Life-cycle cost model and evaluation system for power grid assets based on fuzzy membership degree

Life-cycle cost(LCC)theory can be effectively applied to improve the efficiency and quality of power plant equipment and asset management.However,specific aspects of the LCC calculation and evaluation model require further research for practical application.This paper proposes an LCC assessment model for the management of electric power plant equipment during its service life.A membership function method based on fuzzy logic is used to improve the allocation of modernization and overhaul projects to multiple equipment assets.An LCC assessment model and evaluation system for power equipment are proposed and successfully applied to the equipment and project management of a Guangzhou power plant in the China Southern Power Grid,providing a decision-making mechanism that facilitates efficient operation and optimal utilization of power plant equipment and assets.

Uncertainty entropy-based exploratory evaluation method and its applications on missile effectiveness evaluation

Some attributes are uncertain for evaluation work because of incomplete or limited information and knowledge.It leads to uncertainty in evaluation results.To that end,an evaluation method,uncertainty entropy-based exploratory evaluation(UEEE),is proposed to guide the evaluation activities,which can iteratively and gradually reduce uncertainty in evaluation results.Uncertainty entropy(UE)is proposed to measure the extent of uncertainty.First,the belief degree distributions are assumed to characterize the uncertainty in attributes.Then the belief degree distribution of the evaluation result can be calculated by using uncertainty theory.The obtained result is then checked based on UE to see if it could meet the requirements of decision-making.If its uncertainty level is high,more information needs to be introduced to reduce uncertainty.An algorithm based on the UE is proposed to find which attribute can mostly affect the uncertainty in results.Thus,efforts can be invested in key attribute(s),and the evaluation results can be updated accordingly.This update should be repeated until the evaluation result meets the requirements.Finally,as a case study,the effectiveness of ballistic missiles with uncertain attributes is evaluated by UEEE.The evaluation results show that the target is believed to be destroyed.

The Theory of Membership Degree of Γ-Conclusion in Several n-Valued Logic Systems

Based on the analysis of the properties of Γ-conclusion by means of deduction theorems, completeness theorems and the theory of truth degree of formulas, the present papers introduces the concept of the membership degree of formulas A is a consequence of Γ (or Γ-conclusion) in Lukasiewicz n-valued propositional logic systems, Godel n-valued propositional logic system and the R0 n-valued propositional logic systems. The condition and related calculations of formulas A being Γ-conclusion were discussed by extent method. At the same time, some properties of membership degree of formulas A is a Γ-conclusion were given. We provide its algorithm of the membership degree of formulas A is a Γ-conclusion by the constructions of theory root.

Adjustment Model and Algorithm Based on Ellipsoid Uncertainty

In surveying adjustment models,there is usually some uncertain additional information or prior information on parameters,which can constrain the parameters,and guarantee the uniqueness and stability of parameter solution.In this paper,we firstly use ellipsoidal sets to describe uncertainty,and establish a new adjustment model with ellipsoidal uncertainty.Furthermore,we give a new adjustment criterion based on minimization trace of an outer ellipsoid with two ellipsoid intersections,and analyze the propagation law of uncertainty.Correspondingly,we give a new algorithm for the adjustment model with ellipsoid uncertainty.Finally,we give three examples to test and verify the effectiveness of our algorithm,and illustrate the relation between our result and the weighted mixed estimation.

Multi-objective energy management system for DC microgrids based on maximum membership degree principle

With the increasing quantity of DC electrical equipment, DC microgrids have been paid more and more attention. This paper proposes an approach to multi-objective optimisation of an energy management system(EMS) for a DC microgrid that includes a hybrid energy storage system(HESS). The operating and maintenance cost and the loss of power supply probability(LPSP) of the system are used as optimisation targets. The power flows of all distributed generators(DGs) in the DC microgrid during operating period are optimized. Based on the improved differential evolution(DE) algorithm, and by using the multi-objective non-dominated sorting method and the maximum membership degree principle(MMDP) of fuzzy control, the overall satisfaction degree of Pareto solutions to power flow optimization can be obtained. Simulation results verify the effectiveness of the proposed EMS optimization scheme, which is able to achieve an effective trade-off between the economy and the reliability of microgrid operation.

