美国加州压载水管理的新规定

美国加州国土委员会对来自北美西海岸并挂靠加州港口的船舶实施压载水管理的新规定于2006年3月22日生效。通过对新规定的深入探讨,结合加州国土委员会检查官对船舶压载水管理现场检查的重点,强调船长/船舶压载水管理负责人对此新规定的学习、理解、执行和满足的必要性;同时,提醒预抵达美国加州的船舶,在压载水管理方面必须建立严格的记录,熟悉新规定的相关要求。     

KZ4A型机车压缩机控制模式改进设计

文章从控制原理与控制逻辑两方面介绍对KZ4A型机车压缩机控制模式的改进设计,从而进一步优化了压缩机的工作方式,提高了压缩机的使用寿命与安全系数。     

争创一流科研院所为和谐铁路建设提供技术支撑和理论支持

铁道科学研究院作为全路惟一的多学科、多专业的综合性研究机构,在铁路提速、重载、客运专线建设、青藏铁路建设、安全保障和信息化等重点领域发挥了科技支撑作用。在推进和谐铁路建设过程中,针对存在的不足和差距,铁道科学研究院需要增强战略意识、忧患意识、改革意识和创新意识,以争创世界一流科研院所为目标,全面增强科技创新能力,承担起重大综合科学试验,努力做好技术和信息服务,提高成果转化水平,完善科技创新的体制机制,并着眼行业需求,搭建先进的技术装备平台,不断加快高素质人才培养,为和谐铁路建设提供强有力的技术支撑和理论支持。     

舰船结构可靠性的定性设计方法

提出一种舰船结构可靠性的定性设计方法，在分析故障及其影响的基础上，不断改进设计、消除故障并提出质量控制、使用限制及防救措施的方案，可以保证对舰船结构可靠性的定性要求。     

宁波-舟山港口一体化建设的投融资模式分析

宁波-舟山港口一体化的关键之一是港口建设中的投融资问题。巨大的资金来源必须采取多种投融资模式，而最主要的是提高民资所占比重和实施地主港融资模式。分析宁波-舟山港口一体化建设中采取的主要投融资模式，提出相应的策略，并指出投融资风险。     

内贸集装箱生成量的可拓聚类预测

回顾了我国水路内贸集装箱运输的发展历程,并对我国内贸箱的发展现状和存在的问题进行了分析。在此前提下,选择可拓聚类预测模型应用于内贸箱生成量的预测。通过1997年至2004年有关数据,对2005年内贸集装箱运输生成量进行预测,得到了与实际值比较理想的拟合结果。     

车辆动力荷载分析

采用模态迭加法对复杂结构在多支座随机位移激励下的振动进行随机振动分析，以载重货车为计算对象，求得在不同车速和工况下车辆荷载的均方根值和超载概率，讨论动荷载的概率特征。     

国际首例铣削式挖泥船设计研究

本文介绍了国际首例铣削式挖泥船的开发背景、技术水平、总体设计方案、关键部件及设备等。     

State observer-based sliding mode control for semi-active hydro-pneumatic suspension

This paper proposes an improved virtual reference model for semi-active suspension to coordinate the vehicle ride comfort and handling stability. The reference model combines the virtues of sky-hook with ground-hook control logic, and the hybrid coefficient is tuned according to the longitudinal and lateral acceleration so as to improve the vehicle stability especially in high-speed condition. Suspension state observer based on unscented Kalman filter is designed. A sliding mode controller (SMC) is developed to track the states of the reference model. The stability of the SMC strategy is proven by means of Lyapunov function taking into account the nonlinear damper characteristics and sprung mass variation of the vehicle. Finally, the performance of the controller is demonstrated under three typical working conditions: the random road excitation, speed bump road and sharp acceleration and braking. The simulation results indicated that, compared with the traditional passive suspension, the proposed control algorithm can offer a better coordination between vehicle ride comfort and handling stability. This approach provides a viable alternative to costlier active suspension control systems for commercial vehicles.     

Multimode trip information detection using personal trajectory data

Handheld global positioning system (GPS) devices can serve as a new tool to collect an individual's trip information with advantages of low cost, accurate data, and intensive spatial coverage. Various machine learning algorithms have been explored to detected trip train information in previous studies; however, few of them focused on the evaluation and comparison of the performance and applicability of different models. Meanwhile, according to previous studies, car and bus mode detection is a thorny issue due to their similar travel characteristics, and algorithms still need to be well explored and improved to solve this problem. In this article, an innovative method is proposed to detect trip information, including trip modes, mode-changing time and location, and other attributes, from personal trajectory data. The method is a two-step process. A machine learning algorith-based module (including artificial neural network, support vector machine, random forests, and Bayesian network) is firstly used to identify walk, bicycle, and motorized trip modes (bus or car); we thoroughly compared the performance of these four algorithms. Then a second module, using critical points on the GPS trajectories, is further developed to distinguish car and bus mode, incorporated with GIS map information. Field test results show that the proposed machine learning models can all be applied for walk, bicycle, and motorized mode detection with high detection rates exceeding 90%; however, the algorithms work relatively poorly for bus and car mode detection, with results mostly below 75%. The proposed two-step method can greatly improve bus and car mode detection accuracy by 14–30%. As a result, the average mode detection rates for all the four modes are above 90%. Compared with mode detection results by using only the machine learning algorithm, the proposed two-step method has much better performance in both accuracy and consistency.