Author name cluster

Li Wan

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5 papers

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EAAI Journal 2025 Journal Article

A study on the automation of borehole image auto-recognition based on instance segmentation

Tong Jiang
Fanke Meng
Li Wan

To mitigate the time consumption and inefficiency of manual borehole image data recognition, this paper introduces a instance segmetation-based algorithm model called borehole image auto-recognition (BIAR). The algorithm initially employs the instance segmentation module of the you only look once version 8(YOLOv8)deep learning model to automatically detect and classify structures like dike filling and fractures in borehole wall images. Polynomial fitting formulas are then applied to the segmented mask coordinates to generate structural curves of the detected features. The dip and dip direction of these structures are calculated from the extrema of the fitted curves. Finally, the algorithm outputs the processed borehole images. A comparison between automatic and manual recognition using a dataset of actual borehole images demonstrates the algorithm's ability to quickly and accurately identify fractures and discontinuities, even in the presence of complex borehole wall information. The model's accuracy and robustness are also validated. Validation indicates that the BIAR algorithm can identify and map structural surface features in borehole images at a depth of 100 m within 30 s, achieving over 90% accuracy in identifying structural surfaces. This algorithm provides a highly efficient and accurate method for automatic borehole image analysis in engineering applications.

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EAAI Journal 2025 Journal Article

Dual seismic image collaborative recognition algorithm based on deep learning

Fanke Meng
Tong Jiang
SiXin Zhu
Li Wan

To address the inefficiencies and subjectivity of traditional manual interpretation in seismic data analysis, this paper introduces a deep learning-based dual image collaborative recognition (DICR) model. The model is based on an enhanced you only look once version 8 (YOLOv8) architecture with a dual-stream feature extraction network. A multi-task-optimized Cross Stage Partial Darknet-Path Aggregation Network(CSPDarknet-PANet) backbone processes seismic stacked velocity spectra and seismic trace set data in parallel. The multi-class detection head estimates the probability distribution of energy clusters in the velocity spectrum, while the geometric morphology analysis module analyzes the geometric morphology of seismic reflection events. A novel cross-modal correction mechanism implements a bidirectional feedback system using a velocity-time domain transformation matrix. Iterative parameter optimization continuously aligns detected energy clusters with corrected seismic reflection events. Real seismic datasets were employed for end-to-end evaluation experiments. Across 728 images affected by strong noise interference and waveform distortions, the DICR model achieves an average absolute localization error of 4. 7 % (±1. 3 %) for energy cluster centers. Furthermore, the structural similarity index measure (SSIM) for seismic reflection event reconstruction reaches 0. 912, while processing efficiency is approximately 30 times higher than that of manual interpretation. By incorporating domain knowledge into the deep learning framework via a confidence fusion (a decision-level integration of velocity spectra and gather features using weighted fusion), this model develops an intelligent recognition system with physical interpretability. The error rate is maintained within a strict 5 % confidence interval, ensuring compliance with practical engineering accuracy requirements for seismic exploration.

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YNIMG Journal 2024 Journal Article

Investigating unilateral and bilateral motor imagery control using electrocorticography and fMRI in awake craniotomy

Jie Ma
Zhengsheng Li
Qian Zheng
Shichen Li
Rui Zong
Zhizhen Qin
Li Wan
Zhenyu Zhao

