Nanoparticle emission characteristics and reduction strategies by boost pressure control and injection strategies in a passenger diesel engine

This research attempted to analyze nanoparticles and other harmful exhaust emissions in accordance with injection strategies and air-fuel ratio (AFR) changes for small diesel engines. The emission characteristics were analyzed in the medium-speed condition, which is the main driving range of a diesel engine. In the case of particulate matter (PM), the number of particles was measured, analyzed, and compared to identify the correlation and emission characteristics of nanoparticles by using a dilution device and condensation particle counter (CPC), which are international standards for particle measurement recommended by the Particulate Measurement Programme (PMP). The engine torque tended to be reduced as pilot injections were added, and the torque was increased by the increased boost pressure, but reduced by the exhaust pressure increase in a part of the low-load range. The number of nanoparticles was not influenced greatly by the change in AFR, but the reduction effect on the PM weight was great depending on the boost pressure increase. In addition, the number of nanoparticles tended to increase as the fuel injection timing became closer to TDC in all conditions, and its difference became larger with an increase in AFR. In addition, in the case of the pilot injection, nanoparticle emission showed similar characteristics depending on the main injection timing, but it was increased by advanced injection timing when performing the main injection only, and the number of the nanoparticles increased as pilot injections were added. Last, the optimal conditions for EMS calibration were analyzed by selecting the conditions of torque reduction and NOx increase within 5 % from all of the engine operating conditions; optimized conditions are presented.     

Transportation infrastructure improvement and real estate value: impact of level crossing removal project on housing prices

This paper studies the impact of removing the level crossing, which constitutes traffic hazard to the society, on house prices by conducting a quasi-natural experiment using the Level Crossing Removal Project (LXRP) implemented by the Victoria state government in Australia since 2015. Using a difference-in-differences method, we analyzed the changes in housing prices due to the improvement of transportation infrastructure, gauging the LXRP’s impact on house and unit submarkets separately. We found that the prices for house and unit markets increased significantly after the removal of level crossings, with the value uplift decreasing with distance from the removal site. This paper contributes to the existing literature by adding an empirical study related to the enhancement of infrastructure aiming to improve the traffic safety in the urban context. Unlike previous studies, this study examines the effect of improvement projects for existing infrastructure and provides relevant implications to improve the efficiency of investing public resources in infrastructure improvement.

     

中长周期波浪条件下船舶航迹带宽度研究

针对现行《海港总平面设计规范》中航迹带宽度计算在中长周期波浪条件下存在的不足,对比分析国外航道规范中的研究成果,并通过船舶操纵模拟试验等技术手段,运用船舶外缘包络线法统计分析了不同航速、不同吨级的散货船在各种中长周期波工况下的航迹带宽度,给出了中长周期波浪条件下的波浪附加宽度参考值,并与国外规范中的设计取值进行了对比.     

探究公路勘察设计与环境保护的关联

文章首先分析了公路建设过程中对环境造成的影响,而后阐述了公路工程在路线设计时应注意的环境问题,最后列举了提升公路设计线路与环境环保的举措,希望降低工程在建设过程中对环保造成的不利影响。     

琼州海峡船舶雾航安全管理

船舶在雾中航行,由于能见度低,瞭望、定位困难,极易发生海上交通事故,造成人员、船舶的经济损失及海洋环境的污染。据统计,在雾航中发生的船舶碰撞事故占整个海上碰撞事故总数的70%以上,因此,对雾航安全的研究一直是航运界关注的重点。琼州海峡位于海南岛与雷州半岛之间,是我国南海沟通北部湾和华南沿海的一个主要海峡通道,同时也     

基于多旅行商问题的应急设施服务区划分模型

基于多旅行商问题,增设集散中心需求及应急服务设施资源容量约束条件,以最小化遍历区域内全部集散中心的综合旅行时间成本为优化目标,构建一种应急设施服务区划分模型,确定各应急设施的服务区范围.设计一种复合算法求解模型,首先基于P-中值选址模型的优化理念,形成初始方案;继而加入禁忌搜索算法,结合LKH求解器对模型进行迭代优化求得最优解.基于宁波市北仑区实际拓扑网络进行案例分析,验证了模型和求解方法的有效性.     

VW-MSPC方法在变压器状态评估中的应用研究

针对变压器状态评估变量指标之间具有相关性以及重要性不同的特点,在基于马氏距离的传统多元统计控制图中引入加权马氏距离,设计一种基于变量加权的多元统计过程控制的变压器评估方法。该方法结合关联规则方法给出的变压器关键指标变量体系利用多元控制图的波动检出能力,将变压器各类故障对应的多元实时监测数据转化为一个检验统计量,进而对变压器运行状态进行评估。电流回路过热故障的实例分析表明,相对于现行的变压器状态评估标准,该方法具有更快的故障检出速度;对于已知故障的样本数据的回顾检测表明该方法具有较高的故障检出率。     

基于深度强化学习的智能船舶航迹跟踪控制

[目的]智能船舶的航迹跟踪控制问题往往面临着控制环境复杂、控制器稳定性不高以及大量的算法计算等问题。为实现对航迹跟踪的精准控制,提出一种引入深度强化学习技术的航向控制器。[方法]首先,结合视线(LOS)算法制导,以船舶的操纵特性和控制要求为基础,将航迹跟踪问题建模成马尔可夫决策过程,设计其状态空间、动作空间、奖励函数;然后,使用深度确定性策略梯度(DDPG)算法作为控制器的实现,采用离线学习方法对控制器进行训练;最后,将训练完成的控制器与BP-PID控制器进行对比研究,分析控制效果。[结果]仿真结果表明,设计的深度强化学习控制器可以从训练学习过程中快速收敛达到控制要求,训练后的网络与BP-PID控制器相比跟踪迅速,具有偏航误差小、舵角变化频率小等优点。[结论]研究成果可为智能船舶航迹跟踪控制提供参考。     

Collision avoidance path planning in multi-ship encounter situations

Journal of Marine Science and Technology - Collision avoidance path planning is still one of the essential problems in the design and application of an intelligent maritime navigation system. Its...     

Hybrid model for prediction of bus arrival times at next station

Effective prediction of bus arrival times is important to advanced traveler information systems (ATIS). Here a hybrid model, based on support vector machine (SVM) and Kalman filtering technique, is presented to predict bus arrival times. In the model, the SVM model predicts the baseline travel times on the basic of historical trips occurring data at given time‐of‐day, weather conditions, route segment, the travel times on the current segment, and the latest travel times on the predicted segment; the Kalman filtering‐based dynamic algorithm uses the latest bus arrival information, together with estimated baseline travel times, to predict arrival times at the next point. The predicted bus arrival times are examined by data of bus no. 7 in a satellite town of Dalian in China. Results show that the hybrid model proposed in this paper is feasible and applicable in bus arrival time forecasting area, and generally provides better performance than artificial neural network (ANN)–based methods. Copyright © 2010 John Wiley & Sons, Ltd.