基于相关性分析的指挥信息系统模拟数据集可用性评估算法

田相轩;李军旗;金丽亚;刘正仁;石志强

兵工学报 ›› 2021, Vol. 42 ›› Issue (2) : 399-407.

兵工学报 ›› 2021, Vol. 42 ›› Issue (2) : 399-407. DOI: 10.3969/j.issn.1000-1093.2021.02.017
论文

基于相关性分析的指挥信息系统模拟数据集可用性评估算法

  • 田相轩1, 李军旗2, 金丽亚1, 刘正仁1, 石志强1
作者信息 +

Simulation Dataset Usability Evaluation Algorithm Based on Correlation Analysis for the Command Information System

  • TIAN Xiangxuan1, LI Junqi2, JIN Liya1, LIU Zhengren1, SHI Zhiqiang1
Author information +
文章历史 +

摘要

针对指挥信息系统模拟数据集可用性评估难的问题,提出了基于相关性分析的指挥信息系统模拟数据集可用性评估算法。该算法定义了模拟数据集的相关性评价标准,分别构建了数据集的基本信息矩阵、冗余度张量、交互度张量;给出了交互信息和冗余信息计算的近似公式,通过爱因斯坦求和约束求解数据集的相关关系张量;根据深度学习中损失函数的思想,计算生成数据集与原始数据集可用性张量之间的误差距离。仿真结果表明,所提算法能够较好地表征数据集的可用性,为数据生成算法提供鉴定依据。

Abstract

A correlation analysis-based usability evaluation algorithm (CA-UEA) of the simulation dataset for the command information system is proposed for assessing the usability of simulation dataset for the command information system based on correlation analysis. The relevance evaluation criteria of the simulation dataset are defined. The basic information matrix, redundancy tensor and interaction tensor are constructed separately. Approximate formulas for calculating interactive information and redundant information are proposed. The correlation tensor of dataset is solved by Einstein summation constraints. And the error distance between the generated and original dataset usability tensors is calculated based on the loss function in deep learning. The simulated results show that the proposed algorithm can better represent the usability of dataset and provide the identification basis of the data generation algorithm.

关键词

指挥信息系统 / 可用性 / 相关性 / 模拟数据集

Key words

commandinformationsystem / usability / correlation / simulationdataset

引用本文

导出引用
田相轩, 李军旗, 金丽亚, 刘正仁, 石志强. 基于相关性分析的指挥信息系统模拟数据集可用性评估算法. 兵工学报. 2021, 42(2): 399-407 https://doi.org/10.3969/j.issn.1000-1093.2021.02.017
TIAN Xiangxuan, LI Junqi, JIN Liya, LIU Zhengren, SHI Zhiqiang. Simulation Dataset Usability Evaluation Algorithm Based on Correlation Analysis for the Command Information System. Acta Armamentarii. 2021, 42(2): 399-407 https://doi.org/10.3969/j.issn.1000-1093.2021.02.017

基金

陆军武器装备军内科学研究项目(2020年)

