一种基于功率谱特征参量的水中目标辐射噪声非母板匹配分类识别方法

本文阐述了一种非母板匹配的水中目标辐射噪声分类识别的思想、技术路线及算法的计算机实现关键技术。该算法基于提取水中目标辐射噪声信号功率谱中的最强谱带位置、线谱位置、线谱根数、线谱平均间隔等特征参量，通过特定处理后，直接给出识别结果，不需要附带庞大数据库。经试验验证，该技术突破了传统的母板匹配目标识别的算法思想，能够适用于复杂海况，做到宽容性匹配。识别效率高，识别率达到试验预期要求。     

Centralised reallocation of emission permits using DEA

In this paper a data envelopment analysis (DEA) approach to the problem of emission permits reallocation is presented. It can be used with conventional command and control as well as with an allowance market. It uses a centralized point of view, which represents the common good. In the model it is assumed that firms produce two types of outputs: desirable outputs (i.e. good outputs with positive value for consumers) and undesirable outputs (i.e. bad outputs with negative value for consumers, such as emissions of pollutants). The proposed approach has three phases, which correspond to three objectives that are pursued lexicographically. The three objectives are maximizing aggregated desirable production, minimizing undesirable total emissions and minimizing the consumption of input resources. The relative priority of these objectives is defined by the regulator. The whole approach is units-invariant and does not require information on input and output prices. The approach is applied on a dataset from the Swedish pulp and paper industry.