Outlier detection algorithms for penetration depth data of concrete targets combined with prior knowledge

QIN Shuai; LIU Hao; CHEN Li; ZHANG Lei

doi:10.11883/bzycj-2023-0287

Volume 44 Issue 3

Mar. 2024

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Article Navigation > Explosion And Shock Waves > 2024 > 44(3): 031406

QIN Shuai, LIU Hao, CHEN Li, ZHANG Lei. Outlier detection algorithms for penetration depth data of concrete targets combined with prior knowledge[J]. Explosion And Shock Waves, 2024, 44(3): 031406. doi: 10.11883/bzycj-2023-0287

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QIN Shuai, LIU Hao, CHEN Li, ZHANG Lei. Outlier detection algorithms for penetration depth data of concrete targets combined with prior knowledge[J]. Explosion And Shock Waves, 2024, 44(3): 031406. doi: 10.11883/bzycj-2023-0287

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Outlier detection algorithms for penetration depth data of concrete targets combined with prior knowledge

doi: 10.11883/bzycj-2023-0287

QIN Shuai^{1, 2
,},
LIU Hao^{1, 3},
CHEN Li¹,
ZHANG Lei^{1
,
,}

1.
Institute of Defense Engineering, AMS, Luoyang 471000, Henan, China
2.
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, Heilongjiang, China
3.
College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, China

Received Date: 2023-08-14
Rev Recd Date: 2023-11-23

Available Online: 2023-11-24

Publish Date: 2024-03-14

Abstract

Abstract

Data quality is the basis for the validity and accuracy of data-driven models, and there may be a large number of anomalies in the raw concrete targets penetration depth data. Therefore, to ensure the accuracy of the subsequent data-driven model, it is necessary to eliminate the outlier of the raw data. Compared with the traditional anomaly detection method, the anomaly detection method based on neural network models is more suitable for complex multi-dimensional and unevenly distributed concrete target penetration depth data. However, relying only on the neural network model to fit the raw experimental data ignores the abundant and effective expert prior knowledge, which will reduce the accuracy of the model, and even lead to wrong prediction results due to the limited amount of data of the training sample, data bad pixels, poor data distribution, etc. To this end, an algorithm for outlier detection of concrete target penetration depth data combined with prior knowledge was proposed. Firstly, the back propagation (BP) neural network model is used to fit the distribution of the experiment samples, then the outlier is screened out based on the deviation index, and at last, the anomaly detection performance of the model is evaluated by the empirical algorithm. Based on the characteristics of the experimental data, the batch gradient descent combined with the momentum optimization method is selected to improve the stability and efficiency during training. Furthermore, by adding domain prior knowledge with the BP neural network model to constrain the fitting of the sample data, the model can reflect the influence of additional features during training. The research results show that the BP neural network model is suitable for the outlier detection of the rigid projectile penetrating concrete experiment data. The fusion of reasonable prior knowledge can improve the detection accuracy and the convergence speed of the model, furthermore, integrating different prior knowledge will cause different results.
- concrete penetration,
- neural network,
- prior knowledge,
- anomaly detection

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References(23)

References

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