堰塞坝开发利用综合评价方法与应用

现阶段堰塞坝开发利用工程设计建设大多沿用传统土石坝相关技术理论,应急抢险、风险分析及开发利用可行性分析完成后,缺少针对堰塞坝开发利用效果的定量评价方法。通过分析社会及生态、供水灌溉、发电功能及投资回报等堰塞坝开发利用需求,建立堰塞坝开发利用综合评价指标体系和多级模糊综合评价模型,引入云模型改进的层次分析法确定各指标权重,结合构建的隶属度函数确定各指标隶属度,并以红石岩、唐家山、白格、舟曲泥石流堰塞坝为典型案例,综合评价国内典型堰塞坝开发利用效果。结果表明:堰塞坝开发利用综合评价体系满足需求效果及收益最大化的评价要求,改进的多级模糊综合评价模型较好地解决了评价指标随机性、离散性等问题;综合评价结果与工程实际情况基本相符,评价体系及模型具有较好的适用性。研究成果可为堰塞坝开发利用综合评价提供理论参考和实践依据。

An Initiative Learning Algorithm Based on System Uncertainty

Initiative-learning algorithms are characterized by and hence advantageousfor their independence of prior domain knowledge. Usually, their induced results could moreobjectively express the potential characteristics and patterns of information systems.Initiative-learning processes can be effectively conducted by system uncertainty, becauseuncertainty is an intrinsic common feature of and also an essential link between information systemsand their induced results. Obviously, the effectiveness of such initiative-learning framework isheavily dependent on the accuracy of system uncertainty measurements. Herein, a more reasonablemethod for measuring system uncertainty is developed based on rough set theory and the conception ofinformation entropy; then a new algorithm is developed on the bases of the new system uncertaintymeasurement and the Skowron's algorithm for mining prepositional default decision rules. Theproposed algorithm is typically initiative-learning. It is well adaptable to system uncertainty. Asshown by simulation experiments, its comprehensive performances are much better than those ofcongeneric algorithms.

基于PLC的油田注水全自动监控模型构建

油田注水全自动监控能够减少能耗,为实现油田注水全自动监控,构建一种基于PLC的油田注水全自动监控模型。首先设定监测目标,将注水量、供水量、注水井压力和注水井总流量作为约束条件,并建立基于油压、套压、注水水质、注水量、注水温度等预警指标,采用PID与PLC结合的方法实现油田注水全自动监控。实验结果表明,所提油田注水全自动监控模型在注水量监控、缓存罐液位监测、回水管路液体压力、油压监测等方面具有较高的准确性,能够满足方法设计需求,具有一定应用价值。

基于不确定性的机坪管制系统风险评估

对机坪管制系统进行风险评估是提高机场安全水平的一个重要措施,但如何科学客观地评价机坪管制系统,得到贴近现实的评估结果是一个难题。基于此,建立了机坪管制系统风险评价体系,从组合权重法的观点出发,通过乘法归一化合成评价权重,并在模糊集理论的基础上引入DS证据论对某机场机坪管制系统进行实例评估,得出人员因素是机坪管制系统安全体系的重点这一结论,对机坪管制系统安全改进有较好的指导意义。

基于加权隶属度矩阵的岩体质量优化分级及支护研究

巷道围岩质量分级是巷道围岩稳定性分析、合理支护设计与施工的基础。提高分级结果的准确性,是提升支护效果和巷道围岩稳定性的关键途径。以RMR法为基础,以某一测点的分级因素指标值为例,构建RMR法各分级指标的隶属度矩阵,再结合熵值法确定分级指标的权重,得到加权隶属度矩阵,构造优化后的评分公式,进行指标优化评分和围岩质量分级,然后根据围岩质量分级结果及松动圈监测结果设计支护参数,利用FLAC3D进行模拟计算以巷道围岩塑性区体积和顶底板收敛量为评价指标,研究优化前后的支护效果。研究结果表明:经过优化后的指标评分和围岩等级更加准确,巷道支护参数设计更加合理,数值模拟结果显示其能有效地提升围岩的稳定性,并将优化后的支护设计参数应用于工程,取得了良好的效果。研究成果可有效解决巷道围岩分级因素指标值位于分级边界或分级跨度大的问题,为矿山岩体质量分级与支护设计提供参考。

基于模糊隶属度分布函数的博斯腾湖景观适宜水位确定

采用层次分析法识别博斯腾湖景观关键点,基于湖泊特征水位分析,构建了景观关键点水位和景观适宜度的模糊隶属度分布函数,计算了不同季节博斯腾湖景观适宜水位范围。结果表明:湖泊水质、年旅游业收益是识别博斯腾湖景观关键点的主要因素;博斯腾湖最佳景观水位为1046.70 m,低警戒水位为1045.00 m,高警戒水位为1047.90 m,低、高破坏边缘水位分别为1045.40 m和1047.40 m;不同季节博斯腾湖景观适宜水位存在差异,春季和秋季景观适宜水位范围为1045.90~1047.16 m,夏季为1046.00~1047.00 m,冬季为1045.85~1047.25 m。

