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

doi:10.3969/j.issn.1000-1093.2021.02.017

Abstract

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.

Key words: commandinformationsystem, usability, correlation, simulationdataset

CLC Number:

TIAN Xiangxuan, LI Junqi, JIN Liya, LIU Zhengren, SHI Zhiqiang. Simulation Dataset Usability Evaluation Algorithm Based on Correlation Analysis for the Command Information System[J]. Acta Armamentarii, 2021, 42(2): 399-407.

References

［1］ LI X, 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］ MIN M. 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 of International 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］ LI J 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-lization of 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］ BODMANN B 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］ GOODFELLOW I 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.

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