As one of the most important part of weapon system of systems(WSoS),quantitative evaluation of reconnaissance satellite system(RSS)is indispensable during its construction and application.Aiming at the problem of nonl...As one of the most important part of weapon system of systems(WSoS),quantitative evaluation of reconnaissance satellite system(RSS)is indispensable during its construction and application.Aiming at the problem of nonlinear effectiveness evaluation under small sample conditions,we propose an evaluation method based on support vector regression(SVR)to effectively address the defects of traditional methods.Considering the performance of SVR is influenced by the penalty factor,kernel type,and other parameters deeply,the improved grey wolf optimizer(IGWO)is employed for parameter optimization.In the proposed IGWO algorithm,the opposition-based learning strategy is adopted to increase the probability of avoiding the local optima,the mutation operator is used to escape from premature convergence and differential convergence factors are applied to increase the rate of convergence.Numerical experiments of 14 test functions validate the applicability of IGWO algorithm dealing with global optimization.The index system and evaluation method are constructed based on the characteristics of RSS.To validate the proposed IGWO-SVR evaluation method,eight benchmark data sets and combat simulation are employed to estimate the evaluation accuracy,convergence performance and computational complexity.According to the experimental results,the proposed method outperforms several prediction based evaluation methods,verifies the superiority and effectiveness in RSS operational effectiveness evaluation.展开更多
Operational disposition of electronic countermeasures(ECM)is a hot topic in modern warfare research.Through fully analyzing the characteristics and shortcomings of the traditional operational disposition scheme,a supe...Operational disposition of electronic countermeasures(ECM)is a hot topic in modern warfare research.Through fully analyzing the characteristics and shortcomings of the traditional operational disposition scheme,a super-efficient data envelopment analysis support vector machine(SE-DEA-SVM)method for evaluating the operational configuration scheme of ECM is proposed.Firstly,considering the subjective and objective factors affecting the operational disposition of ECM,the index system of operational disposition scheme is established,and we explain the solution method of terminal indexs.Secondly,the evaluation and algorithm process of SE-DEA-SVM evaluation method are introduced.In this method,the super-efficient data envelopment analysis(SE-DEA)model is used to calculate the weight of index system,and the support vector machine(SVM)method combined with the training samples of evaluation index is used to obtain the input-output model of evaluation value of combat configuration.Finally,by an example(obtaining five schemes),we verify the SE-DEA-SVM evaluation method and analyze the results.The efficiency analysis,comparison analysis,and error analysis of this method are carried out.The results show that this method is more suitable for military evaluation with small samples,and it has high efficiency,applicability,and popularization value.展开更多
针对齿轮箱振动信号复杂多变,导致现有的齿轮箱故障诊断方法诊断精度不高、较弱故障特征容易被噪声淹没等问题,提出了一种基于向量加权平均优化算法(weighted mean of vectors,INFO)、变分模态分解(variational mode decomposition,VMD...针对齿轮箱振动信号复杂多变,导致现有的齿轮箱故障诊断方法诊断精度不高、较弱故障特征容易被噪声淹没等问题,提出了一种基于向量加权平均优化算法(weighted mean of vectors,INFO)、变分模态分解(variational mode decomposition,VMD)和卷积神经网络(convolutional neural network,CNN)的齿轮故障诊断方法。该方法首先采用熵权法将不同位置的振动传感器信号信息进行融合,利用INFO对VMD算法中参数进行优化,并设计一个复合评价指标作为参数优化的评价标准,使用奇异峭度差分谱的方法对敏感分量进行重构;其次,从重构的信号中提取时域、频域特征并输入到CNN模型中进行分类;最后通过Shap(Shapley additive explanations)值法对模型输入特征的重要性进行排序,分析不同特征组合对模型分类和特定故障识别的影响。在东南大学行星齿轮数据集上进行验证,结果表明,利用所提特征组合进行故障诊断,CNN模型故障诊断准确率为98.24%,高于其他特征组合,为行星齿轮箱的故障诊断提供了一组有效的特征指标。展开更多
为提高应用文编写效率,提出一种融合大语言模型(large language model,LLM)与向量知识库(vector knowledge base)的应用文自动生成框架.根据目标应用场景,以人工编写的标准应用文为范本,构建结构化辅助生成文件,并建立相应类型应用文的...为提高应用文编写效率,提出一种融合大语言模型(large language model,LLM)与向量知识库(vector knowledge base)的应用文自动生成框架.根据目标应用场景,以人工编写的标准应用文为范本,构建结构化辅助生成文件,并建立相应类型应用文的向量知识库.利用目标类型应用文的章节标题和用户输入的关键信息在知识库中进行检索,匹配相关文段;设置提示词引导LLM,以召回的参考文段及用户输入的提示信息为参考,使用末级标题作为分割标志,分章节生成应用文文本;最终按规定格式整合全文并输出完整的目标应用文.以应急预案为例,在同一评价标准下使用ChatGPT-4Turbo进行评测,自动生成的应急预案高度趋近于人工编写的质量,二者的文档质量相似度达95.87%.所提方法能够在算力资源有限的情况下突破字数限制,生成符合基本标准的长篇幅应用文,可供人工参考或直接使用,极大提高了编写人员的工作效率.展开更多
基金the National Defense Science and Technology Key Laboratory Fund of China(XM2020XT1023).