融合多源数据的桥梁技术状况指标评定方法

为可靠地评定桥梁技术状况,针对桥梁技术状况评定过程的模糊性和不确定性特点,提出一种融合多源数据的桥梁技术状况指标评定方法。该方法首先通过云模型分别将桥梁技术状况指标的检测值和监测值转化为指标各等级的隶属度,并用隶属度构造改进Dempster-Shafer证据理论的基本信任分配函数,再结合证据冲突/一致度和信息熵确定综合权重系数,对基本信任分配函数进行修正,然后采用Dempster合成规则融合得到综合基本信任分配函数,最后依据最大隶属度原则得到指标的标度,实现桥梁技术状况的可靠评定。采用该方法和《公路桥梁技术状况评定标准》(JTG/T H21—2011,现行评定方法)分别评定某座斜拉桥的索力指标,结果表明,与现行评定方法相比,该方法的评定结果对应的养护对策与实际养护对策更相符,该方法有效、可行。

基于模糊区域生长的医学图像分割

针对肺部图像的特征,论文提出了一种基于模糊隶属度的区域生长的图像分割方法。首先,对原图像进行预处理,并将其二值化;其次根据二值图像确定不同区域轮廓,计算不同区域的质心;再次,根据标注图像自动获取目标区域的质心将其作为区域生长的种子点;最后利用隶属度函数计算种子点邻近像素点的隶属度,根据其隶属度的大小进行区域生长。实验结果表明,论文提出的方法与传统区域生长方法相比能够缓解过分割或者欠分割现象,分割精度比传统方法提高了21.88%,可以分割出更清晰的目标区域,为今后的临床诊断提供帮助。

A new structural reliability analysis method in presence of mixed uncertainty variables

For structures with both random and fuzzy uncertainty,this paper presents a novel method for determining the membership function in fuzzy reliability with the Automatic Updating Extreme Response Surface(AUERS)method.In the proposed method,fuzzy variables are initially converted into a value domain under the given cut level and the extreme point in the domain where the reliability reaches its extreme value is considered.Second,the Particle Swarm Optimization(PSO)algorithm is used to determine the extreme point according to the extreme responses for different sets of random sample inputs.A kriging response surface is subsequently constructed between the random variables and the corresponding extreme points.An automatic updating strategy is then introduced based on the Relative Mean Square Predicted Error(RMSPE)before performing every iteration of reliability analysis.By adding new sample points,the approximate quality of the kriging response surface is improved.Finally,reliability analysis is used to determine the reliability bound under the given cut level.The proposed method assures the accuracy and computation efficiency of the mixed uncertainty reliability analysis results while it prevents the solution from becoming trapped in a local optimum,which occurs in classical optimization methods.Two example analyses are used to demonstrate the validity and advantages of the proposed method.

基于分类风险的半监督集成学习算法

针对当前半监督集成学习算法对无标记样本预测时容易出现的标注混沌问题,文中提出基于分类风险的半监督集成学习算法(Classification Risk-Based Semi-supervised Ensemble Learning Algorithm,CR-SSEL).采用分类风险作为无标记样本置信度的评判标准,可有效衡量样本标注的不确定性程度.迭代地训练分类器,对高置信度样本进行再强化,使样本标注的不确定性逐渐降低,增强半监督集成学习算法的分类性能.在多个标准数据集上验证CR-SSEL的学习参数影响、训练过程收敛和泛化性能提升,实验表明随着基分类器个数的增加,CR-SSEL的训练过程呈收敛趋势,获得较优的分类精度.

ESG信息披露提升了企业投资效率吗?——非财务信息披露的财务作用

基于2010—2022年A股上市公司数据,将商道融绿首次公布上市公司ESG评级作为准自然实验,采用多期双重差分模型实证检验ESG信息披露对企业投资效率的影响效应及作用机制。研究结果显示,ESG信息披露能够有效提升企业投资效率,且这一结论在进行多项稳健性检验后依然成立。机制分析结果表明,抑制企业短视、缓解融资约束和降低环境不确定性是ESG信息披露提升企业投资效率的有效作用渠道。异质性分析发现,ESG信息披露对企业投资效率的促进作用在非国有企业、高数字化水平企业和市场化程度高的地区中更为明显。研究结论在实践层面为构建与完善企业可持续发展体系提供了经验证据,对促使企业积极履行社会责任、推动中国经济高质量发展具有重要意义。

