A template-based knowledge reuse method for generating multitype 3D railway scenes

Constructing 3D railway scenes helps improve the railway information construction ability and the comprehensive railway management level. However, the existing modeling methods have disadvantages such as low flexibility in scene configuration, weak extensibility of scene objects and poor reusability of modeling knowledge. To enable multitype 3D railway scenes to be automatically constructed, a template-based knowledge reuse method was proposed in this paper. First, based on parsing of modeling operation characteristics and modeling knowledge expression, a modeling knowledge template was designed to store modeling knowledge and modeling operations in a parameterized way. The contents of this template were described in terms of semantic information, geometric information, topologic information and reference system information, and the method for instantiating the modeling knowledge template was proposed. Second, a scene configuration template was designed to organize the instantiated modeling knowledge files and scene objects. By the use of a flexible tree structure, the scene configuration template enables extensibility of scene objects. Finally, the two types of templates were parsed in terms of scene organization, linear referencing information, geometric and topologic relationships, and semantic descriptions. In this way, different types of 3D railway scenes were generated automatically. After a prototype system was developed, experiments on automatic modeling of two 3D railway scenes were carried out to verify the template-based knowledge reuse method. The experimental results show that the proposed method can be used to generate multitype 3D railway scenes automatically. With this method, model combination can be configured, model types can be extended and modeling knowledge can be reused

Manager’s and citizen’s perspective of positive and negative risks for small probabilities

So far „risk‟ has been mostly defined as the expected value of a loss, mathematically PL, being P the probability of an adverse event and L the loss incurred as a consequence of the event. The so called risk matrix is based on this definition. Also for favorable events one usually refers to the expected gain PG, being G the gain incurred as a consequence of the positive event. These “measures” are generally violated in practice. The case of insurances (on the side of losses, negative risk) and the case of lotteries (on the side of gains, positive risk) are the most obvious. In these cases a single person is available to pay a higher price than that stated by the mathematical expected value, according to (more or less theoretically justified) measures. The higher the risk, the higher the unfair accepted price. The definition of risk as expected value is justified in a long term “manager‟s” perspective, in which it is conceivable to distribute the effects of an adverse event on a large number of subjects or a large number of recurrences. In other words, this definition is mostly justified on frequentist terms. Moreover, according to this definition, in two extreme situations (high-probability/low-consequence and low-probability/high-consequence), the estimated risk is low. This logic is against the principles of sustainability and continuous improvement, which should impose instead both a continuous search for lower probabilities of adverse events (higher and higher reliability) and a continuous search for lower impact of adverse events (in accordance with the fail-safe principle). In this work a different definition of risk is proposed, which stems from the idea of safeguard: (1Risk)=(1P)(1L). According to this definition, the risk levels can be considered low only when both the probability of the adverse event and the loss are small. Such perspective, in which the calculation of safeguard is privileged to the calculation of risk, would possibly avoid exposing the Society to catastrophic consequences, sometimes due to wrong or oversimplified use of probabilistic models. Therefore, it can be seen as the citizen‟s perspective to the definition of risk