Modeling and simulation of torpedo acoustic homing trajectory with multiple targets

1Introduction Homingtrajectoryisthemostimportantaspectwithin exteriortrajectoryofanacoustichomingtorpedo.Thus,homingtrajectoryshouldreceivemuchattention intorpedotrajectorysimulations.Inthisresearchpro ject,atorpedoacoustichomingtrajectorysimulation progr…     

鱼雷跟踪目标深度起伏机理分析及全弹道数学仿真研究

针对鱼雷跟踪目标时的深度起伏现象，从水声传播原理和鱼雷跟踪目标时深度平面的控制原理进行了理论分析，给出了等效声线的建模方法，通过设置工况条件，对鱼雷进行了全弹道数学仿真，得到了与实际情况基本相符的结果，验证了理论分析的正确性。     

Dangerous driving behavior detection using video-extracted vehicle trajectory histograms

Dangerous driving behavior detection can be used in video surveillance systems to identify dangerous patterns, such as Abrupt Double Lane Change (ALC), Retrograde Driving (RD), and Illegal U-Turn (IT), for traffic design, traffic management, and law enforcement. The purpose of this study is to develop a detection method of dangerous driving behavior based on video surveillance. First, a novel method named trajectory histogram is proposed. A set of trajectory histograms (e.g., control points histogram and velocity histogram) is constructed to represent vehicle motion. Then, a frequently used feature selection method named Minimum Redundancy and Maximum Relevance (mRMR) is introduced to evaluate the most representative trajectory histograms for dangerous driving behavior detection. In addition, a hybrid algorithm, Particle Swarm Optimization-Support Vector Machine (PSO_SVM), is then developed to identify dangerous driving behavior. To validate the performance and effectiveness of the proposed method, several experiments are conducted. The results show that mRMR is better than other representative methods, namely Conditional Mutual Information Maximization (CMIM), Mutual Information Maximization (MIM), and ReliefF, for evaluating the most representative trajectory histograms. Based on the most representative trajectory histograms, the detection accuracy rate of dangerous driving behavior using PSO_SVM is superior to those of the most frequently used classifiers—Naïve Bayesian Classifier (NBC), k-Nearest Neighbor (kNN), and C4.5 decision tree. In addition, we find that the proposed method outperforms the two common approaches for dangerous driving behavior detection in video surveillance systems. Therefore, the proposed method can be widely applied to detect dangerous driving behavior in video surveillance systems.     

Evolutionary trajectory planning of ships in navigation traffic areas

By taking into account certain boundaries of the maneuvering region, along with navigation obstacles and other moving ships, the problem of avoiding collisions in navigation traffic areas was reduced to a dynamic optimization task with static and dynamic constraints. In heavy navigation traffic areas and within coastal zones, correct selection of a ship's speed, when accompanied by the necessary trajectory changes, is a crucial element of safe navigation. This paper presents a modernized version of the evolutionary ship trajectory planning system in which a new parameter was introduced, namely changes of the ship's speed along particular sections of the trajectory. Generally, a safe trajectory is estimated using specialized genetic operators for modifying the trajectory. Here, an additional operator was added to the system that mutates the ship's speed. Sample results present the safe trajectory for a ferry meeting other moving ships and navigational constraints on its way. Received for publication on Dec. 2, 1998; accepted on June 16, 1999     

矿用无人运输车辆轨迹跟踪控制算法研究

矿用无人运输车辆作业环境恶劣,存在大曲率弯道、坡道等非结构化道路明显特征,对无人化运输控制要求高。为改善PID等传统控制算法适应性问题,提高无人驾驶轨迹跟踪的车辆横纵向控制精度,提出一种纯跟踪与PID结合的多点预瞄横向控制、考虑模糊控制表参数拟合的纵向控制方法,减少控制参数的同时提高算法效果。根据传统控制算法设计基础控制器,结合基础算法优势进行横向与纵向控制算法设计,通过硬件在环仿真和实车测试验证算法的性能。试验结果表明,横向控制算法与斯坦利算法相比,车辆路径跟踪精度有明显改善,纵向控制方面,速度跟随误差<1 km/h,保证了车辆驾驶时的平稳性与舒适性。     

