Adaptation and Learning in Coastal Management: The Experience of Five East African Initiatives

This article explores principles of adaptive, learning-based resource management and their practical application in coastal management projects in East Africa. The principles of feedback and adjustment, experimentation, and carefully guided participatory processes that capture widespread knowledge are used to describe the experience of five projects in Kenya, Tanzania, and Mozambique. The findings are drawn from a variety of sources, including site visits and interviews. The main finding is that adaptive methods are a major feature of all projects, and the general approaches used in each case are similar.     

神经网络用于船舶操纵动态特性学习的研究

本文针对BP网络存在的一些问题，提出一种单层神经网络结构，通过大量的仿真试验，对两种网络在学习、预报和适应参数变化等方面的特性进行了对比。结果表明单层神经网络用于学习船舶操纵动态特性，具有收敛速度极快、预测误差较小、能快速跟踪参数变化等优点，可用于船舶操纵特性的实时学习。     

多路视频监控信息传输中网络带宽资源利用的优化策略

针对铁路客运专线运输安全监控系统的特点,研究在网络带宽资源受限条件下的多路视频实时传输最佳策略问题,提出一种基于源端网络流量实时采集的视频流码率控制和网络拥塞避免机制。建立基于这一系统结构的流量控制模型,应用自动控制理论,推导出稳定的流量调节算法。对带宽资源分配的公平性原则进行扩展,通过设置视频源“优先级”,实现网络带宽的“按需”动态自适应调节的算法;同时引入自适应修正因子β和γ,根据调节误差对每路视频流的带宽占用率α进行动态修正。试验数据表明,这一拥塞避免策略对网络瓶颈部分的带宽利用率可达97.4%,调节时间在5 ms左右。     

双层隔振系统单神经元自适应PID控制器

通过推导双层隔振主动控制系统的力传递率，分析了反馈信号的选择对力传递率的影响，指出了双层隔振PID主动控制系统应采用速度或加速度作为反馈控制信号。在此基础上采用单神经元PID控制器对双层隔振系统进行了主动控制仿真，表明该方法对双层隔振系统进行主动控制的可行性，提高了双层隔振系统的隔振能力。     

基于自适应STFT的货车滚动轴承故障诊断

带故障的铁路货车滚动轴承振动信号表现为低频平稳信号与高频的周期性冲击信号的叠加。采用以三阶B样条函数作为窗函数的自适应短时傅立叶变换(STFT)对货车滚动轴承振动信号进行时频分析和故障信息提取。与传统的固定带宽的STFT相比,自适应STFT在不同频段自适应选取窗长,大大提高了振动信号的时频分辨率。应用该方法对197726型货车滚动轴承在内圈剥离、外圈剥离两种故障状态下的振动信号做了分析,求得故障频率分别为61.32 Hz和46.36 Hz,与内外圈的理论故障频率相符,可以有效地诊断出铁路货车滚动轴承内外圈故障。     

Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data

Vehicle longitudinal control systems such as (commercially available) autonomous Adaptive Cruise Control (ACC) and its more sophisticated variant Cooperative ACC (CACC) could potentially have significant impacts on traffic flow. Accurate models of the dynamic responses of both of these systems are needed to produce realistic predictions of their effects on highway capacity and traffic flow dynamics. This paper describes the development of models of both ACC and CACC control systems that are based on real experimental data. To this end, four production vehicles were equipped with a commercial ACC system and a newly developed CACC controller. The Intelligent Driver Model (IDM) that has been widely used for ACC car-following modeling was also implemented on the production vehicles. These controllers were tested in different traffic situations in order to measure the actual responses of the vehicles. Test results indicate that: (1) the IDM controller when implemented in our experimental test vehicles does not perceptibly follow the speed changes of the preceding vehicle; (2) strings of consecutive ACC vehicles are unstable, amplifying the speed variations of preceding vehicles; and (3) strings of consecutive CACC vehicles overcome these limitations, providing smooth and stable car following responses. Simple but accurate models of the ACC and CACC vehicle following dynamics were derived from the actual measured responses of the vehicles and applied to simulations of some simple multi-vehicle car following scenarios.     

Evolutionary crew scheduling with adaptive chromosomes

This paper presents an adaptive evolutionary approach incorporating a hybrid genetic algorithm (GA) for public transport crew scheduling problems, which are well-known to be NP-hard. To ensure the search efficiency, a suitable chromosome representation has to be determined first. Unlike a canonical GA for crew scheduling where the chromosome length is fixed, the chromosome length in the proposed approach may vary adaptively during the iterative process, and its initial value is elaborately designated as the lower bound of the number of shifts to be used in an unachievable optimal solution. Next, the hybrid GA with such a short chromosome length is employed to find a feasible schedule. During the GA process, the adaptation on chromosome lengths is achieved by genetic operations of crossover and mutation with removal and replenishment strategies aided by a simple greedy algorithm. If a feasible schedule cannot be found when the GA’s termination condition is met, the GA will restart with one more gene added. The above process is repeated until a feasible solution is found. Computational experiments based on 11 real-world crew scheduling problems in China show that, compared to a fuzzy GA known to be well performed for crew scheduling, better solutions are found for all the testing problems. Moreover, the algorithm works fast, has achieved results close to the lower bounds obtained by a standard linear programming solver in terms of the number of shifts, and has much potential for future developments.     

减摇鳍自适应控制系统的研究和仿真

Based on the analysis of the fin-stabilizer system,an adaptive control system with on-line identification is used for the controlled object.The control algorithms are given and the computer simulation is carried out.The simulation results are satisfactory.     

智能自适应控制及其在船舶操纵系统中的应用

主要分析了基于模糊控制，专家系统与神经网络的几种智能自适应控制系统的结构，原理，并与传统控制作了比较，介绍了智能自适应控制在船舶操纵系统中的应用情况，最后提出了智能自适应控制理论及应用面临的主要困难与亟需解决的问题。     

Reducing bunching with bus-to-bus cooperation

Schedule-based or headway-based control schemes to reduce bus bunching are not resilient because they cannot prevent buses from losing ground to the buses they follow when disruptions increase the gaps separating them beyond a critical value. (Following buses are then overwhelmed with passengers and cannot process their work quick enough to catch up.) This critical gap problem can be avoided, however, if buses at the leading end of such gaps are given information to cooperate with the ones behind by slowing down.This paper builds on this idea. It proposes an adaptive control scheme that adjusts a bus cruising speed in real-time based on both, its front and rear spacings much as if successive bus pairs were connected by springs. The scheme is shown to yield regular headways with faster bus travel than existing control methods. Its simple and decentralized logic automatically compensates for traffic disruptions and inaccurate bus driver actions. Its hardware and data requirements are minimal.