参考文献


[1]LIX, HUANG H Z, LI X Y,et al. Reliability evaluation for the C4ISR communication system via propagation model[C]∥Proceedings of 2019 Annual Reliability and Maintainability Symposium. Orlando, FL, US: IEEE, 2019.
[2]OUYANG S J, DAI Z J, YAN C X,et al. Operational effectiveness evaluation of maritime C4ISR system based on system dyna-mics [C]∥Proceedings of the 37th Chinese Control Conference. Wuhan, China: IEEE, 2018: 8583-8588.
[3]JIAO Z, YAO P. Capability construction of C4ISR based on AI planning[J]. IEEE Access, 2019, 7: 31997-32008.
[4]XIAO B, LUO P C, CHENG Z J, et al. Systematic combat effectiveness evaluation model based on xg-boost[C]∥Proceedings of International Conference on Reliability Maintainability and Safety. Shanghai, China: IEEE, 2018: 130-134.
[5]HILL R, TOLK A. Open challenges in building combat simulation systems to support test, analysis and training[C]∥Proceedings of 2018 Winter Simulation Conference. Gothenburg, Sweden: IEEE, 2018: 3730-3741.
[6]NIU H, SONG Y, WANG R, et al. Research on integrated simulation training system for warship communication[C]∥Proceedings of International Conference on Smart Grid and Electrical Automation. Changsha, China: IEEE, 2017: 533-537.
[7]KAYA A, OZTURK R, GUMUSSOY C. Usability measurement of mobile applications with system usability scale[C]∥Proceedings of Industrial Engineering in the Big Data Era. Nevsehir, Turkey: Springer, 2019:345-358.
[8]JEON B J, Kim H W. An exploratory study on the sharing and application of public open big data[J]. Information Policy, 2017, 24(3): 27-41.
[9]MIN M. Modeling and implementation of public open data in NoSQL database[J]. International Journal of Internet, Broadcasting and Communication, 2018, 10(3):51-58.
[10]MINM. A data design for increasing the usability of subway public data[J].International Journal of Internet, Broadcasting and Communication, 2019, 11(4):18-25.
[11]HUANG M, LI L L, XUAN P. Evaluating data consistency with matching dependencies from multiple sources[C]∥Proceedings ofInternational Conference on Power Data Science. Taizhou, China: IEEE, 2019: 6-10.
[12]MA S, FAN W F, BRAVO L.Extending inclusion dependencies with conditions [J]. Theoretical Computer Science, 2014, 515(1):64-95.
[13]LI L L, LI J Z, GAO H.Evaluating entity-description conflict on duplicated data [J]. Journal of Combinatorial Optimization, 2016, 31(2): 918-941.
[14]ZHANG Y, WANG H Z, GAO H. Efficient accuracy evaluation for multi-modal sensed data[J]. Journal of Combinatorial Optimization, 2016,32(4):1068-1088.
[15]聂凯, 栾瑞鹏. 基于数据增强的仿真模型验证方法[J].指挥控制与仿真, 2019, 41(3): 92-96.
NIE K, LUAN R P. Validation method of simulation models based on data augmentation [J]. Command Control & Simulation, 2019, 41(3): 92-96.(in Chinese)
[16]TRAGANITIS P, SLAVAKIS K. Big data clustering via random sketching and validation[C]∥Proceedings of Asilomar Conference on Signals Systems and Computers. Pacific Grove, CA, US: IEEE, 2014: 1046-1050.
[17]PACKIANATHER M, KAPOOR B. A wrapper-based feature selection approach using bees algorithm for a wood defect classification system[C]∥Proceedings of System of Systems Engineering Conference. San Antonio, TX, US: IEEE, 2015: 498-503.
[18]LIJ X, RAJAN D, YANG J. Local feature embedding for supervised image classification[C]∥Proceedings of IEEE International Conference on Image Processing. Quebec City, QC, Canada: IEEE, 2015: 1300-1304.
[19]LU M T, YIN J F. A feature metric algorithm combining the wasserstein distance and mutual information[C]∥Proceedings of IEEE International Conference on Progress in Informatics and Computing. Suzhou, China: IEEE, 2018: 154-157.
[20]HOSSAIN M A, PICKERING M. Unsupervised feature extraction based on a mutual information measure for hyperspectral image classification[C]∥Proceedings of International Geoscience and Remote Sensing Symposium. Vancouver, BC, Canada:IEEE, 2011:1720-1723.
[21]ZENG Z L, ZHANG H J, ZHANG R, et al. A novel feature selection method considering feature interaction[J]. Pattern Recognition, 2015,48(8): 2656-2666.
[22]RAO Q, YU B, HE K, et al. Regularization and iterative initia-lizationof SoftMax for fast training of convolutional neural networks[C]∥Proceedings of International Joint Conference on Neural Networks. Budapest, Hungary: IEEE, 2019: 19028828.
[23]HLANDER K. Supporting tensor symmetries in EinSum[J]. Computers & Mathematics with Applications, 2003, 45(4):789-803.
[24]BODMANNB G, SINGH P K. Burst erasures and the mean-square error for cyclic parseval frames[J]. IEEE Transactions on Information Theory, 2011, 57(7):4622-4635.
[25]ALI S S, GHANI M U. Handwritten digit recognition using DCT and HMMs[C]∥Proceedings of International Conference on Frontiers of Information Technology. Islamabad, Pakistan: IEEE, 2014: 303-306.
[26]GOODFELLOWI J, POUGET-ABADIE J,MIRZA M, et al. Generative adversarial nets[C]∥Proceedings of the 28th Annual Conference on Neural Information Processing Systems. Montreal, Canada: Neural Information Processing Systems Foundation, Inc., 2014: 2672-2680.
[27]MUDAVATHU K D B,CHANDRA SEKHARARAO M, RAMANA K V.Auxiliary conditional generative adversarial networks for image data set augmentation[C]∥Proceedings of International Conference on Inventive Computation Technologies. Coimbatore, India: IEEE, 2018: 263-269.


250

Accesses

0

Citation

Detail

段落导航
相关文章

/