舰船监控系统的现状及发展趋势

通过研究国外舰船监控系统的发展状况、发展趋势,分析了国外船舶监控系统自动化、网络化的发展,结合我国科技与国防事业的发展,提出了推进我军舰船监控系统发展的建议。     

基于Petri网考虑中介状态的机械系统失效相关可靠性分析

机械系统因其各子系统、各部件、各单元之间的相互复杂关系,容易受到自身结构与人为因素的影响,导致其发生失效,严重影响了机械系统的可靠性.在利用Petfi网模型对机械系统分析的基础上,并引入"安全-中介-失效"3级工作模式,从安全、中介、失效3个方面来反映机械系统的可靠(完全正常工作)、可靠与失效的中介过渡、失效(完全不能...     

2500m车道/2300客位客滚船消防灭火系统设计

客滚船作为一种既能载运车辆,又同时载运旅客的特殊船型,实现船舶的消防安全目标和功能的要求至关重要。简要介绍2500m车道／2300客位客滚船全船消防灭火系统的设计。重点介绍全船水灭火系统、固定式高压二氧化碳灭火系统、车辆舱固定式压力水雾系统、舱室自动水喷淋系统、机舱局部水雾灭火系统和直升机泡沫灭火系统。     

Scheduled paratransit transport systems

In this paper we focus on ways to provide individualized services to people with mobility challenges using existing modes of public transport. We study the design of an interesting case, in which a bus operating in a public transport route may diverge from its nominal path to pick-up passengers with limited mobility and drop them off at their destination. We have modeled the design problem by a mixed integer-linear program, and we developed an exact Branch and Price approach to solve it to optimality. The proposed approach includes a labeling algorithm in which we introduced appropriate dominance rules, which do not compromise optimality. We have compared the efficiency of our approach with that of related algorithms from the literature. Furthermore, we have used the proposed approach to study key aspects of the system design problem, such as the effect of various constraints on the service level, and the tuning of the system’s parameters to address different transport environments.     

地铁通风小系统风机送电调试的探讨

通过6个案例分析,从不同调试故障的处理方法,归纳出3类常见调试问题,进而提出分3步进行的送电调试,为快速、高效地完成通风小系统风机送电调试任务提供了一条可行的便利途径。     

Asynchronous n-step Q-learning adaptive traffic signal control

Ensuring transportation systems are efficient is a priority for modern society. Intersection traffic signal control can be modeled as a sequential decision-making problem. To learn how to make the best decisions, we apply reinforcement learning techniques with function approximation to train an adaptive traffic signal controller. We use the asynchronous n-step Q-learning algorithm with a two hidden layer artificial neural network as our reinforcement learning agent. A dynamic, stochastic rush hour simulation is developed to test the agent’s performance. Compared against traditional loop detector actuated and linear Q-learning traffic signal control methods, our reinforcement learning model develops a superior control policy, reducing mean total delay by up 40% without compromising throughput. However, we find our proposed model slightly increases delay for left turning vehicles compared to the actuated controller, as a consequence of the reward function, highlighting the need for an appropriate reward function which truly develops the desired policy.     

An insight into linear quarter car model accuracy

The linear quarter car model is the most widely used suspension system model. A number of authors expressed doubts about the accuracy of the linear quarter car model in predicting the movement of a complex nonlinear suspension system. In this investigation, a quarter car rig, designed to mimic the popular MacPherson strut suspension system, is subject to narrowband excitation at a range of frequencies using a motor driven cam. Linear and nonlinear quarter car simulations of the rig are developed. Both isolated and operational testing techniques are used to characterise the individual suspension system components. Simulations carried out using the linear and nonlinear models are compared to measured data from the suspension test rig at selected excitation frequencies. Results show that the linear quarter car model provides a reasonable approximation of unsprung mass acceleration but significantly overpredicts sprung mass acceleration magnitude. The nonlinear simulation, featuring a trilinear shock absorber model and nonlinear tyre, produces results which are significantly more accurate than linear simulation results. The effect of tyre damping on the nonlinear model is also investigated for narrowband excitation. It is found to reduce the magnitude of unsprung mass acceleration peaks and contribute to an overall improvement in simulation accuracy.     

Vibration control in train–bridge–track systems

To study the problems associated with vibration control of train–bridge–track systems a mathematical model with the capability of representing supplementary vibrational control devices is proposed. The train system is assumed as rigid bodies supported on double-deck suspension mechanism with semi-active features. The bridge system is modeled using the modal approach. Vibration control for bridge responses is provided by tuned mass dampers. A non-classical incremental Eigen analysis is proposed to trace the system characteristics across the time. In an example, the capability of the proposed model in investigating the vibration control prospects of a bridge–train system is shown. The results indicate the effectiveness of active suspension mechanism in reducing train's body movements, particularly the pitching angle and the vertical accelerations. Accordingly, the results also verify the potential of TMD devices in reducing the bridge responses at resonance motions.     

High-speed train control based on multiple-model adaptive control with second-level adaptation

Speed uplift has become the leading trend for the development of current railway traffic. Ideally, under the high-speed transportation infrastructure, trains run at specified positions with designated speeds at appointed times. In view of the faster adaptation ability of multiple-model adaptive control with second-level adaptation (MMAC-SLA), we propose one type of MMAC-SLA for a class of nonlinear systems such as cascaded vehicles. By using an input decomposition technique, the corresponding stability proof is solved for the proposed MMAC-SLA, which synthesises the control signals from the weighted multiple models. The control strategy is utilised to challenge the position and speed tracking of high-speed trains with uncertain parameters. The simulation results demonstrate that the proposed MMAC-SLA can achieve small tracking errors with moderate in-train forces incurred under the control of flattening input signals with practical enforceability. This study also provides a new idea for the control of in-train forces by tracking the positions and speeds of cars while considering power constraints.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.