively. We develop analytical expressions for travel between pallet locations for one of these—the fishbone design. We then compare fishbone warehouses that have been optimized for dual‐command to traditional warehouses that have been optimized in the same manner, and show that an optimal fishbone design reduces dual‐command travel by 10–15%. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 54: 389–403, 2009     

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。     

装甲火控可靠性建模

装甲武器是陆军及其两栖机械化部队实施地面突击的主要兵器,也是海军陆战部队实施抢滩登陆、岛屿攻防的主要兵器。装甲火控是装甲武器的重要组成部分,其可靠性直接影响装甲武器的打击效能。两栖装甲武器肩负海上、陆上的作战使命,使用环境更加恶劣,可靠性问题更加突出。描述了装甲火控的一般组成、功能及工作方式,基于两栖装甲武器的使用特点,分析了装甲火控基本任务、工作方式和作战环境的关系,建立了不同任务剖面的可靠性模型,为装甲火控可靠性设计、分析提供了一种参考。     

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.