Coordinated Bayesian optimal approach for the integrated decision between electronic countermeasure and firepower attack

The coordinated Bayesian optimization algorithm (CBOA) is proposed according to the characteristics of the function independence, conformity and supplementary between the electronic countermeasure (ECM) and the firepower attack systems. The selection criteria are combinations of probabilities of individual fitness and coordinated degree and can select choiceness individual to construct Bayesian network that manifest population evolution by producing the new chromosome. Thus the CBOA cannot only guarantee the effective pattern coordinated decision-making mechanism between the populations, but also maintain the population multiplicity, and enhance the algorithm performance. The simulation result confirms the algorithm validity.     

一种复杂系统研究方法——基于多智能体的整体建模仿真方法

本文在对一般复杂系统进行研究分析的基础上,针对目前国内外的研究现状,提出了一种研究复杂系统的新方法－基于多智能体的整体建模仿真方法,对这种方法的操作流程和步骤进行了详细的说明,并介绍了支撑这种方法的ＳＷＡＲＭ仿真平台,最后指出了这种建模仿真方法在军事和经济系统中的应用前景。     

雷达大地回波反射和散射系数的测量

介绍了雷达大地回波特性的一种测量方法 ,包括测量系统框图和基于回波波形的数据处理方法。利用该方法得出了一种地面的反射系数和散射系数 ,该结果对雷达高度表的设计和其它地面反射和散射系数测量均具有参考价值     

C~3CM(C~2W)系统效能评价技术

通过分析研究建立Ｃ３ＣＭ系统效能评价准则,探索了Ｃ３ＣＭ效能评估技术和方法     

System deployment optimization in architecture design

Optimization of architecture design has recently drawn research interest. System deployment optimization （SDO） refers to the process of optimizing systems that are being deployed to activi- ties. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal so- lutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are mini- mized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm （HGA）, as well as the combination of heuristic method and particle swarm optimization （HPSO）. Experi- ment results show that the HPSO method generates solutions faster than particle swarm optimization （PSO） and genetic algo- rithm （GA） in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.     

美国中产阶级的“美国梦”研究——以《兔子,快跑》为例

"美国梦"的概念的出现,反映了美国人积极向上的精神,即人只要努力奋斗就能过上体面人的生活。围绕这种共识,约翰·厄普代克出版的《兔子,快跑》一书,生动地刻画了在美国梦理念下哈利·阿斯特罗姆的人生历程,折射了美国中产阶级面临的困惑与迷茫,并对美国梦进行了深刻的反思。     

瞬变电磁法在一四井煤矿采空区探测中的应用

采用瞬变电磁法(TEM)在河南省新密市境内探查一四井煤矿采空区积水分布情况、断层等构造的含导水性情况及二1煤底板的富水性情况,为矿井防治水工作的合理实施提供依据.     

浅谈变电站自动化系统调试

文章从变电站现场调试工作前的准备到设备投运的整个工作程序和调试方法及注意事项进行了详细介绍,并说明了细节问题应注意的事项。     

Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods

Financial distress prediction (FDP) has been widely considered as a promising approach to reducing financial losses. While financial information comprises the traditional factors involved in FDP, nonfinancial factors have also been examined in recent studies. In light of this, the purpose of this study is to explore the integrated factors and multiple models that can improve the predictive performance of FDP models. This study proposes an FDP framework to reveal the financial distress features of listed Chinese companies, incorporating financial, management, and textual factors, and evaluating the prediction performance of multiple models in different time spans. To develop this framework, this study employs the wrapper-based feature selection method to extract valuable features, and then constructs multiple single classifiers, ensemble classifiers, and deep learning models in order to predict financial distress. The experiment results indicate that management and textual factors can supplement traditional financial factors in FDP, especially textual ones. This study also discovers that integrated factors collected 4 years prior to the predicted benchmark year enable a more accurate prediction, and the ensemble classifiers and deep learning models developed can achieve satisfactory FDP performance. This study makes a novel contribution as it expands the predictive factors of financial distress and provides new findings that can have important implications for providing early warning signals of financial risk.