ABSTRACT. Nuclear-power-plant emergency management is very important, which can be used to avoid nuclear accident and radiation leak accident and the emergency action swiftly taken beyond the normal work procedure to mitigate the accident consequences. Evacuation management for large crowds after accidents involves a number of processes and factors with socio-economic and environmental implications. These processes and factors, as well as their interactions, are associated with a variety of uncertainties. In this study, an interval-based evacuation management (IBEM) model is developed in response to such challenges, based on interval-parameter linear programming (ILP) technique that can tackle uncertainties presented as interval values. The IBEM model is applied to a case study and then solved through an interactive algorithm that does not lead to more complicated intermediate submodels and has a relatively low computational requirement. Two scenarios are analyzed based on different policies of total capital considerations. A number of decision alternatives could be directly generated based on results from the IBEM model, which provide bases for in-depth analyses of tradeoffs among evacuation population, system cost, and constraint-violation risk.
With the development of Internet technology, various network attacks have emerged one after another, seriously affecting the security of many key infrastructures such as finance, energy, and transportation. Therefore, the importance of network asset management is self-evident. How to judge the security of assets and detect lost assets has become an important research topic. This paper proposes a method for detecting lost assets based on feature optimization and activepassive detection. Firstly, it achieves the classification of abnormal traffic by extracting important features of the traffic data. And then, it detects the network assets using the combined active and passive detection method. The experiments show that this method can effectively detect the lost assets in the network and effectively provide an analysis basis for threat analysis and emergency response.
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