基于抽样车辆轨迹数据的信号控制交叉口排队长度分布估计

排队长度是评价信号控制交叉口运行状态的重要参数之一。现有大多数基于抽样车辆轨迹数据的排队长度估计方法可以实现周期级排队长度估计，但是需要信号配时、渗透率或车辆到达分布等实践中难以获取的输入信息。此外，这类方法在低渗透率条件下往往难以确保估计结果的准确性和可靠性，极大地限制了其实用性。因此，提出一种抽样车辆轨迹数据驱动的时段级信号控制交叉口排队长度分布估计方法，可不依赖任何交通流理论模型和前述输入信息实现排队估计。首先，通过理论推导可以证明时段内抽样车辆的停车位置分布和排队长度分布之间可互相转化；然后，提出一种扩展的核密度估计方法来拟合并平滑抽样车辆停车位置分布，从而有效地适应不同日期和周期的轨迹叠加所带来的波动，提高方法的适用性；最后，基于前述推导和拟合的停车位置分布实现时段排队长度分布、平均排队长度和百分位排队长度估计。分别采用仿真和实证数据对上述方法进行验证和评价。结果表明，通过叠加5 d相同时段的抽样轨迹数据，15 min的平均排队长度估计误差仅为1.59 veh，相对误差仅为9%。同时，面向不同分析时长，只要给定超过100 veh抽样车辆的观测样本，无论渗透率高低，所提出的方法在定时或自适应信号控制交叉口都可实现时段排队长度分布的准确估计，其成果可进一步用于信号控制交叉口运行可靠性评估以及多时段定时信号控制的鲁棒优化。     

汽车内饰材料的分类及燃烧特性分析

从使用位置及材质的角度,对汽车内饰材料进行分类,对汽车塑料类及面料类内饰材料进行水平燃烧试验,结合试验数据,分析在试验过程中影响材料燃烧速度的因素,并提出建议。     

基于乘用车内部凸出物试验测评方法研究

目前,国内外有关汽车内部凸出物试验的标准中均只将假人头部作为参考指标;而正面碰撞法规和C-NCAP(2021版)中均将假人的头、颈、胸、骨盆、腿等部位损伤值作为衡量汽车安全性能优劣的重要评价指标。本文通过滑台碰撞试验方法,对汽车内部凸出物试验测评方法进行研究;结果显示:假人的头、颈、胸、腿部均受到不同程度的损伤;尤其是95th假人的骨盆位移量过大,造成假人下半身受到损伤更为严重。因此,本文建议乘用车内部凸出物试验测评方法中,应适度考量假人多个部位及其损伤指标,旨在不断完善我国汽车安全标准体系,进一步提升汽车安全性能,更好地保护驾乘人员的乘车安全。     

基于车联网的汽车自动换道方法研究

为弥补现有汽车自动换道时未考虑周围车辆运动状态变化以及舒适性差和通行效率低等方面的不足,同时提出了在车联网条件下的汽车自动换道方法,主要包括动态轨迹的规划、前馈与反馈相结合的PQ跟踪控制策略两部分,开展Carsim和simulink联合仿真以及实车验证结果表明,与传统非动态自动换道方法相比,该方法能有效解决在换道过程中周围车辆车速变化及车辆突然闯入等情况的难题,明显提高了换道过程中的安全性,由数据分析可知在保证车辆舒适性、稳定性的同时,换道时间缩短了20%,有效提高了车辆换道效率。     

浅论ATM交换技术及发展

结合ATM的特点,简要论述了ATM的工作过程和内部构以及ATM交换技术发展的必要性。