Research on early fire source identification and anti-interference methods in mines based on dual-spectrum imaging technology by WANG Yanlin, PEI Xiaodong, WANG Kai, XU Guang

“…Existing image analysis-based methods for exogenous mine fire detection are affected by complex mining environments and interference sources. …”
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HoloMine: A Synthetic Dataset for Buried Landmines Recognition Using Microwave Holographic Imaging by Emanuele Vivoli, Lorenzo Capineri, Marco Bertini

“…To the best of the authors' knowledge, the dataset is the first of its kind and will help drive further research on computer vision methods to automatize mine detection, with the overall goal of reducing the risks and the costs of the demining process.…”
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Data Mining–Based Model for Computer-Aided Diagnosis of Autism and Gelotophobia: Mixed Methods Deep Learning Approach by Mohamed Eldawansy, Hazem El Bakry, Samaa M Shohieb

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Research on collision avoidance path planning method for mining and anchoring equipment in narrow and restricted space of tunneling laneways by Wenjuan YANG, Ran ZHANG, Xuhui ZHANG, Sihao TIAN, Zeyao WANG, Xili ZHENG, Zhiteng REN, Jicheng WAN, Yuyang DU, Hanbing ZHANG

“…Addressing the challenges of collision detection and collision avoidance path planning during the collaborative operation of mining and anchoring equipment in the narrow and restricted spaces of underground coal mines, this paper proposes a method for collision detection and collision avoidance path planning for mining and anchoring equipment in tunneling lanes based on Deep Reinforcement Learning (DRL). …”
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A Multistep Method for Automatic Determination and Optimization of Microseismic P-Phase Arrival Times in a Coal Mine by Quanjie Zhu, Xiaohui Liu, Xiaoyun Liu, Haitao Chai, Jianjun Shi

“…Through the analysis and validation of seismic data in a mine located in Hebei province, the result shows that this method can pick up the first arrival time of MS signal fast and accurately and automatically judge and remove the error station. …”
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A Spatiotemporal Feature-Based Approach for the Detection of Unlicensed Taxis in Urban Areas by Yun Xiao, Rongqiao Li, Jinyan Li

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Graph neural network approach with spatial structure to anomaly detection of network data by Hao Zhang, Yun Zhou, Huahu Xu, Jiangang Shi, Xinhua Lin, Yiqin Gao

“…Abstract Network anomaly detection using graph-structured data is a critical task in data mining and cybersecurity, involving the identification of unusual patterns within a network by analyzing its structure as a graph. …”
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A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features by Xuhui ZHANG, Yunkai CHI, Yuyang DU, Junying JIANG, Wenjuan YANG, Youjun ZHAO, Jicheng WAN, Yanqun WANG, Chenhui TIAN

“…This study proposed a visual positioning method for TBMs in underground coal mines based on anchor net features.MethodsA three-stream depthwise separable convolutional neural network (TSCR-NET) for image enhancement was employed to estimate the reflection, illumination, and noise in images individually. …”
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A fast video coding algorithm using data mining for video surveillance by Bingyue Xie

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Log Anomaly Detection Method Based on Transformer and Temporal Convolutional Networks by Niandong Liao, Zihan Liu

“…However, the rapid development of distributed technologies has led to the increasing scale and complexity of log data, resulting in existing methods facing difficulties in fully mining log features and information loss during parsing. …”
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Automatic vibration fault detection of coal mine explosion-proof electrical equipment based on One-Class Support Vector Machine by ZHENG Tiehua, WANG Fei, ZHAO Gelan, DU Chunhui

“…As a result, the boundary between normal and fault signals becomes unclear, reducing the accuracy of traditional fault detection methods. To address this issue, an automatic vibration fault detection method for coal mine explosion-proof electrical equipment was proposed based on One-Class Support Vector Machine (OCSVM). …”
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Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation models by Kaoutar Clero, Said Ed-Diny, Mohammed Achalhi, Mouhamed Cherkaoui, Imad El Harraki, Sanaa El Fkihi, Intissar Benzakour, Tarik Soror, Said Rziki, Hamd Ait Abdelali, Hicham Tagemouati, François Bourzeix

