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Jinran Wu

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4 papers

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EAAI Journal 2026 Journal Article

Microseismic source localization and application based on the attention-based deep feedforward neural network model

Zhuangcai Tian
Jiahao Tian
Sen Hua
Liyuan Yu
Hongwen Jing
Jinran Wu

Microseismic events act as early warning indicators for dynamic disasters in the mining, making their precise localization a crucial aspect of disaster prevention and mitigation. This study utilized a numerical model of elastic wave propagation in layered geological strata to derive an accurate heterogeneous velocity model, thus addressing the limitations associated with traditional single-velocity models. By simulating elastic waveform data from 1617 microseismic source points based on the refined velocity model, a comprehensive dataset comprising 12, 936 entries was generated. This dataset includes monitoring point locations, P-wave arrival times, and source coordinates. The Attention-based Deep Feedforward Neural Network (ADFNN) model, incorporating multi-head self-attention and residual modules, was developed for localization. The results indicated that the average localization error for this model was merely 13. 02 m. In comparison, traditional methods such as the Geiger method and the Newton method exhibited localization errors of 30. 07 m and 38. 75 m, respectively, demonstrating accuracy improvements of 56. 7% and 66. 4%. Furthermore, the ADFNN model significantly outperformed the standard Feedforward Neural Network model, which had a localization error of 45. 53 m. Field blasting tests conducted in the actual roadway, which served as the basis for the model, yielded an average localization error of 23. 44 m for the ADFNN model. This result is substantially lower than those obtained using traditional methods and the Feedforward Neural Network model, which reported errors ranging from 27. 34 m to 97. 26 m. The proposed approach effectively addresses the complexities of modeling wave velocity nonlinearity in intricate geological settings, significantly enhancing the accuracy and efficiency of microseismic source localization. This advancement presents a novel solution for achieving high-precision microseismic source localization in mining operations.

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EAAI Journal 2024 Journal Article

Robust autoregressive bidirectional gated recurrent units model for short-term power forecasting

Yang Yang
Zijin Wang
Shangrui Zhao
Hu Zhou
Jinran Wu

Accurate short-term power forecasting (STPF) provides reliable support for the stable operation of power systems. However, due to the randomness of consumer behavior and energy properties, outliers inevitably exist in power series. Considering its negative influence, effectively extracting features from the power series with outliers has become a significant challenge in STPF. This paper develops a robust hybrid model to handle this issue. The proposed model utilizes the robust regression technique to handle outliers. An adaptive rescaled Huber loss is developed to approximate the complex distribution of the actual power series. Moreover, the proposed model applies autoregressive and bidirectional gated recurrent units to extract linear and nonlinear features of power series, respectively. Meanwhile, the attention mechanism extracts the temporal feature through the attention representation, which considers the correlations between different moments. The proposed model obtains the optimal coefficients of determination between predictions and observations on the wind power series as 0. 9629 and power load series as 0. 978, which indicates that the proposed model performs competitive robustness and generalization on the daily operation of renewable energy systems.

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EAAI Journal 2022 Journal Article

An opposition learning and spiral modelling based arithmetic optimization algorithm for global continuous optimization problems

Yang Yang
Yuchao Gao
Shuang Tan
Shangrui Zhao
Jinran Wu
Shangce Gao
Tengfei Zhang
Yu-Chu Tian

In engineering applications, many real-world optimization problems are nonlinear with multiple local optimums. Traditional algorithms that require gradients are not suitable for these problems. Meta-heuristic algorithms are popularly employed to deal with these problems because they can promisingly jump out of local optima and do not need any gradient information. The arithmetic optimization algorithm (AOA), a recently developed meta-heuristic algorithm, uses arithmetic operators (multiplication, division, subtraction, and addition) to solve optimization problems including nonlinear ones. However, the exploration and exploitation of AOA are not effective to handle some complex optimization problems. In this paper, an opposition learning and spiral modelling based AOA, namely OSAOA, is proposed for enhancing the optimization performance. It improves AOA from two perspectives. In the first perspective, the opposition-based learning (OBL) is committed to taking both candidate solutions and their opposite solutions into consideration for improving the global search with a high probability of jumping out of local minima. Then, the spiral modelling is introduced as the second perspective, which is particularly useful in getting the solutions gathering faster and accelerating the convergence speed in the later stage. In addition, OSAOA is compared with other existing advanced meta-heuristic algorithms based on 23 benchmark functions and four engineering problems: the three-bar truss design, the cantilever beam design, the pressure vessel design, and the tubular column design. From our simulations and engineering applications, the proposed OSAOA can provide better optimization results in dealing with these real-world optimization problems.

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EAAI Journal 2021 Journal Article

A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability

Shaotong Zhang
Jinran Wu
Yonggang Jia
You-Gan Wang
Yaqi Zhang
Qibin Duan

In situ observations of suspended sediment concentration (SSC) and hydrodynamics were conducted in the subaqueous Yellow River Delta, China. With the dataset, a new least absolute shrinkage and selection operator (LASSO) regression model with temporal autocorrelation incorporated (temporal LASSO) is proposed for SSC prediction and mechanism investigation in coastal oceans. The model is concise and practical, effectively shrinking the interrelated variables into representative ones, while also achieving one-hour ahead forecasting with both higher accuracy and better interpretability than other data-driven methods. The model interpretability is further validated with direct data analysis from a physical perspective. Specifically, Empirical Mode Decomposition is employed to decouple the measured SSC into intrinsic mode functions (IMFs) and a residual. The periods of each subseries estimated from both zero-crossing and spectrum analysis show that IMF1 physically corresponds to the sediment resuspension by M4 tidal currents, IM F 2 is the M2 tidal advection, IMF3-IMF5 are the resuspension by wind waves, IM F 6 is the spring–neap tidal pumping of sediments. The contributions estimated with the ratio of variance are 12 %, 14 %, 63 %, and 10 %, respectively, over the observation period. The residual is the seasonal variations which can be taken as the background SSC thus not included for variance contribution. Waves make the dominant contribution which verifies the rationality of the LASSO shrinkage and confirms the model interpretability. The temporal LASSO model is shown to be a potential tool for emergency forecasting and mechanism explanation of SSC to benefit ocean environmental engineering management.

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