Introducing macrophages to artificial immune systems for earthquake prediction

Wen, Zhou; Liang, Yiwen

doi:10.1016/j.asoc.2022.108822

Cited by 10 publications

(5 citation statements)

References 31 publications

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“…GA's searching capabilities and boosting of AdaBoost makes the proposed method to be a powerful classifier. In addition, considering the earthquake prediction process is similar to the anomaly detection process of the biological immune system, Zhou et al [19][20][21] [22] introduced dendritic cells algorithm, artificial macrophage classification optimization method, and artificial antigen-presenting cells approach to earthquake prediction. However, ML methods are unable to learn the complex and nonlinear relationship of earthquake features.…”

Section: Earthquake Prediction Methodsmentioning

confidence: 99%

Elite GA-based Feature Selection of LSTM for Earthquake Prediction

Lan

Wen

et al. 2023

Preprint

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Earthquake magnitude prediction is an extremely difficult task that has been studied by various machine learning researchers. However, the redundant features and time series properties hinder the development of prediction models. Elite Genetic Algorithm (EGA) has the advantages in searching optimal feature subsets, meanwhile, Long Short-Term Memory (LSTM) is dedicated to processing time series and complex data. Therefore, we propose an EGA-based feature selection of LSTM model (EGA-LSTM) for time series earthquake prediction. First, the acoustic and electromagnetics data of the AETA system we developed are fused and preprocessed by EGA, aiming to find strong correlation indicators. Second, LSTM is introduced to execute magnitude prediction with the selected features. Specifically, the RMSE of LSTM and the ratio of selected features are chosen as fitness components of EGA. Finally, we test the proposed EGA-LSTM on the AETA data of Sichuan province, including the influence of data in different periods ($timePeriod$) and fitness function weights ($\omega_a$ and $\omega_F$) on the prediction results. Linear Regression (LR), Support Vector Regression (SVR), Adaboost, Random Forest (RF), standard GA (SGA), steadyGA, and three Differential Evolution Algorithms (DEs) are adopted as our baselines. Experimental results demonstrate that all the methods can get the best performance when $timePeriod = 0:00-8:00$, $\omega_a=1$, and $\omega_F=0.8$. Moreover, our proposed approach is superior to state-of-the-art approaches on the evaluation indicators MAE, MSE, RMSE, and $R_2$. Non-parametric tests reveal that EGA-LSTM is significantly different from others and outperforms the standard LSTM.

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Section: Earthquake Prediction Methodsmentioning

confidence: 99%

Elite GA-based Feature Selection of LSTM for Earthquake Prediction

Lan

Wen

et al. 2023

Preprint

Self Cite

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“…Without training the neural network with the excitation function, the output signal would be a basic linear function, resulting in a linear regression model. However, since real-world problems are inherently non-linear, the absence of non-linear functions would render the model meaningless and provide a summary of commonly perceived excitation functions [ 33 ].…”

Section: Predictive Maintenancementioning

confidence: 99%

“…Second, the focus curve function the neural network with the excitation function, the output signal would be a basic linear function, resulting in a linear regression model. However, since real-world problems are inherently non-linear, the absence of non-linear functions would render the model meaningless and provide a summary of commonly perceived excitation functions [33]. inherently non-linear, the absence of non-linear functions would render the model meaningless and provide a summary of commonly perceived excitation functions [33].…”

Section: Function Name Notedmentioning

confidence: 99%

Remaining Useful Life Estimation of MoSi2 Heating Element in a Pusher Kiln Process

Irfan,

Liao,

Taipabu

et al. 2024

Sensors

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The critical challenge of estimating the Remaining Useful Life (RUL) of MoSi2 heating elements utilized in pusher kiln processes is to enhance operational efficiency and minimize downtime in industrial applications. MoSi2 heating elements are integral components in high-temperature environments, playing a pivotal role in achieving optimal thermal performance. However, prolonged exposure to extreme conditions leads to degradation, necessitating precise RUL predictions for proactive maintenance strategies. Since insufficient failure experience deals with Predictive Maintenance (PdM) in real-life scenarios, a Generative Adversarial Network (GAN) generates specific training data as failure experiences. The Remaining Useful Life (RUL) is the duration of the equipment’s operation before repair or replacement, often measured in days, miles, or cycles. Machine learning models are trained using historical data encompassing various operational scenarios and degradation patterns. The RUL prediction model is determined through training, hyperparameter tuning, and comparisons based on the machine-learning model, such as Long Short-Term Memory (LSTM) or Support Vector Regression (SVR). As a result, SVR reflects the actual resistance variation, achieving the R-Square (R2) of 0.634, better than LSTM. From a safety perspective, SVR offers high prediction accuracy and sufficient time to schedule maintenance plans.

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“…In 2022, a new earthquake prediction method based on the AMA was proposed to identify noise and anomalies, and improve prediction accuracy through distance measurement and stochastic gradient descent. Experimental results show that AMA is better than existing earthquake prediction algorithms [28]. Subsequently, a novel immune optimization-inspired NDANKA (using NDANKA and artificial antigen-presenting cell methods) was presented to predict earthquakes.…”

Section: Related Workmentioning

confidence: 99%

A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection

He,

Wang,

et al. 2023

Electronics

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Anomaly in geoacoustic emission is an important earthquake precursor. Current geoacoustic anomaly detection methods are limited by their low signal-to-noise ratio, low intensity, sample imbalance, and low accuracy. Therefore, this paper proposes a clone selection algorithm optimized one-class support vector machine method (CSA-OCSVM) for geoacoustic anomaly detection. First, the interquartile range (IQR), cubic spline interpolation, and time window are designed to amplify the geoacoustic signal intensity and energy change rules to reduce the interference of geoacoustic signal noise and intensity. Secondly, to address the imbalance of positive and negative samples in geoacoustic anomaly detection, a one-class support vector machine is introduced for anomaly detection. Meanwhile, in view of the optimization capabilities of the clone selection algorithm, it is adopted to optimize the hyperparameters of OCSVM to improve its detection accuracy. Finally, the proposed model is applied to geoacoustic data anomaly detection in nine different datasets, which are derived from our self-developed acoustic electromagnetic to AI (AETA) system, to verify its effectiveness. By designing comparative experiments with IQR, genetic algorithm OCSVM (GA-OCSVM), particle swarm optimization OCSVM (PSO-OCSVM), and evaluating the performance of the true positive rate (TPR) and false positive rate (FPR), the experimental results depict that the proposed model is superior to the existing state-of-the-art geoacoustic anomaly detection approaches.

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Introducing macrophages to artificial immune systems for earthquake prediction

Cited by 10 publications

References 31 publications

Elite GA-based Feature Selection of LSTM for Earthquake Prediction

Elite GA-based Feature Selection of LSTM for Earthquake Prediction

Remaining Useful Life Estimation of MoSi2 Heating Element in a Pusher Kiln Process

A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection

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