Effect prediction of time-gaps for adaptive cruise control (ACC) and in-vehicle tasks on bus driver performance

Researchers have devoted a great deal of attention to the effects of driver assist systems on driver performance. This article describes a modeling approach to simulate the effects of time-gaps for adaptive cruise control (ACC) and manual in-vehicle tasks on bus-driver performance. A concept model was built with the knowledge of modularization, parameterization, and parallel processing. By running the model, the predictions for the effects of five levels of time-gaps and two types of in-vehicle tasks were collected in three measures: (1) mean gap, (2) minimum gap, and (3) collision rate. The model performed well in prediction, especially when driving with in-vehicle tasks. Predictions from the model were validated by the experiment with a verified fixed-base bus-driving simulator, used in the authors’ previous studies. Throughout the modeling approach, this research provides a theoretical and accurate way to assess effects of time-gaps and vehicle-equipped interfaces. In follow-up research, the authors will apply this approach to evaluate other driving assist systems (e.g. collision warning systems and navigation systems) to create a customized software kit.     

An evaluation of three statistical methods used to model resource selection

The performance of statistical methods for modeling resource selection by animals is difficult to evaluate with field data because true selection patterns are unknown. Simulated data based on a known probability distribution, though, can be used to evaluate statistical methods. Models should estimate true selection patterns if they are to be useful in analyzing and interpreting field data. We used simulation techniques to evaluate the effectiveness of three statistical methods used in modeling resource selection. We generated 25 use locations per animal and included 10, 20, 40, or 80 animals in samples of use locations. To simulate species of different mobility, we generated use locations at four levels according to a known probability distribution across DeSoto National Wildlife Refuge (DNWR) in eastern Nebraska and western Iowa, USA. We either generated 5 random locations per use location or 10,000 random locations (total) within 4 predetermined areas around use locations to determine how the definition of availability and the number of random locations affected results. We analyzed simulated data using discrete choice, logistic-regression, and a maximum entropy method (Maxent). We used a simple linear regression of estimated and known probability distributions and area under receiver operating characteristic curves (AUC) to evaluate the performance of each method. Each statistical method was affected differently by number of animals and random locations used in analyses, level at which selection of resources occurred, and area considered available. Discrete-choice modeling resulted in precise and accurate estimates of the true probability distribution when the area in which use locations were generated was ≥ the area defined to be available. Logistic-regression models were unbiased and precise when the area in which use locations were generated and the area defined to be available were the same size; the fit of these models improved with increased numbers of random locations. Maxent resulted in unbiased and precise estimates of the known probability distribution when the area in which use locations were generated was small (home-range level) and the area defined to be available was large (study area). Based on AUC analyses, all models estimated the selection distribution better than random chance. Results from AUC analyses, however, often contradicted results of the linear regression method used to evaluate model performance. Discrete-choice modeling was best able to estimate the known selection distribution in our study area regardless of sample size or number of random locations used in the analyses, but we recommend further studies using simulated data over different landscapes and different resource metrics to confirm our results. Our study offers an approach and guidance for others interested in assessing the utility of techniques for modeling resource selection in their study area.     

What enables coexistence in plant communities? Weak versus strong species traits and the role of local processes

Explaining the coexistence of species that basically depend on the same resources has been a brainteaser for generations of ecologists. Different mechanisms have been proposed to facilitate coexistence in plant communities, where space is an important resource. Using a stochastic cellular automaton simulation model we analyze - separately and in combination - the influence of different species traits and processes which alter local competition on the coexistence of plant species over a fixed time horizon. We show that different species traits operate on different time scales in competition. We therefore suggest the concept of weak versus strong traits according to short- or long-term exclusion of species differing in these traits. As a consequence, highly non-linear trade-offs between weak and strong traits can result in communities. Furthermore, we found that trade-offs based on physiological species traits such as plant lifetime, dispersal range and plant growth, did not support broad and long-term coexistence—further processes such as density-dependent mortality and light-dependent colonization were necessary. This suggests that coexistence in plant communities requires (stabilizing) local processes to support the (equalizing) trade-offs in species traits.     