基于主客观组合赋权-云模型的边坡稳定性评价

边坡工程在外力作用下易造成滑坡、泥石流等地质灾害,严重影响周边的人民生命财产安全。选取边坡角、边坡高度、天然容重、孔隙水压力比、黏聚力、内摩擦角等6个评价指标,运用CRITIC法和序关系分析法(G1法)分别计算6个评价指标的客观权重和主观权重,并用博弈论对权重进行组合赋权,最后确定综合权重。结合正向云发生器,计算出边坡不同等级的隶属度,并与CRTIC法评价结果及实际等级进行对比,结果表明:边坡稳定性评价的准确率与权重息息相关,利用CRITIC-G1云模型所得的组合权重准确率高达91.7%,而CRITIC法的准确率仅为75%。随机抽取5组数据检验CRITIC-G1云模型的合理性和可靠性,结果显示CRITIC-G1云模型的边坡稳定性等级评价结果与实际边坡稳定性等级相吻合,证明该方法可以用于边坡稳定性的分析与评价。

链式回转弹仓区间不确定性动力学模型

针对具有区间不确定性参数的辨识问题,提出一种基于区间可能度转换模型的区间不确定性参数的双层嵌套辨识(Double-layer Nested Identification,DNI)方法。通过将待辨识参数分为两类,利用DNI方法辨识出第1类确定性参数,再通过基于DNI思想的区间优化方法优化第2类区间不确定性参数的区间范围;面向嵌套策略类型方法计算量庞大且效率低的问题,选用贝叶斯优化-粒子群优化(Bayesian Optimization-Particle Swarm Optimization,BO-PSO)方法作为内层算法以提高求解效率。DNI方法的内层利用BO-PSO方法计算区间上下界,外层利用改进型布谷鸟搜索(Improved Cuckoo Search,ICS)方法辨识特定参数。为进一步缩短求解时间,提出一种ICS多核极限学习机(ICS-Multiple Kernel-Extreme Learning Machine,ICS-MK-ELM)代理模型,ICS-MK-ELM代理模型克服了人工调节每个核函数超参数的困难,并且模型预测精度明显高于核ELM(Kernel ELM,KELM)和MK-ELM;将DNI方法应用于链式回转弹仓的参数辨识,解决了链式弹仓具有区间不确定性参数的辨识困难的问题,参数辨识结果表明所提DNI方法以及基于DNI思想的区间优化方法具有更高的精度和稳定性。

南葶苈子膨胀度的不确定度评定

目的建立中药南葶苈子膨胀度测定的不确定度评定方法。方法按2020年版《中国药典(四部)》通则2101膨胀度测定法测定膨胀度,分析测定过程中的不确定度来源,量化不确定度分量。结果测定过程中的不确定度来源于药材样品膨胀后的体积、称量、水分测量的相对不确定度,以及膨胀度测量重复性的标准不确定度。所得扩展不确定度为0.3 mL/g,南葶苈子膨胀度为(5.5±0.3)mL/g。结论在现行膨胀度测定法的基础上,建议修订膨胀度测定管的刻度分度(如由0.2 mL改为0.1 mL),“称定质量”项由精确至0.01 g改为0.001 g,以提高南葶苈子膨胀度测定结果的可靠性。

基于隶属度综合分析法从制革固废中回收羊毛纤维

为提高制革固废中毛纤维的利用价值,本实验将隶属度综合分析法与响应面优化法相结合,建立了酶法回收毛纤维的工艺。运用隶属度综合分析法对不同酶法回收的羊毛纤维白度、断裂强度、断裂伸长率和酶解液总蛋白、总糖含量进行赋权计算,以获得的综合评分S为响应值,运用响应面优化法对酶制剂配比进行优化,最终获得羊毛纤维的回收工艺。具体为:含毛制革固废80℃高温水浴1 h,冷却后浸入含有胶原酶2.5 g/L、胰蛋白酶2.5 g/L、菠萝蛋白酶5 g/L的溶液当中,以NaOH溶液调节pH至7.0,40℃酶解5 h,过滤洗涤,提取毛纤维。经测定提取的毛纤维白度61%,纤维断裂伸长率33%,较原毛均有所提升,纤维断裂强度1.08 cN/dteX,为原毛的96%。该研究为制革含毛固废的高值化利用提供了理论参考。