To improve the agility, dynamics, composability, reusability, and development efficiency restricted by monolithic federation object model （FOM）, a modular FOM is proposed by high level architecture （HLA） evolved p...To improve the agility, dynamics, composability, reusability, and development efficiency restricted by monolithic federation object model （FOM）, a modular FOM is proposed by high level architecture （HLA） evolved product development group. This paper reviews the state-of-the-art of HLA evolved modular FOM. In particular, related concepts, the overall impact on HLA standards, extension principles, and merging processes are discussed. Also permitted and restricted combinations, and merging rules are provided, and the influence on HLA interface specification is given. The comparison between modular FOM and base object model （BOM） is performed to illustrate the importance of their combination. The applications of modular FOM are summarized. Finally, the significance to facilitate compoable simulation both in academia and practice is presented and future directions are pointed out.展开更多
针对目前海洋能区划研究中存在的计算复杂、耗时长和成本高等问题,本研究基于改进的多准则决策(Multiple criteria decision making,MCDM)方法和人工神经网络(Artificial neural network,ANN),提出了一种风浪联合开发区划智能模型。为...针对目前海洋能区划研究中存在的计算复杂、耗时长和成本高等问题,本研究基于改进的多准则决策(Multiple criteria decision making,MCDM)方法和人工神经网络(Artificial neural network,ANN),提出了一种风浪联合开发区划智能模型。为降低专家的主观偏差,应用基于层级的模糊权重评估(Fuzzy level based weight assessment,FLBWA)法来计算各评价指标权重;继而结合改进的Borda-全乘比例多目标优化(Borda-multi-objective optimization on the basis of ratio analysis plus full multiplicative form,Borda-MULTIMOORA)法计算开发适宜性指数,从而能够更加准确、高效地得到评价结果;之后,基于灰狼优化算法的反向传播(Grey wolf optimizer with back propagation,GWO-BP)神经网络构建并训练智能模型,将适宜性分析转化为自动化、高效化和智能化的过程;最后,以山东省风浪联合开发区划为例验证该模型的可行性和合理性。根据实例验证,该模型可以实现风浪联合开发区划的智能化,为相关领域的研究和政府规划提供参考。展开更多
基于模型诊断(MBD)方法在不同的环境中有越来越多的用途,包括软件故障定位、电子表格的调试、Web服务和硬件设计,以及生物系统的分析等.受这些不同用途的启发,近年来MBD算法改进成效显著.然而,对体系庞大、结构复杂的系统,需要对现有方...基于模型诊断(MBD)方法在不同的环境中有越来越多的用途,包括软件故障定位、电子表格的调试、Web服务和硬件设计,以及生物系统的分析等.受这些不同用途的启发,近年来MBD算法改进成效显著.然而,对体系庞大、结构复杂的系统,需要对现有方法进一步改进.由于求解诊断解在计算上具有挑战性,因此相继提出了一些通过压缩模型的MBD算法来提高诊断效率,如基于统治的多观测压缩模型(dominated-based compacted model with multiple observations,D-CMMO)算法.对于给定多个观测值且注入1个以上错误需要大量时间的诊断问题,提出了一个新的诊断模型CCM(cardinality-constrained compacted model)来解决.基于基数约束的压缩模型算法使用2种方法对求解过程进行优化:首先,利用系统观测的故障输出和故障组件数量之间的约束关系来限制目标解的范围;其次,通过对假设集采用单次入队方法,进而有效提升MaxSAT(maximum satisfiability)求解器的性能.此外,在ISCAS85和ITC99基准测试用例上的实验结果表明,与目前最新的MBD求解方法D-CMMO相比,上述2种优化方法有效缩小了MBD问题的求解范围,降低MaxSAT求解器搜索目标解的难度,进而能在更短的时间内返回一个诊断解.在平均状况下,CCM方法相比D-CMMO方法求解效率分别提升64.5%和92.8%.展开更多
基金supported by the National Natural Science Foundation of China(6067406960574056).
文摘To improve the agility, dynamics, composability, reusability, and development efficiency restricted by monolithic federation object model （FOM）, a modular FOM is proposed by high level architecture （HLA） evolved product development group. This paper reviews the state-of-the-art of HLA evolved modular FOM. In particular, related concepts, the overall impact on HLA standards, extension principles, and merging processes are discussed. Also permitted and restricted combinations, and merging rules are provided, and the influence on HLA interface specification is given. The comparison between modular FOM and base object model （BOM） is performed to illustrate the importance of their combination. The applications of modular FOM are summarized. Finally, the significance to facilitate compoable simulation both in academia and practice is presented and future directions are pointed out.
文摘针对目前海洋能区划研究中存在的计算复杂、耗时长和成本高等问题,本研究基于改进的多准则决策(Multiple criteria decision making,MCDM)方法和人工神经网络(Artificial neural network,ANN),提出了一种风浪联合开发区划智能模型。为降低专家的主观偏差,应用基于层级的模糊权重评估(Fuzzy level based weight assessment,FLBWA)法来计算各评价指标权重;继而结合改进的Borda-全乘比例多目标优化(Borda-multi-objective optimization on the basis of ratio analysis plus full multiplicative form,Borda-MULTIMOORA)法计算开发适宜性指数,从而能够更加准确、高效地得到评价结果;之后,基于灰狼优化算法的反向传播(Grey wolf optimizer with back propagation,GWO-BP)神经网络构建并训练智能模型,将适宜性分析转化为自动化、高效化和智能化的过程;最后,以山东省风浪联合开发区划为例验证该模型的可行性和合理性。根据实例验证,该模型可以实现风浪联合开发区划的智能化,为相关领域的研究和政府规划提供参考。
文摘基于模型诊断(MBD)方法在不同的环境中有越来越多的用途,包括软件故障定位、电子表格的调试、Web服务和硬件设计,以及生物系统的分析等.受这些不同用途的启发,近年来MBD算法改进成效显著.然而,对体系庞大、结构复杂的系统,需要对现有方法进一步改进.由于求解诊断解在计算上具有挑战性,因此相继提出了一些通过压缩模型的MBD算法来提高诊断效率,如基于统治的多观测压缩模型(dominated-based compacted model with multiple observations,D-CMMO)算法.对于给定多个观测值且注入1个以上错误需要大量时间的诊断问题,提出了一个新的诊断模型CCM(cardinality-constrained compacted model)来解决.基于基数约束的压缩模型算法使用2种方法对求解过程进行优化:首先,利用系统观测的故障输出和故障组件数量之间的约束关系来限制目标解的范围;其次,通过对假设集采用单次入队方法,进而有效提升MaxSAT(maximum satisfiability)求解器的性能.此外,在ISCAS85和ITC99基准测试用例上的实验结果表明,与目前最新的MBD求解方法D-CMMO相比,上述2种优化方法有效缩小了MBD问题的求解范围,降低MaxSAT求解器搜索目标解的难度,进而能在更短的时间内返回一个诊断解.在平均状况下,CCM方法相比D-CMMO方法求解效率分别提升64.5%和92.8%.
