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IEEE International Conference on Industrial Technology (ICIT)

作者： Huaijin Huang Xiaobo Liu Yiting Zheng Dongsen Zhang Hongbo Xiang School of Automation China University of Geosciences Wuhan China Hubei Key Lab of Advanced Control and Intelligent Automation China University of Geosciences Wuhan China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Accurately characterizing geological patterns in reservoir modeling remains a significant challenge due to their inherent spatial heterogeneity and complexity. This paper proposes a Conditional Progressive Growing of GANs (CPGAN), which improves progressive growing of GANs (PGGAN) and Conditional GAN (CGAN). In this approach, the generator architecture of PGGAN is revised to incorporate a conditioning data input pipeline, allowing the generator to learn the features of geological patterns and conditioning data across different scales. In addition, a loss function derived from the conditioning data is introduced. This function defines the distance between the generated reservoir model and the conditioning data, guiding the generator in learning how the conditioning data constrain geological patterns. Experimental results indicate that CPGAN can generate high-quality reservoir models that comply with geological patterns and meet the constraints of the conditioning data.

关键词： Training Solid modeling Three-dimensional displays Geology Pipelines Reservoirs Generative adversarial networks Generators Data models Ice

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Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive control

Trajectory Tracking for Directional Drilling in Underground ...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Jiajun Wu Chengda Lu Hao Li Hongchao Wei Min Wu School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Automation China University of Geosciences Wuhan China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China China Coal Research Institute Beijing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the $H_{\infty}$ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment.

关键词： Drilling Uncertainty Trajectory tracking Stability criteria Predictive models Taylor series Coal mining Trajectory Thermal stability Predictive control

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Adapting Human Mesh Recovery with Vision-Language Feedback

arXiv

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arXiv 2025年

作者： Xu, Chongyang Huang, Buzhen Zhang, Chengfang Feng, Ziliang Wang, Yangang College of Computer Science Sichuan University Chengdu610065 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education The School of Automation Southeast University Nanjing210096 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China

Human mesh recovery can be approached using either regression-based or optimization-based methods. Regression models achieve high pose accuracy but struggle with model-to-image alignment due to the lack of explicit 2D-3D correspondences. In contrast, optimization-based methods align 3D models to 2D observations but are prone to local minima and depth ambiguity. In this work, we leverage large vision-language models (VLMs) to generate interactive body part descriptions, which serve as implicit constraints to enhance 3D perception and limit the optimization space. Specifically, we formulate monocular human mesh recovery as a distribution adaptation task by integrating both 2D observations and language descriptions. To bridge the gap between text and 3D pose signals, we first train a text encoder and a pose VQ-VAE, aligning texts to body poses in a shared latent space using contrastive learning. Subsequently, we employ a diffusion-based framework to refine the initial parameters guided by gradients derived from both 2D observations and text descriptions. Finally, the model can produce poses with accurate 3D perception and image consistency. Experimental results on multiple benchmarks validate its effectiveness. The code will be made publicly available. © 2025, CC BY.

关键词： Mesh generation

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Design and Hardware-in-the-loop Validation:A Fractional Full Feed-forward Method for Grid Voltage in LCL Grid-connected Inverter systems

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CSEE Journal of Power and Energy systems 2023年第5期9卷 1720-1731页

作者： Qingyi Wang Binlei Ju Yudi Lei Dan Zhou Shuai Yin Danyun Li the School of Automation China University of Geosciencesand Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhan 430074China.

The harmonic disturbance in the background grid is a problem that must be considered in the design of a gridconnected ***,the full feed-forward method cannot completely suppress the harmonic disturbance in theory and is sensitive to *** tackle these problems,a fractional full feed-forward method of grid voltage is proposed in this ***,the mathematical model of the full feed-forward method is deduced,and the differences with the theoretical solution,which can suppress all harmonics,are ***,the parameter equation,the harmonic suppression performance,stability analysis and the implementation process of this method are *** with the full feed-forward method,the proposed method not only further improves the harmonic suppression performance,but also reduces the order of the mathematical model of the differential term in the feed-forward *** addition,the proposed method can be used to flexibly design feed-forward coefficients by selecting the order of suppressed ***,the proposed method is validated by a hardware-in-the-loop experiment on a MT real-time control platform NI PXIE-1071.