Predicting a ‘tree change’ in Australia's tropical savannas: Combining different types of models to understand complex ecosystem behaviour

In this study, key ecological modelling limitations of a process-based simulation model and a Bayesian network were reduced by combining the two approaches. We demonstrate the combined modelling approach with a case study investigating increases in woody vegetation density in northern Australia's tropical savannas. We found that by utilising the strengths of a simulation model and a Bayesian network we could both forecast future change in woody vegetation density and diagnose the reasons for current vegetation states. The local conditions of climate, soil characteristics and the starting population of trees were found to be more important in explaining the likelihood of change in woody vegetation density compared to management practices such as grazing pressure and fire regimes. We conclude that combining the strengths of a process and BN model allowed us to produce a simple model that utilised the ability of the process model to simulate ecosystem processes in detail and over long time periods, and the ability of the BN to capture uncertainty in ecosystem response and to conduct scenario, sensitivity and diagnostic analysis. The overall result was a model that has the potential to provide land managers with a better understanding of the behaviour of a complex ecosystem than simply utilising either modelling approach in isolation.     

Industrial waste management simulation game and its educational effect

The purposes of this study were to develop the Industrial Waste Game and to examine the validity of this game as a tool for environmental education. The aim of this game is to enable players to understand the social dilemma between individual interest of hazardous dumping and the social cost of purifying pollution, and to find a solution to the social dilemma by providing the monitoring and sanction system for illegal dumping or other efficient systems. The game was played by 213 undergraduate students who were divided into 40 groups of 5 to 6 people. One of 4 combinations of monitoring and penalty conditions was assigned to each group. The players were asked to answer questionnaires concerning their interests and understanding of the industrial waste issue before and after the game to evaluate educational effects. The results indicated that the players increased their awareness of industrial waste problems and came to understand that these problems were caused not by psychological factors such as immorality of the illegal dumper but mainly by social structural factors like the social dilemma. Through playing the game and participating in post-game discussion, players were able to obtain interesting experience and gain motivation to learn more.     

模具成形过程中虚拟仿真环境研究

此文提出了一种基于先进制造技术、并行工程、知识工程及网络技术于一体的虚拟仿真系统思想——VS,对其组织方式、结构模型、环境关键技术和发展前景作了尝试性地探讨研究。这将有助于整个模具制造业找到一个新的应用发展突破点,真正体现高速、高效、高质的优势。     

工业危险源仿真预警技术

概述了应用数字仿真技术于液化天然气风险评价、蒸汽云爆炸后果预测以及液氨储罐泄漏监控预警系统等的国内外研究与发展现状，给出了计算机仿真结果和现场实测数据之间的对比曲线。     

北江大堤洪水风险图信息管理系统中仿真模型的开发研究

“北江大堤洪水风险信息管理系统”中洪水仿真模型的研究, 是根据北江大堤可能出现的溃堤风险,模拟了北江大堤溃决后保护区内洪水的演进过程,模型的计算结果提供的淹没水深、淹没范围、淹没历时以及流速等洪水风险信息, 为北江大堤洪水风险图的绘制提供了依据     

生态动力学模型在太湖水质模拟中的应用

考察了西澳大利亚大学水研究中心开发的生态动力学模型CAEDYM与三维水动力模型ELCOM的耦合模型在太湖的应用效果,用该模型模拟了2005年5月~6月太湖的水温、总氮、硝氮、氨氮、总磷、磷酸盐、叶绿素a,通过实测数据进行参数率定。并用2005年8月的实测资料作为初始条件,9月的模拟资料与实测资料进行对比,对模型进行了验证。结果显示模型较好地模拟了太湖的水质变化和分布。     

利用GMS模拟技术分析矿坑涌水对周围村庄的影响

运用地下水数值模拟技术,通过GMS地下水数值模拟软件对矿区地下水动态进行模拟,对矿坑涌水给矿区周围地下水环境带来的影响进行预测,并通过预测结果说明在环境影响评价中如何应用数值模拟技术分析矿坑涌水对周围村庄的影响。