粗糙模糊环境下电力用户负荷响应潜力评估

深度探索用户负荷可调节潜力是国家电力市场精细化发展的迫切需求。为有效感知电力用户负荷综合响应潜力,提出一种模糊粗糙环境下的混合评估模型。首先,从经济性、用户特性、负荷特性、信息特性等4个维度构建负荷响应潜力指标体系;其次,充分考虑评估中个体判断的模糊性和群体偏好的多样性,采用模糊粗糙数对个体语义评估信息进行处理和集结;然后,将模糊粗糙熵权法和逐步加权评估比率分析法(step-wise weight assessment ratio analysis,SWARA)相结合确定指标综合权重,并采用基于模糊粗糙数的改进多属性边界逼近区域比较法(multi-attributive border approximation area comparison,MABAC)计算电力用户针对属性函数的负荷响应潜力综合评估值,从而获取潜力排序结果;最后,以多个行业的电力用户负荷综合响应潜力评估为例,验证所提模型的有效性。

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.

综合利用率评估的民机结构维修间隔确定方法

确定结构维修间隔是民机结构维修大纲中的关键内容之一,民机利用率对维修间隔制定的合理性有很大影响。基于环境损伤条件下的民机结构,通过分析不同利用率下影响金属结构环境损伤的指标的敏感性,提出了民机结构维修间隔与利用率匹配模型。在该模型中,应用序关系分析法-属性重要度相结合的组合赋权方法,对不同利用率下影响金属结构环境损伤的指标进行组合权重评估,确定金属结构环境损伤的总等级,并根据环境损伤等级-维修间隔回归方程确定了不同利用率下的民机结构维修间隔,最后采用灰色关联分析法验证了不同利用率下各指标评价值与理想值的关联度。以某型飞机中央盒段上壁板内表面维修间隔确定为例,对所提出的模型进行验证分析,利用模型得到的维修间隔与维修审查委员会报告(maintenance review board report,MRBR)规定相比偏小,鉴于该文件规定的为最低工程要求,从而确定该维修间隔满足要求,证明了模型的有效性。研究成果表明,建立的民机结构维修间隔与利用率匹配模型的适用性较强,可以有效解决国产飞机结构维修大纲中维修间隔确定时利用率考虑不足的问题,具有较高的工程应用价值。

SWARA方法在药品临床综合评价中的应用现状

药品临床综合评价是药品供应保障决策的重要技术工具,其要求评价主体采用多种评价方法和工具对药品开展多维度、多层次证据的综合评判。多准则决策分析(MCDA)是药品临床综合评价的重要方法,包括权重赋值和综合评价。逐步加权评估比率分析法(SWARA)是MCDA的一种权重赋值方法,较其他方法可更简洁、准确地确定指标权重。本文对SWARA的方法进行介绍,并系统综述了SWARA在药品临床综合评价中的应用现状。结果显示,目前,SWARA已应用于多个研究领域。在医药学领域内,研究者利用SWARA建立MCDA模型,通过咨询专家团队意见,计算出每个药物评价准则的具体权重值。SWARA的优势在于其按照相应步骤,通过征询专家意见或判断,为药物评价准则的权重提供了一个全新的赋值方式,以解决医疗领域中的MCDA问题;但其在解决复杂决策问题时具有一定的主观性和不确定性,也可能存在评价准则筛选不充分、覆盖主题不全面等问题,应用时需加以注意。