关键词： Fractional differential full feed-forward method hardware-in-the-loop LCL inverter

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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Geological Reservoir Modeling Method Based on Conditional Progressive Growing of GANs

Geological Reservoir Modeling Method Based on Conditional Pr...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Huang, Huaijin Liu, Xiaobo Zheng, Yiting Zhang, Dongsen Xiang, Hongbo School of Automation China University of Geosciences Wuhan430074 China China University of Geosciences Hubei Key Lab of Advanced Control and Intelligent Automation Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798331521950

关键词： Petroleum reservoir evaluation

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

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Load Prediction Model of Gas Boiler Generator Set Based on CNN-LSTM

Load Prediction Model of Gas Boiler Generator Set Based on...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： Xu, Yan Wu, Min Hu, Jie Peng, Fu-Sheng Zhang, Wen Song, Wen-Shuo Li, Hui-Hang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789819647521

With the proposal of carbon peak and carbon neutrality targets and the construction of a new power system, improving energy efficiency and reducing carbon emissions have become the key to the sustainable development of industrial boiler generator sets. However, the current load scheduling of gas boiler power plants is mainly based on manual experience, which limits achieving more efficient and stable operation. Accurate short-term load forecasting can help dispatchers to make reasonable production schedules. Therefore, this paper proposes a hybrid load forecasting model for generator sets based on convolutional neural networks combined with long short-term memory networks. First, the Isolation Forest algorithm eliminates anomalies in the historical data set. Then, variables related to unit load are selected by combining mechanistic analysis with the Spearman algorithm. Considering the multiple operating conditions and high complexity of unit loads, the CNN-LSTM network is used to fully extract the spatial and temporal characteristics to build the load prediction model. Finally, experiments are conducted using actual production data, showing that the proposed method is effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric load dispatching

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Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive control

Trajectory Tracking for Directional Drilling in Underground ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wu, Jiajun Lu, Chengda Li, Hao Wei, Hongchao Wu, Min School of Future Technology China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an710077 China China Coal Research Institute Beijing100013 China

ISBN: (纸本)9798331521950

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the H∞ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment. © 2025 IEEE.

关键词： Predictive control systems

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Fault Localization and State Estimation of Power Grid under Parallel Cyber-Physical Attacks

arXiv

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arXiv 2025年

作者： Ren, Junhao Zhao, Kai Zhang, Guangxiao Liu, Xinghua Zhai, Chao Xiao, Gaoxi School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Institute of Catastrophe Risk Management Nanyang Technological University Singapore639798 Singapore Future Resilient Systems Singapore-ETH Centre Singapore138602 Singapore School of Electrical Engineering Xi’an University of Technology Xi’an710048 China Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Parallel cyber-physical attacks (PCPA) refer to those attacks on power grids by disturbing/cutting off physical transmission lines and meanwhile blocking transmission of measurement data to dwarf or delay the system protection and recovery actions. Such fierce hostile attacks impose critical threats to the modern power grids when there is a fusion of power grids and telecommunication technologies. In this paper, we investigate the fault diagnosis problem of faulty transmission lines under a broader spectrum of PCPA for a linearized (or DC) power flow model. The physical attack mechanism of PCPA includes not only disconnection but also admittance value modification on transmission lines, for example, by invading distributed flexible AC transmission system (D-FACTS). To tackle the problem, we first recover the information of voltage phase angles within the attacked area. Using the information of voltage phase angle and power injection of buses, a graph attention network-based fault localization (GAT-FL) algorithm is proposed to find the locations of the physical attacks. By capitalizing on the feature extraction capability of the GAT on graph data, the fault localization algorithm outperforms the existing results when under cyber attacks, e.g., denial of service (DoS) attacks. A line state identification algorithm is then developed to identify the states of the transmission lines within the attacked area. Specifically, the algorithm restores the power injection of buses within the attacked area and then identities the state of all the transmission lines within the attacked area by solving a linear programming (LP) problem. Experimental simulations are conducted on IEEE 30/118 bus standard test cases to demonstrate the effectiveness of the proposed fault diagnosis algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

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