“…Rockfalls are among the frequent hazards in underground mines worldwide, requiring effective methods for detecting unstable rock blocks to ensure miners' and equipment's safety. …”
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AI-Driven Predictive Maintenance in Mining: A Systematic Literature Review on Fault Detection, Digital Twins, and Intelligent Asset Management by Luis Rojas, Álvaro Peña, José Garcia

“…The findings highlight the increasing adoption of deep learning, reinforcement learning, and digital twins for anomaly detection and process optimization. Additionally, AI-driven methods are improving sensor-based data acquisition and asset management, extending equipment lifecycles while reducing failures. …”
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Shoreline Change Detection Using DSAS: Case Study in PT IWIP Mining Industrial Area, North Maluku Province, Indonesia by Nurhalis Wahidin, Adi Noman Susanto, Irham, Zulhan Arifin Harahap, Salnuddin, Muhammad Aris

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Enhancing Autonomous Truck Navigation in Underground Mines: A Review of 3D Object Detection Systems, Challenges, and Future Trends by Ellen Essien, Samuel Frimpong

“…The findings of this work show that the mid-level fusion method for combining different sensor suites enhances robust detection. …”
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Application of CycleGAN-based low-light image enhancement algorithm in foreign object detection on belt conveyors in underground mines by Anxin Zhao, Qiuhong Zheng, Liang Li

“…Abstract Clear monitoring images are crucial for the safe operation of belt conveyors in coal mines. However, in underground environments, low illumination and uneven brightness can significantly degrade image quality, thereby affecting the detection of foreign objects in coal flow and reducing the reliability of safety monitoring equipment. …”
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Research on Android malware detection method based on multimodal feature fusion by Ge Jike, He Mingkun, Chen Zuqin, Ling Jin, Zhang Yifan

“…Existing Android malware detection methods mainly use single-modal data to characterize program features, but fail to fully mine and fuse different feature information, resulting in unsatisfactory detection results. …”
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Network traffic anomaly detection method based on multi-scale characteristic by Xueyuan DUAN, Yu FU, Kun WANG, Taotao LIU, Bin LI

“…Aiming at the problem that most of the traditional network traffic anomaly detection methods only pay attention to the fine-grained features of traffic data, and make insufficient use of multi-scale feature information, which may lead to low accuracy of anomaly detection results, a network traffic anomaly detection method based on multi-scale features was proposed.The original traffic was divided into sub-sequences with multiple observation spans by using multiple sliding windows of different scales, and the multi-level sequences of each sub-sequence were reconstructed by wavelet transform technology.Multi-level reconstructed sequences were generated by Chain SAE through feature space mapping, and a preliminary judgment of abnormality was made by the classifiers of each level according to the errors of the reconstructed sequences.The weighted voting strategy was adopted to summarize the preliminary judgment results of each level to form the final result judgment.Experimental results show that the proposed method can effectively mine the multi-scale feature information of network traffic, and the detection performance of abnormal traffic is obviously improved compared with traditional methods.…”
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Research on dust identification and concentration detection method based on machine vision by Luyang TU, Qinghua CHEN, Yingsong CHENG, Bingyou JIANG

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Lightweight coal miners and manned vehicles detection model based on deep learning and model compression techniques: A case study of coal mines in Guizhou region by Beijing XIE, Heng LI, Zheng LUAN, Zhen LEI, Xiaoxu LI, Zhuo LI

“…Compared to various lightweight architectures and advanced detection models, this method demonstrates excellent accuracy, lower computational costs, and better real-time performance, providing a feasible coal mine pedestrian-vehicle detection method for resource-constrained coal mine scenarios, meeting the deployment requirements of coal mine video surveillance and enabling real-time alerts for intelligent inspection of coal mine pedestrian-vehicles.…”
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