In some practical applications modeled by discrete-event systems(DES),the observations of events may be no longer deterministic due to sensor faults/failures,packet loss,and/or measurement uncertainties.In this contex...In some practical applications modeled by discrete-event systems(DES),the observations of events may be no longer deterministic due to sensor faults/failures,packet loss,and/or measurement uncertainties.In this context,it is interesting to reconsider the infinite-step opacity(∞-SO)and K-step opacity(K-SO)of a DES under abnormal conditions as mentioned.In this paper,the authors extend the notions of∞-SO and K-SO defined in the standard setting to the framework of nondeterministic observations(i.e.,the event-observation mechanism is state-dependent and nondeterministic).Obviously,the extended notions of∞-SO and K-SO are more general than the previous standard ones.To effectively verify them,a matrix-based current state estimator in the context of this advanced framework is constructed using the Boolean semi-tensor product(BSTP)technique.Accordingly,the necessary and sufficient conditions for verifying these two extended versions of opacity are provided as well as their complexity analysis.Finally,several examples are given to illustrate the obtained theoretical results.展开更多
针对传统机组运行约束挤压风电并网空间及固体废弃物堆存量激增造成的环境污染问题,配置光热电站与电加热联合运行促进风电消纳,引入垃圾焚烧电厂与电转气联合运行实现CO_(2)再利用,提出一种含光热电站(concentrating solar power plant...针对传统机组运行约束挤压风电并网空间及固体废弃物堆存量激增造成的环境污染问题,配置光热电站与电加热联合运行促进风电消纳,引入垃圾焚烧电厂与电转气联合运行实现CO_(2)再利用,提出一种含光热电站(concentrating solar power plant,CSP)及垃圾焚烧电厂(waste to energy plant,WTE)的虚拟电厂低碳优化调度模型。基于非参数核密度估计和Frank-Copula函数构建风电和光热电站出力联合分布模型,并利用蜉蝣优化K-means聚类算法得到典型场景;构建电加热与光热电站联合运行模型,并在垃圾焚烧-电转气精细化碳利用模型的基础上,引入阶梯碳交易机制进一步约束系统碳排放;以虚拟电厂总运行成本最低为目标,提出一种基于混合策略改进的水循环算法进行求解。仿真结果表明,所建立模型能够有效促进风电消纳并降低系统碳排放。展开更多
Adaptive cluster sampling （ACS） has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the fi...Adaptive cluster sampling （ACS） has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the final sample sizes, in this study, a k-step ACS based on Horvitz-Thompson （HT） estimator was developed and an unbiased estimator was derived. The k-step ACS-HT was assessed first using a simulated example and then using a real survey for numbers of plants for three species that were characterized by clustered and patchily spatial distributions. The effectiveness of this sampling design method was assessed in comparison with ACS Hansen-Hurwitz （ACS-HH） and ACS- HT estimators, and k-step ACS-HT estimator. The effectiveness of using different k- step sizes was also compared. The results showed that k-step ACS^HT estimator was most effective and ACS-HH was the least. Moreover, stable sample mean and variance estimates could be obtained after a certain number of steps, but depending on plant species, k-step ACS without replacement was slightly more effective than that with replacement. In k-step ACS, the variance estimate of one-step ACS is much larger than other k-step ACS （k 〉 1）, but it is smaller than ACS. This implies that k-step ACS is more effective than traditional ACS, besides, the final sample size can be controlled easily in population with big clusters.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.61903274,61873342,61973175the Tianjin Natural Science Foundation of China under Grant No.18JCQNJC74000。
文摘In some practical applications modeled by discrete-event systems(DES),the observations of events may be no longer deterministic due to sensor faults/failures,packet loss,and/or measurement uncertainties.In this context,it is interesting to reconsider the infinite-step opacity(∞-SO)and K-step opacity(K-SO)of a DES under abnormal conditions as mentioned.In this paper,the authors extend the notions of∞-SO and K-SO defined in the standard setting to the framework of nondeterministic observations(i.e.,the event-observation mechanism is state-dependent and nondeterministic).Obviously,the extended notions of∞-SO and K-SO are more general than the previous standard ones.To effectively verify them,a matrix-based current state estimator in the context of this advanced framework is constructed using the Boolean semi-tensor product(BSTP)technique.Accordingly,the necessary and sufficient conditions for verifying these two extended versions of opacity are provided as well as their complexity analysis.Finally,several examples are given to illustrate the obtained theoretical results.
文摘针对传统机组运行约束挤压风电并网空间及固体废弃物堆存量激增造成的环境污染问题,配置光热电站与电加热联合运行促进风电消纳,引入垃圾焚烧电厂与电转气联合运行实现CO_(2)再利用,提出一种含光热电站(concentrating solar power plant,CSP)及垃圾焚烧电厂(waste to energy plant,WTE)的虚拟电厂低碳优化调度模型。基于非参数核密度估计和Frank-Copula函数构建风电和光热电站出力联合分布模型,并利用蜉蝣优化K-means聚类算法得到典型场景;构建电加热与光热电站联合运行模型,并在垃圾焚烧-电转气精细化碳利用模型的基础上,引入阶梯碳交易机制进一步约束系统碳排放;以虚拟电厂总运行成本最低为目标,提出一种基于混合策略改进的水循环算法进行求解。仿真结果表明,所建立模型能够有效促进风电消纳并降低系统碳排放。
文摘Adaptive cluster sampling （ACS） has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the final sample sizes, in this study, a k-step ACS based on Horvitz-Thompson （HT） estimator was developed and an unbiased estimator was derived. The k-step ACS-HT was assessed first using a simulated example and then using a real survey for numbers of plants for three species that were characterized by clustered and patchily spatial distributions. The effectiveness of this sampling design method was assessed in comparison with ACS Hansen-Hurwitz （ACS-HH） and ACS- HT estimators, and k-step ACS-HT estimator. The effectiveness of using different k- step sizes was also compared. The results showed that k-step ACS^HT estimator was most effective and ACS-HH was the least. Moreover, stable sample mean and variance estimates could be obtained after a certain number of steps, but depending on plant species, k-step ACS without replacement was slightly more effective than that with replacement. In k-step ACS, the variance estimate of one-step ACS is much larger than other k-step ACS （k 〉 1）, but it is smaller than ACS. This implies that k-step ACS is more effective than traditional ACS, besides, the final sample size can be controlled easily in population with big clusters.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.5027150)湖南省教育厅一般项目(the Common Project of Bureau of Education of Hunan Province under Grant No.05C410)