BACKGROUND: The rapid development of neurosurgical techniques, such as awake craniotomy, has increased opportunities to explore the mysteries of the brain. This is crucial for deepening our understanding of motor control and imagination processes, especially in developing brain-computer interface (BCI) technologies and improving neurorehabilitation strategies for neurological disorders. OBJECTIVE: This study aimed to analyze brain activity patterns in patients undergoing awake craniotomy during actual movements and motor imagery, mainly focusing on the motor control processes of the bilateral limbs. METHODS: We conducted detailed observations of patients undergoing awake craniotomies. The experimenter requested participants to perform and imagine a series of motor tasks involving their hands and tongues. Brain activity during these tasks was recorded using functional magnetic resonance imaging (fMRI) and intraoperative electrocorticography (ECoG). The study included left and right finger tapping, tongue protrusion, hand clenching, and imagined movements corresponding to these actions. RESULTS: fMRI revealed significant activation in the brain's motor areas during task performance, mainly involving bilateral brain regions during imagined movement. ECoG data demonstrated a marked desynchronization pattern in the ipsilateral motor cortex during bilateral motor imagination, especially in bilateral coordination tasks. This finding suggests a potential controlling role of the unilateral cerebral cortex in bilateral motor imagination. CONCLUSION: Our study highlights the unilateral cerebral cortex's significance in controlling bilateral limb motor imagination, offering new insights into future brain network remodeling in patients with hemiplegia. Additionally, these findings provide important insights into understanding motor imagination and its impact on BCI and neurorehabilitation.

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YNIMG Journal 2021 Journal Article

A novel method to simultaneously record spinal cord electrophysiology and electroencephalography signals

Feixue Wang
Libo Zhang
Lupeng Yue
Yuxuan Zeng
Qing Zhao
Qingjuan Gong
Jianbo Zhang
Dongyang Liu

The brain and the spinal cord together make up the central nervous system (CNS). The functions of the human brain have been the focus of neuroscience research for a long time. However, the spinal cord is largely ignored, and the functional interaction of these two parts of the CNS is only partly understood. This study developed a novel method to simultaneously record spinal cord electrophysiology (SCE) and electroencephalography (EEG) signals and validated its performance using a classical resting-state study design with two experimental conditions: eyes-closed (EC) and eyes-open (EO). We recruited nine postherpetic neuralgia patients implanted with a spinal cord stimulator, which was modified to record SCE signals simultaneously with EEG signals. For both EEG and SCE, similar differences were found in delta- and alpha-band oscillations between the EC and EO conditions, and the spectral power of these frequency bands was able to predict EC/EO behaviors. Moreover, causal connectivity analysis suggested a top-down regulation in delta-band oscillations from the brain to the spinal cord. Altogether, this study demonstrates the validity of simultaneous SCE-EEG recording and shows that the novel method is a valuable tool to investigate the brain-spinal interaction. With this method, we can better unite knowledge about the brain and the spinal cord for a deeper understanding of the functions of the whole CNS.

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TIST Journal 2020 Journal Article

Exploring Correlation Network for Cheating Detection

Ping Luo
Kai Shu
Junjie Wu
Li Wan
Yong Tan

The correlation network, typically formed by computing pairwise correlations between variables, has recently become a competitive paradigm to discover insights in various application domains, such as climate prediction, financial marketing, and bioinformatics. In this study, we adopt this paradigm to detect cheating behavior hidden in business distribution channels, where falsified big deals are often made by collusive partners to obtain lower product prices—a behavior deemed to be extremely harmful to the sale ecosystem. To this end, we assume that abnormal deals are likely to occur between two partners if their purchase-volume sequences have a strong negative correlation. This seemingly intuitive rule, however, imposes several research challenges. First, existing correlation measures are usually symmetric and thus cannot distinguish the different roles of partners in cheating. Second, the tick-to-tick correspondence between two sequences might be violated due to the possible delay of purchase behavior, which should also be captured by correlation measures. Finally, the fact that any pair of sequences could be correlated may result in a number of false-positive cheating pairs, which need to be corrected in a systematic manner. To address these issues, we propose a correlation network analysis framework for cheating detection. In the framework, we adopt an asymmetric correlation measure to distinguish the two roles, namely, cheating seller and cheating buyer, in a cheating alliance. Dynamic Time Warping is employed to address the time offset between two sequences in computing the correlation. We further propose two graph-cut methods to convert the correlation network into a bipartite graph to rank cheating partners, which simultaneously helps to remove false-positive correlation pairs. Based on a 4-year real-world channel dataset from a worldwide IT company, we demonstrate the effectiveness of the proposed method in comparison to competitive baseline methods.

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