文摘As one of the most important part of weapon system of systems(WSoS),quantitative evaluation of reconnaissance satellite system(RSS)is indispensable during its construction and application.Aiming at the problem of nonlinear effectiveness evaluation under small sample conditions,we propose an evaluation method based on support vector regression(SVR)to effectively address the defects of traditional methods.Considering the performance of SVR is influenced by the penalty factor,kernel type,and other parameters deeply,the improved grey wolf optimizer(IGWO)is employed for parameter optimization.In the proposed IGWO algorithm,the opposition-based learning strategy is adopted to increase the probability of avoiding the local optima,the mutation operator is used to escape from premature convergence and differential convergence factors are applied to increase the rate of convergence.Numerical experiments of 14 test functions validate the applicability of IGWO algorithm dealing with global optimization.The index system and evaluation method are constructed based on the characteristics of RSS.To validate the proposed IGWO-SVR evaluation method,eight benchmark data sets and combat simulation are employed to estimate the evaluation accuracy,convergence performance and computational complexity.According to the experimental results,the proposed method outperforms several prediction based evaluation methods,verifies the superiority and effectiveness in RSS operational effectiveness evaluation.
基金This work was supported by the Military Postgraduate Funding Project(JY2019C055)Hunan Province Postgraduate Scientific Research Innovation Project(CX20200029).
文摘Operational disposition of electronic countermeasures(ECM)is a hot topic in modern warfare research.Through fully analyzing the characteristics and shortcomings of the traditional operational disposition scheme,a super-efficient data envelopment analysis support vector machine(SE-DEA-SVM)method for evaluating the operational configuration scheme of ECM is proposed.Firstly,considering the subjective and objective factors affecting the operational disposition of ECM,the index system of operational disposition scheme is established,and we explain the solution method of terminal indexs.Secondly,the evaluation and algorithm process of SE-DEA-SVM evaluation method are introduced.In this method,the super-efficient data envelopment analysis(SE-DEA)model is used to calculate the weight of index system,and the support vector machine(SVM)method combined with the training samples of evaluation index is used to obtain the input-output model of evaluation value of combat configuration.Finally,by an example(obtaining five schemes),we verify the SE-DEA-SVM evaluation method and analyze the results.The efficiency analysis,comparison analysis,and error analysis of this method are carried out.The results show that this method is more suitable for military evaluation with small samples,and it has high efficiency,applicability,and popularization value.
文摘针对齿轮箱振动信号复杂多变,导致现有的齿轮箱故障诊断方法诊断精度不高、较弱故障特征容易被噪声淹没等问题,提出了一种基于向量加权平均优化算法(weighted mean of vectors,INFO)、变分模态分解(variational mode decomposition,VMD)和卷积神经网络(convolutional neural network,CNN)的齿轮故障诊断方法。该方法首先采用熵权法将不同位置的振动传感器信号信息进行融合,利用INFO对VMD算法中参数进行优化,并设计一个复合评价指标作为参数优化的评价标准,使用奇异峭度差分谱的方法对敏感分量进行重构;其次,从重构的信号中提取时域、频域特征并输入到CNN模型中进行分类;最后通过Shap(Shapley additive explanations)值法对模型输入特征的重要性进行排序,分析不同特征组合对模型分类和特定故障识别的影响。在东南大学行星齿轮数据集上进行验证,结果表明,利用所提特征组合进行故障诊断,CNN模型故障诊断准确率为98.24%,高于其他特征组合,为行星齿轮箱的故障诊断提供了一组有效的特征指标。
文摘为提高应用文编写效率,提出一种融合大语言模型(large language model,LLM)与向量知识库(vector knowledge base)的应用文自动生成框架.根据目标应用场景,以人工编写的标准应用文为范本,构建结构化辅助生成文件,并建立相应类型应用文的向量知识库.利用目标类型应用文的章节标题和用户输入的关键信息在知识库中进行检索,匹配相关文段;设置提示词引导LLM,以召回的参考文段及用户输入的提示信息为参考,使用末级标题作为分割标志,分章节生成应用文文本;最终按规定格式整合全文并输出完整的目标应用文.以应急预案为例,在同一评价标准下使用ChatGPT-4Turbo进行评测,自动生成的应急预案高度趋近于人工编写的质量,二者的文档质量相似度达95.87%.所提方法能够在算力资源有限的情况下突破字数限制,生成符合基本标准的长篇幅应用文,可供人工参考或直接使用,极大提高了编写人员的工作效率.
