Artificial Intelligence Enhanced Reliability Assessment Methodology With Small Samples

Cai, Baoping; Sheng, Chaoyang; Gao, Chuntan; Liu, Yonghong; Shi, Mingwei; Liu, Zengkai; Qing, Feng; Liu, Guijie

doi:10.1109/tnnls.2021.3128514

Cited by 35 publications

(11 citation statements)

References 36 publications

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“…However, in actual regional LAI estimation, a few measured samples are obtained, which is difficult to train well in general neural networks. Aiming at using small samples to estimate parameters, it can be solved in two ways: one is to use Bayesian estimation with incorporating prior information to reduce severely biased estimates [26] or light-weight network to complete regression estimation [27,28], the other is to realize data enhancement for small samples by deep learning [29,30]. Adversarial networks can be used in computer vision to realize unsupervised dual learning in image-to-image translation [31].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Leaf Area Index Estimation With CROP-DualGAN Network

Dong

Zhu

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Quantitative estimation of regional leaf area index (LAI) is an important basis for large-scale crop growth monitoring and yield estimation. With the development of deep learning, theoretically, the use of neural networks can effectively improve the accuracy of LAI estimation, but sufficient training samples are often required due to a large number of network parameters. In an actual regional LAI quantitative estimation, there are only a few samples, which is difficult to train in networks. Therefore, a crop dual-learning generative adversarial network (CROP-DualGAN) was proposed in this article for data enhancement of small samples to estimate regional LAI. The method uses dual learning to generate hyperspectral reflectance and corresponding LAI, including two groups of generative adversarial networks, in which the generator is used to generate data that conforms to the distribution of the training set, and the discriminator is used to judge the true or false generated samples. The generators and discriminators are constantly optimized in the confrontation so that the distribution of generated data is closer to that of training samples. In single crop type experiments, 30 training samples with enhanced in VGG16 achieved the R 2 of cereal, maize and rape seed as 0.921, 0.990 and 0.956, and in SSLLAI-Net achieved the R 2 of cereal, maize and rape seed as 0.971, 0.991 and 0.962. In multiple crop types experiments, the result is lower than individual crop estimation, but higher than that of without enhancement. Finally, non-parametric test is used to prove that most improvement in LAI estimation is significant, and the accuracy won't decrease when improvement is not significant. In all, proposed method is universal and can effectively help benchmark models to improve regional LAI estimation accuracy with neural networks.

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Section: Introductionmentioning

confidence: 99%

Enhanced Leaf Area Index Estimation With CROP-DualGAN Network

Dong

Zhu

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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“…Therefore, it is incomplete to infer the enemy's target intention based on the characteristic information at a single time. With the development of artificial intelligence [15][16], data fusion [17][18] and deep learning [19][20][21][22], the computer can handle a large number of complex data with high performance and high speed.…”

Section: Introductionmentioning

confidence: 99%

“…Kong et al [21] proposed a sensor placement methodology of hydraulic control system to improve the performance of fault diagnosis of the hydraulic control system. Cai et al [22] proposed an artificial intelligence enhanced reliability assessment methodology to improve the accuracy of the reliability assessment of a test product.…”

Section: Introductionmentioning

confidence: 99%

Learning Embedding Features Based on Multisense-Scaled Attention Architecture to Improve the Predictive Performance of Air Combat Intention Recognition

Wang

Lin

et al. 2022

IEEE Access

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In modern air combat, acquiring the opponent's air combat intention is one of essential prerequisites to evaluate the air combat situation effectively and master the battlefield initiative. On account of multi-dimensional and temporal characteristics of the target state, a recognition model is proposed to identify tactical intention of aerial target based on a multi-sense-scaled attention architecture. First of all, the multi-dimensional feature information, including target state attributes, battlefield environment, target attributes and so on, is constructed as target feature. Secondly, the non-numerical information, such as battlefield environment characteristics, enemy and friend attributes of targets, radar status, maneuver type, etc, is transformed into numerical data. For the purpose of subsequent data processing, the flight speed, altitude, RCS, etc, in the target status information are normalized into the same dimension. Furthermore, a target intention recognition model with multiple sense-scaled attention mechanism is designed to depict the target state, attributes and the information of the battlefield environment from multiple dimensions, which is convenient to be close to the actual combat. The BiLSTM neural network is used to learn the deep-seated information in the air combat intention feature vector, and the attention mechanism is used to adaptively allocate the network weights. The air combat feature information with different weights is introduced into the softmax function layer for intention recognition. Compared with the traditional air tactical target intention recognition model, the proposed model effectively improves the efficiency of air target tactical intention recognition as well as affords important theoretical significance and reference value for the auxiliary combat system.

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“…Tagade et al [21] applied a deep Gaussian process algorithm for lithium-ion battery health monitoring. Cai et al [22] presented an artificial intelligence enhanced model reliability by combining Bayesian neural networks (BNNs) and differential evolution (DE) algorithms, which proved that artificial intelligence can effectively improve the performance of the model. Kong et al proposed a sensor placement methodology of a hydraulic control system to determine the optimal number and position of sensors based on a discrete particle swarm algorithm [23].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multiscale Permutation Entropy-Based Fault Diagnosis and Inconsistency Evaluation Approach for Lithium Battery of E-Vehicles

2022

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Lithium battery is a complex nonlinear time-varying system with several inconsistencies. The fault diagnosis method has difficulty making an early diagnosis of battery faults without obvious abnormalities. In fact, voltage inconsistency is a representative fault response. Therefore, the monitoring of voltage inconsistency is highly important for the safe and reliable operation of lithium batteries in Evehicles. The entropy method does not rely on an accurate analysis model and expert experience. Moreover, it does not consider the complex fault mechanism and system structure. Hence, it has gradually attracted widespread attention. Given such attention, a hybrid fault diagnosis method combining multiscale permutation entropy (MPE) and coefficient of variation (CV) is presented in this paper, and the improved MPE fault diagnosis model based on 3-sigma is emphasized. First, MPE and the 3-sigma rule are used to calculate the threshold, and the voltage inconsistency of the battery is judged by the threshold. Then, the location of the faulty cells is located by the CV. The superiority of the proposed method is proven by experimental data from the Yunzhitong platform of CRRC Electric Vehicle Co., Ltd. and a comparison of frontier methods. The proposed approach is feasible and promising in real E-vehicle applications. INDEX TERMS Electric vehicles;Lithium battery; multi-scale permutation entropy (MPE); coefficient of variation (CV) rule; 3-sigma rule

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Artificial Intelligence Enhanced Reliability Assessment Methodology With Small Samples

Cited by 35 publications

References 36 publications

Enhanced Leaf Area Index Estimation With CROP-DualGAN Network

Enhanced Leaf Area Index Estimation With CROP-DualGAN Network

Learning Embedding Features Based on Multisense-Scaled Attention Architecture to Improve the Predictive Performance of Air Combat Intention Recognition

A Hybrid Multiscale Permutation Entropy-Based Fault Diagnosis and Inconsistency Evaluation Approach for Lithium Battery of E-Vehicles

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